Capital Goods & Strategic Skill Council (CGSSC), in partnership with Tata Consulting Engineers (TCE), today launched Project UTKARSH, a CSR-led skilling initiative to equip engineering students with industry-relevant skills and prepare them for careers in India’s growing EPC, and manufacturing infrastructure sector, along with the launch of CGSSC’s newsletter, ‘Yantrashilp’. The programme was inaugurated at Don Bosco Technical Institute (DBTI), Okhla, New Delhi, in the presence of senior government, industry and skilling leaders from TCE, CBC and CGSSC.

Project UTKARSH has been designed to bridge the gap between technical education and industry requirements by offering industry aligned training in three critical engineering disciplines—Civil Design, Piping & Instrumentation Design, and Electrical Design. The initiative will initially benefit technical/ vocational students across Delhi-NCR and Jamshedpur, equipping them with industry-relevant competencies and enhancing their employability.

CGSSC is an apex skill body with representation from industries like Larsen & Tourbo, MSME, pan-India trade associations for the empowerment of capital goods manufacturing and engineering services, and strategic manufacturing sectors. The recent initiative with TCE (Tata Consulting Engineers) for their ‘Utakarsh’ – a CSR Initiative is a step towards providing industry skilled workforce, quality training in technical domain for sustainable livelihood with the society in manufacturing industries infrastructure.

CGSSC has developed these programmes in collaboration with TCE by creating specialised trades’ Qualification Packs aligned with the Skill India. With a portfolio of more than 60 industry-driven Qualification Packs across capital goods, advanced manufacturing, Industry 4.0 and strategic sectors, CGSSC continues to play a pivotal role in developing a future-ready workforce that meets the evolving needs of Indian industry.

Speaking at the launch, Chief Guest Ms. Alka Mittal, Member, Capacity Building Commission (CBC), said, “this initiative is about creating opportunities and unlocking the potential of our young people. Through hands-on learning, industry exposure and employability-focused training, we want every student to move forward with greater confidence and skills. India’s youth are our greatest strength, and by investing in their capabilities today, we are building a more skilled, confident and future-ready nation.”

Addressing the gathering, Ms. Nidhi Mehandiratta, CHRO & CSR Head, Tata Consulting Engineers, said, “The vision behind this programme is to prepare young people to shape the future of the nation. We have already seen encouraging results, with nearly 600 students completing the programme. Our next step is to take this proven model nationwide through collaboration with the Capital Goods & Strategic Skill Council (www.cgssc.org) enabling more young people across states to build confidence, recognise their unique strengths and become future-ready.”

Ms. Shalini Singh, CEO, CGSSC, said, “India’s journey towards becoming a global manufacturing and innovation hub will require a skilled and adaptable workforce. Our focus is not merely on training youth, but on creating an industry-aligned ecosystem where classroom learning, hands-on training and employability skills come together to prepare young people for both today’s jobs and the opportunities of tomorrow.”

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EVTOL: Navigating Certifications, Air Mobility Regulations, and Battery Energy Density

As per Vertex Market Research, the eVTOL Aircraft Market is poised for significant growth during the next decade. Global eVTOL Aircraft market is projected to achieve a Compound Annual Growth Rate (CAGR) of approximately 30% during the forecast period from 2026 to 2035.

In 2025, worldwide eVTOL Aircraft Market stood at around US$ 600 Million. The Global eVTOL Aircraft Market size is projected to reach an estimated US$ 18 Billion by 2035. Vertex projects the eVTOL aircraft unit sales estimated to cross 4,000 – 5,000 units by 2035, up from around 250 unit sales deliveries recorded in 2025 (consisting primarily of uncrewed autonomous platforms and military evaluation prototypes).

eVTOL Aircraft Market is driven by several structural drivers such as growing urbanization and increasing traffic congestion, and the rising demand for clean, efficient, and sustainable transportation modes. Technological advancements in electric propulsion systems and battery technologies, coupled with growing consumer demand for fast, efficient, and flexible point-to point urban mobility, are accelerating the adoption of eVTOL platforms.

Additionally, increasing ongoing public-private investments, and strategic collaborations are fueling product development and deployment. The eVTOL aircraft market is also benefiting from a growing range of applications, from air taxis and urban shuttle services to emergency medical transport, private aviation, leisure air tours, and defense and logistics use cases. The integration of vertiports, electric charging infrastructure, and smart city mobility ecosystems is further catalyzing the commercial rollout of eVTOL aircraft globally.

The 2026 Freeze Point

Many players within the eVTOL industry consider 2026 to be the crucial “freeze point” for eVTOL aircraft development. To meet intended 2027-28 launch dates, OEMs will need to firm up aircraft design and crucial technology decisions over the next twelve months. Future alterations to designs and technologies at a late stage of certification could compromise project timelines and costs significantly. Regulators are solidifying the certification standards and operational requirements for these new aircraft, and therefore manufactures have turned attention from aircraft design and development towards regulatory compliance, testing and certification, making 2026 a significant point for the transition.

The eVTOL regulation landscape is rapidly advancing as governments and aviation authorities work to implement clear paths for commercialization. In late 2025, Europe solidified its lead when EASA finalized certain components of its SC-VTOL certification scheme, offering aircraft manufacturers increased regulatory clarity during design and approval stages.

In March 2026, commercialization was pushed forward in the U.S. When the FAA selected six eVTOL companies including Joby and Archer to move forward into a second phase of its eIPP allowing pre-certified aircraft to begin operating in a controlled environment.

Increased global regulatory cooperation occurred in June 2026 when the FAA and EASA met to discuss the alignment of certification standards-a necessary precursor to allowing aircraft certified in one region to operate efficiently in another. China pushed its ambitions to lead advanced air mobility when revised civil aviation regulations will take effect in July 2026 mandating local governments to harmonize management of low-altitude air corridors and vertiports, ultimately promoting increased rollout speeds, reduced regulatory risks and greater opportunity for widespread eVTOL commercialization across the globe.

Battery Chemistry and Energy Density: The Key to eVTOL Scalability

Vertex states that the future growth of the eVTOL sector will be greatly determined by battery technology. Jet fuel provides a very high energy density, but current batteries offer a significantly lower energy density and therefore range, payload capacity and operational efficiency. One difficulty in operating eVTOL craft is that there are two distinct power requirements – one to provide a high power burst for landing, takeoff and hovering and another for sustainable, efficient flight (cruise flight). Conventional battery chemistries are unable to provide both a high power and energy density simultaneously, prompting eVTOL manufacturers to develop a combination of different battery chemistries, with one system used for landing, takeoff and hovering flight, and another used for cruise flight.

As per Vertex Market Research, the eVTOL technology road-map indicates a steady increase in battery energy densities; current aviation-grade lithium ion batteries achieve energy densities in the range 250-300Wh/kg and are capable of short-range operations. The arrival of semi-solid state and silicon-anode batteries with higher energy densities of 300-350Wh/kg are projected to deliver a 30-50% increase in range. Further ahead, solid-state and lithium-metal batteries with predicted energy densities exceeding 400-500Wh/kg may deliver substantial increases in flight range, higher payloads and improved economics. Overall, battery innovation should prove one of the most critical enablers for large-scale eVTOL commercialization and fleet development up to 2035.

EVTOL Market Concentration and Competitive Landscape

As per Vertex, the Global eVTOL Aircraft Market is segmented based on System Component, Lift Design & Configuration, Propulsion Type, Mode of Operation, Range, Maximum Take-Off Weight (MTOW), and Application. By System Component, the market comprises Aerostructures & Interiors, Avionics, Batteries & Cells, Electric Motors & Engines, Software & Platforms, and Other Systems. Based on Lift Design & Configuration, it is categorized into Multirotor, Lift + Cruise, Vector Thrust, and Other Configurations. By Propulsion Type, the market includes Battery Electric, Hybrid Electric, and Hydrogen Electric aircraft.

Based on Mode of Operation, the market is segmented into Piloted, Semi-Autonomous, and Autonomous platforms, while Range is classified into Up to 200 km and Above 200 km. By MTOW, the market is divided into Up to 500 kg, 501–1,500 kg, and Above 1,500 kg aircraft. Based on Application, the market covers Air Taxis, Air Shuttles & Air Metros, Private Transport, Air Ambulance & Medical Emergency Services, Leisure & Recreational Flights, and Other Applications, reflecting the industry’s broadening commercial use cases.

Innovation and strategic alliances are rapidly reshaping the Global eVTOL Aircraft Market as firms strive to reach certification, develop scaled manufacturing, and commence commercial operations. Key participants include Archer Aviation, ARC Aero Systems, AutoFlight, BETA Technologies, EHang, Electra Aero, Eve Air Mobility, Jaunt Air Mobility, Joby Aviation, LIFT Aircraft, Manta Aircraft, Sarla Aviation, SkyDrive, The ePlane Company, Vertical Aerospace, Volant Aerotech, Wisk Aero, and XTI Aerospace, which are heavily investing in obtaining aircraft certifications, developing battery technology, enabling autonomous flight, scaling manufacturing processes and forging commercial ties with airlines, infrastructure companies, and mobility service operators. Investments and technology partnerships continue to define competition in this rapidly evolving sector as it moves from concept development to full commercial application.

In contrast, the market is extremely challenging, with multiple leading developers not able to keep on commercially developing its product for long term. Several eVTOL programs have been abandoned, suspended, or driven to bankruptcy because of lacking funding, delays in certification, technical complexities, or not clear commercial feasibility. There have been a number of setbacks such as Lilium, Volocopter, Overair, Supernal (Hyundai), Urban Air Mobility programs (Airbus), Nexus program (Textron), Urban Aeronautics and Kitty Hawk, which underscore the extensive funding required, regulatory complexities, and execution risks associated with bringing eVTOLs from concept to commercial operation.

About Vertex Market Research

Vertex Market Research is a trusted and reliable market intelligence and consulting firm headquartered in Australia, with research and sales operations across the United States and India. We deliver high-quality market insights, industry analysis, and customized research solutions that help businesses make informed strategic decisions.

Recognized as a reputable source for market research, Vertex Market Research is committed to accuracy, transparency, and data-driven excellence. Our team of experienced analysts and industry experts provides actionable intelligence across diverse sectors, empowering organizations worldwide to identify opportunities, mitigate risks, and achieve sustainable growth.

Vertex Market Research & Consulting (VMRC) is a one-stop destination for your all research requirements. We are a market intelligence and consulting firm with comprehensive experience and rich knowledge of the market research business intelligence, and advisory industry.

Vertex Market Research & Consulting (VMRC) has a vast repository of the latest market research reports, and top company profiles, along with the latest market statistics released by our dedicated industry experts. Our vast repository of research reports across various categories gives you a complete view of the ever-evolving trends and current topics worldwide.

At Vertex Market Research & Consulting (VMRC) the data bank is monitored by teams of subject matter experts which are validated by various statistical data and trending content.

Being a reliable market research company, Vertex Market Research & Consulting (VMRC) guarantee readers of reliable information based on the study of factual data of the past, and present scenarios of the market.

What Vertex Market Research & Consulting (VMRC) Do – Our Services :

Vertex helps clients make informed business decisions. Vertex offers market intelligence research ensuring relevant and fact-based research across a range of industries including chemicals, materials, energy, healthcare, and technology.

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Garbage, for most investors and business leaders, remains one of India’s least examined infrastructure stories. Yet beneath the country’s approximately 62 million tonnes of municipal solid waste generated every year, a resource-recovery industry projected to reach nearly USD 12 billion by 2030 is taking shape. The businesses that learn to design and integrate this system, rather than merely service individual parts of it, could define the sector’s next decade.

When we decided to dedicate this year’s edition of LogiPulse to waste management, a few people quietly raised an eyebrow. Logistics and supply chains were readily accepted as serious commercial subjects, but waste was still seen mainly as a sanitation or environmental issue. That reaction convinced us that the sector deserved closer attention. Across conversations with investors, operators and policymakers, we repeatedly encountered the same underlying problem: regulations existed, capital was available and technology could be sourced, but the operating system connecting these elements was incomplete.

A manufacturer could not fulfil its Extended Producer Responsibility obligation because there was no authorised recycler within an economically viable distance. A hospital was paying a substantial premium for biomedical-waste disposal because the nearest Common Biomedical Waste Treatment Facility was three districts away. A waste-to-energy project had capital, technology and a municipal contract, but poor feedstock quality restricted the plant to approximately 40% of its designed capacity. The problem was not a lack of intent or investment. The system architecture was broken.

The global context makes this failure more striking. The world generates nearly 2.24 billion tonnes of municipal solid waste annually, but less than one-fifth undergoes meaningful recovery. The recoverable value permanently destroyed, including metals, plastics, organics and rare earths, is estimated at USD 80–120 billion every year. At the same time, the global waste-management industry is valued at approximately USD 2.08 trillion. An industry of this scale spends enormous capital on collection, transportation and disposal while recovering only a fraction of the value embedded in the material it handles. This is no longer merely an operational-efficiency issue; it is a systems-level design failure and, simultaneously, one of the largest untapped opportunities within the circular economy.

India presents a particularly important paradox. The country has established the Solid Waste Management Rules 2016, E-Waste Management Rules 2022, Biomedical Waste Management Rules 2016, Plastic Waste Management Rules 2022 and Battery Waste Management Rules 2022. Yet only around 30% of municipal solid waste undergoes formal processing. The regulatory architecture is substantially in place, but the enforcement and processing infrastructure required to activate it remains inadequate. Formal capacity being developed today is therefore being built ahead of a demand wave that regulation has already created, but has not yet fully delivered.

International experience shows that technology alone does not determine performance. Germany recovers approximately 67% of materials and has made landfill use almost negligible, while the European Union average is close to 48%, supported by formal collection exceeding 90%. The difference lies in consequence-driven enforcement, producer accountability through EPR, sustained infrastructure investment and the integration of informal workers into the formal system. These are the institutional foundations on which recovery technologies become economically effective.

India’s waste value chain is fragmented across segregation, collection, transfer, sorting, processing and final disposal. These activities are managed by different institutions operating with different incentives, data systems and performance measures. A weakness at one stage degrades the economics of every stage that follows. The annual losses identified in our analysis include USD 8–12 billion of material value from inadequate source segregation, USD 1.5–3 billion of revenue from the collection gap, USD 1–1.5 billion of avoidable transfer and transportation costs, USD 1.5–2 billion from sorting and Material Recovery Facility discounts, USD 0.7–1.2 billion from processing assets operating at only 40–60% utilisation, and more than USD 2.4 billion in deferred liabilities arising from non-compliant disposal sites.

The most important intervention is source segregation. When organic waste is mixed with dry recyclable material at the point of generation, contamination progressively becomes irreversible. India’s urban source-segregation compliance rate is approximately 45%. Every 10-percentage-point improvement in compliance can reduce downstream processing costs by 15–20%, while simultaneously increasing the value of recovered material. This demonstrates why processing plants cannot be evaluated independently of their upstream collection and feedstock architecture.

The sector’s economics are equally counterintuitive. The most visible activities are not necessarily the most profitable. Collection and transportation generally operate at EBITDA margins of 5–10%. Margins rise to 15–25% in Material Recovery Facilities, 20–30% in waste-to-energy, 25–40% in e-waste processing, and 30–40% in pharmaceutical and biomedical waste management. Technology platforms and the integration layer can potentially generate margins exceeding 40% at scale. Mid-chain recovery opportunities, including e-waste, biomedical waste, battery recycling, MRF networks and EPR-compliance platforms, offer indicative target IRRs ranging from 22% to 40%.

E-waste illustrates this value-recovery opportunity particularly clearly. Global e-waste generation reached approximately 62 million tonnes in 2022, growing at 4–5% annually, while only 22.3% was formally collected and recycled. India generates around 3.2 million tonnes of e-waste each year, making it the world’s third-largest generator. This annual stream contains more than USD 2.4 billion of embedded recoverable value at 2025 prices, yet formal recyclers capture less than 20% of it.

The concentration of recoverable metals is remarkable. Mobile phones contain approximately 0.4 kilograms of gold per tonne of devices, compared with only 1–4 grams per tonne of mined gold ore. Urban mining can therefore offer gold concentrations 50–100 times higher than conventional mining. The decisive competitive differentiator is processing technology: hydrometallurgical systems can achieve gold recovery rates of 95–99%, compared with 60–80% through conventional smelting. When this capability is combined with EPR-backed processing fees, the resulting business model becomes more defensible. Compliance revenue can form the base case, while recovered-metal sales provide commodity-linked upside.

Attero Recycling provides an operating example of this model, with 500,000 tonnes of licensed capacity, more than 250 EPR clients across 12 export markets, and a hydrometallurgical processing backbone. Its significance lies not merely in scale, but in the combination of technology, regulatory compliance and multiple revenue streams.

Battery recycling is the next extension of the urban-mining opportunity. The first substantial wave of end-of-life electric-vehicle batteries is expected between 2028 and 2032. Operators investing in lithium-ion recycling capacity today are positioning themselves ahead of a regulation-driven feedstock cycle that is likely to expand significantly. The estimated window to build ahead of this wave is only three to four years. Early investment can create advantages in technology, approvals, feedstock relationships and EPR-compliance infrastructure.

Waste-to-energy presents a different investment proposition. India currently has 14 operational plants against an estimated requirement of more than 200, while utilisation of available potential remains at approximately 6%. The bottleneck is not simply capital or technology; it is the sequence in which infrastructure is being developed and the quality of contractual risk allocation.

Mixed Indian municipal waste frequently has calorific value below 1,200 kcal per kilogram, while most plants require approximately 1,500–2,000 kcal per kilogram. Large-scale energy conversion cannot operate reliably unless segregation and Refuse-Derived Fuel preprocessing are established first. RDF also creates an independent off-take opportunity through the cement industry, which is targeting a 25% thermal substitution rate by 2030. Municipal concessions must additionally provide longer tenors, indexed tipping fees, assured feedstock standards and enforceable payment-security mechanisms. Waste-to-energy is therefore best understood as a sequencing and contract-design challenge, rather than merely a technology problem.

Biomedical and pharmaceutical waste have a more structurally protected demand profile. India generates approximately 800–900 tonnes of biomedical waste every day, with volumes growing at 7–8% annually. Around 198 Common Biomedical Waste Treatment Facilities are registered, although the gap between registration and active operation remains significant outside major cities.

Hospitals cannot avoid generating biomedical waste, nor can they use informal disposal channels without exposure to criminal liability. Standard biomedical waste commands approximately ₹8–15 per kilogram, while specialised pharmaceutical streams, including cytotoxic compounds, hormones and expired injectables, command ₹25–50 per kilogram. The authorisation process for a new treatment facility takes approximately 18–24 months, creating a meaningful regulatory moat. Consequently, EBITDA margins of 30–40% among leading facilities are not merely the result of exceptional operating performance; they are supported by captive demand, route density, compliance-led pricing and high barriers to entry.

Technology is also changing the economics of the entire chain. AI-based route optimisation has produced 20–25% savings in fuel and maintenance in Indian smart-city pilots. IoT-enabled bins have improved collection efficiency by 20–40% and reduced overflow incidents by 60–70%. AI-driven optical sorters can process more than 80 items per minute with accuracy exceeding 95%, while blockchain-based EPR traceability is improving the credibility of compliance certificates and could eventually enable them to develop into tradeable financial instruments.

The larger opportunity, however, is not any single technology. It is the integration layer connecting material-flow tracking, incentive coordination, compliance management, processing capacity and data monetisation within one operating architecture. The future waste-management company may resemble a technology and financial-information platform that also manages the physical movement of material. The data generated by that network could ultimately become more valuable than the tipping fees collected.

The risks should not be underestimated. Feedstock quality remains the highest-probability and highest-impact risk across waste-to-energy, composting, RDF and MRFs. Municipal contracts can weaken otherwise viable projects through inadequate tenors, poor tariff design and uncertain payment security. Technology investments may underperform in operating environments without adequate training and accountability. Non-EPR e-waste and battery-recycling volumes remain exposed to commodity prices, while poorly planned formalisation can displace informal workers without securing the collection networks they currently support.

None of these risks is a structural reason to avoid the sector. They are reasons to design investments more carefully. Stronger models will secure feedstock before deploying capacity, build close to regulatory moats, treat compliance-linked income as the base case, structure commodity revenue as upside and create long-term contractual protection around capital-intensive assets.

India does not require another round of broad regulation. It requires credible enforcement, wider development of transfer stations and systematic replication of successful municipal systems such as the Indore model. For operators, the economically sound sequence is to begin with high-value recovery and then integrate backwards to secure feedstock. For capital providers, the opportunity lies in identifying where regulatory demand, infrastructure scarcity, processing capability and data ownership converge.

The fundamental shift is from waste handling to system integration, from compliance administration to capital formation, and from treating discarded material as a disposal problem to designing it as a resource-recovery system. The companies and cities that make this transition first will not merely participate in India’s waste economy. They will shape how it operates for the next several decades.

About the Author :

Mr. Sanjeev Jain  
Managing Partner,
Amicus Growth Advisors  

Mr. Sanjeev Jain is a Fellow Chartered Accountant and Registered Insolvency Professional with over 30 years of strategic finance and governance leadership across logistics, telecom, manufacturing, and infrastructure.

As former CFO of GATI and AFL / FedEx, Mr. Sanjeev Jain has led EBITDA improvement, business transformation, and operational efficiency programs for mid-to-large enterprises, and brings strong expertise in financial strategy, M&A, dispute resolution, and regulatory matters.

As Managing Partner at Amicus Growth Advisors, Mr. Sanjeev Jain leads CFO and Board Advisory engagements covering turnarounds, fundraising and investor relations, due diligence, and performance improvement.

Mr. Sanjeev Jain also serves on the boards of high-growth companies in the logistics and infrastructure sectors, supporting long-term strategy and value creation for promoters and leadership teams.

Mr. Sanjeev Jain can be contacted at:

LinkedIn | Email | Website

About Amicus Growth Advisors :

At Amicus Growth Advisors, we help businesses unlock growth, improve performance, and build lasting value.

From business transformation and operational turnaround to M&A, fundraising, and board advisory, Amicus Growth Advisors partner closely with founders, boards, and investors to navigate complexity and deliver measurable results.

With decades of experience across logistics, manufacturing, infrastructure, and services, Amicus Growth Advisors team combines strategic insight with hands-on execution.

Whether you’re scaling up, restructuring, or preparing for your next phase of growth, Amicus Growth Advisors is there to help you move forward with clarity and confidence.

Amicus Growth Advisors can be contacted at :

Website | E-mail | LinkedIn | Instagram | Facebook | WhatsApp | Phone : +91 98200 75360

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Where the migration stands

The question facing institutions today is no longer whether they can send and receive ISO 20022 messages: on the cross-border network they have no alternative. The question is whether they can do anything useful with what those messages carry. The technical difficulty of the migration was never primarily about message syntax. It was about the depth of the data model, the state of the systems and data behind it, and the regulatory and commercial layers now building on top of the standard. The current phase is where the programme’s toughest challenges collide.

Sourcing structured data, not just parsing it

ISO 20022’s XML-based data model is far richer than MT’s fixed-format fields, and that richness now has a firm, imminent deadline attached. A hybrid address option was introduced with the November 2025 standards release, opening a one-year grace period in which structured, hybrid or unstructured addresses are all still accepted on cross-border payment messages. That grace period ends on 14 November 2026, when fully unstructured addresses will be rejected outright. Purpose codes and Legal Entity Identifiers have moved from optional enrichment to expected content. The difficulty is not building a parser for this data; it is sourcing it, at speed, from customer records that were often captured as unvalidated free text for decades.

Example: a corporate client’s address on file reads simply “123 Business Park, London.” Under the rules taking effect in November 2026, this must be decomposed into building number, street name, town, postcode and country as discrete, individually addressable elements — data the bank never validated at account opening and now has a few months left to retrieve or infer.

Integration debt relocated, not discharged

Financial institutions have had to enhance or replace core systems to accommodate ISO 20022, in coordination with counterparties, payment networks and service providers. At the market-infrastructure layer this has substantially happened: the Eurosystem’s T2 platform, CHAPS, FedNow, CHIPS and the Fedwire Funds Service have all completed native cutovers. Inside individual institutions, however, comparatively few reengineered their core banking systems. Most placed a translation gateway at the perimeter and left internal formats untouched.

Example: a bank receives a fully structured pacs.008 payment instruction at its gateway, translates it back into a legacy MT-shaped internal record to post to the core ledger, and in doing so discards the structured purpose code and remittance detail the sender took care to include.

Mapping and conversion risk, now internalised

While MT and ISO 20022 ran in parallel, the network itself absorbed format mismatches, rebuilding payment data centrally so a message could be delivered in whichever format the receiver could handle, but coexistence service has ended. Every remaining mapping decision now falls to the institution’s own edge, and a field dropped between a gateway and a core system produces no network-level exception, it simply disappears.

Example: a remittance information field longer than a legacy back-office system’s character limit is silently truncated at the gateway. The payment settles; the invoice references the beneficiary needed to reconcile it does not survive the trip.

Parallelism has moved, not ended

Coexistence for cross-border payments has closed, and MT is no longer accepted directly for that traffic. Two qualifications matter. First, SWIFT did not impose a hard technical block: institutions still sending MT can route it through a paid contingency conversion service, subject to escalating penalties, and MT remains in active use in domestic systems, closed user groups, and certain cash-management reporting messages that were given a longer runway than the main deadline. Second, and more importantly, parallel running has not ended for institutions themselves, it has simply moved behind the gateway, where it is cheaper to maintain, less scrutinised, and considerably harder to see.

Example: a bank’s payments team reports full ISO 20022 compliance while its reconciliation and sanctions-screening tools still operate on an MT-derived internal message format, maintained by a small team few people in the organisation know still exists.

Testing as a recurring obligation, not a project

Comprehensive testing was necessary to confirm systems processing ISO 20022 messages worked correctly with counterparties and market infrastructure. Shared standards repositories, readiness portals and in-flow validation matured considerably during the migration. What has proved more durable is that testing has become an annual operating obligation rather than a one-off migration cost: usage guidelines are refreshed with each yearly standards release, and any refresh can tighten a validation rule or change a field’s optionality.

Example: an institution that stood down its ISO 20022 testing team after go-live finds a batch of outbound payments rejected the following November, after a routine annual usage-guideline update quietly restricted a code list its interface still relies on.

A skills gap that has moved from syntax to semantics

Schema literacy, reading and producing valid XML, turned out to be the easier problem, largely solved by tooling. What remains scarce is semantic competence: knowing which of several plausible elements a given business fact belongs in, and how a market’s usage guideline narrows the base standard. This is data stewardship, not message engineering, and it sits awkwardly between technology, operations and compliance functions. At the same time, the generation fluent in MT is retiring precisely when residual MT expertise is still needed for domestic rails and archived traffic.

Example: two operations analysts at the same bank populate the same ISO 20022 field differently for an identical transaction type, each technically valid under the schema, producing inconsistent downstream reporting that only surfaces during an audit.

Regulation assuming structured data, not merely permitting it

This has been the fastest-moving area of the programme, and it is unambiguous: regulators now build requirements on the assumption that structured data exists. The Financial Action Task Force’s revised Recommendation 16 tightens originator and beneficiary information expectations in a manner aligned with ISO 20022 fields. The EU’s Instant Payments Regulation has driven a sequence of hard obligations on receiving, sending and verifying payees. In UK, enhanced-data requirements on CHAPS have been phased in for defined payment types.

Example: a payment that would have cleared without incident under MT is now held for manual review because a payee-verification service flags a mismatch between the structured beneficiary name and the name on file, a control that could not have existed before the data was structured.

A business case still waiting to be funded

The upfront premium for the migration that is system enhancements, testing, training and dual-running costs has now largely been paid. That makes the current period the first honest test of the business case, and results are uneven. Institutions that pushed structured data through to reconciliation, screening and liquidity forecasting have seen real gains in straight-through processing. Institutions that terminate the data at a gateway have a compliance certificate and little else. The benefits of ISO 20022 were never an automatic by-product of the migration; they are a separate, largely unfunded project.

Example: a bank’s compliance team can now screen structured sanctions fields with far fewer false positives, but the productivity gain never reaches the wider organisation because no budget was allocated to extend structured-data use beyond the screening engine itself.

Conclusion

Retiring MT was never the objective; it was the entry requirement. The technical challenges that remain sourcing rich data, retiring internal translation layers, sustaining annual testing discipline, closing the semantic gap between usage guidelines, and funding the second project of turning structured data into operational value are harder than the messaging change itself, and less visible, because most of them no longer generate the kind of network-level exception that used to force attention. Institutions that treat the current phase as complete because the network is single format are likely to discover otherwise at their own front door.

About the Author

Mr. Saravanakumar Dinakaran  
Senior Managing Consultant – IBM

Mr. Saravanakumar Dinakaran is a Industry Consultant with expertise in various areas of Investment Banking and Securities Markets Industry.

Mr. Saravanakumar Dinakaran is holding 20 plus years of experience in variety of Information technology services and solution providers for global capital market participants such as Investment banks, Buy & Sell side firms, financial service providers and market regulator.

Mr. Saravanakumar Dinakaran is Interested to work in process improvement and regulatory compliance projects and assignments related to Derivatives (OTC & ETD) market operations.

Mr. Saravanakumar Dinakaran have strong experience in the Report Warehouse development project successfully launched in the Blockchain concept Distributed Leger Technology (DLT) for the Global leader in Depository and clearing services.

Mr. Saravanakumar Dinakaran is Bestowed with the following Certifications:

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

Mr. Saravanakumar Dinakaran can be contacted at:

LinkedIn

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Over the last few months, I have been closely observing the evolution of Artificial Intelligence, Robotics, Cloud Computing, Quantum Computing, and Sustainable Energy. While many people view these as separate industries, I believe they are becoming one interconnected technology ecosystem that will define the next several decades of economic growth.

At the same time, a second reality is emerging.

The AI conversation is shifting away from blind excitement and toward practical business value. We are moving from experimentation to execution, from hype to measurable outcomes, and from isolated innovation to integrated transformation.

The organizations that understand this shift early will become the leaders of the next industrial revolution.

The AI Industry Is Growing Up

For the last few years, AI discussions were dominated by possibilities:

Today, the market is becoming more realistic.

Business leaders are asking new questions:

These questions are healthy because true technological transformation is never about hype. It is about sustainable value creation.

The companies winning today are not necessarily deploying the most advanced AI models. They are deploying AI where it delivers measurable business outcomes.

The Two Realities of AI: Bottlenecks and Breakthroughs

We are witnessing two parallel realities.

Reality 1: The Bottlenecks

Cost Pressures

Organizations are discovering that running advanced AI models at scale is expensive.

Many enterprises are now evaluating whether they should rely on external AI providers or build optimized models internally.

The industry is rapidly moving toward:

The question is no longer “Can AI do this?”

The question is:

“Can AI do this profitably?”

Agentic AI Is Progressing More Slowly Than Expected

Fully autonomous agents remain a huge opportunity, yet real-world deployment continues to face challenges:

Businesses operating in healthcare, financial services, manufacturing, and logistics cannot afford costly mistakes.

As a result, many organizations are adopting a Human + AI model rather than complete automation.

Enterprise Caution

Large companies are becoming more selective about where AI can be trusted.

Critical functions still require:

This does not mean AI is failing.

It means AI is maturing.

Reality 2: The Breakthroughs

While challenges exist, innovation continues at unprecedented speed.

Scientific Discovery

AI is increasingly accelerating scientific research.

Material discovery, drug development, climate modeling, and advanced simulations are now benefiting from AI-powered analysis that can reduce years of research into significantly shorter cycles.

Mass Adoption

For the first time in history, AI capabilities are reaching billions of users through platforms they already use daily.

This creates a powerful feedback loop:

• More users
• More data
• Better models
• Faster innovation

Next-Generation Models

AI models continue to improve in:

The future is not about one giant model.

The future is about ecosystems of specialized intelligence working together.

The Ultimate Technology Value Chain

Many investors and business leaders examine AI, Cloud, Quantum Computing, and Sustainable Energy independently.

I believe this is a mistake.

These technologies form a single value chain.

AI & Robotics: The Brain and Hands

AI provides intelligence.

Robotics provides execution.

Together they create physical automation capable of working in factories, warehouses, hospitals, farms, and homes.

Future robots will not simply follow programmed instructions.

They will:

Just like human workers.

Cloud Computing: The Nervous System

Advanced AI requires massive computational resources.

Most enterprises cannot host these capabilities locally.

Cloud infrastructure provides:

Without the cloud, modern AI cannot operate at enterprise scale.

Cloud is the digital nervous system connecting everything.

Quantum Computing: The Accelerator

As AI models become larger and more sophisticated, classical computing eventually faces limitations.

Quantum computing represents the next major leap in processing capability.

Potential applications include:

Quantum computing may not replace classical computing soon, but it will significantly accelerate solving the world’s most complex problems.

Sustainable Energy: The Foundation

This is perhaps the most overlooked part of the technology revolution.

Every AI model, cloud data center, and future quantum computer consumes enormous amounts of energy.

Without sustainable energy, technological growth becomes unsustainable.

That is why global technology companies are investing heavily in:

Sustainable Energy is not a separate industry anymore.

It is becoming the foundation of the digital economy.

The Missing Piece: Cultural Intelligence

One area that receives far less attention is cultural adaptation.

Technology often assumes a standardized global user.

Human behavior does not work that way.

Consider something as simple as eating a meal.

Different regions use:

A robot trained on generic global data may perform impressively in a laboratory but fail immediately in a local household environment.

The future of AI will not belong only to the companies with the most data.

It will belong to the companies with the most relevant data.

Why Local Intelligence Matters

For AI and robotics to truly become universal, three major developments are required:

1⃣ Integrated AI Ecosystems

Today’s technology landscape is fragmented.

Imagine combining:

The productivity gains could be extraordinary.

2⃣ Real-Time Validation Systems

Future AI systems must continuously evaluate their own decisions.

This means:

Trustworthy AI will outperform merely powerful AI.

3⃣ Hyperlocal Data Infrastructure

The world needs more:

True intelligence is contextual.

Global intelligence without local understanding remains incomplete.

India’s AI Opportunity

India stands at a remarkable crossroads.

With over 1.4 billion people, millions of businesses, and one of the world’s most vibrant digital ecosystems, the opportunity is enormous.

Yet AI adoption remains relatively early compared to its full potential.

Many organizations are still limiting AI usage to:

The next stage will be enterprise-wide transformation.

Where India Can Leapfrog

Manufacturing

Healthcare

Retail

Logistics

Enterprise Functions

The opportunity is not simply AI adoption.

It is AI-driven business transformation.

The Leadership Challenge

The future does not belong solely to data scientists.

It belongs to leaders who can connect:

Technology alone does not create value.

Business outcomes create value.

The leaders who understand both technology and business transformation will become the architects of the next growth cycle.

Final Thoughts

We are entering a new era where AI, Robotics, Cloud Computing, Quantum Computing, and Sustainable Energy are converging into one interconnected ecosystem.

At the same time, success will require more than technology.

It will require:

The biggest winners of the next decade will not be those chasing every new technology trend.

They will be those who understand how all these technologies work together to solve real-world problems at scale.

The future is not AI alone.

The future is the intelligent integration of technology, energy, infrastructure, and human understanding.

About the Author :

Mr. Chandrakumar T 
Enterprise Sales Leader
Honeywell Technologies

Mr. Chandrakumar is an Enterprise Sales Leader with 20+ years of experience helping organizations accelerate growth through telecom, IT infrastructure, AIDC, and automation solutions.

Mr. Chandrakumar has partnered with 60+ strategic enterprise accounts across India, consistently achieving 100–120% of quota targets while building long-term, trust-based relationships.

Mr. Chandrakumar approach blends proven sales methodologies such as Challenger Sale and Miller Heiman with deep expertise in SaaS, cloud, and cybersecurity platforms.

Mr. Chandrakumaris passionate about translating complex technologies into business outcomes that improve efficiency, reduce costs, and create measurable value for customers.

Mr. Chandrakumar believes in being a trusted advisor to customers – guiding them through digital transformation journeys and enabling sustainable success.

Mr. Chandrakumar actively share insights on automation, SaaS, and enterprise sales strategies, engaging with peers, customers, and industry leaders to exchange ideas and best practices.

Mr. Chandrakumar is Bestowed with the following Licenses & Certifications:

https://www.linkedin.com/in/chandrakumar-t-b495bb22/details/certifications/

Mr. Chandrakumar can be contacted at :

LinkedIn | E-mail | Website 

#ArtificialIntelligence #AI #MachineLearning #GenerativeAI #Robotics #CloudComputing #QuantumComputing #SustainableEnergy #DigitalTransformation #ERP #SaaS #Automation #Industry40 #Industry50 #Innovation #FutureOfTech #TechStrategy #DeepTech #CleanTech #Infrastructure #Leadership #BusinessTransformation #IndiaGrowthStory #Technology #TechTrends

The post India’s AI Moment appeared first on Industry4o.com.

Artificial intelligence is becoming one of the most important forces in healthcare. Every day, we see new models, new tools, and new promises about how AI can support diagnosis, reduce workload, improve patient care, and transform clinical operations.

But as healthcare leaders, engineers, researchers, and clinicians look toward the future, one question matters more than ever:

Are we building AI systems that simply produce answers, or are we building systems that truly understand the patient journey?

This distinction is important.

Healthcare is not just a data problem. It is a context problem.

A patient is not defined by one lab result, one diagnosis code, one medication, or one clinical note. A patient’s health story is built over time through many connected signals: lab trends, symptoms, medications, procedures, diagnoses, vitals, imaging reports, provider notes, and changes that may happen slowly across months or years.

In many healthcare systems, this information exists, but it is often fragmented. One part may sit inside an electronic health record. Another may be hidden in clinical notes. Another may appear in lab reports, claims, medication history, or previous encounters.

The challenge is not only collecting this data.

The challenge is connecting it in a meaningful way.

That is where the next generation of healthcare AI needs to evolve.

Prediction Alone Is Not Enough

Many AI systems today are designed to predict risk. They may predict the likelihood of disease, hospital readmission, adverse events, or clinical deterioration.

Prediction is valuable, but in healthcare, prediction alone is not enough.

If an AI system says, “This patient is at high risk,” the clinician still needs to know:

Why is this patient at risk?
Which clinical signals contributed to that risk?
How did the patient’s condition change over time?
Are the lab trends, medications, and diagnoses connected?
Is this a temporary abnormality or part of a larger pattern?
Can the explanation be trusted?

Without clear reasoning, AI becomes difficult to use in real clinical workflows.

Healthcare decisions require trust. Trust requires transparency. And transparency requires more than a final answer.

It requires explanation.

This is why the future of healthcare AI should focus not only on prediction, but also on clinical understanding.

The Patient Story Matters

In real healthcare, timing matters.

A lab value from today may look normal when viewed alone, but when compared with results from the past five years, it may reveal a slow decline. A medication may seem unrelated until it is connected with symptoms, abnormal lab results, and prior diagnoses. A clinical note may contain an early warning sign that does not appear clearly in structured data.

This is especially important in conditions such as chronic kidney disease, sepsis, oncology, adverse drug events, and long-term disease progression.

These conditions are rarely understood from one data point. They require a broader view.

They require systems that can look across time.

This is where longitudinal patient intelligence becomes powerful.

A strong healthcare AI system should be able to analyze how a patient’s health has changed over time, identify patterns, connect related events, and present those insights in a way that clinicians can understand quickly.

The goal is not to replace clinical judgment.

The goal is to support it.

 Why Knowledge Graphs Matter in Healthcare AI

One of the biggest opportunities in healthcare AI is the use of knowledge graphs.

A knowledge graph can help connect different parts of a patient’s clinical history. Instead of treating each data point separately, it can represent relationships between patients, conditions, lab results, medications, procedures, symptoms, and clinical events.

For example, a patient may have a history of diabetes, hypertension, changing kidney function, medication adjustments, and abnormal lab values. Individually, these details may appear across different systems. But together, they can form a meaningful clinical pattern.

A knowledge graph helps AI understand these relationships more clearly.

It gives structure to complex healthcare data.

It allows the system to answer not just “what happened,” but also “how things are connected.”

This is important because healthcare is relationship-driven. Conditions influence medications. Medications influence lab results. Lab trends influence risk. Past diagnoses influence future outcomes.

When AI systems can understand these connections, they become more useful, more explainable, and more aligned with how clinicians think.

The Role of Small Language Models

Large language models have shown impressive capabilities, but healthcare does not always need the largest model for every task.

In many clinical workflows, focused and efficient models can be more practical.

Small language models can be designed for specific healthcare use cases, such as extracting clinical entities, summarizing patient risk, identifying medication-related concerns, or generating clinician-readable explanations.

They can also be more cost-effective, easier to control, and better suited for regulated environments when designed properly.

The future may not be one large model doing everything.

It may be a system of specialized models working together, supported by clinical rules, knowledge graphs, retrieval systems, validation layers, and human oversight.

This approach can make healthcare AI more reliable.

It can also reduce hallucination, improve traceability, and make the system easier to evaluate.

AI Should Explain in Human Language

One of the most important parts of healthcare AI is communication.

Clinicians do not need a system that simply returns a score.

They need a system that explains the reason behind the score.

A useful AI system should be able to say:

The patient’s risk appears elevated because kidney function has declined over multiple lab cycles, blood pressure has remained high, and there is a history of diabetes that may contribute to disease progression.

That type of explanation is far more useful than a number alone.

Healthcare AI should translate complex data into clear clinical narratives.

It should help clinicians see the key signals faster.

It should reduce the time spent searching across disconnected records.

It should bring important patterns to the surface.

Most importantly, it should communicate in a way that supports action.

Building Healthcare AI Responsibly

Healthcare AI must be built with discipline.

This means strong engineering, strong evaluation, and strong governance.

A healthcare AI system should be tested not only for accuracy, but also for reliability, fairness, explainability, privacy, and safety. It should be validated against real clinical workflows. It should be reviewed by clinicians. It should be monitored over time.

The system should also make its reasoning traceable.

If an AI system provides a clinical recommendation or risk explanation, users should be able to understand which data points contributed to that output.

This is how trust is built.

Not through impressive demos.

Not through marketing language.

But through reliable systems that can be evaluated, audited, improved, and safely used in real environments.

The Real Opportunity

The real opportunity in healthcare AI is not just automation.

It is clarity.

Healthcare teams are already dealing with information overload. The problem is not that clinicians lack data. The problem is that they often have too much data, spread across too many places, with too little time to connect everything manually.

AI can help by reducing noise and surfacing what matters.

It can support earlier detection of risk.

It can help identify patterns across years of patient history.

It can assist with adverse drug event review.

It can summarize complex clinical timelines.

It can support diagnostic reasoning while keeping humans in control.

This is where AI can create real value.

Not by replacing healthcare professionals, but by giving them better tools to make faster, safer, and more informed decisions.

The Future of Healthcare AI

The next chapter of healthcare AI should be built around four principles:

Connection, because patient data must be understood across systems and timelines.

Explanation, because clinicians need to know why an AI system reached a conclusion.

Trust, because healthcare decisions require transparency, validation, and safety.

Impact, because the goal is not technology for technology’s sake, but better care, better workflows, and better outcomes.

Healthcare AI should not stop at prediction.

It should connect the dots.

It should explain the patient story.

It should support clinical judgment.

It should reduce burden.

It should help healthcare teams move from fragmented data to meaningful insight.

That is the future worth building.

A future where AI is not just intelligent, but responsible.

Not just powerful, but understandable.

Not just automated, but clinically meaningful.

And not just focused on answers, but focused on helping people receive better care.

Mr. Rahul Reddy Hanumanthgari is an AI Engineering Leader, researcher, and IEEE Senior Member specializing in building production-grade AI systems for healthcare and enterprise platforms.

Mr. Rahul currently contributes to AI-driven engineering initiatives at Labcorp, where his work focuses on applying large language models, agentic AI workflows, and intelligent automation to improve clinical data processing and diagnostic operations.

With over a decade of experience in software engineering, automation, and distributed systems, Mr. Rahul now focuses on designing scalable AI platforms that integrate large language models, retrieval-augmented generation (RAG), and modern AI orchestration frameworks to transform complex healthcare data into actionable insights.

Mr. Rahul is also an active contributor to applied AI research, with work exploring areas such as fairness evaluation in large language models, AI-assisted clinical decision support, and intelligent patient monitoring systems. His research and engineering work aim to advance responsible, trustworthy, and scalable AI systems in real-world environments.

In addition to his technical work, Mr. Rahul regularly engages with industry leaders and C-level executives, helping organizations understand and adopt AI-driven strategies for innovation and operational transformation. He has also delivered expert-level talks and technical presentations on AI engineering, intelligent automation, and emerging AI architectures.

As a judge, Mr. Rahul brings a practitioner’s perspective on technical depth, scalability, and real-world impact, evaluating innovations not only for novelty but also for their ability to translate advanced AI research into reliable, production-ready systems.

Mr. Rahul Reddy Hanumanthgari is Bestowed with the following Certifications :

https://www.linkedin.com/in/rahul-reddy-hanumanthgari/details/certifications

LinkedIn  | ORCID 

The post AI in Healthcare appeared first on Industry4o.com.

Unplanned Downtime Isn’t a Maintenance Issue ; It’s a Margin Crisis

The Core Shift: From Run-to-Failure → Predict-and-Prevent

In discrete manufacturing (automotive, electronics, aerospace, industrial equipment),
unplanned downtime is one of the largest and least visible profit leaks.

Industry Reality:

Large manufacturing plants lose ~$532,000 per hour due to unplanned downtime
( Forbes / Siemens )

So What?

Plants are losing millions not because machines fail, but because failures are not
predicted in time.

AI Unlocks the “Golden 48 Hours” Humans Can’t See

Why Traditional Maintenance Always Reacts Too Late

Conventional systems rely on fixed thresholds and alarms, they trigger only after
damage is already underway.

Capability Reactive / Preventive AI-Driven Predictive Detection Threshold-based
(late) Pattern-based (early) Accuracy High false positives 80-90% ML accuracy Intervention Window Minutes 24-48 hours Downtime Impact Fully unplanned 30 – 50% reduction

What AI Sees Differently

Evidence:

AI models (Random Forest, LSTM) can predict failure cycles with ~80% accuracy,
providing 24–48 hours of lead time
(Journal of Industrial Engineering; McKinsey)

So What?

Predicting failure early creates a 30–50% efficiency buffer operations never had
before.

Cutting Downtime Requires Architecture, Not Just Algorithms

Why AI PdM Succeeds Only with a Structured Data-to-Decision Pipeline

Plants achieving consistent 30–50% downtime reduction follow a disciplined,
repeatable architecture.

The 5-Step PdM Operating Model

What Leaders Do Differently

So What?

AI PdM becomes a new reliability operating model, not a dashboard experiment.

30–50% Downtime Reduction Is Now an Industry Benchmark

Why the ROI Is Proven Across Discrete Manufacturing

AI Predictive Maintenance is no longer experimental, it is repeatable at scale.

Real-World Evidence

• Automotive (GM):

• Semiconductor Fab:

• Heavy Machinery:

Beyond Downtime

So What?

AI PdM has moved from best practice to competitive baseline.

The Competitive Window Is Closing Fast

Why AI PdM Is a Margin Play Today, and Survival Tomorrow

The gap between AI-enabled plants and legacy maintenance models is widening.

Market Signals

Risk of Inaction

Final Recommendation : Where to Start

Run a 4-week Critical Asset Audit to identify the top 3 machines where a 30%
downtime reduction delivers immediate ROI.

Closing Thought:

AI doesn’t eliminate maintenance work, it eliminates surprises.

And in discrete manufacturing, surprises are the most expensive failures of all.

About the Author :    

Mr. Gulshan Kumar Saini
Senior Director
Samsung Electronics

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:

The post AI Predictive Maintenance appeared first on Industry4o.com.

Going by the Accounting Principles, an Organization is to be Construed, Conceptualized and Carried-on for Perpetual Existence – in colloquial terms, Ever Lasting & Never Ending for Generations to come. But in reality, there are Business Exits – for Strategic, Tactical or sometimes Conditions induced reasons.

When Exits are planned and structured, as in the case of Strategic and Tactical Decisions, its very controlled and the impact of which is carefully taken into consideration prior to implementation. In the 3rd case, Business Conditions induced Exits are usually uncontrolled and therefore are undesirable, resulting in People Issues and Emotional Conflicts, Business issues apart.

As the Famous adage goes “Today, You need to run to remain in the same place. And to Succeed, You need to run faster than the fastest.” This is same for Businesses. To remain where they are, they have to run atleast to match the speed of Industry Growth. And if they are to Grow, they have to run faster than their Competitors – as Growth comes only by acquiring Market and Business Share.

One of the essential aspects of Growth, is to Change and Change Constantly, Consistently, Continuously. As much Change and Growth are inseparable, they are essentially People Dependent and hence, Leadership Oriented. Apex Level Changes – are supposed to be Strategic leading to Compounded results, Operational Level Changes are Tactical leading to Incremental results and Execution Level Changes are for Sustainable results.

It’s all about 3Cs – Conviction, Commitment & Consistency.

Growth comes essentially because of the ability of the Organization to easily Adapt to the Changes effected by SPELT (Social, Political, Economic, Legal and Technological) factors. Other way to manage Change is to create one. Such strategies also define Market Leaders, Followers and Laggards. There are both merits & demerits in being either one.

Lag Vs Lead Indicators

Businesses thrive on Revenues, Profits and Cash Flows. Prudent Leadership will agree these are results based on past and facts. Certainly they are required for Business continuity and therefore entrusted with Executional Level. Growing Organizations usually focus on influencing SPELT environment to their advantage. Focus then shifts to Market Share, Business Share, New Businesses/ New Customers/ New Markets and New Products.

Failures as stepping stones to success

Growth oriented Leadership encourage Changes, despite knowing the risk of Failures. Its important to accept that there is always a 50% probability to succeed if we try, which otherwise is zero. Successful Organizations actually celebrate failures – wherein it incentivizes to discuss, understand and analyze so as to not repeat them. This is essentially helpful when its 100% transparent and organization wide. Learning from Failures then becomes a part of the Business Process, well documented and widely accessible.

Actions and Inactions:

Actions can be Predictive, Proactive, Reactive and Responsive. Each of these have their own consequences – while being Predictive and Proactive may be choices that induce Change and thus Growth, Reactive and Responsive are essential to sustain businesses. If we don’t Act and failure to act appropriately at an appropriate time may cause irreparable consequences. Ofcourse, to wait and watch is also an action but with limitations, sometimes when overused becoming a liability.

Fundamentals

Many effective Leaders who are Change Makers believe in questioning the Fundamentals. This is certainly needed in a dynamic, fast changing Business Environment. For Business Sustainability, even to change the Fundamentals, there is a need to understand and believe the prevailing fundamentals to know what to change and what not to change. Change for the sake of Change has to be avoided and therefore Change & its impact should be measurable. Reliability then is a result of Repeatability which relies on Processes & People Capability.

Learning & Growing

Growth comes out of the ability of the Organization to Learn and Lead, which means People who are part of the Change and Growth process should be willing to Learn and Grow and thus Grow the Organization. As much as People and Processes are inseparable, Organization and People are.

Cannibalization is an effective Business Strategy which comes out of Learning from past and from customers. When the same applied for People, its Learning to move out of old knowledge and habits (comfort zone) to acquire new knowledge and new skills.

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 an Intrapreneur with hands on experience establishing medium & large scale end to end Value Chain Manufacturing Organizations (from Castings to Finished Products) for Indian Corporates and MNCs, driving Growth & Profitability by Building, Sustaining and Growing High Performance Teams.

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 CHANGE is GROWTH appeared first on Industry4o.com.

Key Takeaway: Reinvention isn’t some Silicon Valley luxury. It’s survival. Any organization that wants to keep up in a world where data, tech, and priorities change overnight needs to get good at this (and fast).

Introduction: The Power of the Pivot

Picture this: a scrappy team in San Francisco, founders of a startup called Odeo, suddenly find their business model flattened by Apple’s iTunes. Most people would’ve called it a day. But not these folks. They tossed out their old idea and doubled down on something wild: an app for sharing what you’re doing in just 140 characters. That gamble gave us Twitter, and changed how the world connects.

Across the city, another crew had their own mess. Their app, Burbn, did everything from check-ins, to games, to social stuff, to photos. But users only cared about posting pictures with cool filters. So, Kevin Systrom and Mike Krieger made a tough call. They stripped Burbn down to just photo sharing and turned it into something called “Instagram.” The result? A cultural juggernaut that changed how we capture the world.

While it is certain that a majority of people have never heard of Odeo or Burbn, Twitter (now “X”) and Instagram have become household terms. Strategic reinvention put them both on the map, and they’re here to stay. At least for now.

So, what does any of this have to do with lean data governance? Actually, more than you might think. Twitter and Instagram didn’t win at reinventing themselves strategically by piling on features or more rules. They won because they knew when to focus, when to simplify, and when to let go of anything that didn’t matter. That’s the heart of lean data governance: cut the clutter, stay focused, and build systems that serve the business. Not the other way around.

The Trap of Traditional Governance

Let’s be real. Most organizations start with good intentions for data governance. They want to keep data safe, follow the rules, and use data responsibly. But here’s the problem: old-school data governance frameworks were built for a slower world. When things speed up, those same frameworks turn into roadblocks.

All those committees, sign-offs, and endless paperwork? They add friction, not value. Teams get tangled up in bureaucracy instead of actually doing something meaningful with data. It’s like trying to sprint with weights around your ankles. Sure, you can move forward, but doing it that way is so much harder than it should be.

Lean data governance takes a page from the Twitter and Instagram playbook. Instead of tweaking the old system, it asks the question: “What if we started fresh?” Rather than being satisfied with only rebranding, it goes a step further and encompasses real transformation; that is, putting users first, making bold decisions, and knowing when to move on. That’s what sets effective data governance apart from the usual red tape.

There are five cornerstones upon which this “strategic reinvention” mindset is built: focus, strategic constraints, user-centric design, timing and execution, and building on strengths. Let’s break them down one at a time.

Focus: The Instagram Principle

Rather than merely trimming the fat, Instagram went all in on what mattered and ditched everything else. Systrom and Krieger weren’t aiming for a “simpler” app. They wanted something so clear in its purpose that it could really take off.

I’ve seen it over and over: when companies pick one thing to do really well, and ignore the urge to please everyone, they win. They figure out what makes them special and cut out the distractions.

Data governance has the same problem. Too many organizations try to cover every possible scenario, and end up with a mess of policies that slow everyone down. Lean data governance borrows that Instagram mindset: find what truly matters and get rid of the rest.

Strategic Constraints: The Twitter Lesson

Twitter’s 140-character limit (later 280) may seem like a tech quirk, but it was a deliberate choice. That boundary forced people to get creative and gave Twitter its unique style.

In data governance, strategic constraints work the same way. Instead of endless approvals and red tape, lean governance gives teams clear rules (such as data quality standards, who can access what, retention timelines, and so forth), then lets them get to work. These boundaries keep things on track without smothering innovation.

The goal isn’t just to make things run smoother. It’s about giving people room to innovate, but within clear boundaries.

User-Centric Design: Building for People, Not Just Policy
Look at Twitter and Instagram. They show how user-focused design can drive big changes. Twitter didn’t invent hashtags in a boardroom. Users started tagging their tweets, and the company just paid attention and made it official. Over on Instagram, filters went beyond simply making photos look nice. They turned regular folks into artists, making everyone feel like their photos were worth sharing.

Good data governance takes the same approach. Instead of treating people like compliance problems to manage, smart organizations see them as partners who add value. When you bring data users into the process, you build systems that actually help them work, not slow them down. Adoption rates go up, and suddenly, data governance goes from being a box to check off to becoming a competitive advantage.

Timing and Execution: The Decisive Edge

Timing really matters. Twitter made its move before Odeo tanked; Instagram shifted gears just as mobile photography took off. Both saw the turning point and jumped in before anyone else could own the space.

Data governance works the same way. The hard part isn’t knowing what needs to change, but rather, making it happen at the right moment. Too many organizations treat lean governance as just another tech project and forget the cultural shift that makes it stick. If you wait until your old systems totally fall apart, you’re in for a rough ride. The best results come when you start changing things while the old ways still sort of work; when the team is frustrated but not in full crisis mode yet.

Building on Strengths: Evolution, Not Revolution

One thing stands out: neither Twitter nor Instagram tossed out what already worked. Twitter’s real-time messaging became microblogging. Instagram’s photo filters built the backbone for social sharing. Instead of rebuilding from the ground up, they adapted what they already had.

That’s exactly how organizations should approach data governance. Don’t throw away the systems that deliver value. Figure out what’s working, cut the red tape, and evolve. Period. No need for a dramatic overhaul; just build on what you know works.

Conclusion: The Courage to Reinvent

Here’s the bottom line. If you want to reinvent your organization, you need vision and discipline to execute. Like Twitter and Instagram, you have to drop what’s not working, but hang on to what really drives the business. It takes courage to let go of comfortable routines and focus on what actually moves you forward. But if you get it right, lean data governance sets you apart. Decisions get faster, friction drops, and you gain a real edge in a world where data rules.

Summary Box:

Twitter and Instagram prove that transformation is both possible and necessary for anyone ready to focus, simplify, and take action. For lean data governance to be effective, forget about simply piling on more rules and calling it done. Stand up and make every process count.

“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. Dr. Joe Perez 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, Dr. Joe Perez embarked on a path that ultimately led him to the dynamic world of data-driven Information Technology.

In the early 1990s, Dr. Joe Perez transitioned into IT, starting as a Computer Consultant at NC State University. Over the years, Dr. Joe Perez’s 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. Joe 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 (NCDHHS). His impressive journey continued with promotions to Senior Systems Specialist and Team Leader, showcasing his versatility and leadership capabilities.

In addition to his full-time responsibilities at NCDHHS, Dr. Joe Perez assumed the role of
fractional Chief Technology Officer at a North Carolina firm in October 2020. An Amazon best-selling author with over 21,000 followers on LinkedIn and numerous professional certifications, he is a highly sought-after international keynote speaker and recognized expert in data analytics and visualization.

Dr. Joe Perez’s contributions have not gone unnoticed. Dr. Joe Perez is a recipient of the IOT Industry Insights 2021 Thought Leader of the Year award, the 2023 Gartner Peer Community Ambassador of the Year award, and Top Ten Global Thought Leader for several categories at Thinkers360 in four consecutive years (2023-2026). Dr. Joe Perez holds memberships in several prestigious Thought Leader communities, and was recently inducted into the United Nations Global Network of Data Officers and Statisticians. Dr. Joe Perez’s reach extends to more than twenty countries worldwide, where he impacts thousands through his speaking engagements, articles, and status as a Distinguished Fellow in the Public Sector Network’s Future Government Institute.

Beyond his professional achievements, Dr. Joe Perez’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 through involvement in his church’s Hispanic ministry. Dr. Joe Perez 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, Dr. Joe 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. Dr. Joe Perez’s journey is a testament to the remarkable achievements of a truly exceptional individual, and more importantly, to the amazing grace of God.

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 :

E-mail | LinkedIn | Web | Sessionize | FaceBook  | Twitter | YouTube

Also read Dr.Joe Perez‘s earlier articles:

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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
CCISO | DPO| DTO| CISA | CRISC | CISM | CGEIT | PCSM | IAPP AIGP | ISO42001: LA
Program Director: Cybersecurity & Data Privacy
Tata Consultancy Services

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 :

LinkedIn

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.

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.

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?

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

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.

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

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

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:

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.

Military use cases of ASI

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:

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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Abstract

AI and machine learning are fundamentally transforming capital markets beyond automation across three critical areas: market microstructure analysis, portfolio construction, and advanced risk modelling. Real-world implementations by JPMorgan’s LOXM, BlackRock’s Aladdin, and Morgan Stanley’s Portfolio Risk Platform demonstrate measurable improvements in trade execution, risk assessment, and optimization. These technologies enable real-time processing of vast data and complex market relationship identification, achieving 25-40% performance improvements while managing trillions through AI-enhanced platforms.

Core Applications in Market Operations

Market Microstructure Analysis

Advanced algorithmic systems have become powerful platforms in processing vast quantities of data simultaneously, revealing concealed market behaviours and emerging profit opportunities. JPMorgan Chase exemplifies this technological evolution through their groundbreaking LOXM (Liquidity Optimization Execution Machine), which leverages Azure Machine Learning to optimize client order execution. This intelligent system continuously analyses historical trade patterns while dynamically adjusting to evolving market conditions. The results demonstrate remarkable performance improvements: a 40% reduction in processing time and 25% improvement in successful trade execution, underscoring the transformative impact of AI-driven market analysis on modern trading infrastructure.

Portfolio Construction Evolution

Advanced intelligent systems are revolutionizing portfolio management through three key innovations:

Dynamic Real-Time Rebalancing Unlike conventional portfolio management with predetermined rebalancing schedules, intelligent systems enable continuous optimization. These platforms monitor market volatility, asset correlations, and macroeconomic developments in real-time, automatically adjusting allocations to capture opportunities while minimizing risk. This eliminates human delays and maintains optimal distribution without manual intervention, particularly valuable for evolving markets.

BlackRock’s Aladdin (Asset Liability Debt and Derivative Investment Network) exemplifies this advancement, utilizing machine learning to analyse thousands of portfolios daily. The platform’s growth reflects its effectiveness: investment advisory and administration fees from Aladdin-based assets under management surged from $355.5 million in 2023 to $926.5 million in 2024.

As of 2020, Aladdin oversees more than $21.3 trillion in assets, with technology services revenue reaching a record $1.6 billion in 2024.

Fig 1: Aladdin Technology Services Revenue (2018-2024)

Alternative Data Integration Modern portfolio systems extend beyond conventional financial metrics like earnings reports and price movements, incorporating diverse data sources including satellite imagery, ESG sustainability indicators, consumer transaction patterns, geospatial intelligence, and supply chain analytics. This comprehensive data integration enables AI-driven platforms to identify subtle market trends, consumer behaviour shifts, and geopolitical risks, facilitating more strategic and anticipatory investment decisions.

BlackRock’s Aladdin Climate demonstrates this capability by quantifying climate-related risks and opportunities in financial terms, combining climate science, policy projections, asset information, and financial modelling to generate climate-adjusted valuations and risk assessments.

Personalized Risk Management Intelligent systems customize portfolio strategies based on individual investor characteristics, including risk tolerance, investment objectives, and timeline preferences. These platforms analyse behavioural patterns and financial goals to dynamically modify asset allocations, implement protective hedging strategies, and recommend personalized approaches—whether targeting aggressive growth, steady income generation, or capital preservation.

JPMorgan’s Athena platform illustrates this personalization capability, delivering comprehensive cross-asset risk assessment, pricing, and trade management solutions. The system processes billions of daily risk calculations while achieving a 30% reduction in processing time and approximately 80% decrease in hourly calculation costs.

Risk Modelling Advancement

Modern computational approaches have transformed risk assessment by:

1. Identifying Complex, Non-Linear Relationships Between Market Variables

Traditional linear risk models miss complex market correlations. Advanced machine learning—neural networks and decision trees—captures non-linear relationships conventional models overlook. JPMC’s AI research demonstrates this through risk-sensitive decision trees using reinforcement learning for trade execution, helping investors make better risk-adjusted decisions by understanding underlying price dynamics.

The application of these technologies has achieved impressive results and inspired broader uses of deep neural networks for agent representation in trading applications. Deep Q-trading systems have emerged as effective solutions for algorithmic trading, while LSTM (Long Short-Term Memory) representations of reinforcement learning trading agents have demonstrated significant potential in market applications.

2. Enhancing the Accuracy of Tail Event Predictions

Extreme market events like crashes are difficult to predict using traditional models that inadequately weigh risks. AI-driven deep learning and Bayesian networks improve tail risk forecasting by analysing vast datasets. Morgan Stanley’s Portfolio Platform, combined with BlackRock’s Aladdin Risk Engine, helps foresee potential impacts of market shocks on portfolios.

These advanced systems simulate a wider range of market scenarios, utilizing thousands of potential risk factors to help investors and their financial advisors understand how various risks may affect their holdings, including both internal investments and external positions. Morgan Stanley has further strengthened this capability through their patent (US8370241B1) for systems and methods that utilize probabilistic networks—essentially Bayesian networks—to analyse financial models.

This patented approach enables dynamic modelling of financial variables and their interdependencies, significantly aiding in the assessment of complex risk scenarios and tail events. By incorporating these sophisticated AI-driven techniques, the platform enhances portfolio resilience through proactive risk mitigation strategies, allowing investors to better prepare for and respond to extreme market conditions that could otherwise cause substantial losses.

3. Adapting to Shifts in Market Regimes

Financial markets undergo structural shifts from monetary policy, geopolitical events, or liquidity crises, altering dynamics. Traditional models struggle with real-time adjustment, while intelligent systems use adaptive learning algorithms to detect regime shifts early through pattern recognition.

S&P’s Kensho AI platform NERD identifies 142 million entities—companies, people, organizations, events, places—across market regimes, connecting them to S&P Capital IQ data . This entity recognition monitors multiple information streams simultaneously, providing early warning signals for market changes and enabling dynamic investment strategy recalibration before risks materialize.

AI-driven platforms continuously learn from market data and adapt to regime shifts, unlike static traditional models, ensuring risk strategies remain effective and protecting investors against unexpected transitions.

Conclusion

The next evolution in intelligent financial systems will likely centre on quantum computing integration, federated learning across institutions, and autonomous trading ecosystems that require minimal human intervention. Financial institutions should prioritize three strategic imperatives: investing in scalable cloud infrastructure to handle exponentially growing data volumes, developing cross-functional AI expertise through targeted talent acquisition and training programs, and establishing robust governance frameworks to ensure ethical AI deployment while maintaining regulatory compliance. Organizations that proactively build these capabilities today will be positioned to capitalize on emerging technologies like quantum-enhanced portfolio optimization and real-time regulatory reporting systems.

The organizations that will thrive in this intelligent finance era are those that view AI not merely as a technological upgrade, but as a fundamental reimagining of how financial markets can operate more efficiently, transparently, and effectively for all participants.

The dawn of intelligent financial systems is not a distant future—it is happening now, reshaping capital markets one algorithm at a time.

About the Authors :

Mr. Bhushan Joshi
Competency Leader for Banking ISV, Financial Markets and Wealth Management
IBM

Mr. Bhushan Joshi is Competency Leader of Financial Markets and Wealth in IBM India.

Mr. Bhushan Joshi has 25+ years of experience in Driving Business Transformation for clients in Banking, Capital Markets and Wealth Management Industry.

Mr. Bhushan Joshi  has played variety of roles including setting up new practices, working with business units and ISV ecosystem partners to drive growth, in program management and working on strategic consulting engagements for CxO level clients at Global Banks in Legacy Modernization with Microservices and Cloud, Business Architecture, Process Strategy and Optimization, Package Implementations, Vendor selection and Innovation.

Mr. Bhushan Joshi Specialties are Consulting, Digital Banking, Capital Markets and Wealth Management

Mr. Bhushan Joshi is Bestowed with the following Licenses & Certifications :

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

Mr. Bhushan Joshi can be contacted at:

LinkedIn

Mr. Saravanakumar Dinakaran
Senior Managing Consultant
IBM

Mr. Saravanakumar Dinakaran is a Managing Consultant at IBM with over 23
years of experience leading business transformation initiatives across Financial Markets and Investment Banking.

Mr. Saravanakumar Dinakaran brings deep expertise in front-office trading support, risk management, regulatory reporting, and end-to-end business analysis.

Mr. Saravanakumar Dinakaran with strong domain knowledge across all major asset classes—including Equities, Debt, Derivatives, and OTC products—he has successfully partnered with leading global banks and  regulatory bodies to deliver complex, high-impact programs.

Mr. Saravanakumar Dinakaran recent work focuses on applying AI-driven capabilities to modernize capital markets, enhance risk controls, and accelerate digital transformation across the financial ecosystem.

Mr. Saravanakumar Dinakaran is Bestowed with the following Licenses & Certifications :

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

Mr. Saravanakumar Dinakaran can be contacted at:

LinkedIn

References and Sources

https://www.linkedin.com/pulse/ai-powered-trading-financial-revolution-driven-guy-massey-8wole/

https://thefinancialtechnologyreport.com/blackrocks-powerful-aladdin-platform-over-20-trillion-and-counting/

https://s24.q4cdn.com/856567660/files/doc_financials/2025/ar/BLK_AR24.pdf

https://www.blackrock.com/aladdin/products/aladdin-climate

https://www.efinancialcareers.com/news/2022/05/jpmorgan-technology-jobs-and-spending

https://advisor.morganstanley.com/the-stalwart-group/articles/investing/portfolio-risk-management-prepare-unexpected?

https://patents.google.com/patent/US8370241B1/en

https://kensho.com/nerd

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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 :

E-mail | LinkedIn | Web | Sessionize | FaceBook  | Twitter | YouTube

Also read Dr.Joe Perez‘s earlier articles:

The post Data Storytelling – Part 2 appeared first on Industry4o.com.

Have become an avid anlayser of History of Businesses, as part of my recent Professional Engagement of evaluating Companies. Its intriguing to see different styles of the Business Managers, probably defined by their personalities and personal experiences. I repeat, irrespective of Size and Stature, its clear that a Leader’s Decision (Entrepreneur or Professional) is completely dependent on his experience. Once bitten, twice shy types mostly. This is probably from where Resistance to Change also emulates.

In Business & in Real Life, there are two approaches – Outside-In and Inside-Out..

Typical Entrepreneurs, Professionals and Leaders believe in Outside-In as the best approach – laying emphasis on SPELT (Social Political Economic Legal & Technological Environment, summed up in SWOT Analysis) based decisions. Which is quite logical …which also follows Porters Principles of Five Forces.

However in my view, true Innovators and Disruptors believe in Inside-Out approach. It typically envisions a scenario which believes the Market and Society (which is essentially the Market) at large may not know what they want. This in Management Jargon is referred to as Concept Selling & Creativity. I always vouch for this approach.

While the first approach is believed to understand and mitigate risks, in the second approach, risk is the essence and hence returns are disproportionately high as a reward for the high stakes. Ofcourse, failures will always be there…it takes confidence and courage to take the second approach. World over, its a proven fact that, successes even if it is around 10% (meaning as high as 90% failures), will more than Compensate and Contribute to Growth & Profitability. This approach is what brings Leap Frog Growth rather than market dependent incremental growth as in the first approach.

For the first approach, Competitiveness, Consistency and Efficiencies should be at their best to sustain success. Whereas, by nature, the second approach inbuilds inefficiency and thrives only on newness and market attractiveness – mostly due to the First Mover advantage.

In my assessment, for first one, good examples shall be Lee Iacocca… for the second i would rate Akio Marita, Steve Jobs and more recently, Elon Musk. Google, Microsoft, Intel, N-Vidia – off late i believe have lost out on their abilities to be creative – may be due market pressures and share holder expectations, these always being the limiting factors
especially when businesses are investor driven, which in the recent years are too many.

In Indian context, i place L&T, Jindals, Tatas in first category. Though many may not agree, I place Mukesh Ambani, Damanias and Adanis in the second category.

Drawing parallel to our personal lives, many try to evaluate others (Outside – In approach) , rather than trusting themselves (Inside – Out approach). In the process we become judgmental, either consciously or otherwise. Though colloquial, easiest examples could Indian Arranged Marriages as an example for the First Approach and Love Marriages for the second approach.

Irrespective of the approaches, which mostly is associated with Personality, ability to recognise that going is not right and take course correction at that instant is what ensures sustainable success. Then its all about timing – a withdrawal or an exit strategy.

About the Author :

Dr. NITHYANANDAN DEVARAAJ (NITHIN) BE., MBA., Ph.D
President – Coimbatore Management Association
Past National Secretary – The Institute of Indian Foundrymen.
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 an Intrapreneur with hands on experience establishing medium & large scale end to end Value Chain Manufacturing Organizations (from Castings to Finished Products) for Indian Corporates and MNCs, driving Growth & Profitability by Building, Sustaining and Growing High Performance Teams.

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 : +9194879 46900

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

Also read Dr. NITHYANANDAN DEVARAAJ’s earlier articles:

The post Personal Or Professional, Its all about Style and Substance… appeared first on Industry4o.com.

This article appears in two parts. Part One lays the groundwork and core principles; Part Two moves into narrative craft, delivery, and what the future holds.

Picture the scene: the Circus Maximus on a sweltering afternoon, grit in the air and a restless hush before the breakneck charge. A chariot team snaps forward. The crowd erupts. In the middle of that roar, there’s a name that once rattled across Rome: Gaius Appuleius Diocles. He raced, and he endured. For 24 years he tilted around those turning posts, surviving a career that often ended boys before it made men, and he retired with numbers that still pull the eye: an inscription records total winnings of 35,863,120 sesterces. Historians trying to translate that into today’s terms land all over the map, with estimates frequently cited in the tens of billions of dollars, a spread that reflects just how thorny it is to price the ancient world in modern money. Depending on which yardstick you use (wheat prices, soldier pay, grain for the city) the picture shifts, and with it the story.

That’s the puzzle, isn’t it? Numbers alone can be cold. They’re rows in a ledger until someone shows us what they mean. Data storytelling is the craft of turning those rows into meaning, linking facts with narrative and visuals so people can understand, remember, and act—technical experts and non-specialists alike. Done well, it moves beyond reporting an outcome and invites the audience to see what matters and why.

Start with memory. Why does Diocles stick? Not just because the sum is huge, but because we can feel the stakes; that is, the dust, the danger, the near misses, the mass of people whose fortunes rose and fell with every lap. Story is the glue. If the inscription were only a number, it would read like a bookkeeping entry. Framed through a life lived on a knife’s edge, the number acquires weight. In business, the same principle applies: a quarterly report that strings together figures without context fades from mind; a narrative that shows which customers moved, why a region sagged, what bets paid off, and what to try next stays with readers and shapes decisions. Narrative makes data sticky and shared, the kind people can recall in the elevator after the meeting.

The second thread is perspective. Those wildly different conversions of Diocles’ fortune reflect different lenses; different choices about how to compare purchasing power across an empire and two thousand years. If your baseline is grain for Rome or pay for a legion, you end up with very different modern equivalents. Context reframes the whole picture. It works the same way with a modern dashboard: a sudden dip in sales might look catastrophic in isolation, but add seasonality, a competitor’s launch, or a supply hiccup, and the narrative changes shape. Context is the difference between alarm and insight.

Third, scale. Billions of anything (be they dollars, sesterces, or page views) are hard to feel. The attempts to translate Diocles’ winnings into today’s money are really attempts to make scale graspable: think in lunch rations, soldier salaries, or the grain needs of a city, and the mind finds purchase. With modern datasets, the same move works. Strip away what’s not useful, select the few things that are, and turn abstractions into something people can visualize and compare. Visuals are your ally here. Charts and diagrams serve as cognitive scaffolding that let patterns and exceptions show themselves.

Finally, action. Even the inscription that immortalized Diocles was a kind of public performance, a message carved in stone. It said: look what this man achieved. It must have lit sparks in the heads of young drivers and fans, shaping ambition and allegiance. That’s what we want from data stories: for people to do something because they now see clearly. A good story closes with a concrete next step and an implied “why now,” nudging a roomful of stakeholders from nodding along to moving their budgets and calendars.

The Fabric of a Data Story: how numbers become a narrative that moves people

Building a strong data story blends analysis with craft. It starts long before you open your slide deck, and it doesn’t end at the last chart.

Purpose and audience

Begin with the point. What question are you trying to answer or decision you want to affect? “Increase market share,” “reduce churn,” “shorten the onboarding cycle”—each aims you at different data and a different arc. With Diocles, the “question” could be: what made him extraordinary, and how do we measure it? The framing shapes the evidence you collect and how you talk about it.Then consider who’s listening. Executives need strategic clarity and the one or two levers that matter; marketing teams often want customer stories, channel insights, and the “so what” for campaigns; technical teams care about method and caveats. Tune the voice, the level of detail, and the visuals to each audience so they can follow along and care enough to act.

Finding the signal

Once you’re clear on purpose and audience, go to the data. Clean it, join it, stress-test it. You’re looking for the few things that explain most of the outcome: a trend, a break, a relationship you didn’t expect, an outlier that reframes the rest. Think like a reporter with a beat—curious, a little skeptical, stubborn about the truth.

With Diocles, that “beat” might include his races and finishes, the teams he drove for, the payouts, and the length of his career; 24 years, which was unusually long for such a dangerous sport. He raced in more than four thousand events and won 1,462 times, often surging late to take the lead, a tactic that thrilled crowds and built his legend. Those facts guide the story you’d tell about his skill, durability, and showmanship. With those pieces in place, this is a natural pause before we move from discovery into the making of a story that holds together in the room.

“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 :

E-mail | LinkedIn | Web | Sessionize | FaceBook  | Twitter | YouTube

Also read Dr.Joe Perez‘s earlier articles:

The post Data Storytelling – Part 1 appeared first on Industry4o.com.

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

The post Myth of Big Data appeared first on Industry4o.com.