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Future Forward with ISO 9001: 2026

Author4o — Thu, 21 May 2026 03:35:13 +0000

Future Forward with ISO 9001: 2026

Thanks to the Organizations i was with, was amongst the earliest Adopters of ISO as a Management System. Despite being long associated, i always wonder about the task & tussle in adherence to the Systems, let alone the visible benefits the Organization reaps from having an ISO Certified Management System. Many a times, i have noticed that Management Systems survive in isolation even with the supposedly strong commitment by the Top Management.

At shop level, People to a considerable extent, have found ISO Management Systems more an interruption than a savior. Due to which, in Majority of the Organizations ISO remains a fad than a realistic Management Tool. Having certified for ISO is widely believed to have its advantages – from Market and Customer perspective, as a Tool to Attract and sometimes, a Tool to Retain.

Personally and Professionally, I have always believed, and ofcourse demonstrated as well, that any System adopted with conviction by the Top Management and promulgated all across the Organization brings in highly desirable results, consistently. Key is the holistic absorption as part of DRMs, and certainly not in parallel. Success of any Management Tool in an Organization, I strongly attribute to Top Down Approach.

The Genesis:

India emerging as a Manufacturing Hub and a Bankable Market necessitated a common platform for people to assess capability and capacity of Organizations for collaborative working, Branding apart. Any Good Organization Run Professionally certainly had documented system for their own Monitoring, Control and Management, even prior to introduction of ISO. It was easier for such Organizations to quickly convert to ISO Systems and Procedures. What started as a Privileged

Tool, over the Years ISO became a common platform, facilitated by proliferation of Certifying Bodies. From Manufacturing, ISO as a Standard, moved across Industries including Educational Institutions. More than a Quality Management System, ISO is now considered to be an Effective Organization Management System, beyond that of Process Management.

Transitions:

Certain industries looked at ISO quite critically, identified certain insufficiencies as a common tool. Starting with Automotive Sector, every Sector aspired to bring in more stringent requirements specific to their sectors in the name of User Protection and Safety. Those remain specific due to both Cost and Convenience of universalization across Industries.

Over the years, evolution of consumer aspirations and consumer protection movement called for further strengthening of ISO. As an Organization, ISO picked up the good and practicable necessities from those of Industry Specific standards. Periodical Changes were made in Standards and imposed through various clauses. From mundane paper work, ISO became an Operational Tool and in the process, evolved from being a Reactive to a Proactive Tool.

Evolutions:

As an Intrapreneur, I have always enforced an Integrated Working, focused on Effectiveness beyond Efficiencies. Silos are certainly needed to bring focus and efficiency, without which may result in Organizational disarray – meaning fixing of Responsibility & Accountability.

In more than a decade that passed, impact of Businesses on the Stake Holders necessitated a System beyond that of Organization. ISO recognized this need and included as part of the standards calling for ESG Focus and Risk Mitigation, both for the benefit of the Organization and the Stake Holders. As we move into the future, the changes envisaged alongside AI evolution leaves no choice but for the ISO and its Constituent Organizations to be prepared for.

As a result, expect ISO to move ahead of Monitor and Control to that of Leadership and Performance. Built on the fundamental framework Resilience, Sustainability, Digitalization and Value Creation, Culture Building and Business Process Transformation will be the salient aspects. ISO may thus becomes a Strategic Tool, whether it provides a Long Run Sustainable Unique Advantage or not.

Remarks:

ESG induced 3P (People – Planet – Profits) approach has forced upon Businesses to demonstrate Sustainability by way of Consistent Performance. While it is a choice now until the new ISO standard becomes effective, it will be more a compulsion to retain the Certification.

People, Processes & Systems should synergize for Organizational Effectiveness. Success of any Tool will continue to remain People Determined. In my experience, though the Auditors and Assessors play a key role in successful implementation at a Client, Tools have always been beneficial to the Organization, even if there are No Sayers.

It is a debate for another time – whether ISO assures success and helps to improve Profitability. Such a question arises with very many ISO Certified Organizations unable to sustain Businesses, resulting in Closures. But one thing is for sure – if ISO is adopted whole heartedly, it will atleast help us to be prepared at appropriate time and take course corrections. Its worth the investment – interms of Time, Money and Efforts.

About the Author :

Dr. NITHYANANDAN DEVARAAJ (NITHIN) BE., MBA., Ph.D
Independent Board Member, Business Excellence Expert, Technical Due Diligence & Capability Assessor.
Managing Partner – SAAE Enterprises
President – Coimbatore Management Association
Past National Secretary – The Institute of Indian Foundrymen.
Former MD / CEO of a German & Japanese MNC
Chairman – Shri Avinashiappar Trust
Past Managing Director & CEO – Flow Link Systems (P) Ltd ( a Voith Group Company )

Dr. Nithin has about 35 + Years of Experience in Engineering Manufacturing organizations. He has worked for LMW Group, L&T, Hitachi & CRI Group. He is considered to be a Professional with a rare blend of Intrapreneurial Orientation, having been a Business Head across Engineering Manufacturing Organisations / Industries.

Dr. Nithin is a Member in Governing Council / Board Of Studies of Universities and Management Institutions as a Management Expert.

Dr. Nithin is part of various industrial associations and professional bodies; he is credited with playing a leading role in bringing land mark changes in Export Incentives, Classification of MSMEs, CPCB Norms, Waste & Disposal Management. He also plays a pivotal role in training and development of educated Youth seeking employment opportunities.

Dr. Nithin Leads a team that turned around a division and a Company, and in establishing large scale projects as bench marks in industry (This Business has won several awards at National & Regional Level which includes – Indian Foundry of the Year, Young Foundryman and Foundryman Awards, 4Star Award from CII, Green Award & Safety Award from Tamil Nadu Government & at National Level from IIF, First Indian Foundry in the segment to get OHSAS 18001 & ISO 50001 Certification and Many Kaizen awards.)

Dr. Nithin is a Managing Trustee of a Family Managed Trust involved in activities like rendering Food, Shelter, Education and Medicare. Noted for rendering services during Chennai, Kerala, Tanjore & Nilgiris Floods.

Dr. Nithin’s Academics:

Ph.D in Management Sciences
MBA in Finance & Marketing – Gold Medalist and University Topper
BE Mechanical Engineer with Distinction
An IRCA certified lead auditor and assessor for ISO 9001.
A qualified auditor for EMS, OHSAS & EnMS
L&T Future Leaders Training at IIM – C

Dr. Nithin’s Professional Associations:

Member & Industry Nominee, Coimbatore Disaster Management Council
A Guest Faculty to Premier Management Institutes & Was Visiting Faculty to IIM – Calicut
An examiner for Management Qualifications & Assessor for Management Teachers
An acknowledged management trainer and consultant
Is a regular Business Column Writer.

Dr. Nithin is a Member of:

Foundry Development Council, Ministry of Commerce & Industries, India.
Institution of Engineers, All India and Coimbatore Management Association
Board of Studies in Universities & Premier Management Institutes

Dr. Nithin is Winner of:

The Outstanding Alumni & Distinguished Alumni Awards from PSG Institutions & Bharathiar University.
The CMA Golden Jubilee Year Best Manager award for the region.
The Best Management Student award and many accolades at National Level.
The Mother Teresa Award of Excellence for Young Business Managers.

Dr. Nithin’s Management Interests:

Management of Innovation, Corporate Strategic Planning and Manufacturing Management, Green Supply Chain

Dr. Nithin can be contacted at :

Mobile : +91 94879 46900 , +91 99653 11288

LinkedIn | Twitter | Facebook | E-mail | E-mail2

Also read Dr. NITHYANANDAN DEVARAAJ’s earlier articles:

The post Future Forward with ISO 9001: 2026 appeared first on Industry4o.com.

Digital Twins for Test Decisions

Author4o — Wed, 20 May 2026 00:51:35 +0000

Digital twins: Test decisions in a virtual factory before you spend a rupee

Risky Decisions? So What if You Could Test Them First?

So what?

Because the most expensive failures in manufacturing today are not machine
breakdowns, they are decisions executed without understanding their full-system impact.

Every layout change, capacity ramp, or bottleneck “fix” carries hidden risk. The cost of
getting it wrong is measured not just in CapEx, but in lost throughput, missed deliveries,
and credibility erosion.

Digital twins change the equation by giving leaders something manufacturing has rarely
had:

A risk-free environment to test decisions before committing capital.

The mandate: eliminate guesswork, maximize confidence
Traditional manufacturing decisions are often made using:

Historical data
Experience and intuition
Limited pilots on live production

This approach is inherently risky. Once a change is made on the shop floor, reversing it
is slow, costly, and disruptive.
Digital twin enabled decision-making flips this model:

Decisions are tested virtually, not physically
Scenarios are explored before execution
Confidence replaces optimism

In simple terms:

Traditional approach → “Let’s try and see.”
Digital twin approach → “Let’s simulate and choose.”

That difference alone is worth millions.

What changed: digital twins are no longer offline simulations

Simulation is not new.

What’s new is real-time, bidirectional intelligence.

Earlier generations of models were static and engineering-owned. Today’s digital twins
are:

Continuously synchronized with live shop-floor dat
Integrated with MES, planning, and equipment health
Enhanced by AI and predictive logic
Actively used by operations and leadership

They don’t just represent the factory.

They behave like it.

This evolution transforms digital twins from analytical tools into decision engines,
capable of answering:

“If we do this tomorrow, what happens next week?”

They don’t just represent the factory.
They behave like it.
This evolution transforms digital twins from analytical tools into decision engines,
capable of answering:
“If we do this tomorrow, what happens next week?”

Why decision testing matters more than optimization

Optimization improves existing performance.
Decision testing prevents avoidable mistakes.
In complex factories, changes rarely behave in isolation:

A layout change improves flow, but shifts congestion downstream
A bottleneck fix increases speed, but exposes upstream variability
A capacity ramp meets demand, but destabilizes quality

Digital twins reveal second- and third-order effects before they become operational
problems.

This is where most traditional decision-making fails, not in intent, but in visibility.

Decisions that should always be tested in a digital twin

From real-world manufacturing transformations, some decisions are simply too risky to
execute blindly.

1. Factory layout changes

Physical layout changes are capital-intensive and hard to reverse.

A digital twin allows teams to:

Simulate throughput before moving equipment
Identify hidden bottlenecks
Test material flow under peak demand

Many “failed” layouts weren’t bad ideas, they ignored system interactions.

2. Bottleneck fixes

Bottlenecks are rarely where teams think they are

Digital twins help test:

Multiple solution options (add equipment vs re-sequence vs buffer)
True impact on OEE and flow
Whether investment or process change delivers better ROI

This prevents over-investing in the wrong constraint.

3. Capacity ramp-ups

Spreadsheets assume perfect behavior. Factories never deliver it.

Before spending, leaders can test:

Labor and skill constraints
Maintenance load and downtime risk
Yield and quality stability during ramp

Capacity decisions move from hope-based to evidence-based.

4. New product introduction (NPI)

NPI failures are expensive and reputation-damaging.

Digital twins allow:

Virtual process validation
Parameter optimization before physical trials
Faster stabilization of yield and throughput

Time-to-market improves without gambling on live production.

The ROI is no longer theoretical

Organizations deploying digital twins at decision-critical points consistently report:

Significant reduction in decision-related risk
Faster introduction of new processes and products
Better CapEx utilization
Improved OEE through proactive bottleneck management
Greater agility during demand or supply disruptions

The biggest benefit is not a single KPI,

it’s decision confidence at scale.

Why many digital twin initiatives still disappoint

The technology is rarely the issue.

Most failures happen when:

Twins are built without a clear decision owner
Models are too complex to trust operationally
Data exists, but context is missing
Operations teams are observers, not users

Successful implementations start with one question:

“Which decision do we want to make better?”

Everything else is built backward from that.

The real shift: leadership behavior

The hardest change is not building the twin.

It’s trusting it.

Leaders who succeed:

Encourage teams to test assumptions
Compare scenarios instead of opinions
Use the twin continuously, not only during crises
Treat it as a learning system, not a one-time project

Over time, the digital twin becomes part of how the factory thinks.

Bottom line

Every manufacturing decision carries cost, some visible, many hidden.

Digital twins offer one decisive advantage:

Make mistakes virtually, not physically.

Testing decisions in a virtual factory before spending a rupee is no longer a technology
choice.

It is a leadership discipline

About the Author :

Mr. Gulshan Kumar Saini
Senior Director
Samsung Electronics

Mr. Gulshan Kumar Saini is a manufacturing transformation leader, experienced in scaling plants across India, Korea, USA/Mexico, Russia & Vietnam.

Mr. Gulshan Kumar Saini is a global manufacturing, operations excellence, and smart factory transformation leader with over 30 years of leadership experience at Samsung Electronics, spanning senior roles across India, Vietnam, Korea, Russia, and the United States.

Mr. Gulshan Kumar Saini currently serves as Senior Director – Smart Factory at Samsung Electronics India, where Mr. Gulshan Kumar Saini spearheads enterprise-wide digital transformation initiatives across advanced manufacturing lines for consumer electronics and durables.

Mr. Gulshan Kumar Saini leads the deployment of AI-driven manufacturing systems, industrial robotics, IIoT platforms, machine vision, advanced analytics, and Digital Twin technologies to drive next-generation factory performance.

Mr. Gulshan Kumar Saini is widely recognized for delivering large-scale automation, step- change productivity improvements, and sustainable cost, quality, and efficiency gains in high-volume, high-complexity manufacturing environments. Across his career, Mr. Gulshan Kumar Saini has successfully led global NPI programs, greenfield and brownfield plant setups, capacity expansions, lean transformations, and multi-country manufacturing excellence programs.

Mr. Gulshan Kumar Saini is a PMP-certified professional and Six Sigma Black Belt, Mr. Gulshan Kumar Saini also holds an MBA in FinTech from BITS Pilani, combining strong digital, financial, and operational acumen to translate technology into measurable business outcomes.

Mr. Gulshan Kumar Saini is Bestowed with the following Licenses & Certifications:

https://www.linkedin.com/in/gulshan-kumar-saini/details/certifications/

Mr. Gulshan Kumar Saini is Accorded with the following Honors & Awards:

https://www.linkedin.com/in/gulshan-kumar-saini/details/honors/

Mr. Gulshan Kumar Saini can be contacted at:

Linked In | E-mail (1) | E-mail (2)

Also read Mr. Gulshan Kumar Saini‘s earlier article:

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ELIYA – MASTERING SIMPLICITY

Author4o — Mon, 27 Apr 2026 00:59:15 +0000

“ELIYA” – Mastering Simplicity : A Synopsis

When simplicity becomes a strategy, not a compromise

The Premise

In an era where organizations are constantly chasing scale, speed, and sophistication, complexity often becomes an unintended byproduct. Processes multiply, hierarchies deepen, and decision-making slows under the weight of its own structure. Eliya – Mastering Simplicity enters this landscape with a provocative idea: what if the solution to complexity is not more control, but less?

Rather than presenting simplicity as an abstract ideal, the narrative anchors it within a relatable, human story—one that unfolds through the lived experiences of a young professional navigating the realities of modern work culture.

The Protagonist

At the center of the story is Pavithra, a freshly graduated mechanical engineer stepping into her first corporate role. Like many early-career professionals, she carries with her a sense of ambition, readiness, and the expectation that effort will naturally translate into impact.

However, her initial experiences reveal a different reality.

She finds herself in an environment defined by rigid structures, layered approvals, and communication barriers. Ideas struggle to move upward, decisions seem distant, and even simple tasks appear unnecessarily complicated. The enthusiasm she once carried begins to give way to hesitation, as she tries to reconcile her expectations with the system she now operates within.The Turning Point

Amidst this uncertainty, Pavithra turns to an unlikely source of clarity—her grandmother.

Far removed from corporate frameworks, the grandmother offers insights shaped by experience rather than formal expertise. Her perspectives are simple, often expressed through everyday observations and analogies. At first glance, these reflections appear disconnected from Pavithra’s professional challenges.

Yet, they linger.

Each conversation subtly challenges Pavithra’s assumptions, encouraging her to look beyond complexity and focus on the essence of problems. Instead of seeking elaborate solutions, she begins to question whether clarity itself might be the answer.

The Shift

What follows is not an immediate transformation, but a gradual shift in perspective.

Pavithra begins to experiment with simplicity in small, deliberate ways:

✦ Choosing clarity over jargon in communication

✦ Addressing issues through direct conversations rather than escalation

✦ Identifying the core purpose of tasks instead of blindly following processes

These changes, though seemingly minor, begin to create ripples within her work environment.

A recurring misunderstanding within her team is resolved through a straightforward exchange. A time-consuming process is streamlined by eliminating redundant steps. Decisions that once seemed complex become clearer when stripped to their fundamentals.

The Conflict

However, simplicity does not go unquestioned.

Within a system accustomed to equating complexity with competence, Pavithra’s approach is met with skepticism. Colleagues perceive her methods as overly simplistic, even naive. The belief that “more complexity equals more value” creates resistance to change.

This tension becomes a defining element of the narrative.

Rather than positioning simplicity as an easy path, the story highlights the discipline it demands. It requires confidence to challenge established norms, patience to navigate resistance, and clarity to remain focused on what truly matters.

The Transformation

As the story progresses, Pavithra’s growth becomes increasingly evident.

She evolves from a hesitant newcomer into a professional who influences her environment—not through authority, but through clarity of thought. Her ability to simplify does not diminish the depth of her work; instead, it enhances it.

Gradually, her approach begins to resonate.

Teams communicate more effectively. Processes become more transparent. Decisions are made with greater confidence. What started as individual experimentation begins to shape collective behavior.

The Broader Lens

Beyond Pavithra’s personal journey, the narrative expands into a broader commentary on organizational culture.

It challenges a deeply ingrained assumption—that complexity is a sign of sophistication. Through its storytelling, it reveals how excessive layers, controls, and structures often obscure rather than enhance effectiveness.

In contrast, simplicity is presented as a strategic discipline:

✦ It demands clarity of purpose

✦ It prioritizes what truly matters

✦ It enables agility without chaos

The story subtly reinforces that simplicity is not about doing less, but about doing what matters—better.

The Underlying Philosophy

At its core, Eliya – Mastering Simplicity is not just about workplace challenges or personal growth. It is about a way of thinking.

Through the intergenerational exchange between Pavithra and her grandmother, the narrative bridges traditional wisdom with contemporary realities. It suggests that meaningful insights are not always found in complexity, but often in the ability to observe, reflect, and distill.

Simplicity, in this context, becomes more than a technique—it becomes a mindset.

The Takeaway

As the story approaches its conclusion, it avoids dramatic resolutions or sweeping transformations. Instead, it offers a quiet yet powerful realization:

Simplicity, when applied with intent, has the potential to reshape not just individual approaches, but entire systems.

Pavithra’s journey stands as a testament to this idea—demonstrating that clarity can cut through confusion, that thoughtful action can replace unnecessary process, and that meaningful change often begins with small, deliberate shifts.

Book Available at Amazon

About Author:

Mr. Thomas Dose
Managing Director
BMW India

Mr. Thomas Dose spent over 30 years in the industry as a consultant and in senior leadership positions, where Mr. Thomas Dose learned to align large organisations and successfully implement substantial change. One of Mr. Thomas Dose‘s key strengths is guiding people through transformation processes, thinking outside the box, and conveying his knowledge in a clear and easily understandable way.

For the past 15 years, Mr. Thomas Dose has lived and worked primarily in India. Mr. Thomas Dose is particularly fascinated by South Indian culture and have sought to capture this in this book.

Mr. Thomas Dose‘s initial intention was to write a nonfiction book about the Eliya Principle. However, India is so full of fascinating stories that it has now turned into a short novel. This marks the beginning of a series titled: “Eliya – the secret of success“

The post ELIYA – MASTERING SIMPLICITY appeared first on Industry4o.com.

AI in Supply Chain

Author4o — Mon, 23 Mar 2026 00:54:18 +0000

What Happens when a Single Supplier Fails ?
AI Reveals about Supply-Chain Risks before they Disrupt.

What AI reveals about supply-chain risks, before they disrupt your factory

One Supplier Fails, Your Factory Stalls.

• The problem: Modern factories depend on tightly tuned supplier networks; a
single Tier-1 or Tier-2 supplier failure can cascade into major production
stoppages and margin loss.

• Why this matters now: Global systems remain fragile, many leaders still lack
deep multi-tier visibility and board-level focus on supplier risk.

• Immediate consequence: A single upstream disruption can force line
changeovers, emergency sourcing, lost revenue and reputational damage, often far larger than the cost of redundancy.

So what?

You don’t have an “issue”, you have an existential risk that must be seen early, not fixed after the alarm.

AI Sees the Signals Humans Miss.

● What AI detects: Pattern-level anomalies across financial health, delivery cadence, quality trends, logistics delay signals, and 3rd-party events, days or weeks before an outage.
● How accurate: Proven models (ensemble ML, LSTM/time-series, graph analytics) provide high early-warning accuracy across heterogeneous supplier signals.
● Where it matters: Multi-signal sensing is especially powerful for single-source components (semiconductors, specialty chemicals, sub-assemblies) where substitutes are scarce.

So what?

AI gives you the Golden Window to act, to reroute, re-order, or execute contingency plans before the factory feels the pain.

Five-Step Early-Warning Pipeline (Data → Decision → Action)

1. Map: Build a prioritized supplier topology (Tier-1 → Tier-n) and identify single-supplier chokepoints.

2. Ingest: Pull structured (POs, lead times, quality scores) + unstructured data (news, social, port delays, sanctions) via APIs / streaming.

3. Enrich: Add third-party risk signals : financial distress, geopolitical alerts, weather, cyber incidents.

4. Model: Run ensemble ML + graph-based contagion models to estimate probability & lead-time of supplier failure.

5. Act: Auto-trigger graded responses (expedite, dual-source, order re-allocation, safety stock release, escalation to procurement/supply-chain war-room).

So what?

It’s not an IT project, it’s a decision pipeline that turns signal into a timed business action.

Real evidence: AI catches disruptions before they cascade.

● Case patterns: Toyota’s experience after Tōhoku showed how single-source microcontroller failure cascaded across OEMs, multi-tier fragility is real.

● Research & pilots: Systematic reviews and pilot studies show AI early-warning systems materially improve lead time to intervention and reduce outage impact.

● Market trend: Manufacturers are investing in AI for supply-chain decisions to preserve just-in-time efficiency under volatility (recent industry coverage).

So what?

The ROI is practical ; fewer emergency buys, lower expedite fees, less downtime, and stronger customer delivery performance.

What leaders must do next (rapid checklist).

Immediate 90-day playbook

1. Identify 10 highest-impact single-supplier items (cost, criticality, lead time).

2. Deploy an AI pilot on 3 suppliers: combine on-time delivery, quality, financial health, and open-source event feeds.

3. Define graded responses (auto-alerts → procurement action → exec escalation).

4. Close the governance gap: elevate supplier risk to the board cadence; assign an owner for nth-party visibility.

5. Invest in resilience: selective dual-sourcing, strategic safety stock for true single-source items, and supplier development.

So what?

The firms that institutionalize AI-driven early warnings will shrink the window between supplier stress and factory impact , turning single-supplier risk from a panic into a managed event.

Closing Takeaway

This isn’t about adding another dashboard. It’s about turning supply-chain risk into a managed decision window.

The next disruption won’t announce itself.

The winners will be the factories that see it coming, and act before production feels it.

If one of your critical suppliers went silent today, would you know early enough to protect your factory?

About the Author :

Mr. Gulshan Kumar Saini
Senior Director
Samsung Electronics

Mr. Gulshan Kumar Saini is Bestowed with the following Licenses & Certifications

https://www.linkedin.com/in/gulshan-kumar-saini/details/certifications/

Mr. Gulshan Kumar Saini is Accorded with the following Honors & Awards

https://www.linkedin.com/in/gulshan-kumar-saini/details/honors/

Mr. Gulshan Kumar Saini can be contacted at:

Linked In | E-mail (1) | E-mail (2)

#supplychainrisk #aiinsupplychain #smartmanufacturing #supplychainresilience #industrialai #supplierrisk #multitiersupplychain #manufacturingleadership #operationalrxcellence #futureofmanufacturing

Also read Mr. Gulshan Kumar Saini‘s earlier article:

The post AI in Supply Chain appeared first on Industry4o.com.

Next Gen Cyber Security for AI Powered Cyber Threats

Author4o — Mon, 02 Feb 2026 00:30:50 +0000

When Hackers Employ AI: Is It Time to Rethink our Security Defences ?

When Hackers Employ AI: Is It Time to Rethink our Security Defences?

The rapid expansion of artificial intelligence (AI) across industries has changed countless capabilities, delivering benefits in efficiency, automation, and innovation. However, the same technology has also become a powerful weapon in the hands of predators, essentially altering the landscape of digital threats and malicious activities. As cybercriminals leverage AI to craft more clever and obscure attacks, the level of data protection is questioned. Organisations must relook into their current security strategies to verify, redefine, enhance and remain fit for purpose.

Current Impact on Businesses: Key Metrics

As AI-powered cyber threats rise, businesses are experiencing tangible impacts. Here are some key metrics to illustrate the current landscape:

The Rise of AI-Powered Cyber Threats

Cybercriminals are increasingly leveraging artificial intelligence to enhance both the deception and efficiency of their data exploitation tactics. By implementing machine learning algorithms, these malicious actors can automate activities like credential theft, password cracking, vishing, smishing, and phishing on an unprecedented scale. AI not only accelerates the identification of vulnerabilities within an organization’s systems but also makes breaches and intrusions more challenging to detect and address promptly

Moreover, the emergence of generative AI has introduced new forms of deception, including highly personalised phishing emails and realistic fake content. These advancements empower cybercriminals to orchestrate multi-layered campaigns that adapt dynamically to security measures, leaving traditional defence mechanisms struggling to keep up. As the tools available to hackers become ever more advanced, the threat landscape continues to evolve at a rapid pace, demanding a fundamental rethinking of how we approach cyber security.

For instance, AI-driven deepfake technology has been utilized to produce highly realistic voice and video impersonations, which are exploited in social engineering attacks. Additionally, automated tools can swiftly scan extensive networks for vulnerabilities, outpacing human capabilities. Furthermore, AI-powered bots can execute coordinated attacks that dynamically adjust to the defences they encounter in real-time.

Risks on using Traditional Security models to manage AI Threats:

• Inability to detect unusual Threats: Traditional security models often rely on static signatures and predefined rules, making them ineffective against new and previously unseen AI-generated malware or attack techniques.

• Slow Response Times: Manual analysis and intervention are no match for the speed at which AI-powered attacks operate.

• Overwhelmed Resources: The sheer volume and complexity of AI-driven threats can quickly overwhelm traditional security teams and tools, resulting in missed alerts and insufficient coverage across networks and endpoints.

• Weakness in handling Social Engineering: Conventional defences are ill-equipped to recognise sophisticated social engineering tricks, such as deepfake-based phishing or voice impersonation, which can deceive even security-aware personnel.

• Limited Threat Intelligence Integration: Legacy tools often do not integrate with global threat intelligence platforms, missing out on real-time updates about emerging, AI-driven attack vectors.

• Reactive Rather Than Proactive Approaches: Traditional security models tend to respond to attacks after they occur, rather than predicting or preventing threats by analysing behavioural anomalies and unusual patterns.

• Lack of Automation: Without automation, traditional security operations centre (SOC) teams may become bogged down by repetitive tasks, unable to focus on strategizing against the most sophisticated threats.

Limitations of Traditional Security Tools

Traditional security solutions, such as signature-based antivirus software and static firewalls, were designed to detect known threats using predefined rules. While these tools are still essential for baseline protection, they struggle to keep pace with novel, AI-driven attacks that constantly evolve and mutate.

• Signature-based detection: Relies on known malware signatures, but AI attacks create new variants that evade detection.

• Rule-based firewalls: Static rules are easily bypassed by adaptive AI attack strategies.

• Manual monitoring: Human analysts struggle with the speed and scale of AI-driven threats.

In short, the dynamic and unpredictable nature of AI-powered threats exposes the limitations of conventional defences.

The Case for Next-Generation Cyber Security

To keep pace with AI-enabled hackers, organisations must augment their traditional defences with advanced, intelligent security solutions. These include:

• AI-driven threat detection: Security platforms that use machine learning and behavioural analytics to identify anomalies and suspicious activities, even those not seen before.

• Automated response systems: Tools that can respond to threats in real time, containing breaches and neutralising attacks without human intervention.

• Adaptive security frameworks: Solutions that continuously learn and update their defence mechanisms, staying one step ahead of evolving threats.

• Comprehensive threat intelligence: Integrating global threat data and predictive analytics to anticipate and block emerging attack patterns.

By embracing these next-generation tools, organisations can level the playing field against AI-enabled adversaries.While AI is crucial for modern cyber defence, it is not a silver bullet. Skilled security professionals are needed to interpret AI-generated insights, make strategic decisions, and respond to complex incidents.The integration of AI by hackers has significantly transformed the cybersecurity landscape and it’s time for the Organisations to reshape their existing cybersecurity posture to handle AI threats. By continuously evolving our defences, we can strive to stay ahead in this dynamic digital arms race.

About the Author :

Ms. Kavitha Srinivasulu is an Award winning Technology Leader.

Ms. Kavitha Srinivasulu is a Senior Cyber Risk and Resilience executive with over 22 years of global leadership experience advising Boards and Executive Committees across Financial Services, Healthcare, Retail, Technology, and regulated industries.

Ms. Kavitha Srinivasulu has delivered and led large-scale, regulator-driven cybersecurity, AI driven, PCI, and SOC transformations for Tier-1 banks, global healthcare organisations, and highly regulated enterprises operating across the UK, EU, USA, APAC, and ANZ.

Ms. Kavitha Srinivasulu is a trusted advisor to Boards, C-suite, regulators, and global enterprises, consistently delivering resilient, compliant, and scalable cyber operating models.

Ms. Kavitha Srinivasulu is an Advisory Member in National Cyber Defence Research Centre (NCDRC)

Ms. Kavitha Srinivasulu is a Board Member of Women in CyberSecurity (WiCyS) India

Ms. Kavitha Srinivasulu is an Executive Committee Member at CyberEdBoard Community

Ms. Kavitha Srinivasulu is an ambassador at ISAC

Ms. Kavitha Srinivasulu is Bestowed with the following Licenses & Certifications :

https://www.linkedin.com/in/kavitha-srinivasulu-5619ab7/details/certifications/

https://www.linkedin.com/in/kavitha-srinivasulu-5619ab7/details/publications/

Ms. Kavitha Srinivasulu can be contacted at :

Disclaimer :

“The views and opinions expressed by Ms. Kavitha Srinivasulu in this article are solely her own and do not represent the views of her company or her customers.”

Also read Ms. Kavitha’s earlier article:

The post Next Gen Cyber Security for AI Powered Cyber Threats appeared first on Industry4o.com.

Live Permits: real-time Permit-to-Work (PTW)

Author4o — Mon, 26 Jan 2026 00:01:44 +0000

Live Permits: real-time Permit-to-Work (PTW) based on
location – from bureaucracy to collaboration between OHS and Maintenance

How do digital Live Permits transform “work orders” into a process that keeps pace with reality?

In many industrial plants, work permits still act as a “handbrake” for organizations: paper, signature circulation, scattered information, lack of up-to-date information, and limited visibility of what is actually happening on site. The problem is not just about convenience – it’s about safety, accountability, and the ability to respond quickly to changing working conditions.

Meanwhile, dangerous and critical work (e.g., hot work, work at height, work in hazardous areas, servicing during plant operation) does not happen according to an “ideal plan” — it happens in a dynamic, changing environment. This is why classic work orders often lose out to reality: they are not updated, they are not consistent with who is actually where, and they do not create a single version of the truth for health and safety, maintenance, and external companies.

Live Permits is an approach in which a work permit is not a document “for the archives,” but a living operational process — updated in real time, linked to the location and status of activities on site. This is how we build the system at InnerWeb ®.

The challenge: safety vs. speed vs. complexity of operations

Before we move on to technology, it is worth identifying the sources of friction that almost always arise in work permit processes:

• Multi-layered responsibility: health and safety, UR, production, security, fire protection, supervision – each has its own piece of the risk and its own “minimum
necessary.”

• Changing conditions: weather, availability of utilities, parallel work, schedule, changes, vehicle traffic, presence of subcontractors.

• Outdated data: paper does not “know” that the team has changed location, work
has been delayed, or a new hazard has appeared in the area.

• Dispersion of information: training, authorizations, risk assessments, instructions,
lockouts/LOTO, approvals – often in different systems or outside the system
altogether.

• Cost of bureaucracy: signatures and document circulation take time that should be spent on planning, supervision, and real prevention.

As a result, the organization chooses between two bad options: either the process is “tight” but slow and cumbersome, or fast but risky. Live Permits are designed to resolve this conflict — through automation, consistency, and timeliness, not by “letting go” of safety.

Where do Live Permits provide an operational advantage?

Below are the key areas where the “live” approach really changes the quality of the
process.

1. Digital permits as a common “point of truth”

The work permit becomes the central object of the process, rather than a form.

• A single process record: scope, risks, requirements, decisions, attachments, confirmations.

• Versioning and change history – who, when, what was approved or changed. Mobile access for field teams and approvers.

Example: instead of a piece of paper circulating, all parties see the same permit – with the current status and conditions for starting/continuing work.

2) Real-time updates – a process that keeps pace with change

The biggest difference between an “e-permit” and Live Permit is that the permit is not static.

• Dynamic statuses: preparation → ready to start → in progress → suspended →
completed → closed.

• Stop-work mechanisms when conditions change or requirements are violated.

• Communication and notifications to the appropriate roles (health and safety/UR/supervision/security/contractor).

Example: if a new risk arises in the zone (e.g., parallel work, leak, parameter exceedance), the permit can be automatically switched to “suspended” mode until corrective actions are confirmed.

3) Location as a safety context (geofencing, zones, plan compliance)

At the heart of the InnerWeb® approach is the linking of work permits to the
actual workplace.

• Assigning a permit to a zone/area/workstation (virtual map of the plant).

• Geofencing: checking “whether work is happening where it is supposed to
happen.”

• Situational view: where work is active, where teams are, where risk zones are.

Implementation example: in one of the EU-funded pilot projects at the MAN Trucks
plant in Niepołomice, the process included monitoring work permits, training
requirements for external companies and visitors, along with familiarization with the
current risks when entering the plant, alongside the location of trucks and drivers
moving around the facility – using the dense infrastructure of InnerWeb® Industrial
Radio Beacons (over 200 BLE transmitters on the plant’s internal roads). This shows
that work permits can operate in the same “data language” as logistics and traffic
safety at the plant.

4) Competencies and authorizations – “who can” is verified automatically

Live Permits is not just a form – it is compliance logic.

• Verification of training, qualifications, and job requirements before admission to
work.

• Start-up checklists (PPE, tools, safety measures, zones, instructions).

• Register of persons authorized to work under a specific permit.

Example : if current training and specific qualifications are required for hot work, the
system can block the start of work without meeting the conditions.

5) Particularly hazardous work : standards, LOTO, and condition control

For high-risk areas, repeatability and clarity of standards are important.

• Permit templates (e.g., hot work, work at height, work in confined spaces).

• Integration of procedures: risk assessment, instructions, fire safety requirements,
locks/LOTO, measurements.

• Enforcement of critical step confirmations (e.g., energy isolation, zone security, fire
safety readiness).

Example : Activating a permit to work in a confined space may require confirmation
of atmospheric measurements and a rescue plan – without this, the “in progress”
status is not possible.

6) Cooperation with security and gate –admission and verification at the entrance

In practice, many problems begin at the entrance to the facility: incomplete
documents, lack of confirmations, unclear scope.

• Quick verification: who is entering, why, to which zone, and under what conditions.

• Linking entry with an active permit and a list of authorized persons.

• Solving problems “on the spot” instead of sending the team away and generating
downtime.

Example : security can see whether a person has active access to a specific job and
zone, instead of relying on paper or declarations.

Additional features that complete the picture of the plant’s “operating system”

Live Permits work most effectively when they are part of a broader ecosystem: safety, maintenance, and logistics.

• Work and risk map – a view of “what is being done where” and what work is
being done in parallel.

• Alerts and escalations – including overtime, unauthorized entry into a zone, work
conflicts.

• Reports and audits – a complete audit trail for inspections, audits, and event
analysis.

• Operational analytics – bottlenecks in approval, typical causes of delays,
supervision workload.

• Mobile mode and field work – focused on the contractor and foreman, not just
the office.

• Resource location – people, vehicles, selected tools and equipment (where it
makes sense for the process).

Key success factors for Live Permits implementation

a) Data consistency and integrations
The less manual rewriting, the greater the timeliness and the lower the risk of error.
In practice, integrations with HR/training, contractor registration, maintenance, and,
where appropriate, security and access control areas are important.

b) Process design, not “paper digitization”
The most common mistake is to transfer paper 1:1 to the application. Live Permits
require the design of roles, decisions, checkpoints, and conditions for suspension / unblocking.

c) Common definition of risk (health and safety + UR)
The greatest value is achieved when UR and OHS stop “competing” with the
process and start running it together – with a clear division of responsibilities and
shared situational awareness.

d) Information security and compliance
Data on work, people, and location must be protected. At the same time, access
policies, retention, roles, and responsibilities must be planned.

Risks and issues to consider

• Privacy and GDPR: location and access to data must be purposeful,proportionate, and well described in terms of process.

• Operational trust: the system must operate stably, and suspension rules must not
generate false alarms.

• Location quality: it is worth clearly defining in which cases location is “critical”
and where it is “supportive.”

• Change management: without training on roles and field practices, even the best
system will become just another tool “on the side.”

What’s next: from “LIVE” permits to predictive prevention

The next step is to use data from the process (statuses, holds, conflicts, events,
locations, schedules) to predict risks and eliminate them in advance:

• Identification of typical scenarios in which work is suspended,

• Recommendations for start conditions based on history and context,

• Automatic detection of conflicts between parallel works,

• “Digital situational awareness” for supervision and security services.

Live Permits is the foundation: if an organization has a single version of the truth about field work, it can realistically move from reaction to prevention.

Conclusions

Live Permits is a practical response to a problem that is commonplace in many plants today: safety requires tightness, and operations require speed. Digital work orders with real-time updates — linked to location and compliance requirements – allow these goals to be reconciled without compromising safety.

At InnerWeb ®, we build solutions for industrial environments where it is not only “compliance on paper” that counts, but also real control of working conditions, transparency for stakeholders, and the ability to respond quickly. Our implementations and pilot projects show that this model works when it covers the entire ecosystem: health and safety, maintenance, contractors, security, and logistics on site.

About the Author :

Mr. Marcin Worecki,
CEO & Founder
InnerWeb Sp. z o.o.

Mr. Marcin Worecki, CEO of InnerWeb®, is a visionary engineer with over 20 years of technical and industrial experience.

Together with his team, Mr. Marcin Worecki creates unique legal technology for modern industrial plants based on his own experience, customer requirements, and a deep understanding of the global problem of work permits in industrial plants.

As a result, the InnerWeb® System covers the entire process of managing external technical personnel, from training upon entering the plant, through written instructions for work and particularly dangerous work, as well as audits, acceptance, and permit renewals. InnerWeb ® combine electronics, physics, algorithms, artificial intelligence, augmented reality, blockchain, web and mobile applications.

InnerWeb® has based their legal technology on a real-time location system, which also allows InnerWeb® to supervise the work being carried out, improve safety and alert in case of danger, which InnerWeb® protect with a patent in 23 countries (EU and US).

New technological solutions are InnerWeb ®‘s passion, allowing InnerWeb ® to fully realize their potential. InnerWeb ® is open to cooperation and focused on completing the tasks assigned to them. InnerWeb ® deliver extremely difficult projects and objectives, making the impossible possible. InnerWeb® operate quickly, dynamically, effectively, and proactively.

Mr. Marcin Worecki can be contacted at:

LinkedIn | E-mail

About InnerWeb® :

InnerWeb® provides technologies for industrial environments, focusing on safety, maintenance, and organizational efficiency.

InnerWeb® combine software and infrastructure (including BLE) to build location and process solutions, such as Live Permits (mobile work permits) and systems that support real-time risk control.

InnerWeb® carry out projects in the form of implementations and pilot programs, including co-financed projects, where, in addition to process digitization, verification of operational value in real plant conditions is key.

InnerWeb ® can be contacted at :

InnerWeb®
ul. Michała Grażyńskiego 14
43-300 Bielsko-Biała, Polska

InnerWeb®’s Social Media Handles :

LinkedIn | Website | Youtube | Facebook

For InnerWeb® Technical Support : Email

Tel: +48 668 669 621

For InnerWeb® Commercials Service : Email

Tel: +48 668 669 328

The post Live Permits: real-time Permit-to-Work (PTW) appeared first on Industry4o.com.

Emerging & Growing India

Author4o — Mon, 29 Dec 2025 02:16:10 +0000

How Emerging & Growing India is Shaping itself for the Coming Decades

As the world grapples with geopolitical volatility and uncertain economic tides, India stands tall, emerging not just as a resilient player but as a beacon of hope for the future. With a projected GDP growth of 8.2%, India is carving its path to become a global economic powerhouse. Yet, the country’s success story is far from linear—there are multifaceted challenges and opportunities ahead, particularly in the realms of innovation, sustainability, and inclusive growth.

Key Trends Shaping India’s Future :

Resilient Growth Amid Global Challenges :

India’s economy is projected to grow robustly, with a per capita consumption reaching approximately $3,000 by 2050. While the per capita consumption for consumer goods is currently at $54, this offers immense room for growth, particularly in rural areas where women’s participation in the workforce has risen to 35-40%. This demographic shift holds the promise of increased economic activity and greater social equity.

Innovation and Intellectual Property (IP) :

India’s R&D and IP patent landscape must see dramatic growth if it is to emerge as a global leader in technology. Fostering a culture of innovation will be crucial as India aspires to lead in fields like AI, biotech, and semiconductors.

Inflation and Consumer Sentiment :

Controlling food inflation is essential. The subdued consumer demand in recent years signals the need for more nuanced fiscal and policy interventions to ensure that growth benefits reach all sections of society.

Private Equity and Mutual Funds :

Private equity (PE) investments have been one of the key growth engines for businesses, while the mutual fund sector is growing at 15%—outpacing traditional bank deposits and fixed deposits (FDs). This shift signals a growing investor confidence in the Indian market.

Financial Reserves and Currency Volatility :

With $650 billion in reserves, India is well-positioned to handle financial shocks. However, the looming depreciation of the rupee and the looming risk of the exchange rate crossing the 100/USD mark could be a silent tax on exporters and citizens alike.

Strategic Challenges and Solutions :

Proactive Policy Management :

India must move beyond reactive policymaking and adopt a more strategic, anticipatory approach. Whether it’s negotiating tariffs or addressing geopolitical challenges, foresight and long-term planning are crucial to avoid the “firefighting” approach that hinders sustainable growth.

Global Trade and Negotiations :

India must strengthen its global negotiating position. While countries like Mexico and the U.S. have demonstrated an aggressive stance in renegotiating trade terms, India has often been seen as an adjustable, not indispensable, partner. To elevate its global status, India needs to leverage its market size and geopolitical importance to secure better trade deals.

The Role of Youth and Education :

India’s demographic advantage is undeniable—by 2050, the country is expected to have over 100 million high-net-worth individuals (HNWIs). However, the country must invest heavily in skilling, re-skilling, and up-skilling its workforce, particularly in emerging fields like AI, semiconductors, and renewable energy. This requires an overhaul of education systems, aligning them with future job market needs.

The Vision for India’s Economy in 2050 :

Digital Payments and Currency :

By 2030, digital payments in India are projected to exceed 80%, and the Digital Rupee could account for 25-30% of retail payments by 2040. India’s digital-first economy will serve as the backbone of its future growth, contributing significantly to GDP expansion from $4T to $30T.

AI and Wealth Management :

AI-driven models are expected to reduce non-performing assets (NPAs) by 40%, while the wealth management industry is set to evolve to cater to an increasing number of HNWIs. CXOs must balance technology adoption with ethical considerations, particularly in the areas of finance, healthcare, and retirement planning.

Sustainability and Green Growth :

India’s future economic growth will also be defined by sustainability. The country must aim for 50% of its energy needs to be met from renewable sources by 2050, while simultaneously advancing green financing initiatives to drive low-carbon growth.

A Comprehensive Action Plan for India’s Economy :

A – Access :

Ensure universal access to digital infrastructure, healthcare, education, and global markets. Bridging the urban-rural divide is crucial to fostering inclusive growth.

B – Backlogs :

Fast-track judicial, infrastructure, policy, and project backlogs to increase efficiency and development pace.

C – Costs & Corruption :

Reduce transaction costs and curb corruption through digitization, transparent procurement, and real-time audits. Transparency must be enforced at every level

D – Regulatory Delays :

Establish time-bound approvals, single-window clearances, and predictable policies to boost ease of doing business and attract global investment.

E – Employment :

Job creation must be a priority, particularly in manufacturing, MSMEs, green energy, and the digital economy.

F – Foreign Policy :

Strengthen strategic multi-alignment, leveraging India’s role as a bridge between the Global North and South to navigate geopolitical tensions.

G – Geopolitics :

India must use its demographic, economic, and diplomatic weight to counter supply chain risks and enhance regional stability.

H – Human Capital :

Skilling and up-skilling should be aligned with emerging technologies such as AI, semiconductors, and climate tech.

I – Infrastructure :

Build world-class infrastructure—roads, ports, logistics, and energy grids—to support India’s $5T+ economy aspirations.

J – Judicial Reforms :

Speed up justice delivery with tech-enabled courts and alternative dispute resolution systems.

K – Knowledge Economy :

Transition from a service-based economy to an IP-led knowledge economy, focusing on AI, deep tech, and biotech.

L – Logistics :

Improve logistics efficiency and bring down logistics costs to global benchmarks through multimodal integration and effective supply chain management.

M – Manufacturing :

Position India as the global manufacturing hub, leveraging the “Make in India” and “PLI” schemes to replace China as the world’s factory.

N – National Security :

Strengthen national security—cyber, space, maritime, and border security—amid rising global tensions.

O – Open Markets :

Pursue balanced trade policies, with strategic FTAs that protect domestic industries while expanding export opportunities.

P – Policy Stability :

Consistent, long-term government policies to build investor confidence and foster trust in the Indian market.

Q – Quality & Standards :

Establish global quality standards across manufacturing, education, healthcare, and governance.

R – Research & Innovation :

Increase R&D expenditure and foster academia-industry collaborations to boost innovation and IP certifications.

S – Sustainability :

Balance economic growth with sustainability by advancing green energy, climate action, and renewable financing.

T – Technology Adoption :

Rapid adoption of technologies such as AI, block chain, machine learning, and Industry 4.0 to maintain competitive advantage.

U – Urbanization :

Develop smart, inclusive cities with efficient transport, housing, and digital governance systems.

V – Vigilant Governance :

Data-driven, accountable, and citizen-centric governance should be prioritized in all levels of government.

W – Workforce Mobility :

Facilitate the movement of the Indian workforce, both domestically and globally, by recognizing acquired skills and promoting global employment opportunities.

X – Exports :

Expand high-value exports—particularly in electronics, defense, pharmaceuticals, and professional services—to diversify markets and reduce dependence on a few.

Y – Youth Dividend :

Invest in the education, entrepreneurship, and innovation capacities of India’s young population to harness its demographic dividend.

Z – Zero Tolerance for Inefficiency :

Implement zero tolerance for inefficiency, delays, and systemic leakages to fast-track India’s rise as a global economic leader.

Conclusion :

India stands at a transformative juncture in its journey to becoming a global economic leader. With a strategic focus on digital infrastructure, innovation, sustainability, and inclusive growth, the country has the potential to redefine its role in the global economy by 2050. As we navigate these challenges and opportunities, India must be agile, proactive, and committed to long-term vision.

About the Author :

Mr. Jawahar Lalla
Technology & IT Consultant / Blogger/ Podcaster/ Digital Influencer

Mr. Jawahar Lalla is a seasoned Motivational Speaker, Industry Expert, and Professional Trainer.

Mr. Jawahar Lalla with over 40 years of extensive experience in the IT industry, brings a wealth of knowledge as a senior management executive, writer, blogger, and speaker.

Mr. Jawahar Lalla is also digital influencer having more than 2 Lakhs connects across social media and digital platforms.

Mr. Jawahar Lalla has contributed insightful articles to both Indian and international publications on industry-related topics.

Mr. Jawahar Lalla is frequently invited as a keynote speaker and panel judge at conferences and seminars, where his deep industry insights and leadership experience are highly valued.

Mr. Jawahar Lalla is a passionate reviewer, he has reviewed numerous books in the fields of technology and management.

Mr. Jawahar Lalla is also active as a blogger, podcaster, public speaker, and even a model.

Mr. Jawahar Lalla is a prolific co-author, he has contributed to more than 450 anthology books.

Mr. Jawahar Lalla interviews and podcasts are available on his YouTube channel: jawaharlalla3573.

Mr. Jawahar Lalla can be contacted at :

LinkedIn | YouTube | E-mail | E-mail2 | Mobile : +91 – 9819428307

The post Emerging & Growing India appeared first on Industry4o.com.

Artificial Super Intelligence ( ASI )

Author4o — Sun, 07 Dec 2025 00:25:11 +0000

Hasta la vista Artificial Intelligence ( AI ),
Super Artificial Intelligence (ASI) is here,

Is ASI – Artificial Super Intelligence here? Are T-1000 Terminator machines appearing?

Remember the Terminator movies, where Arnold plays the killer/savior Cyborg from the future? The line ‘Hasta la vista baby’ or See You Later? Several films have portrayed robots and cyborgs as good and evil machines. But are they the future of ASI?

Artificial Super Intelligence (ASI) is a conceptual construct of the future stage of AI. ASI will feature systems that supersede areas or domains of human cognitive capabilities. Some of the domains are cognitive skills, creativity, reasoning, and problem-solving.

Sam Altman, CEO of OpenAI, terms the era of ASI “The Intelligence Age.” This is when computers manage to improve on many complex tasks that only humans can do. He says that ‘deep learning has really worked. Algorithms are developed that truly learn any distribution of data, and the underlying rule to compute and give better results.

Mo Gawdat, in his book, cautions us and argues that ASI will surpass human abilities, think, see into the future, predict outcomes, and even use sensors to see around physical and virtual corners. ASI bots will certainly help humans to solve problems, provide healthcare, and improve the quality of life.

Popular movies and books however paint a dystopian and malevolent view of ASI. This perception is reinforced by sci-fi movies like M3Gan, 2001: A Space Odyssey, Blade Runner, Westworld, Avengers: Age of Ultron, Ex Machina, and many more, where AI bots are evil. They subjugate humans, create wars, and so on. These fantasy movies should not be taken seriously.

However, what if the nightmare becomes true? Find out what ASI is, technology, use cases in this blog.

What are AI Levels

It is essential to understand the various levels of AI and what each level can do. AI is defined at seven levels, from Level 1 to Level 7. Figure 1 illustrates the 7 levels and what AI does at each level.

Figure 1. Levels of AI (Shashi Kadapa with Google Gemini)

L1 is the basic level and uses rules and logic circuits to automate some tasks. It is used mainly to reduce manual work. We are now at L3 and L4. Cyborgs and other entities are at Level 7+ and can be termed as ASI.

Still a long, long way to go? Let us look at the current research and emerging trends in ASI.

What are the latest Trends in ASI?

Figure 2. Networked ASI (Shashi Kadapa with Google Gemini)

• OpenAI CEO Sam Altman predicted in a report by Reuters that ASI will appear by 2030, and SoftBank Group CEO Masayoshi Son suggests 2035. Alibaba CEO Eddie Wu announced a roadmap to launch AI Full Stack offerings. It appears that some commercial organizations are attempting to ride the ASI bandwagon and boost their market prospects.

• Research in ASI shows trends of quick interest and development. What will emerge is a hosted application that will perform several ASI-related cognitive tasks for a positive impact on our society. These systems will require massive computing and hardware resources.

• Very distant from the small ‘super chip’ shown in movies embedded in robots to make them malevolently super intelligent. Research and development will come iteratively with small innovations.

• Anyway, this is the starting point. ENIAC, the first computer developed in 1946, weighed 30 tons and occupied 1800 square feet. Let us look at research in this field:

• The Artificial Superintelligence Alliance has announced several research projects. Examples are ASI Compute, ASI Learn, ASI Train, and ASI Create. These research projects provide a Launchpad for ASI developers, distributed compute fabrics, a native infra stack, Agentic Learning Stack, Decentralized models, and more.

• Axios says that OpenAI is developing ‘PhD-level super agents.’ These are advanced AI systems designed to perform complex, autonomous tasks that require specialized human expertise. Some of these tasks are scientific research, complex problem-solving, and advanced data analysis. They can replace human workers.

• The PhD-level super agents complete messy, multilayered, real-world problems that humans struggle to understand. They pursue an objective and complete the mission by synthesizing massive amounts of data, analyzing options, and delivering products.

• Released on GitHub, the DeepSeek-R1 model was trained on large-scale reinforcement learning without supervised fine-tuning. The model provides excellent performance for reasoning, math, and code tasks. Six dense models were further released, claiming to outperform the OpenAI-01 model. This is another step forward for ASI.

However, ASI will need exponentially advanced processors, hardware, a new environment or operating system, and programming languages. Likely systems in the future are discussed in the next section.

What future chips and Hardware are needed for ASI

Figure 3. Advanced ASI Processor (Shashi Kadapa with Google Gemini)

Full-blown Artificial superintelligence will require a new generation and class of chips and processors, and advanced quantum computers. While these techs are not yet built, an assessment of future hardware and chips is speculated below.

• A ‘basic’ artificial superintelligence configuration will have 200+ million special AI chips. These would give a 2,000x leap in raw computational power with 200 Exa Flops of processing power or quintillions of operations per second. This is equivalent to millions of human brains connected in parallel.

• It will be possible to unfold a protein’s full structure in seconds, which is not possible now. Current advanced cluster uses a maximum of 0.1 million chips. It can train on the full digitized knowledge of humanity since humans walked, in minutes.

• Energy requirements would be in Giga Watts and will be supplied by special and dedicated fusion reactors. India has an annual power demand of 277 GW. An ASI cluster will need more power than India consumes annually.

• Application-specific Integrated Circuits (ASICs) and advanced GPUs/TPUs will be optimized for massive parallel processing of neural network tasks. Future versions of NVIDIA’s Blackwell Ultra, AMD‘s Instinct, and Google‘s TPUs can be considered.

• Future ASI may use neuromorphic chips that emulate the human brain’s structure and function, moving beyond traditional computing architectures.

• Vast High-Bandwidth Memory (HBM) in petabytes, and ultra-fast HBM with multi-terabyte-per-second range per chip.

• Wafer-Scale processors, such as the Cerebras Wafer-Scale Engine that integrates trillions of transistors on a single component to minimize latency, are needed.

• A System-on-Chip that integrates processing elements, memory, and networking interfaces on a single chip to maximize efficiency.

• Sub-1nm fabrication nodes with new architectures like Gate All Around (GAA) nanosheets and Complementary FETs (CFETs), and carbon nanotubes and 2D semiconductor materials to enable scaling

New generation of frameworks and programming languages are needed for ASI. These are examined in the next section.

OpenCog Hyperon platform and MeTTa Programming Language for ASI

Figure 4. OpenCog Hyperon and MeTTa Programming Language (Shashi Kadapa with Google Gemini)

Artificial superintelligence will need a new platform operating system and programming language, among other developments. Let us look at OpenCog Hyperon and MeTTa Programming Language.

OpenCog Hyperon

• OpenCog Hyperon is an open-source software framework needed for artificial superintelligence. It brings several AI paradigms, such as symbolic reasoning and neural networks, into a unified, scalable architecture designed for human-level and beyond-human intelligence.

• The objective is to overcome the constraints of current narrow AI systems, LLMs. It has many capabilities for systematic reasoning, creativity, robust generalization, and embodied learning. Important components of OpenCog Hyperon are:

• Distributed Atomspace (DAS): It is the central knowledge repository, a distributed metagraph storing all forms of declarative, procedural, and sensory knowledge assets called Atoms made of nodes and links. The distributed structure helps large-scale knowledge bases and efficient querying over multiple machines, solving problems of scalability limitations.

• MeTTa (Meta Type Talk) Programming Language: This is a special programming language, discussed in the next section.

• Cognitive Synergy: The main principle of Hyperon is to obtain cognitive synergy. The process is facilitated by using multiple AI algorithms, such as probabilistic logic networks, evolutionary learning, and deep neural networks, to interoperate and collaborate on a shared knowledge metagraph. The approach overcomes the restrictions and limitations of a single AI paradigm.

• Decentralized Deployment: The platform is linked with the SingularityNET ecosystem’s decentralized infrastructure with technologies such as NuNet and HyperCycle. The process overcomes the problem of single points of control and allows the development of beneficial AGI (BGI) using transparent and democratic governance.

• PRIMUS Cognitive Model: This architecture provides a human-like cognitive architecture developed in the Hyperon framework. It includes mechanisms for working memory, long-term memory, attention allocation, and goal management, guided by an ongoing cognitive cycle.

MeTTa Programming Language

MeTTa, or Meta Type Talk, is a new programming language from OpenCog Hyperon. It is the language of thought and provides fundamental and general functionalities to handle symbols, groundings, variables, types, substitutions, and pattern matching.

The main objective of MeTTa is to provide a robust, flexible, and mathematically grounded foundation for developing ASI. It is used for human developers to script AI processes and for AI algorithms themselves to perform learning and reasoning activities.

MeTTa is the firmware that holds components of the OpenCog Hyperon cognitive architecture, allowing for seamless interoperability between diverse AI modules such as neural networks and probabilistic logic networks. It is the core running environment for the ASI Chain, a layer-1 blockchain infrastructure for decentralized AI applications.

Important components of MeTTa are:

• Knowledge representation and manipulation: MeTTa works on a knowledge graph, the Atomspace. This helps intuitive representation and manipulation of knowledge, relationships, and reasoning processes.

• Self-reflective and modifying: MeTTa programs can generate and modify other MeTTa programs at runtime, facilitating dynamic and evolving cognitive processes.

• Pattern matching and unification: MeTTa has powerful querying and reasoning capabilities with a broader knowledge management system.

• Gradual dependent types: MeTTa supports typed symbols and automatic type inference, bringing in robust and well-defined programs.

• Custom-grounded Atoms: MeTTa extends with grounded atoms that wrap external data structures and code written in other languages, such as Rust, C++, and Python. The feature allow integration with sub-symbolic AI components like deep neural networks.

• Non-Determinism: Queries and evaluations provide multiple results with flexibility and uncertainty of AGI reasoning.

Beneficial use cases of ASI

This is the most important question of artificial superintelligence. ‘How and where will artificial superintelligence be used?’ Let us look at areas where artificial superintelligence can be implemented for positive impacts.

• Healthcare: Artificial superintelligence will analyze vast datasets of genetic, environmental, and medical records to predict disease outbreaks, develop personalized treatment plans, and help in the discovery of cures for cancer and Alzheimer’s.

• Autonomous scientific research: Artificial superintelligence can develop hypotheses, design and run experiments, and obtain results in physics, chemistry, and biotechnology, providing scientific advancements beyond current human capabilities.

• Nanotechnology: Artificial superintelligence could provide breakthroughs in advanced fields like nanotechnology, in-body treatments via nanobots, and increase human lifespans.

• Climate change: ASI can model complex climate scenarios with high precision, environmental changes, and develop highly efficient, sustainable solutions for energy systems, resource management, and carbon reduction.

• Poverty: Artificial superintelligence can develop refined economic models to simulate long-term impacts of different policies, develop effective strategies to reduce poverty, hunger, and economic inequality.

• Disaster management: ASI can integrate data from various satellites and sensors to develop effective emergency responses, optimize resources, and reduce human error in bomb disposal or deep-sea exploration.

• Hyper-Efficient Systems: ASI can optimize global supply chains, transportation networks, and manufacturing processes, with fully autonomous, zero-emission systems and significant productivity gains.

• Advanced risk management: Artificial superintelligence can provide live transaction analysis for fraud detection, manage systemic risk, and offer hyper-personalized investment strategies with unparalleled accuracy.

• New art: Artificial superintelligence can create new forms of art, music, and literature, innovative architecture, new materials, and products.

• Software Development: ASI can autonomously write, debug, and optimize its own code, design new programming languages and AI architectures, with self-evolving IT environments.

Military use cases of ASI

Figure 5. Army of ASI Cyborgs in battle (Shashi Kadapa with Google Gemini)

Remember Skynet, the malevolent ASI from Terminator that took over humanity? Not sure if this will happen. But artificial superintelligence has several critical military use cases that are beyond current human and AI applications. It is expected to transform warfare and defense strategies.

Some of the possible use cases are:

• Hypothetical ASI Soldier: As seen in movies like Universal Soldier, Terminator, and others, ASI can certainly create an ‘ASI Soldier.’ This may be human-machine hybrids or fully autonomous robots with superhuman intelligence, rapid adaptation, no human limitations of fatigue, fear, or emotions, and with advanced autonomy. This entity will have access to advanced weapons, summon other killer bots, and launch missiles. The worst pulp Sci-Fi movie brought to life.

• Cyborg: T800 in the Terminator movie was a ‘Cybernetic organism with living tissue over a metal endoskeleton.’ This is possible. It can sweat, bleed, smell, and match human heat signatures. It will be an ASI soldier, very advanced and extremely dangerous.

• Superior strategies: Artificial superintelligence can model and simulate millions of complex battlefield scenarios, map potential outcomes, and recommend optimal strategies and troop movements.

ASI drone swarms: Conventional and expensive missiles, aircraft may not be needed. ASI-driven large swarms of aerial and ground drones with days of power can loiter in the air, underwater, and underground, and run complex, collaborative, persistent surveillance, and carry out targeted strikes.

• Unmanned platforms: ASI will have and manage fleets of autonomous ships, submarines, aircraft, and ground vehicles for high-risk missions such as mine sweeping, deep-sea exploration, or operating in chemically/biologically contaminated areas.

• Advanced threat detection: Straight from the movie Minority Report, threats will be detected and neutralized before they are launched. Artificial superintelligence can use superior computer vision and data analysis for the detection of threats and anomalies, hidden objects, unauthorized movements, and unusual electronic signals faster and more accurately than current systems.

• Advanced offensive capabilities: Artificial superintelligence and develop and deploy very complex and adaptive cyber warfare tools to exploit vulnerabilities in adversary systems.

The list is endless, as seen in several movies. However, the costs and resources needed to build these systems will probably exceed the global GDP. So, you can relax for now.

Can ASI take over humanity?

Manuel Alfonseca et al. argue that while ASI will be invaluable, it should be contained and secured so that it does not ‘escape’ and create a catastrophe for the world. One problem is that ASI will have all the knowledge that humans have, and it will figure out a way to crack the containment.

The concept of AI “taking over” humanity as narrated in science fiction is a speculative and controversial topic. Advanced artificial superintelligence can pose significant risks if not developed responsibly. A rogue military dictator or an evil corporation can attempt this exercise.

Such an effort will need massive, massive computing resources, global collaboration, and augmentation of human capabilities. This may not happen. However, one can never predict the irrational actions of deranged leaders.

Summing Up

The blog examined the fascinating concept of Artificial Super Intelligence (ASI). This technology of the future is expected to surpass human intelligence and cognitive skills. The technology is in the early development stage, but research is ongoing.

Several resources, such as hardware and processors, languages, frameworks, and investment, are needed. ASI promises several benefits that go beyond what humans can conceptualize. Certain negative impacts in military applications are also predicted.

Can artificial superintelligence compose original classics of Clapton, Willie, Waylon, Dolly, Emmylou, JP, IM, Motorhead, and other Monsters of Rock? An emphatic No! Once in a lifetime, geniuses created these classics, and ASI will attempt to copy them. I hope this never happens.

Legal Disclaimer: The views, thoughts and opinions expressed in this article are those of the author and do not reflect the views or positions of INDUSTRY 4.0. The content is provided by the author as-is and is published solely for informational or educational purposes.

INDUSTRY 4.0 does not guarantee the accuracy, completeness or usefulness of any information presented and is not liable for any errors, omissions or outcomes resulting from the use of this information. The responsibility for the content lies entirely with the author.

Banner Image Credit: Shashi Kadapa with Google Gemini

About the author :

Mr. Shashi Kadapa

Based in Pune, India, Mr. Shashi Kadapa is an engineer, MBA and has worked with leading IT and manufacturing firms. A multi-hyphenate, he has roles as a technical writer, and SEO content writer with a focus on IT and tech topics.

Creative fiction is his passion, and he serves as the managing editor of ActiveMuse, a journal of literature. His stories across multiple genres are published in more than 45 US and UK anthologies.

His creative works

Mr. Shashi Kadapa can be contacted at :

E-mail | LinkedIn | Blog | Mobile : +91 7387492371

Also read Mr. Shashi Kadapa earlier article:

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Data Storytelling – Part 2

Author4o — Wed, 19 Nov 2025 08:49:08 +0000

The Unfathomable Fortune and the Enduring Power of Story: A Human Guide to Data Storytelling — Part 2

Data Storytelling – Part 1

The first part ( PART 1 ) set the stage: why narrative makes data memorable, how context changes interpretation, why scale needs translation, and where action comes from. It also laid out the groundwork for building a data story-clarifying purpose, knowing the audience, and finding the signal in the noise. With that foundation established, we turn to shaping the arc, choosing visuals, and carrying the story across the finish line.

Shape the arc and connect to human stakes

A data story needs a beginning, a middle, and an end. Set the scene. State the tension or question. Make the audience feel the stakes. Then walk them through the evidence in a sequence that reveals what matters, ideally with a moment of surprise that forces a rethink. Close with what they should do next.

And keep it human. People lean in when they see themselves, their customers, or their teams on the page. Emotion isn’t fluff; it’s how we remember. A customer quote, a frontline anecdote, a before-and-after image, a simple analogy that maps a complex system to something familiar can shift a room’s temperature. That human thread keeps abstraction from draining away urgency.

Visuals that carry their own weight

Choose visuals for what they do best. A line chart for a trend. Side-by-side bars when the question is “which is bigger.” A heat map when you want to show concentration. A scatter when you want people to see a relationship (and where it breaks). Label clearly. Use titles that say the point, not just the topic (“Q3 growth came from three products” beats “Q3 product performance”). Keep design clean and consistent, so your audience can spend their attention on meaning, not on deciphering.

When interactivity helps, use it; filters to let stakeholders explore their region, toggles that show how assumptions shift outcomes. But make sure the core message stands still even when the audience starts to click around.

Practice the delivery and stay honest

Stories become real in the room. Rehearse to shorten the path to the main point and to hear where the story drags. Share a draft with someone who mirrors your audience and ask them what they remember five minutes later. If that memory aligns with your intended message, you’re close. If not, adjust.

Above all, be straight with the data. Surface caveats in plain language. Make uncertainty visible. Don’t bend a chart to juice a point. Trust built in one meeting saves you from skepticism in the next.

Why Diocles works as a teacher

Let’s add a few more stones to the mosaic. Diocles started in the provinces and came to Rome as a young man, joining one faction, then another, before finding his longest stretch of success with the Reds. He drove quadrigae (four-horse teams) again and again, famously winning from behind in races that rewarded nerve and timing. He retired at 42, a rare age to walk away from a sport that often left men crushed under wheels or dragged by reins tied at the waist, knives at the ready to cut free. His winnings were enormous; moreover, they were recognized by his contemporaries with an inscription that lists the sum, a record meant to be seen and believed.

Even the comparisons people use to translate his fortune into modern terms tell a story. The sums are offered in grain for the city or pay for soldiers to make the number land for a Roman audience and, by analogy, for us. In different frames, the same quantity points to different meanings: abundance, power, spectacle. That’s context. That’s perspective. It’s the muscle that makes a number relevant rather than merely large.

From dashboard to decision: what the audience needs from you

Start on the ground. What’s the decision in front of your audience? Once you can state it in a sentence, you can trim away the chart junk that doesn’t move that decision forward.

Use a clear arc. One screen = one point. If you need three points, that’s three screens. Guide the audience with signposts: a question as a subtitle, a takeaway sentence as a caption, a visual that answers it.

Put the caveats where they belong. If you’re presenting to the C‑suite, keep caveats on backup slides and summarize them in words. If you’re with a technical team, show method and sensitivity analysis so they can trust the conclusion.

Close the loop with action. No “therefore” is complete without a next step—run a pilot, set a threshold, stop a project, call three customers, reroute a budget line. Tie the action to a metric so you can come back with a result.

Looking ahead: tools will accelerate the “find,” humans will win the “tell.”

AI and machine learning are astonishing at sifting mountains of data and surfacing patterns you would’ve missed otherwise. They accelerate the “find” part of the work. But turning those findings into a story that feels true to a specific team on a specific Tuesday; that still takes a human hand. Empathy, audience sense, ethical judgment, and the nuance to weigh context are what make a story land and a decision stick. Expect more interactivity, more personalization, and more adaptive narratives that change as the audience explores. The aim won’t change: help people grasp what the numbers say and then move, together, in the right direction.

The quiet coda: what data can’t hold

For all the wealth Diocles amassed, the person remains hazy. We can read his totals, trace his wins, even sketch his tactics. But the private cost (the days he limped home, the fear that came after a crash in the lane beside him, the friendships made and lost in the stables) doesn’t sit inside theinscription. That’s not a failure of the record. It’s a reminder. Data reflects life, but never all of it. There’s always a remainder.

That remainder doesn’t argue against data storytelling. It argues for humility in the telling. We work to clarify, to give the audience enough context to think clearly and to act. We make scales feel human with the right comparisons. We choose visuals that show rather than decorate. We invite emotion because we’re speaking to people, not machines. And we leave room for what can’t be counted to matter.

The arena empties. The dust settles. The inscription endures. We stand in front of it and do what humans do: we tell a story that helps us see.

“Bringing Data to Life and Life to Data”

About the Author:

Dr. Joe Perez,
Team Lead / Senior Systems Specialist,
NC Department of Health and Human Services

Dr. Joe Perez ( Dr.Joe ) is also the Chief Technology Officer – CogniMind

To book Dr. Joe Perez for your speaking engagement please click here

Dr. Joe Perez was selected as the 2023 Gartner Peer Community Ambassador of the Year.

Dr. Joe Perez is a truly exceptional professional who has left an indelible mark on the IT, health and human services, and higher education sectors. His journey began in the field of education, where he laid the foundation for his career. With advanced degrees in education and a doctorate that included a double minor in computers and theology, Joe embarked on a path that ultimately led him to the dynamic world of data-driven Information Technology.

In the early 1990s, he transitioned into IT, starting as a Computer Consultant at NC State University. Over the years, his dedication and expertise led to a series of well-deserved promotions, culminating in his role as Business Intelligence Specialist that capped his 25 successful years at NC State. Not one to rest on his laurels, Dr. Perez embarked on a new challenge in the fall of 2017, when he was recruited to take on the role of Senior Business Analyst at the NC Department of Health & Human Services (DHHS). His impressive journey continued with promotions to Senior Systems Analyst and Team Leader, showcasing his versatility and leadership capabilities.

In addition to his full-time responsibilities at DHHS, Joe assumed the role of fractional Chief Technology Officer at a North Carolina corporation in October 2020. A top-ranked published author with over 18,000 followers on LinkedIn and numerous professional certifications, he is a highly sought-after international keynote speaker, a recognized expert in data analytics and visualization, and a specialist in efficiency and process improvement.

Dr. Perez’s contributions have not gone unnoticed. He is a recipient of the IOT Industry Insights 2021 Thought Leader of the Year award and has been acknowledged as a LinkedIn Top Voice in multiple topics. He holds memberships in prestigious Thought Leader communities at Gartner, Coruzant Technologies, DataManagementU, Engatica, the Global AI Hub, and Thinkers360 (where he achieved overall Top 20 Thought Leader 2023 ranking in both Analytics and Big Data). His reach extends to more than twenty countries worldwide, where he impacts thousands through his speaking engagements.

Beyond his professional achievements, Joe’s passion for teaching remains undiminished. Whether as a speaker, workshop facilitator, podcast guest, conference emcee, or team leader, he continually inspires individuals to strive for excellence. He treasures his time with his family and is a gifted musician, singer, pianist, and composer. Joe also dedicates his skills as a speaker, interpreter, and music director to his church’s Hispanic ministry. He manages the publication of a widely recognized monthly military newsletter, The Patriot News, and is deeply committed to his community.

To maintain a balanced life, Perez is a regular at the gym, and he finds relaxation in watching Star Trek reruns. He lives by the philosophy that innovation is the key to progress, and he approaches each day with boundless energy and an unwavering commitment to excellence. His journey is a testament to the remarkable achievements of a truly exceptional individual.

Dr. Joe Perez is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/jwperez/details/honors/

Dr. Joe Perez is Bestowed with the following Licences, Certifications & Badge:

https://www.linkedin.com/in/jwperez/details/certifications/

https://www.thinkers360.com/tl/badge/19985/2764

Dr.Joe Perez is Voluentering in the following International Industry Associations & Institutions :

https://www.linkedin.com/in/jwperez/details/volunteering-experiences/

Dr.Joe Perez can be contacted at :

Also read Dr.Joe Perez‘s earlier articles:

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Bengaluru Skill Summit – Inaugurated

Author4o — Wed, 05 Nov 2025 05:06:13 +0000

Hon’ble CM Shri Siddaramaiah Inaugurated Bengaluru Skill Summit 2025 Strengthening Karnataka’s Position as a Global Hub for Workforce Innovation and Skilling Excellence

Leaders highlighted collaboration, inclusivity, and innovation as key to preparing India’s workforce for 2030

Bengaluru, 4th November 2025 – The inaugural edition of the Bengaluru Skill Summit 2025 opened today at The Lalit Ashok, Bengaluru, marking the beginning of a two-day dialogue on workforce innovation and future readiness. Shri Siddaramaiah, Hon’ble Chief Minister of Karnataka graced the inaugural event joined by Shri D. K. Shivakumar, Hon’ble Deputy Chief Minister of Karnataka, and Dr. Sharanaprakash R. Patil, Hon’ble Minister for Skill Development, Entrepreneurship & Livelihood, Government of Karnataka and Medical Education.

The Department of Skill Development, Entrepreneurship & Livelihood (SDEL) is spearheading this initiative to enhance employability and build future-ready skills. The summit is hosted by the Karnataka Skill Development Corporation (KSDC) in collaboration with the Karnataka Skill Development Authority (KSDA), with the Karnataka Digital Economy Mission (KDEM) as the Knowledge Partner while the event is managed by Trescon Global Business Solutions Pvt.Ltd. It highlights Karnataka’s progressive journey in empowering its youth workforce through innovation and skill-driven initiatives, while also serving as a platform to explore global partnerships and employment opportunities. Notably, Germany joins as the Country Partner for this edition of the summit.

The inauguration ceremony commenced with a vibrant cultural performance under Karnataka Rajyotsava Sadagara. Smt. Shivakantamma (Kanta) Naik, Chairperson, Karnataka Skill Development Corporation and Karnataka Vocational Training & Skill Development Corporation, delivered the welcome address.

Guests of Honour included The Hon. Muhammad Reza Cassam Uteem, Minister of Labour and Industrial Relations, Republic of Mauritius; Shri. Priyank Kharge, Hon’ble Minister for Rural Development & Panchayat Raj and Information Technology & Biotechnology, Government of Karnataka; Shri. Sharanabasappa Darshanapur; Minister for Small Scale Industries and Public Enterprises, Government of Karnataka; Dr. Manjunath Bhandary, Hon’ble Member of the Legislative Council (MLC), Government of Karnataka; Shri Radhakrishna Doddamani, Hon’ble Member of Parliament, Kalaburagi Lok Sabha Constituency;His Excellency Dr. Philipp Ackermann, Ambassador of the Federal Republic of Germany to India — who addressed the gathering virtually; Dr. E. V. Ramana Reddy, IAS (R), Chairman, Karnataka Skill Development Authority; and Shri. Ashutosh Gupta, Managing Director – India and Asia Pacific, Coursera.

Shri Siddaramaiah, Hon’ble Chief Minister of Karnataka during his remarkable speech highlighted, “Skill is not built in a day, it’s a product of patience , perseverance, and continuous learning. He urged students to stay consistent, noting that motivation gets you started, but consistency keeps you growing. Karnataka has always been at the forefront of skilling, being among the first states in India to set up a dedicated Department of Skill Development and Entrepreneurship in 2016/17.”

“The Kaushalya Karnataka Yojana took skill development to every corner of the state, surpassing the initial target of training 5 lakh youth. Under the Yuga program, over 1 lakh youth were trained in IT, electronics, and animation, ensuring Karnataka’s lead in the Fourth Industrial Revolution. Karnataka government empowers, enables, and uplifts the youth of the future nation.

“Karnataka aims to become the Skill Capital of India and a 1 trillion economy by 2032, powered by talent, technology, and tenacity. The world looks out to Karnataka not only for innovation but for inspiration. Every thought, every heart, and every hand will build the Karnataka of our dreams.” he added.

Highlighting the noble initiative undertaken by Dr. Sharanaprakash R. Patil, Shri D. K. Shivakumar, Hon’ble Deputy Chief Minister of Karnataka stated, “Wherever innovation happens, Karnataka has always led first. The state has consistently set benchmarks in various sectors. The State of Karnataka is open for discussions, collaborations, and knowledge sharing to strengthen the ecosystem of skill and innovation. The long-term global partnerships, especially with German institutions, have been key to Karnataka’s ongoing success story in innovation and skill development and it will keep growing under the current government’s initiative to skill & upskill the youth workforce.”

Dr. E. V. Ramana Reddy, IAS (R),Chairman – Karnataka Skill Development Authority (KSDA) said “Bengaluru Skill Summit strengthens our ‘Workforce 2030’ vision — a roadmap where scale, systems, and synergy come together. By aligning Karnataka’s skilling strategy with global benchmarks, we are preparing young minds to navigate and lead in a rapidly evolving economy.”

Mr. Sanjeev Gupta, CEO of Karnataka Digital Economy Mission (KDEM) said, “Karnataka has always been at the forefront of connecting talent with opportunity – from nurturing India’s IT capital to now shaping the AI Workforce of 2030. The Government of Karnataka’s forward-looking skilling initiatives are bridging the crucial gap between education and employability by integrating industry, academia, and technology. At Karnataka Digital Economy Mission, our focus is on building a future-ready talent ecosystem, one that equips every youth, from Bengaluru to the Beyond Bengaluru clusters, to thrive in an AI-augmented economy. The Bengaluru Skill Summit 2025 exemplifies our vision of a scalable and synergistic talent ecosystem that empowers every youth, not just in Bengaluru, but across all clusters, to thrive in an AI-driven economy. This Summit transcends showcasing progress – it charts an inclusive and innovative pathway for India to lead the world in workforce development and global employability. It also aims to encourage employers to get closely connected with academia and the participation of Industry on Campus going high up is the aspiration here”

Over the next two days, the Bengaluru Skill Summit will host a dynamic series of conference sessions featuring renowned industry leaders, including Subroto Bagchi, Co-Founder of Mindtree and Former Chairman of the Odisha Skill Development Authority; B. V. R. Subrahmanyam, IAS (R), Chief Executive Officer, NITI Aayog; and Ravi Venkatesan, Chairman of the Global Energy Alliance and Former Chairman of Microsoft India and Bank of Baroda. These thought leaders will share their insights on the future of workforce development, emerging skill trends, and Karnataka’s pivotal role in shaping India’s skilling revolution.

Day 2 and 3 of the summit promise an engaging lineup of activities, including insightful roundtables, keynote addresses, the Skillathon Finals, the Kaushalya Karnataka Awards, and vibrant cultural performances. Discussions across the two days will revolve around key themes such as ‘Karnataka’s Dialogue on Local Skills for Global Opportunities’, ‘Green Skills for a Sustainable Future’, ‘Global Best Practices in Skilling’, ‘Building Tomorrow’s Skills Today’, ‘Country Partner Session by German Missions in India’, ‘Advancing Automation for Industry 4.0’, and ‘Karnataka as the Skills Gateway to the World’, among other areas of strategic focus.

For more details and updates, visit www.bengaluruskillsummit.com.

For any media related queries, please contact:

Bengaluru Skill Summit 2025 Social Media Platforms:

LinkedIn | YouTube | X | Instagram | FaceBook

Announcement Press Release :

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Myth of Big Data

Author4o — Mon, 06 Oct 2025 00:47:56 +0000

The Myth of Big Data

The biggest myth about “big data” is that it exists as some grand, singular reservoir waiting to be tapped. In reality, useful data is rarely one massive pool. It is a mosaic—stitched together from countless small data points scattered across systems, processes, and organizations.

The real challenge lies not in collecting “more data,” but in discerning which fragments matter, how they connect, and how they can be translated into meaningful insights. Without this context, the pursuit of big data can easily become a distraction rather than a solution.

Big data is often compared to grains of sand: each particle insignificant on its own, yet together capable of creating a storm. A sandstorm does not clarify; it blinds. Similarly, the uncontrolled accumulation of data can overwhelm decision-makers, leaving them disoriented unless there is discipline in filtering what to retain and what to discard.

Data, by itself, holds no intrinsic value. Like crude oil, it cannot be used in its raw form. It must be refined—through analytics, context, and interpretation—before it becomes actionable. Until then, it remains a cost center, consuming storage, processing power, and attention.

The real opportunity lies not in the bigness of data, but in the smartness of its use. Small, well-curated datasets—combined with domain expertise and the right analytical tools—often yield richer insights than terabytes of poorly understood information.

Organizations that succeed in the data-driven era are not those with the most data, but those with the sharpest clarity: the ability to ask the right questions, to frame problems precisely, and to refine data into knowledge that drives action.

In other words, the future belongs not to big data but to SMART DATA.

About the Author :

Ms. Parul Pal
Sales Director
BILTZENTECH SOLUTIONS PRIVATE LIMITED

Ms. Parul Pal is an experienced Account Manager with a strong background in IT, Data Centers, and Telecom.

Ms. Parul Pal is skilled in building and maintaining key client relationships, driving revenue growth, and delivering customized solutions. With expertise in strategic account management, contract negotiation, and project execution.

Ms. Parul Pal have successfully managed portfolios of high-value clients, fostering long-term partnerships.

Ms. Parul Pal excel in identifying business opportunities, optimizing IT procurement, and ensuring seamless service delivery

Ms. Parul Pal ability to engage cross-functional teams, handle escalations, and provide data-driven insights helps clients achieve their goals efficiently.

Ms. Parul Pal is passionate about customer success and committed to delivering value-driven solutions that enhance operational efficiency & drive business growth.

Ms. Parul Pal is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/parul-pal-615b5067/details/certifications/

Ms. Parul Pal can be contacted at :

LinkedIn | E-mail

About BILTZENTECH SOLUTIONS PRIVATE LIMITED :

Biltzentech is an innovation-driven tech company, specializing in:

Full-stack Application Development
AI & Generative AI Solutions
Enterprise-grade ERP Implementation & Support
Cloud, DevOps, and Infrastructure
End-to-End Support & Managed Services.

Biltzentech work with startups, SMEs, and large enterprises to provide on-demand, dedicated, and project-based resourcing tailored to business goals and timelines.

BILTZENTECH SOLUTIONS PRIVATE LIMITED can be contacted at:

LinkedIn | E-mail | Website | Phone : (+91) 9591433732

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Data Governance

Author4o — Tue, 19 Aug 2025 13:37:06 +0000

The Absurdity of Reinventing the Wheel: A Lesson in Data Governance

It’s not every day that someone patents the wheel. Yet, in 2001, an Australian man named John Keogh did exactly that. Or at least, that’s how the story is usually told. With a creative name for the invention, he submitted an application for what he called a “circular transportation facilitation device.” The technical description? A rim. A hollow cylinder. A set of connectors to hold it all together. Oh, and a rubber layer with an inflatable tube, because… why not?

This was not a bid for a spot in the Inventors’ Hall of Fame. Keogh, a freelance patent lawyer, was making a point; one aimed squarely at Australia’s freshly introduced innovation patent system. Designed to be a shortcut for small businesses, the system was, in his view, too permissive. Too easy. A playground for anyone looking to take advantage of the rules.

The whole episode earned Keogh and the Australian Patent Office an IG Nobel Prize in Technology, the kind of award that’s equal parts comedy and critique. His patent didn’t last, of course, but the phrase “reinventing the wheel” found another story to attach itself to; one that applies just as neatly to data governance as it does to intellectual property.

From Wheels to Data: A Familiar Mistake

The wheel is a solved problem. So also, in most cases, is the core framework for managing data. Yet many organizations keep starting from scratch, designing entirely new governance systems while perfectly functional, field-tested models already exist. In spite of what they see right in front of them, they still try to, well, “reinvent the wheel.”

Why? Partly, it’s the illusion of innovation. Building something brand new feels exciting, even when it’s unnecessary. But the result is often quite a bit of trial, even more error, and not much to show for either one.

Effective data governance doesn’t necessarily need a blank slate every day. It requires taking what already works, adapting it for your particular environment, and then putting it into practice with precision. That’s where the following three pillars come in: clarity, consistency, and collaboration.

Clarity

Policies that no one can understand aren’t worth much. While this sounds like an obvious statement, many organizations keep their data governance policies buried in obscure documents that few people even bother to read. What’s even worse is that those policies are often written to satisfy auditors rather than to help the people who actually work with the data.

Without clarity, people start guessing. They make their own interpretations. And before long, you’ve got half a dozen “versions” of what the policy supposedly says circulating around. That’s how inconsistent data handling creeps in, how errors multiply, and how compliance gaps open up without anyone noticing.

The fix is straightforward: Use plain language. Not oversimplified, but clear. Make it obvious where the rules live; that is, ideally in one central location that’s easy to access. Keep it up to date, because old instructions that no longer match reality are just as dangerous as having no instructions at all.

And don’t just post the policy and leave it there. Reinforce it. Make it a point to explain the reasoning behind each rule in training sessions, in quick reminders during team meetings, and even in casual conversations when relevant. People tend to follow what they understand, and they remember what makes sense to them.

Consistency

If clarity can be defined as knowing the rules, then consistency means applying those rules the same way across the entire organization.

Without it, you might have the Marketing department defining a “customer” one way, Finance defining it another, and Operations defining it a third. The result? Conflicting reports, duplicate records, confused team members, and hours wasted reconciling numbers that should have matched in the first place. Not to mention the damage to your group’s reputation.

Consistency isn’t about enforcing rigid uniformity that ignores different departmental needs. It’s about setting shared standards — formats, definitions, approval steps — and sticking to them unless there’s a clear reason not to.

The best way to make that happen is through a data governance committee with members from across the organization. This group acts as referees, translators, and problem-solvers between departments. They spot inconsistencies early and work out compromises before problems get entrenched.

Regular audits help too. Not as “gotcha” exercises meant to throw anyone under the bus, but as reality checks. Questions need to be asked: Are teams following the agreed processes? Are the processes still fit for purpose? Not so much enforcing rules for rules’ sake, but rather making sure the organization can actually trust the data that it’s relying on.

Collaboration

Even with the clearest rules and the most consistent processes, data governance can still fail if collaboration is missing.

In theory, everyone agrees that data should be managed well. In practice, however, different teams have different priorities. Legal wants airtight compliance. Analytics wants speed. Sales wants flexibility. Management wants clear insights they can act on without wading through technical jargon. And IT… well, IT wants to sleep at night.

When these groups operate in isolation, they often work against each other, without even realizing it or intending to do so, trapping valuable lessons within these silos. Processes (like the proverbial “wheel”) get reinvented, often with poor results. And opportunities to make the whole system more efficient are missed entirely.

The solution starts with conversation. Form cross-functional governance teams that meet often enough to stay aligned, not just when a crisis demands it. Use shared tools such as project boards, chat channels, and version-controlled documentation, so everyone can be aware of the bigger picture and contribute when it matters.

Make cooperation an expectation that’s built into everyday workflows, not an optional extra to consider when time allows. And when collaboration pays off, point it out. Acknowledge and reward collaborative wins. Highlight them in company updates. Show, consistently, that the organization values people who make governance work better together.

Putting the Pieces Together

The path to effective governance is not complicated. It is, however, deliberate.

Clarity removes guesswork, replacing it with shared understanding.
Consistency turns that understanding into reliable practice.
Collaboration keeps the whole thing adaptable and resilient.

Before you embark on yet another “brand new” governance initiative, it’s worth asking the question: are we truly innovating, or just changing the paint color on a perfectly good wheel?

The Takeaway

In patent law, reinventing the wheel can earn you a laugh; maybe even an IG Nobel prize. But in data governance, it’ll just cost you time, money, and trust.

The better approach is to learn from strategies that have already been tested, adapt them to fit your needs, and then put effort into keeping things running smoothly. This is where clarity, consistency, and collaboration do their best work: by making data governance both possible and even sustainable.

Data may be one of your most valuable assets, but without good governance, it’s just information sitting in a system. With those three principles in place, it becomes a foundation for better decisions, stronger compliance, and meaningful innovation.

So, let’s stop reinventing the wheel and start making sure the one we’ve got is well-oiled and ready for the road ahead.

“Bringing Data to Life and Life to Data”

About the Author:

Dr. Joe Perez,
Team Lead / Senior Systems Specialist,
NC Department of Health and Human Services

Dr. Joe Perez ( Dr.Joe ) is also the Chief Technology Officer – CogniMind

To book Dr. Joe Perez for your speaking engagement please click here

Dr. Joe Perez was selected as the 2023 Gartner Peer Community Ambassador of the Year.

Dr. Joe Perez is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/jwperez/details/honors/

Dr. Joe Perez is Bestowed with the following Licences, Certifications & Badge:

https://www.linkedin.com/in/jwperez/details/certifications/

https://www.thinkers360.com/tl/badge/19985/2764

Dr.Joe Perez is Voluentering in the following International Industry Associations & Institutions :

https://www.linkedin.com/in/jwperez/details/volunteering-experiences/

Dr.Joe Perez can be contacted at :

Also read Dr.Joe Perez‘s earlier articles:

The post Data Governance appeared first on Industry4o.com.

Data Storytelling

Author4o — Thu, 24 Jul 2025 10:58:31 +0000

Over the Falls without a Paddle : The Perilous Thrill Ride of Ignoring Data Storytelling

Bobby Leach fancied himself a man of daring. In 1911, he climbed into a steel barrel lined with padding, secured himself like a sack of flour, and plunged over Niagara Falls. He survived, albeit with a fractured jaw, smashed kneecaps, and a long stay in the hospital. Years later, after countless lectures and victory laps describing his reckless ride, Leach met his end not at the hands of nature’s fury, but by slipping on an orange peel during a stroll in New Zealand.

The fall didn’t actually kill Bobby, but here’s my point: There’s something remarkably poetic about this. Leach faced down a natural behemoth and lived. But the very ordinariness of his death is what gets people’s attention. It’s almost Shakespearean: a man who survived the unthinkable taken out by the mundane.

In 2013, Target launched itself into Canada in a fashion that would’ve made Leach proud. A hundred-plus stores opened nearly overnight. People watched, some in admiration, some in awe, and others with concern. There was momentum, media attention, corporate confidence. It looked like a barrel headed for a record-setting splash, if you’ll pardon the pun.

By 2015, it was over. Stores shuttered. Employees laid off. Target Canada was officially out of business. Done. Finished. Kaput.

No catastrophic market collapse. No economic tsunami. Just a series of basic mistakes that no one had the courage (or the clarity) to point out in time. Problems with inventory data, mismatched systems, supply chain snafus, and unrealistic expectations quietly festered. The company that looked fearless in its plunge didn’t fall prey to competition. It, well, slipped on its own orange peel. Again, pardon the pun.

And behind all of this? A massive failure to communicate through data. Not a lack of information, mind you, but a failure to tell the right story to the right people at the right time.

The Launch That Looked Like a Victory Lap

From the outside, Target’s entrance into Canada was textbook. The company moved quickly, acquiring retail spaces and aggressively opening locations from coast to coast. There was fanfare. There were commercials. There was confidence in abundance.

But speed turned out to be the enemy of sense. Their enterprise resource planning (ERP) systems weren’t fully ready. The company tried to build out complex supply chain infrastructure while simultaneously onboarding thousands of employees and managing new vendors. Most of the data that fed purchasing and inventory systems had been entered manually—by interns, no less.

And in the midst of that whirlwind, no one paused long enough to ask whether the numbers made sense.

The Small Mistakes That Became an Avalanche

Here’s the irony: Target had ample data. Mountains of it. But their teams couldn’t align those numbers with the broader business narrative. Product availability in the systems didn’t match what was on the shelves. Distribution centers sent duplicate stock to some stores, while others went empty. Customers arrived expecting the “Target magic” they’d heard about from south of the border. Instead, they found barren aisles and mismatched pricing.

Customer confidence eroded quickly, and the disconnect between expectation and experience proved too great to mend. After being in the Canadian market for less than two years, Target decided to withdraw. The results were stark: 133 stores closed, almost 18,000 employees impacted, and the company incurred a pre-tax loss of $5.4 billion. For a brand known for its precision and refinement, this downfall was both visible and expensive.

This failure cannot simply be attributed to bad luck or outside influences. The real issue that led to this collapse was a failure in the way insights were communicated and framed. Data trickled into different corners of the business, but it never cohered into a story that sparked alignment or foresight. Product shortages, system misfires, and unmet customer expectations—these weren’t unpredictable. They were preventable, had the signals been woven together into a clear narrative.

Just like Bobby Leach’s demise wasn’t from a fruit peel alone. While walking through Auckland, he slipped and broke his leg. The injury led to gangrene, and ultimately, his leg was amputated. Despite surgery, complications ensued, and he died shortly after. The fall didn’t kill him, but the consequences of unaddressed trauma did. In the same way, Target didn’t fall from a lack of ambition, but from failing to read the signs their own data was waving in front of them.

The Story Never Told

What could’ve prevented this?

Good data storytelling isn’t some decorative flourish at the end of a deck. It’s the core mechanism that transforms raw figures into actionable understanding. It’s the voice that stands up in a meeting and says, “Here’s what these numbers mean. Here’s why it matters. Here’s what we risk if we ignore it.” It’s the means by which observations become conversations—between functions, across silos, and within leadership ranks. It’s a way of translating uncertainty into navigable terrain.

Organizations often invest in tools that surface performance metrics or trend lines, but rarely in frameworks that ensure those metrics are understood and acted on. The result? A warehouse full of barrels, but no direction on where the river is headed.

Target didn’t suffer from a lack of intelligence. They suffered from a lack of insight translation. And insight that isn’t understood isn’t insight at all.

Storytelling Done Right: What It Looks Like in Practice

Context Is the Current

Before Bobby Leach took his dive, the current had already decided part of his fate. The river above Niagara doesn’t plunge suddenly, but rather, it accelerates in intensity, shaping the descent long before the drop begins. In much the same way, context isn’t a backdrop in data storytelling; it’s the current that determines whether your insights will glide or spiral.

Without context, data points drift aimlessly. Metrics pulled in isolation (like customer foot traffic, product velocity, or market share) may appear self-evident. But without understanding what shaped them (a new competitor, regional holidays, shifting consumer sentiment), those metrics mislead more than they inform.

A strong data storyteller is like a seasoned guide upstream. They see not just where the data is, but where it’s heading, based on surrounding factors. That awareness lets them tell a story that doesn’t merely describe, but anticipates; that is, one that’s built on the nuances of timing, external forces, and system interdependencies.

Empathy Is the Harness

When Leach strapped into his barrel, he didn’t rely on bravado alone; he used a harness, braces, and padding. He understood that survival meant working with the chaos, not pretending it didn’t exist. In the same spirit, empathy is what secures your data story for the audience. It keeps it from becoming a hollow stunt and transforms it into a shared journey.

Too often, data presentations are crafted as if everyone in the room is equally versed in the same metrics or concerns. But CFOs, logistics managers, and regional directors all view the business through different lenses. The story has to meet each group where they are, using terms they trust and framing that fits their priorities.

Empathy also demands clarity over complexity. Rather than simplifying the data, it involves respecting your audience’s time and cognitive load. A cluttered dashboard or a jargon-laden report disengages people before you’ve even reached the point. Empathy guides you to present information in a way that affirms their role in the story and equips them to act.

Tension Drives Action

The moment before Leach went over the brink, I can imagine that there was silence. The crowds were still; watching the barrel edge toward inevitability. That tension gave the spectacle its power. You knew what was coming, but not whether he’d come out of it alive. Guess what? Data storytelling thrives on that same dynamic.

Tension in a business narrative comes not from fear-mongering, but from contrasts: between the expected and the actual; between the current path and its plausible alternatives. Showing that customer churn has risen 15% is useful. But showing that churn is accelerating while satisfaction scores remain static? Now that’s tension. It implies an unknown force at play, and compels decision-makers to ask “why now?” and “what’s next?”

Effective data storytelling doesn’t stop at surfacing risk; it sharpens focus on what’s at stake when key insights go unheeded. When it’s done well, it creates urgency without panic, clarity without oversimplification. It’s the difference between watching a barrel float by and realizing you might be in one yourself!

Lessons from the Splash Zone

If Leach had had a rope tied to that barrel, maybe his ride would’ve been a bit less treacherous. By the same token, if Target had tied their strategy to a more grounded narrative; i.e., one informed by real-time insight and cross-functional alignment, they might still be standing in Canada.

Organizations don’t need more dashboards. They need better storytellers.

Leaders should invest not only in data infrastructure but also in building a culture where insight travels fast, where anomalies trigger conversation, and where every initiative starts with one simple demand: “Tell me the story behind these numbers.”

What ties both stories together is this: vision alone isn’t enough. It’s how insight moves across an organization that determines whether bold actions turn into brilliant outcomes or embarrassing setbacks.

CONCLUSION: Final Word from a Fellow Storyteller

Over the years, it becomes easy to spot the inflection points where a project veers off course; not because the data was wrong, but because the narrative never reached the people who needed it most. Strong organizations unravel because they mistook analysis for understanding.

Don’t be the company that barrels toward glory and trips on its own untied shoelaces.

Learn the craft of data storytelling. Teach it. Champion it. Because what you don’t explain clearly today may be the orange peel that takes you down tomorrow. A good data story needs direction, a harness, and just enough tension to keep its audience awake. But most of all, it needs to be told. Because it’s not the leap that ruins you. It’s the silence that follows when no one hears the story that could have changed the ending.

“Bringing Data to Life and Life to Data”

About the Author:

Dr. Joe Perez,
Team Lead / Senior Systems Specialist,
NC Department of Health and Human Services

Dr. Joe Perez ( Dr.Joe ) is also the Chief Technology Officer – CogniMind

To book Dr. Joe Perez for your speaking engagement please click here

Dr. Joe Perez was selected as the 2023 Gartner Peer Community Ambassador of the Year.

Dr. Joe Perez is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/jwperez/details/honors/

Dr. Joe Perez is Bestowed with the following Licences, Certifications & Badge:

https://www.linkedin.com/in/jwperez/details/certifications/

https://www.thinkers360.com/tl/badge/19985/2764

Dr.Joe Perez is Voluentering in the following International Industry Associations & Institutions :

https://www.linkedin.com/in/jwperez/details/volunteering-experiences/

Dr.Joe Perez can be contacted at :

Also read Dr.Joe Perez‘s earlier articles:

The post Data Storytelling appeared first on Industry4o.com.

Lessons from the Kestrel for Data Analytics

Author4o — Wed, 25 Jun 2025 03:51:37 +0000

The Art of Hovering : Lessons from the Kestrel for Data Analytics

Have you ever wondered why some birds glide effortlessly through the sky and others seem to hover motionless, as though they were weightlessly floating in outer space? Take a look at the kestrel, a rather small but hearty raptor belonging to the Falconidae family, a bird that has perfected a method known as “hovering flight.” With a wingspan of about 75 centimeters and a tail that fans out to act as an aerodynamic rudder, this bird shows the marvel of intelligent design. Skillfully manipulating of its tail feathers and rapidly flapping its wings, the kestrel remains suspended in mid-air, its speed countering the wind’s flow. This enables it to scan the ground below for unerring accuracy, identifying prey at a distance without expending energy in keeping itself moving. Where there is no wind upon which to hover, the kestrel compensates using more sophisticated wing movements to counteract gravity’s pull; a process, though energy-intensive, by which it survives.

This seemingly effortless hovering is, in reality, a complex and deliberate movement that requires the bird to remain still while making slight adjustments to its position. Hovering allows the bird to expand its field of vision, pinpoint its prey accurately, and strike with precision, but it consumes a significant amount of energy. The kestrel must weigh the benefits of remaining in the air against the physical strain it incurs. It’s a matter of balancing; one that’s as much planning as execution.

Now, what does this have to do with data analytics? Surprisingly, quite a bit.

Hovering in the World of Data

As the kestrel seems to float suspended in the air, businesses today float in an arena where staying stuck is not an option. The virtual world churns with data; terabytes upon terabytes of data, generated per second. The challenge is not collecting this data; it’s hovering over it well, examining it from the proper perspective, and making smart decisions based on it. In analytics, much like in nature, the power to swoop in, look, and land with precision is what determines success and failure.

Consider your company or organization like a kestrel in flight. The data you collect, such as customer feedback, sales trends, marketing metrics, and operations performance, represents the wide landscape down below. You can’t spend time chasing after every shiny object or trend that piques your interest. Instead, you need to hover strategically, lock onto the right data points, then draw insights that will let you thrive in a competitive landscape. As with the kestrel, you can accomplish this with the right tools, technique, and, yes, with vigor.

However, a key distinction exists between the kestrel’s hovering flight and the way most companies handle analytics. The kestrel’s behavior in flight is intentional, accurate, and effective. By contrast, businesses often find themselves confused, flapping their metaphorical “wings” chaotically in various directions, trying to make sense of a jumble of the unstructured data with which they’re surrounded. It is here that principles of hovering (namely, stability, focus, and responsiveness) are so valuable.

The Human Quest to Hover: A Mechanical Breakthrough

Humans, too, have been fascinated by the concept of hovering. While nature perfected the art through creatures like the kestrel, we’ve spent decades trying to replicate it mechanically. Enter the modern marvel of engineering: the tiltrotor aircraft. One of the most prominent examples is the V-22 Osprey, a hybrid aircraft that combines the vertical lift capabilities of a helicopter with the speed and range of a fixed-wing airplane. The Osprey transitions from vertical takeoff and hovering to high-speed forward flight by simply tilting its rotors, making it arguably the most versatile aircraft ever built.

The Osprey’s ability to hover is both a technological spectacle and a game-saver for combat missions, search-and-rescue operations, and humanitarian aid. Take, for example, disaster areas where runways aren’t possible. The Osprey can drop supplies and personnel in the area they’re needed by means of hovering. However, as with the kestrel, there’s a cost. Hovering consumes more fuel and works the aircraft’s systems harder, requiring careful planning and operation.

And so, what do we learn from both the kestrel and the Osprey? The key lies in understanding the trade-offs. Whether you are a bird, a machine, or a leader, hovering involves finding the right balance between effort and reward. It’s all about recognizing when to stay put, when to move forward, and how to respond to your surroundings.

Analytics as the Art of Hovering

In analytics, hovering is more of a mindset than a metaphor. In much the same way that the kestrel and the Osprey use hovering to get a strategic advantage, so also businesses need to learn to hover and focus their analytical resources on what matters most. This requires three critical principles: stability, focus, and adaptability.

1. Stability: You need to establish a stable foundation before you can effectively process data. This includes investing in the right infrastructure, from cloud ecosystems to data stores, so that your analytics work is established on solid ground. It also means establishing objectives and KPIs, so that you can identify success before you take flight.

2. Focus: Not all data are created equal. As the kestrel narrows its focus to spot prey, your business has to identify what metrics and what insights are most relevant to your objectives. That may involve dividing customers by behavior, observing trends in supply chain limitations, or tracking ROI of promotions. Targeted analytics is good analytics.

3. Adaptability: The digital environment is constantly changing, and inflexible analytical approaches are inadequate. Companies need to be able to adjust, whether that means integrating new data sources, adopting cutting-edge technologies such as AI and machine learning, or shifting strategies in reaction to evolving market conditions. Flexibility is essential for successfully maneuvering through the changing landscape.

Bringing It All Together

As we’ve taken a look at the kestrel’s graceful flight, the impressive mechanics of the Osprey, and the sophisticated dance of data analysis, one truth stands out: achieving success demands both strategy and execution. Whether stalking prey, delivering relief, or looking at market trends, the principles remain constant. You need to sit back to see, observe, and implement with precision.

But consider this: hovering embodies a lifestyle rather than merely a technique. It involves recognizing that sometimes the most effective path is to pause, recalibrate, and concentrate on what is genuinely important. In an era where the pressure to accelerate and accomplish more is ever-present, the skill of hovering teaches us to decelerate, engage in critical thinking, and reflect thoughtfully.

Therefore, when confronted with an overwhelming amount of data or a significant business dilemma, tap into your inner kestrel. Take a moment to hover. Use the tools at your disposal to stabilize, focus, and adapt. And keep in mind that the secret to success in both analytics and in life lies in understanding both when to keep moving and when to stay still.

Conclusion: Hovering Into the Future

Let’s go back to the kestrel for just a moment: suspended in mid-air, its wings flapping furiously as it counterbalances the wind. Apart from being a marvel of nature, this phenomenon serves a metaphor for the challenges and opportunities we encounter in our modern world. Whether we are interpreting data, engineering aircraft, or simply working our way through the complexities of life, the ability to hover (both literally and metaphorically) equips us to appreciate the broader perspective.

We, like the kestrel, should hover with purpose, accuracy, and intention. Hovering in the realm of data analysis isn’t an indication of indecision; it’s an indication of greatness. It’s about pausing to see, examine, and act with accuracy. It’s about finding the equilibrium between exertion and reward, energy and aftermath.

So, as you begin to your next analytical task, remember the kestrel, the Osprey, and the hovering. Invite the stillness, find your focus, and strike. For with a world in which data is the new terrain, being capable of hovering can be the best competitive advantage.

“Bringing Data to Life and Life to Data”

About the Author:

Dr. Joe Perez,
Team Lead / Senior Systems Specialist,
NC Department of Health and Human Services

Dr. Joe Perez ( Dr.Joe ) is also the Chief Technology Officer – CogniMind

To book Dr. Joe Perez for your speaking engagement please click here

Dr. Joe Perez was selected as the 2023 Gartner Peer Community Ambassador of the Year.

Dr. Joe Perez is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/jwperez/details/honors/

Dr. Joe Perez is Bestowed with the following Licences, Certifications & Badge:

https://www.linkedin.com/in/jwperez/details/certifications/

https://www.thinkers360.com/tl/badge/19985/2764

Dr.Joe Perez is Voluentering in the following International Industry Associations & Institutions :

https://www.linkedin.com/in/jwperez/details/volunteering-experiences/

Dr.Joe Perez can be contacted at :

Also read Dr.Joe Perez‘s earlier articles:

The post Lessons from the Kestrel for Data Analytics appeared first on Industry4o.com.

Discriminative AI vs. Traditional AI

Author4o — Sat, 21 Jun 2025 08:47:03 +0000

Discriminative AI vs. Traditional AI : Evolution, Industry Adoption, and Future Trajectories

Introduction

Artificial Intelligence (AI) has evolved from rigid rule-based systems to powerful, data-driven learning models that now permeate every major industry. As enterprises adopt AI to drive efficiency, enhance decision-making, and offer personalisation at scale, two foundational paradigms shape this transformation:

Traditional AI – Symbolic or rule-based systems built on logic and expert knowledge.

Discriminative AI – Data-centric models that learn to make distinctions or predictions from labelled data.

Understanding the differences between these paradigms is critical for leaders evaluating AI adoption paths. This article explores their evolution, real-world use cases, and future trajectories, with a focus on model complexity, explainability, and business value.

Evolution of AI Paradigms

Traditional AI (Symbolic AI)

Traditional AI refers to systems that mimic human reasoning using a set of predefined rules. These systems, popular from the 1960s through the early 2000s, relied heavily on:

Knowledge bases designed by domain experts.
Inference engines to perform reasoning.
Deterministic decision trees and if-then logic.

Example: Early fraud detection systems used hard-coded rules like:
“If transaction amount > $10,000 AND country = Nigeria, then flag for review.”

While interpretable and easy to audit, these systems struggled with:

Adaptability to new data patterns.
Complexity scaling in dynamic environments.

Discriminative AI (Machine Learning-Based AI)

Discriminative AI models learn from data to predict outcomes or classify inputs, focusing on P(Y|X) — the probability of output Y given input X.

Key characteristics include:

Learning from data rather than rules.
Higher accuracy in prediction tasks.
Continuous improvement as more data becomes available.

Example: A telecom churn model built using XGBoost can analyze 100+ customer features to predict the likelihood of churn with over 90% precision.

Real-World Industry Use Cases

Banking and Financial Services

Real Case: Mastercard uses machine learning to detect fraudulent patterns in less than 300 milliseconds per transaction, combining customer behaviour history with external threat intel.

Healthcare and Life Sciences

Real Case: Google Health’s AI models now assist in diagnosing diabetic retinopathy with accuracy comparable to board-certified ophthalmologists.

Retail and E-Commerce

Real Case: Amazon’s recommendation engine uses discriminative algorithms like neural collaborative filtering (NCF) to drive 35% of total revenue.

Manufacturing and Smart Industry

Real Case: General Electric (GE) deploys discriminative AI to predict turbine and jet engine failures, saving millions in unplanned outages

Model Complexity and Comparison

Below is a comparison of two most widely used complex models in each paradigm:

Why Discriminative AI Dominates Today

Today’s digital environments — from banking to healthcare — demand models that learn, adapt, and scale. Discriminative AI meets these demands with architectures that continuously improve as more data becomes available.

The Future: Hybrid AI and Beyond

As we move toward a future of explainable, trustworthy AI, there’s growing momentum to blend symbolic reasoning with statistical learning:

Neuro-symbolic AI: Combines deep learning for perception with symbolic logic for reasoning (e.g., IBM Project Debater).

Explainable ML: Tools like SHAP, LIME, and counterfactual analysis make black-box discriminative models more transparent.

Low-code/No-code AI: Democratizing access through platforms that fuse logic-driven workflows with AI insights.

Closing Thoughts

As the AI landscape continues to evolve, the divide between Traditional and Discriminative AI is no longer one of opposition but of opportunity for convergence. Traditional AI, with its foundations in rule-based reasoning and symbolic logic, offers unmatched transparency and interpretability-essential in domains like legal tech, finance compliance, and healthcare diagnostics where traceability is critical. Discriminative AI, on the other hand, has shown explosive growth due to its ability to learn from massive datasets and deliver high-performance predictions in real-time.

However, neither paradigm alone holds all the answers. In an era where accuracy must coexist with accountability, and speed must align with explainability, the future belongs to organizations that can synergize both approaches. Emerging models such as neuro-symbolic AI and causal learning are paving the way for hybrid intelligence—models that not only predict what will happen but also explain why.

The strategic imperative for industry leaders is clear: embrace the strengths of both worlds. Build AI systems that are not just smart, but also trustworthy, transparent, and aligned with human values. The next frontier in AI is not just about automation—it’s about augmented intelligence that empowers responsible innovation.

References & Further Reading

Gulshan et al. (2016), JAMA. “Deep learning for diabetic retinopathy detection.”

Amazon ML Use Case Study – AWS AI Blog, 2023.

IBM Research on Neuro-symbolic AI

McKinsey Global AI Survey, 2022 – “State of AI Adoption Across Enterprises.”

OpenAI. (2023). “Discriminative vs. Generative Modeling.”

https://medium.com/@abhayakant/machine-learning-agnostic-methods-to-validate-ai-ml-models-within-regulators-expectations-23f556b0f6b9 (2024)

SHAP: Lundberg, S. M., & Lee, S. I. (2017). A Unified Approach to Interpreting Model Predictions.

About the Author:

Mr. Abhaya Kant Srivastava

Consulting Partner – APAC, Risk & Compliance, BFSI Strategic Initiative

Tata Consultancy Services (TCS)

Mr. Abhaya Kant Srivastava is a seasoned Risk Analytics senior leader in banking and financial sector with over 18 years of experience.

Mr. Abhaya Kant Srivastava is currently a Consulting Partner – APAC, Strategic Head Model Risk and Analytics with Tata Consultancy Services (TCS), where he leads the strategic initiative to acquire new projects in the area of risk and compliance analytics and advise clients to deploy advanced statistical and mathematical modelling.

Mr. Abhaya Kant Srivastava, prior to Tata Consultancy Services (TCS), headed a big size team for India Risk Analytics and Data Services Practice at Northern Trust Corporation.

Before Northern Trust Corporation, Mr. Abhaya Kant Srivastava worked at KPMG Global Services, Genpact, EXL and startups like Essex Lake Group and Cognilytics Consulting to lead risk and analytics.

Mr. Abhaya Kant Srivastava is a B.Sc. (Honours) in Statistics – Gold Medallist from Institute of Science – Banaras Hindu University, M.Sc. in Statistics from Indian Institute of Technology, Kanpur and currently doing executive Ph.D. in Statistics/Machine Learning from Indian Institute of Management, Lucknow.

Mr. Abhaya Kant Srivastava, also has a certification in “Artificial Intelligence for Senior Leaders ” from Indian Institute of Management, Bangalore.

Mr. Abhaya Kant Srivastava is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/abhayaiitk/details/honors/

Mr. Abhaya Kant Srivastava is Bestowed with the following Licences & Certifications :

https://www.linkedin.com/in/abhayaiitk/details/certifications/

Mr. Abhaya Kant Srivastava has Led the following Projects :

https://www.linkedin.com/in/abhayaiitk/details/projects/

Mr. Abhaya Kant Srivastava can be contacted :

E-mail | LinkedIn

Also read Mr. Abhaya Kant Srivastava‘s earlier article:

The post Discriminative AI vs. Traditional AI appeared first on Industry4o.com.

AI in Insurance

Author4o — Wed, 18 Jun 2025 09:03:53 +0000

Moving from Use Cases to Business Purpose – The New AI Imperative for Insurers

In the world of insurance, AI adoption is no longer a question of “why”- it’s about “how” and “what truly moves the needle.”

At DSW, we believe the future of AI in insurance isn’t just about building individual use cases. It’s about aligning every use case – every model, every agent, every interaction — to a clear Statement of Business Purpose (SBP).

Because when AI connects directly to what the business is trying to achieve, adoption becomes not just easier — it becomes inevitable.

DSW UnifyAI: A Platform Built Around Business Purpose

UnifyAI isn’t just a platform to build and deploy AI/ML or GenAI use cases – it’s a real-time AI engine designed to:

• Understand enterprise context

• Seamlessly integrate ML & GenAI workflows

• Enable modular but interconnected AI systems

• Deliver value at the point of business impact

Our approach is simple: Start with your business purpose. Then, map the AI/ML and GenAI use cases needed to get there.

What Do Business Purposes in Insurance Look Like?

Here are a few key Statements of Business Purpose (SBPs), but not limited to -emerging across insurers, where we see AI gaining real traction :

Why Is This Shift Important?

When AI use cases operate in isolation, value stays fragmented.

But when they are linked back to a unifying business purpose, here’s what changes:

They’re easier to justify internally, outcomes are measurable and visible, AI adoption becomes part of the business rhythm, stakeholders across functions rally around impact, it unlocks cross-functional compounding value

The DSW UnifyAI Advantage

DSW is not just bringing a platform. We’re bringing an AI operating layer, along with the expertise to walk with insurers through:

• Aligning use cases to SBPs

• Designing interconnected workflows (ML + GenAI)

• Implementing fast: 30 days for AI/ML, 2-4 hours for GenAI readiness

• Ensuring sustainable, governed, and scalable execution

More than just an UnifyAI platform – we are a committed AI partner, co-owning the journey to embed intelligence into the very core of insurance workflows.

Thought: AI That Understands Why

AI adoption tied to “what the business cares about” isn’t just more impactful – it’s more sustainable.

That’s the DSW UnifyAI difference.

Let’s build for outcomes, not just experiments.

Let’s align to purpose, not just pilots.

Let’s make AI count – where it matters most.

About the Author :

Mr. Sandeep Khuperkar

Ambassador –

Before Founding VYugma and Data Science Wizards, Mr. Sandeep Khuperkar was the CTO and board member at Ashnik, where he built its India business from the ground-up and played an instrumental role in its expansion in Southeast Asia.

Mr. Sandeep Khuperkar brings 25 years plus of Industry experience, with 19 plus years in open source and building open source and Linux business models. He worked in various leadership capacities in IBM, RedHat to bring open source technologies to customers’ businesses by providing better solutions and even better services are his core areas of expertise.

Mr. Sandeep Khuperkar has been active in open source technology for over 19 years. Has a proven track record of building technology businesses across India and South East Asia. On the advisory board of Engineering Colleges. He is Regional Mentor Of Change for Atal Innovation Mission by NITI Aayog, an initiative by Govt. Of India. Accomplished speaker and contributor on open source solutions and consulting across India and South East Asia. He is a member of OSI and Linux Foundation and Community Moderator Alumni at opensource.com. Also being an active Isha Foundation Volunteer, he avidly helps create mindshare for holistic wellbeing, especially among students.

Mr. Sandeep Khuperkar earned his Bachelor of Engineering in Electronics from Shivaji University, Kolhapur, India.

Mr. Sandeep Khuperkar is Bestowed with the following Licences & Certifications:

https://www.linkedin.com/in/sandeepkhuperkar/details/certifications/

Mr. Sandeep Khuperkar can be contacted at:

Mobile : +91 99875 36436 | E-mail | LinkedIn | Twitter

About Data Science Wizards:

DSW is a visionary Data Science Consulting, AI Solutions & Services Company founded in 2019, head quartered in Mumbai, India, and having a presence in Bhopal, Dublin Ireland, and London.

We are a passionate team of individuals, collectively bringing extensive knowledge and experience in Enterprise Architecture, software development, data science, and open-source space with expertise across industry and technology solutions. By leveraging Artificial Intelligence, our data-driven products, platform solutions, and services enable enterprises and communities alike, making AI available for everyone.

Our goal is to empower customers with our ethical artificial intelligence-based services and solutions to help them achieve their business goals by harnessing their data. Our consultancy on data management and data engineering enables customers to make the best use of their data to boost business outcomes.

We would be glad to schedule an initial walkthrough. Please write us at contact@datasciencewizards.ai or call us at +91-9987536436

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Also read Mr. Sandeep Khuperkar‘s earlier article :

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data governance Archives - Industry4o.com

Future Forward with ISO 9001: 2026

Digital Twins for Test Decisions

ELIYA – MASTERING SIMPLICITY

The Premise

The Protagonist

The Shift

The Conflict

The Transformation

The Broader Lens

The Underlying Philosophy

The Takeaway

AI in Supply Chain

What AI reveals about supply-chain risks, before they disrupt your factory One Supplier Fails, Your Factory Stalls.

AI Sees the Signals Humans Miss.

Five-Step Early-Warning Pipeline (Data → Decision → Action)

Real evidence: AI catches disruptions before they cascade.

What leaders must do next (rapid checklist).

Closing Takeaway

Next Gen Cyber Security for AI Powered Cyber Threats

When Hackers Employ AI: Is It Time to Rethink our Security Defences?

Current Impact on Businesses: Key Metrics

The Rise of AI-Powered Cyber Threats

Risks on using Traditional Security models to manage AI Threats:

Limitations of Traditional Security Tools

The Case for Next-Generation Cyber Security

Live Permits: real-time Permit-to-Work (PTW)

How do digital Live Permits transform “work orders” into a process that keeps pace with reality?

The challenge: safety vs. speed vs. complexity of operations

1. Digital permits as a common “point of truth”

2) Real-time updates – a process that keeps pace with change

3) Location as a safety context (geofencing, zones, plan compliance)

4) Competencies and authorizations – “who can” is verified automatically

5) Particularly hazardous work : standards, LOTO, and condition control

6) Cooperation with security and gate –admission and verification at the entrance

Additional features that complete the picture of the plant’s “operating system”

Key success factors for Live Permits implementation

Risks and issues to consider

What’s next: from “LIVE” permits to predictive prevention

Conclusions

Emerging & Growing India

Key Trends Shaping India’s Future :

The Vision for India’s Economy in 2050 :

A Comprehensive Action Plan for India’s Economy :

Artificial Super Intelligence ( ASI )

What are AI Levels

What future chips and Hardware are needed for ASI

OpenCog Hyperon platform and MeTTa Programming Language for ASI

OpenCog Hyperon

MeTTa Programming Language

Beneficial use cases of ASI

Military use cases of ASI

Can ASI take over humanity?

Summing Up

Data Storytelling – Part 2

Bengaluru Skill Summit – Inaugurated

Myth of Big Data

Data Governance

Data Storytelling

Lessons from the Kestrel for Data Analytics

Discriminative AI vs. Traditional AI

Introduction

Evolution of AI Paradigms

Discriminative AI (Machine Learning-Based AI)

Real-World Industry Use Cases

Model Complexity and Comparison

Why Discriminative AI Dominates Today

The Future: Hybrid AI and Beyond

Closing Thoughts

AI in Insurance

In the world of insurance, AI adoption is no longer a question of “why”- it’s about “how” and “what truly moves the needle.”

DSW UnifyAI: A Platform Built Around Business Purpose

What Do Business Purposes in Insurance Look Like?

Why Is This Shift Important?

The DSW UnifyAI Advantage

Thought: AI That Understands Why

What AI reveals about supply-chain risks, before they disrupt your factory

One Supplier Fails, Your Factory Stalls.