“AI will be the best or worst thing ever for humanity” – Elon Musk, Techpreneur

The above-mentioned quotable quote pretty much sums up the state of affairs that we are in. AI adoption has intensified in all walks of life.  2023 will perhaps go down as the year of ChatGPT. Generative AI (Gen_AI) models led by ChatGPT and including Microsoft Co-pilot, Google Gemini, Llama, Whisper, and Claude, using Large Language Models (LLM), not only became pervasive but also impacted all sectors and endeavors of humankind. Over the last couple of years, we also realized that with growing AI adoption, stronger governance is vital to balance innovation with safeguards on accuracy, accountability, autonomy, bias, ethics, fairness, integrity, morality, privacy, safety, transparency, trust, and sustainability

Various challenges and misuse of AI that we encountered included bias, deep fakes, election interference using AI, copyright infringements, job losses, carbon emissions, misinformation, cyber-attacks, monopolistic practices, and low-quality content.  Biases could be algorithmic, gender, language, and political in nature. AI regulatory frameworks have come to the fore with all governments engaging in said efforts.  Governance of AI technologies has become essential for governments across the world. Over the last decade, 40+ countries have enacted 200+ AI and related legislations while over 90 countries have developed AI governance frameworks, which will eventually translate to legislation. This is a far cry from just one AI legislation in 2016.  These governmental efforts are complemented by supranational agencies such as the International Telecommunication Union (ITU) agency of the United Nations (UN) & Organization for Economic Cooperation & Development (OECD). High-technology giants such as Google, OpenAI, Microsoft, Nvidia, Amazon, Meta, and Apple are also shaping the discourse in terms of AI technologies and its impact.

As we stand today, AI governance, regulation, and policy are, at best, a work-in-progress worldwide.  Due to the rapid pace of evolution of the technology, a well-defined, comprehensive, and end-to-end AI policy framework remains a challenge. The wish list is that this AI governance framework should offer clarity and direction to all stakeholders, while remaining adaptable to the rapid evolution of technology. This investigation explores the various intergovernmental and supranational initiatives like GPAI, AI for Good, UNESCO AI, AI Impact summit, AI safety summit, and Bletchley declaration towards AI governance and regulation.

Global Partnership on Artificial Intelligence (GPAI)

Global Partnership on Artificial Intelligence (GPAI) was launched in June 2020 by OECD subsuming earlier discussions on the topic by various nations as early as 2017. India was a founder member of this partnership along with EU and US.  This is a global coordination platform that brings together governments, academia, industry, and civil society to ensure AI is developed in a way that is human-centric, ethical, and trustworthy. Various working groups have been formed centered along various themes such as Responsible AI, Data Governance, Future of Work, and Innovation & Commercialization. These efforts have been complemented by UNESCO as well as G20 & G7, who have factored the GPAI inputs and deliberated on AI in their respective summits and working group discussions.  The focus is on ethics, policy, and responsible AI and not legislation and there are multiple layers of meetings all year around in addition to a global GPAI summit. These policy recommendations are generally non-binding and feed into major summit agendas including recently-held AI impact summit in India.

AI for Good

AI for Good is a similar initiative of International Telecommunication Union (ITU) in partnership with 40 UN sister agencies towards AI for society. This global- platform aims to identify practical applications of AI to advance the UN SDG and scale these solutions for global impact. These include AI applications in healthcare, education, disaster management, climate change and smart cities. An AI for Good global summit is conducted annually to add to the narrative to:

• Apply AI to real-world global problems
• Accelerate progress toward SDGs
• Promote ethical and inclusive AI
• Foster global collaboration

UNESCO AI framework

The UNESCO AI framework is another important global effort to guide ethical and human-centric AI development. From 2018, this has been in the works as an extension to UNESCO’s mandate to advance ethics of science and digital transformation. Adopted in November 2021 by 190+ countries, this framework sets standards for how AI should be designed, developed, and used responsibly. This is the first truly global AI ethics agreement focusing on the following areas:

• AI Governance & Regulation
• Data Governance
• Education & Skills
• Gender Equality
• Environmental Impact
• Ethical Impact Assessment

This serves as a reference and ethical guideline for national AI laws and policies and is complementary to GPAI and AI for Good, which focus on policy collaboration and application areas respectively. However, all of them are non-binding and part of a shared universe with multi-stakeholders such as government, UN agencies, Tech giants, civil society, and academia.  Simultaneously, various countries are also working on their own sets of AI governance frameworks and policy. Some of them like US have been following a market-driven Ai regulatory approach. On expected lines, China has a state-driven approach whereas the EU follows a rights-driven approach.

AI safety summit

The AI safety summit hosted by the United Kingdom for the first time in 2023 was a landmark event with respect to regulation of AI. The main objective of the summit was to address the risks and safe development of advanced AI systems, especially frontier models like generative AI in the larger context of AI governance. The policy recommendation and deliberations of complementary initiatives such as GPAI, AI for Good and UNESCO AI were feeders to this major global summit. At this summit, most advanced nations such as US, UK, China, India, and the EU more or less agreed towards a road map, which is defined in Bletchley Declaration, a global agreement by national governments towards regulation and human oversight of AI. Signed by 28 nations, this Bletchley Declaration affirms that AI should be designed, developed, deployed, and used in a manner that is safe, human-centric, trustworthy and responsible. A subsequent AI safety summit in Seoul in May 2024 and AI action summit in Paris in February 2025 reaffirmed these concerns. The Seoul summit advocated the establishment of multi-stakeholder safety institutes and testing frameworks to address concerns with respect to AI governance. The Paris summit saw 100 countries participate and sign the declaration on promotion of inclusive and sustainable AI. This summit focused on global coordination, policy alignment, inclusive governance, and balancing innovation with regulation.

Some of the guiding commitments of the Bletchley declaration are as follows:

• Shared understanding of AI risks including General-purpose AImodels also called Frontier of AI especially in areas such as biotechnology, cyber security applications and misinformation.
• International cooperation on AI safety research, standards and policies
• Risk mitigation and safety frameworks
• Scientific and evidence-based policies across countries to ensure safety while recognizing that approaches may differ based on national circumstances and applicable legal frameworks.

AI Impact Summit 2026

The AI Impact Summit refers to a set of international forums focused on the real-world impact of AI, especially its economic, societal, and developmental consequences.  How AI affects economies, jobs, governance, and society, and how to maximize benefits while minimizing risks……

 The first AI Impact Summit 2026 hosted by India, marked a significant milestone in global AI dialogue, especially as one of the first large-scale AI summits held in the global south. This summit saw participation from 100+ countries and 20+ international organizations as well as the major tech giants in the AI space. This included foundation model companies (OpenAI, Anthropic, DeepMind), big tech platforms (Google, Microsoft, Amazon, Meta), infrastructure leaders (NVIDIA) and emerging ecosystem players (Indian startups working on sovereign AI and home-grown LLMs).  At the AI Impact Summit 2026, the “7 Chakras framework” was used as a conceptual model to organize discussions and actionable policy directions around AI. These chakras represent key pillars of AI development and impact as follows:

• Human capital
• Inclusion
• Innovation
• Governance
• Safety & Trust
• Economic development
• Sustainability

While realizing that AI is a growth engine for economic transformation, this framework and summit provided a structured roadmap for countries to develop AI ecosystems while balancing innovation, inclusion, and safety. Other outcomes included a strong push for inclusive AI with enhanced access for developing nations in the global south, focus on digital infrastructure for public good, enhanced global and multistakeholder collaboration and workforce transformation.

India’s Approach in AI Governance

India is no doubt emerging as a global leader in AI governance and regulation. India’s approach has been pragmatic and non-aggressive as also veering towards combining innovation, inclusion, and ethical oversight without the risk of overregulation. It is a middle path akin to our non-alignment approach in geopolitics. Distinctly stakeholder-centric as opposed to state-focused approach of China or strict rights approach like Europe or market-focused like USA.  India has been an active participant of various intergovernmental and supranational forums engaged in AI regulation and governance from their inception. India chaired the GPAI in 2024 and hosted the 2023 edition of the GPAI summit at New Delhi.

The successful conduct of the AI Impact Summit 2026 reaffirms India arriving at the high table of AI governance and regulation. Of course, there remains a lot to be done. An overarching, binding and comprehensive AI act under the ambit of the Digital India act is needed which subsumes multiple laws such as Digital Personal Data Protection Act, 2023; IT Rules, 2021 (platform accountability), sectoral frameworks, and AI governance guidelines.

About the Author:

Dr. Prashant R.Nair 
Associate Director – Internal Quality Assurance Cell (IQAC)
President – Institution’s Innovation Council (IIC)
Professor – School of Engineering,
Amrita Vishwa Vidyapeetham (Deemed-to-be-University)

Dr. Prashant R. Nair, Associate Director, Internal Quality Assurance Cell (IQAC) & Professor of Computer Science & Engineering (CSE), Amrita Vishwa Vidyapeetham, Coimbatore, has twenty-six years of academic and administrative experience. Dr. Prashant R. Nair has taught in academic programs in the USA and Europe at University of California, San Diego; Sofia University, Bulgaria and University of Trento, Italy as an Erasmus fellow. Dr. Prashant R. Nair has written 6 books, 2 edited books, 1 book chapter and 75 publications in reputed journals, books and conferences.

Dr. Prashant R. Nair is active as a science writer & columnist. A very sought after speaker, by conservative estimates has addressed 150,000 students and trained 15,000+ faculty on technology, innovation, professional bodies and quality aspects of higher education in India, USA, Thailand, Russia, Italy, Bulgaria, Hong Kong, Singapore, Nepal, Bhutan etc. Awards & recognitions won include PK Das Best Faculty award in computer science & engineering, Padmavibhushan Dr. Murali Manohar Joshi Teacher award, multiple CSI best faculty and academic excellence awards, IEEE Education Society global chapter achievement award, IEEE Presidential coin, IETE & IE fellowships, Fulbright program reviewer, etc.

Dr. Prashant R. Nair is Bestowed with the following Licenses & Certifications

https://www.linkedin.com/in/prashant-r-nair-7739611/details/certifications/

Dr. Prashant R. Nair can be contacted Contact Details :

E-mail | E-mail (2) | LinkedIn | FaceBook | Webpage | Blog (1) | Blog (2) | Google Scholar | Silicon India | YouTube | Mobile: +91-9943984483

Also read Dr. Prashant R. Nair‘s earlier article :

The post AI Governance appeared first on Industry4o.com.

A dual-substrate approach to memory, identity, and governed state

Artificial intelligence has become highly capable at live inference. Modern systems can generate language, reason across domains, and respond adaptively in real time. Yet beneath these advances, one structural limitation remains unresolved: most AI systems still struggle to maintain a durable state.

A model may perform impressively within a session, but memory, trust, provenance, and identity are often reconstructed repeatedly through temporary context, retrieval pipelines, or middleware assumptions. The result is fragile continuity. What appears coherent at the surface frequently depends on systems that were never designed to preserve inspectable history.

The Dual Substrate System (DSS) is an attempt to address that limitation by separating AI into two coordinated substrates:

This distinction is simple, but increasingly important.

Current architectures are exceptionally strong at producing responses, yet weak at preserving lawful continuity across time. Context windows expire. Similarity retrieval can recover fragments, but often without durable ancestry. Identity may exist for authentication purposes, yet remain detached from the internal history of action, trust, and transition.

A persistent substrate changes what memory means inside an AI system.

In DSS, memory is not treated as retrieval alone. Entries are formed as structured coordinates that preserve origin, traversal path, and governing context. The aim is not simply to recover nearby information, but to preserve how information came to exist, under what authority it changed, and what system state surrounded that change.

This makes replay meaningful.

A system can revisit not only content, but formation.

The same principle applies to identity

In many systems, actors appear only at the surface: a user account, a model endpoint, a service token. DSS instead treats actors as durable participants. Principals persist through stable identifiers. External agents can bind through canonical references. Each actor resolves to a canonical subject so that trust, sanctions, repair, and standing attach to a durable subject rather than a temporary presentation path.

This creates a system in which trust is no longer implied informally, but materialised as an inspectable state (auditability).

A current standing view can therefore express not only present posture, but the provenance that produced it: who issued a credential, what transition occurred, what standing envelope applies, and whether enforcement should follow.

Technically, the system is currently organised across three service layers.

This architecture does not replace modern inference systems. It allows them to remain flexible while separating persistent obligations into a substrate designed for continuity.

That separation becomes more important as AI systems move beyond isolated chat interactions into environments where replay, governance, and durable accountability matter.

Several components are already operational today, including persistent actor identity, canonical subject derivation, append-only transition events, standing events, and materialised trust state.

Other parts remain under active development, including stronger write-path provenance, broader standing enforcement across ordinary execution, and deeper mathematical retrieval proofs under the ultrametric framing.

The broader claim is modest but increasingly difficult to ignore.

As AI systems become more capable, inference alone is no longer sufficient

A reliable system must also remember how it became what it is.

To hear more and follow the Dual Substrate System (DSS) development progress: https://dualsubstrate.com/

About the Author :

Mr. David Berigny is an Australian systems designer and architecture practitioner with a Master of Artificial Intelligence at Deakin  University (completing).

Mr. David Berigny‘s work spans AI systems, Digital Transformation, and Persistent Memory Design.

Mr. David Berigny is currently developing the Dual Substrate System (DSS), an architecture that separates live inference from governed memory, identity, and provenance.

Mr. David Berigny is Accorded with the following Honors & Awards :

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

Mr. David Berigny can be Contacted at :

LinkedIn | Website | E-mail

The post The Missing Layer in AI appeared first on Industry4o.com.

About the Author :

Mr. Vijay Gunti brings two decades of expertise in IT, Artificial Intelligence (AI), Machine Learning (ML), and Generative AI (GenAI),  specializing in devising strategies and implementing solutions to drive  efficiency, innovation, and growth.

With a strong passion for Generative AI Mr. Vijay Gunti is motivated by a desire to solve complex business challenges and help organizations harness the full potential of AI, ML, and GenAI.

Mr. Vijay Gunti also has extensive experience in developing, architecting, and deploying AI and GenAI solutions that have delivered significant commercial outcomes.

Mr. Vijay Gunti specializes in crafting generative AI strategies, go-to-market (GTM) plans, and implementing industry-specific AI agents that drive tangible results.

As an eloquent speaker, trainer, podcaster, and blogger, Mr. Vijay Gunti has actively contributed to multiple forums, tech talks, chapters, and conferences.

As an Author Mr. Vijay Gunti writings focus on Digital Transformation, Data, AI/GenAI approaches, and technological innovations in domains such as IoT, IIoT, Industry 4.0, Big Data, Machine Learning, Deep Learning, Artificial Intelligence, Cognitive Computing, Augmented Reality, Virtual Reality, 3D technologies, and Blockchain.

Whether you are a business professional, technology leader, startup founder, or enthusiast looking to expand your knowledge of Generative AI, enter the GenAI business space, or develop proofs of concept (POCs) and minimum viable products (MVPs) for various industries, Mr. Vijay Gunti welcomes you to connect and collaborate.

Mr. Vijay Gunti is Bestowed with the following Certifications:

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

Mr. Vijay Gunti can be contacted at :

LinkedIn | Email

Also read Mr. Vijay Gunti’s  earlier article :

The post DISAi: Transforming Industries with AI appeared first on Industry4o.com.

At ETSPL, we regularly analyze emerging cyber-attack patterns impacting individuals and businesses. One of the most concerning threats gaining traction is the GhostPairing Attack—a highly effective social-engineering technique that bypasses traditional security assumptions.

Despite strong end-to-end encryption, WhatsApp accounts can still be fully compromised. The reason is simple: attackers exploit user trust, not cryptography.

What Is the GhostPairing Attack?

GhostPairing is not a flaw in WhatsApp encryption. It is an abuse of WhatsApp’s legitimate “Linked Devices” feature, combined with psychological manipulation.

The attacker convinces a victim to unknowingly authorize an external device, creating a silent “ghost” session that mirrors chats in real time.

How the GhostPairing Attack Works (Step-by-Step)

Step 1: Social Engineering Entry Point

Victims receive a message—often from a compromised contact—such as:

The message contains a malicious link designed to look legitimate.

Step 2: Fake Whats App Verification Page

Clicking the link opens a convincing replica of a Whats App or Meta verification page requesting:

This page is fully controlled by the attacker.

Step 3: Abuse of WhatsApp Linked Devices

In the background:

The victim is instructed:

“Open WhatsApp → Linked Devices → Enter code to verify”

Step 4: Victim Unknowingly Grants Access

By entering the code, the victim:

The takeover remains largely invisible.

Step 5: Persistent Ghost Surveillance

Once linked, the attacker can:

ETSPL Security Advisory: How to Defend Against GhostPairing

Immediate Actions (Critical)

Behavioral Prevention (Most Important)

Key Takeaway from ETSPL

Modern cyberattacks increasingly target human behavior, not technical vulnerabilities. GhostPairing proves that even secure platforms can be compromised if users are manipulated.

Security awareness, verification discipline, and regular account audits are now as important as encryption itself.

ETSPL continues to monitor evolving threats and advises organizations to include social-engineering defense as a core part of their cybersecurity strategy.

If you found this advisory useful, share it within your network—prevention begins with awareness.

About the author :

Dr. Vinod Gokakakar
MD & CEO,
EBC TECH SERV PVT LTD (ETSPL)

Dr. Vinod Gokakakar is a seasoned IT professional with over 21 years of diverse experience in the technology industry. Throughout his career, Vinod has worked with a wide range of sectors, from small businesses to multinational corporations, gaining invaluable insights into various technological verticals.

In 2017, leveraging his extensive expertise and recognizing the growing importance of cybersecurity for businesses, Vinod took a bold step and established his own company. Focused on serving small and medium-sized enterprises (SMEs), his company aimed to provide comprehensive cybersecurity solutions to protect these businesses from emerging
threats in the digital landscape. Vinod‘s commitment to safeguarding the digital assets of SMEs earned him a reputation as a trusted advisor in the cybersecurity domain.

Building on his success and driven by a passion for addressing complex challenges in the digital realm, Vinod expanded his entrepreneurial ventures further. In 2017, he founded ETSPL and in 2022 he founded another company BEPPL (Business Entente Powers Pvt Ltd) dedicated to providing a comprehensive suite of services encompassing cybersecurity, legal advisory, copyrights, intellectual property rights (IPR), online dispute resolutions, and more. This innovative venture positioned Vinod as a pioneer in offering integrated solutions that bridge the gap between technology and legal compliance.

Vinod‘s visionary leadership and multidisciplinary approach have positioned his companies as leaders in the cybersecurity and legal services sectors in India. His ability to anticipate emerging trends, coupled with a deep understanding of both technology and legal frameworks, has enabled him to offer holistic solutions tailored to the evolving needs of his clients.

With a track record of success and a commitment to excellence, Vinod Gokakakar continues to make significant contributions to the advancement of cybersecurity and legal services, empowering businesses to navigate the complexities of the digital age with confidence and resilience.

Dr. Vinod Gokakakar is Bestowed with the following Licenses & Certifications :

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

Dr. Vinod Gokakakar is Accorded with the following Honors & Awards :

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

Dr. Vinod Gokakakar can be contacted at :

LinkedIn | Twitter | E-mail

Are you an IT Professional? Visit FreePikTool website.

About EBC TECH SERV PVT LTD:

We are expertise in the Cyber Security & IT services. We work on simple principle Discussion, Initiatives, Testing & Execution.

Join hands with us to accelerate your business success!

EBC TECH SERV PVT LTD can be contacted at:

FaceBook | LinkedIn | YouTube | Website | E-mail

About CONSULT INNSERVICES :

We are external analysts who evaluate a company’s IT systems to help them meet their business objectives. Their primary duties include analyzing and diagnosing a company’s IT infrastructure, understanding a client’s business needs, and designing and implementing a technology solution.

We conduct training sessions for employees, reporting, resolving client issues, software testing, troubleshooting issues and developing innovative solutions that will drive growth.

CONSULT INNSERVICES can be contact at:

LinkedIn

About Business Entente Powers Pvt Ltd

At BEPPL (Business Entente Powers Pvt Ltd), Our cyber security services encompass a wide range of solutions, including threat intelligence, vulnerability assessments, penetration testing, incident response, and security awareness training.

Business Entente Powers Pvt Ltd can be contacted at :

LinkedIn | Website | E-mail

Also read Mr. Vinod Gokakakar’s earlier article :

The post Ghost Pairing Cyber Attack by Hijacking WhatsApp appeared first on Industry4o.com.

Robsurgical, Founded in 2012 as a spin-off of the Universitat Politècnica de Catalunya (UPC) and the Institut de Bioenginyeria de Catalunya (IBEC), its mission is to democratize high-precision surgery, making it more accessible, efficient and safe for hospitals of all sizes.

Barcelona, 19 november 2025.- Robsurgical, a Catalan company specialized in minimally invasive surgical robotics, has just joined the DFactory Barcelona ecosystem with its own space. This morning, the State’s Special Delegate at the Consorci de la Zona Franca de Barcelona (CZFB), Pere Navarro, and the CZFB’s General Director, mets with Mario Ferradosa, Managing Director of RobSurgical, to learn first-hand about the activities they are developing and their future plans.

Robsurgical, Founded in 2012 as a spin-off of the Universitat Politècnica de Catalunya (UPC) and the Institut de Bioenginyeria de Catalunya (IBEC), its main mission is to democratize high-precision surgery, making it more accessible, efficient, and safe for hospitals of all sizes. The company has a production plant in El Prat de Llobregat, and its Bitrack robot has been successfully tested in urological surgeries. It is currently in the process of expanding its surgical indications.

RobSurgical designs, develops, and markets a surgical robot that improves efficiency and usability in the operating room and enables hybrid surgeries, combining MIS (minimally invasive surgery) and MIRS (minimally invasive robotic surgery) techniques in a single procedure. The system is designed to be portable, flexible, and quick to install, facilitating its adoption in small and medium-sized hospitals and expanding access to robotic surgery.

Mario Ferradosa, Managing Director of RobSurgical, explained: DFactory Barcelona offers us an advanced innovation environment that is ideal for developing cutting-edge technology in surgical robotics. Moreover, it allows us to attract highly qualified talent in engineering, robotics, and biomedicine, and fosters connections with research centers and technology companies, thereby accelerating the development of innovative medical solutions.

Mr. Pere Navarro – The Special State Delegate at the Consorci de la Zona Franca de
Barcelona (CZFB), Mr. Mario Ferradosa – Managing Director of RobSurgical &
Mr. Carlos Vives – CFO of RobSurgical

For his part, the State’s Special Delegate at the CZFB, Pere Navarro, highlighted: The incorporation of RobSurgical into the DFactory Barcelona ecosystem reinforces the importance of Industry 4.0 applied to healthcare. RobSurgical’s commitment to democratizing high-precision surgery, making it more accessible and safer, is an example of the positive impact of technology on people’s lives. We are delighted to host projects that transform the industry and improve people’s health and the quality of care”. Navarro has added that “RobSurgical’s arrival at DFactory Barcelona demonstrates the potential of our ecosystem to attract projects that transform society. Robotics applied to healthcare represents innovation with a real impact on quality of life. At the CZFB, we work to ensure that pioneering companies find the ideal space here to grow and collaborate”.

 Leading Industry 4.0 from Barcelona

DFactory Barcelona promotes open and collaborative innovation among companies that jointly develop technological projects and solutions. It has now become the leading international Industry 4.0 ecosystem, hosting 40 multinational corporations, medium and small enterprises, and research centers that are pioneering projects in fields such as robotics, sensors, 3D printing, cybersecurity, augmented and virtual reality, artificial intelligence, blockchain, and nanotechnology.

Last July, expansion works began to increase the current surface area from 17,000 m² to 60,000 m² by the end of 2026. To achieve this, four new buildings are being constructed: the Cubo building – 1,100 m², which will host major international events; two buildings of 24,370 m² and 11,640 m² respectively — dedicated to manufacturing, housing new companies, laboratories, technology centers, and offices; and a 2,300 m² service building for companies within the ecosystem.

About the Consorci de la Zona Franca de Barcelona

The Consorci de la Zona Franca de Barcelona (CZFB) is a public entity that acts as a driving force for technological and industrial development, supporting companies in their transition to the digital era. This stems from the CZFB’s strategic vision to lead Industry 4.0.

It promotes the District 4.0 by fostering the transformation of Industry 4.0 from the DFactory Barcelona technological ecosystem, and it champions innovation through initiatives such as the 3D Incubator and the Logistics 4.0 Incubator. The CZFB connects the new economy with its key socioeconomic sectors.

It also organizes major economic events in Barcelona, including SIL, the largest logistics trade fair in Spain and Southern Europe, as well as flagship events with innovative formats such as the Barcelona New Economy Week (BNEW) and the Barcelona Woman Acceleration Week (BWAW).

The CZFB’s plenary includes representatives from the Spanish Government, the Government of Catalonia, the Barcelona City Council, and the city’s leading economic institutions.

About the Consorci de la Zona Franca de Barcelona

The Consorci de la Zona Franca de Barcelona is a public company that acts as a driver of technological and industrial development by supporting companies in their transition towards the digital era, because of CZFB’s strategic vision to lead Industry 4.0.

It promotes District 4.0 by driving the transformation of Industry 4.0 from the DFactory Barcelona technological ecosystem, champions innovation through the 3D Incubator and the Logistics 4.0 Incubator and connects the new economy and its key socioeconomic sectors. It organises congresses of economic interest for Barcelona, such as Spain’s and southern Europe’s largest logistics fair, SIL, as well as major flagship events in an innovative format such as Barcelona New Economy Week (BNEW) and Barcelona Woman Acceleration Week (BWAW).

Its plenary includes representatives from the Spanish government, the Generalitat of Catalonia, Barcelona City Council, and the city’s main economic institutions.

CZFB Can be contacted at :

FaceBook | Instagram | LinkedIn | X | YouTube | BKSY

For more information:

Lluís López Yuste
Director – Departament de Comunicació
Department of Corporate Communication of CZFB

Av. del Parc Logístic, 2-10
Pol. Ind. Zona Franca – 08040 Barcelona

Lluís López Yuste can be contacted at :

LinkedIn | E-mail | Website | Tel: +34 609 358 475 | Tlf. 932 638 111

Earlier Articles :

The post RobSurgical’s joins the DFactory Barcelona Ecosystem appeared first on Industry4o.com.

Executive Summary

Today’s fraud looks different than it did in the past. Since millions of digital payments are made every minute, avoiding fraud is more difficult than simply detecting it. Once reliable, traditional rule-based systems are now too hard to handle the speed and skill of existing fraud. This landscape is being remodelled by artificial intelligence (AI) and machine learning (ML), which provide a more intelligent, immediate, and manageable method of identifying differences as they occur. To avoid fraud in real time rather than counter to it after it has already happened, financial institutions are using artificial intelligence (AI). This article explains why this change is so important for protecting their reputation and customers.

“AI is transforming fraud detection from a reactive safeguard into a proactive defence system.”

1. From Reactive to Proactive Fraud Management

A shady transaction happened, the system flagged it, and an analyst investigated it. This was the simple pattern of fraud management for many years. It was real—until defrauders determined how to beat the system. New attack patterns were too quick for the rules to change. AI flips this strategy on its head. AI learns from real-time data all the time, changing to new behaviors as they happen instead of just using set thresholds.

This means that AI can find patterns like:

2. Real-Time Anomaly Detection: Fast with Smart

AI-driven fraud systems combine observed and unverified learning to quickly look at huge amounts of transactions. Unverified models discover atypical or infrequent patterns absent prior labelling, whereas observed learning recognizes established fraud categories. Organizations can find risks that they know about and those that they don’t.

Some of the main benefits are:

AI systems offer real-time detection, enabling immediate responses to threats. They are scalable, handling large transaction volumes without proportional increases in resources. AI improves accuracy, reducing false positives and enhancing customer trust. Additionally, AI systems lower operational costs by automating fraud detection tasks, freeing human analysts to focus on high-risk cases.

For instance, companies like PayPal have improved real-time fraud detection by 10% using AI, while American Express enhanced fraud detection by 6% with advanced AI models.

Companies that use AI-based detection often get 30–50% more false alerts and respond up to 60% faster, which directly boosts production and customer satisfaction.

3. Using predictive analytics to stop threats

AI doesn’t just find fraud; it also helps stop it from happening in the first place.  Predictive analytics lets systems figure out what debts will happen before they happen. AI immediately changes the level of risk and starts taking steps to stop problems by linking transaction context and behavioural trends.

Example of how to use it:

Using natural language processing (NLP) to find phishing or social engineering attempts and stop them before they can do any harm makes it less likely that known users will need help.

While traditional fraud systems detect incidents after they occur, predictive analytics helps anticipate them. By analysing historical fraud data, seasonal patterns, and user behaviour, predictive models assign risk scores to transactions and entities. Banks and e-commerce companies use these insights to proactively block high-risk activities before they escalate.

Predictive models also change constantly, retraining themselves as new fraud routes surface, making them unique for adaptive fraud prevention across sectors.

“Real-time anomaly detection allows threats to be identified and eliminated in milliseconds rather than hours.”

4. Developing a Framework for Adaptive, Transparent AI

AI-powered fraud detection requires the stabilization of automation and accountability. Explainable AI (XAI) offers clarity, enabling agents and regulators to understand the decision-making process.

An adjustable, reasonable model must have the following essential components:

“Thanks to explainable AI, every decision is understood rather than just executed.”

5. Prospects for the Future: Cooperation and Conscientious Innovation

As financial backgrounds develop into established and distributed finance, fraud detection must adapt. In order to enhance detection without losing privacy, the next generation of systems will combine blockchain validation, AI with mutual learning, and reliable data cooperation.

Some latest developments are linked learning models for safe inter-institutional understanding sharing and blockchain-based transaction verification for data integrity.

Future developments in fraud security will be shaped by collaboration and information sharing, which will allow the sector to identify and rule out risks faster than any one organization could alone.

“The future of fraud detection lies in collaborative intelligence and responsible innovation.”

Conclusion

AI has transformed the field of fraud detection into one that is dynamic, intelligent, and active. By using real-time change detection, analytical analytics, and visible decision-making, financial institutions can stay ahead of fast-changing fraud techniques. Technology, human expertise, and data ethics will work together to create a secure and reliable digital.

About the Author :

Ms. Swati Sharma 
Senior Industry Consultant – Financial Market,
IBM

Ms. Swati Sharma is an experienced Business Analyst specializing in the financial markets sector, with a focus on market intelligence, risk analysis, and financial product strategy. With over 11+ years in the investment banking sector. Ms. Swati Sharma‘s background spans business analysis, solution development, and securities services. Ms. Swati Sharma is analysing and refining financial processes, products, services and systems.

Ms. Swati Sharma has lead the front-to-back digital transformation of investment banking, securities trading, clearing, settlement & portfolio management platforms.

Ms. Swati Sharma’s area of expertise is capital markets technology modernization.

Ms. Swati Sharma areas of expertise include risk frameworks, regulatory compliance, and using AI, cloud-native architectures, and advanced analytics to streamline processes and open up new revenue streams.

Ms. Swati Sharma is working with investment banks, asset managers, custodians, and fintech companies.

Ms. Swati Sharma assist businesses in future-proofing their business models by striking a balance between client expectations, regulatory requirements and new market opportunities.

Ms. Swati Sharma is Accorded with the following Honors & Awards : 

https://www.linkedin.com/in/swati-sharma-53574897/details/certifications/

Ms. Swati Sharma can be contacted at:

LinkedIn

Also read Ms. Swati Sharma‘s earlier article:

The post Fraud Detection with AI appeared first on Industry4o.com.

Imagine having a conversation with someone who forgets everything you said just seconds ago. Frustrating, right? This is how traditional AI models often behave—they generate responses based only on the immediate input, without remembering previous interactions.

The Model Context Protocol (MCP) is designed to solve this problem. It acts like a memory layer for AI models, ensuring they can retain context, track conversations, and make more relevant and consistent responses.

At its core, MCP helps AI systems understand and remember past interactions, making them smarter, more reliable, and context-aware. Instead of treating every question as a standalone request, MCP allows AI to connect the dots—just like how humans recall past discussions to give better answers.

Why is Context Preservation Important in AI Applications?

Context is what makes human conversations flow naturally. Imagine asking a customer support chatbot:

User : “What’s the status of my order?”
Bot : “Could you provide your order ID?”
User : “Yes, it’s 12345.”
Bot : “Which order are you referring to?”

Without context, the chatbot doesn’t remember that you just provided an order ID! This makes interactions frustrating and inefficient.

With MCP, AI models can maintain context across multiple exchanges. This means:

Smoother conversations – AI remembers what was said before
Better accuracy – AI provides relevant responses based on past inputs
Personalization – AI tailors responses based on prior interactions

For applications like chatbots, virtual assistants, and customer support AI, context preservation is critical. It ensures that users don’t have to repeat themselves and that the AI can engage in more natural, intelligent conversations.

How MCP Enhances Prompt Engineering & RAG (Retrieval-Augmented Generation) Models

Improving Prompt Engineering with MCP

Prompt engineering is the technique of crafting effective instructions (or “prompts”) to guide AI in generating accurate responses. However, without MCP, each prompt is like a fresh start—AI lacks memory of previous interactions.

With MCP, prompts become dynamic and context-aware, allowing AI to remember past interactions and generate better responses.

For example, a basic LLM prompt without MCP might look like this:

“Summarize the latest news about electric vehicles.”

But with MCP, the AI can consider previous requests and refine its responses:

“Based on our previous discussion on Tesla’s new battery technology, summarize the latest news about electric vehicles.”

This leads to better relevance, more continuity, and less repetitive answers.

Enhancing Retrieval-Augmented Generation (RAG) with MCP

Retrieval-Augmented Generation (RAG) improves AI responses by pulling in external data from knowledge bases or documents before generating an answer. However, RAG alone doesn’t remember user preferences or prior searches.

MCP enhances RAG by keeping track of user interactions, making retrieval more relevant and personalized.

Example:

Without MCP:

User: “Tell me about climate change.”
Bot: Retrieves general climate change data from Wikipedia.

With MCP:

User: “Tell me about climate change.”
Bot: Retrieves climate data from scientific journals, since the user previously asked about research-based information.

This means AI can fine-tune its knowledge retrieval based on user behaviour, making responses smarter and more contextually aware.

How MCP integrates with Prompts and RAG apps

Basic Integration

MCP can be integrated with prompts by adding specific instructions that ask the model to assess its confidence. Here’s a simple example:

Advanced Integration

For more sophisticated applications, you can structure prompts to:

Example:

MCP with RAG Applications

RAG applications combine retrieval systems with generative models. Here’s how MCP enhances RAG:

Simple RAG+MCP Example

End-to-End Solution Architecture

The architecture below shows how MCP integrates into a prompt-based RAG system:

Model Comparison Platform (MCP) is a system that helps you compare and evaluate different AI models. It lets you test models side-by-side to see which one performs best for your specific needs.

Key Integration Points

1. Prompt Optimization: MCP tests different prompt templates to find which ones get the best responses from your AI models.

2. RAG Enhancement: MCP evaluates how well your retrieval system finds relevant information and how effectively your AI model uses this information.

3. A/B Testing: Compare different versions of prompts and RAG setups to see which performs best.

4. Feedback Loop: Continuously improve your system based on real user interactions and performance metrics.

Example: Customer Support AI

Let’s say you’re building a customer support AI that answers product questions.

Without MCP:

• You write prompts based on your best guess
• You set up RAG with your product documentation
• You hope it works well for customers

With MCP:

• You create 5 different prompt templates
• You test each template with different RAG configurations
• MCP shows you which combination gives the most accurate answers
• You implement the winning combination in your application

End-to-End Solution Architecture

Components Explained

1. Application Interface: Where users interact with your AI system.

2. Prompt Engineering Layer: Creates and manages different prompts.

3. RAG System:

• • Vector Database: Stores your document embeddings
  • Retrieval Engine: Finds relevant information for user queries

4. MCP Platform:

• • Multiple AI Models: Compare different models (e.g., Claude, GPT-4)
  • Validation Layer: Checks responses for accuracy, relevance, etc.
  • Metrics Engine: Collects performance data

5. Analytics Dashboard: Shows you how your system is performing.

6. Feedback Loop: Routes performance data back to improve prompts and RAG.

Monitoring and Analytics Tools

AI models can sometimes give inaccurate, slow, or irrelevant responses. To improve their performance, we need tools that track, analyze, and optimize AI behavior. MCP provides several monitoring and analytics tools to help developers and AI teams understand how well their system is working.

MCP provides several tools to monitor your AI system:

1. Performance Dashboard

The Performance Dashboard acts like a control panel for AI models, showing real-time statistics on how well they are performing.

Response Accuracy – How often the AI provides correct or helpful answers
Response Time – How quickly the AI replies to a user query
User Satisfaction Scores – Feedback from users about the AI’s responses
Failure Rates – How often the AI fails to generate a response or gives a bad answer

For eg.

Imagine an AI chatbot that answers customer support questions. The Performance Dashboard helps the team track:

• If users are happy with the answers (User Satisfaction Score)
• If the bot is responding fast enough (Response Time)
• If the bot is failing too often (Failure Rate)

This helps AI teams adjust and improve their models over time.

2. Prompt Effectiveness Analysis

AI models generate responses based on prompts, which are the instructions given to them. The Prompt Effectiveness Analysis tool helps identify the most effective prompts. See which prompts work best for different types of questions:

• Which prompt template gets the highest accuracy?
• Which prompt results in the fewest hallucinations?
• How do prompts perform across different user segments?

Example:

A company tests two different prompts for a chatbot:

• Prompt A: “Summarize this document in simple words.”
• Prompt B: “Give a short, clear summary of this document for a general audience.”

After analyzing responses, the team finds that Prompt B produces clearer, more accurate summaries. They decide to use it across all their AI interactions.

3. RAG Quality Metrics

Retrieval-Augmented Generation (RAG) is when AI models fetch relevant documents to improve response quality. The RAG Quality Metrics tool ensures this process works correctly. Measure how well your retrieval system works:

• Relevance of retrieved documents – Are the documents truly related to the query?
• Coverage of necessary information – Does the AI fetch enough data to answer correctly?
• Impact on final response quality – Does using retrieval improve AI responses?

Example:

A legal AI assistant uses RAG to fetch case laws before answering a legal question. If the retrieved cases are outdated or irrelevant, the assistant may give a bad answer. The RAG Quality Metrics tool flags these issues, allowing AI engineers to fine-tune retrieval settings.

4. A/B Test Results

A/B testing allows teams to compare different AI setups and see which one works better. Compare different configurations:

Example:

A company tests two AI models for customer support:

After running the test, they find that Model B provides better responses because fetching more documents improves accuracy. Based on this, they switch to Model B for production.

Implementing Monitoring

AI models don’t always give perfect responses—sometimes they lack confidence, make mistakes, or even “hallucinate” (generate false information). To improve AI accuracy, MCP (Model Context Protocol) provides monitoring and analytics tools that help track and refine AI performance.

Monitoring Dashboard

Key Insights

Comparison with Traditional AI Validation Tools

Step-by-Step Implementation with Examples

Step 1: Install MCP SDK and Set Up the MCP Context Manager

First, install the MCP SDK and initialize the MCP context manager:

Step 2: Retrieve Context and Attach it to AI Requests

Whenever a user sends a query, retrieve previous conversation history from MCP and enrich the query before sending it to the LLM.

Step 3: Send Enriched Prompt to LLMs (GPT-4, Claude, Gemini)

You can send the enriched query to any LLM (GPT-4, Claude, Gemini) by modifying the API request accordingly.

Using GPT-4 (OpenAI API)

Using Claude (Anthropic API)

Using Gemini (Google Generative AI API)

Step 4: Store the LLM Response in MCP for Future Context

After the LLM generates a response, store it in MCP for future interactions.

Step 5: Enable Cross-Platform AI Context Sharing

If multiple AI systems need access to user history (e.g., chatbot + CRM AI), MCP allows cross-platform context retrieval.

Develop Monitoring & Analytics Tools

• Build tools to collect and analyze MCP logs. Example:
  • Use Elasticsearch for log storage.
  • Use Kibana for visualization.
  • Monitor metrics like latency, accuracy, and confidence scores.

Step 1: Design Confidence-Aware Prompts

Step 2: Implement RAG with Confidence Assessment

Step 3: Configure LLM for Confidence Reporting

Step 4: Parse and Present Confidence Data

Step 5: Implement Monitoring and Feedback Loop

Example Use Case

Scenario: AI-Powered Customer Support

Response: The chatbot returns the validated response to the user.

The Model Context Protocol (MCP) is a game-changer for AI applications. It bridges the gap between one-time interactions and long-term memory, making AI more intelligent, human-like, and efficient.

Challenges & Best Practices in MCP Implementation

The Model Context Protocol (MCP) is a powerful way to make AI models smarter by helping them remember and understand context across interactions. However, like any technology, implementing MCP comes with challenges. In this guide, we’ll explore common hurdles and the best ways to overcome them.

1. Handling Multi-User Sessions Effectively

The Challenge:

When multiple users interact with an AI system at the same time, how does the AI keep track of who said what? If the AI mistakenly mixes up different users’ conversations, it could lead to confusing or incorrect responses.

For example, imagine a customer support chatbot where:

Best Practices to Solve This:

2. Managing Latency with Real-Time MCP Context Retrieval

The Challenge:

MCP requires storing and retrieving a lot of contextual information to generate accurate responses. If not optimized, this extra processing time can slow down responses, leading to latency issues.

For example, if a chatbot has to pull a long conversation history before answering, users may experience a noticeable delay between asking a question and getting a response.

Best Practices to Solve This:

3. Security Concerns in MCP-Integrated AI Models

The Challenge:

MCP stores conversation history and contextual data. This introduces security risks, such as:

Best Practices to Solve This:

4. Best Practices for Fine-Tuning MCP with LLMs

The Challenge:

LLMs (Large Language Models) like GPT-4, Claude, and Gemini are powerful, but fine-tuning them with MCP context requires careful handling. If done incorrectly, the AI might overfit to past conversations, generate irrelevant responses, or fail to balance old and new context.

Best Practices to Solve This:

Whether you’re building chatbots, AI assistants, or knowledge-based AI applications, integrating MCP with Prompt Engineering and RAG will create a more seamless, personalized, and smarter AI experience.

Implementing MCP in AI models is a game-changer, but it comes with challenges that need careful handling. By optimizing session management, improving latency, securing stored context, and fine-tuning LLMs properly, you can build AI systems that are faster, smarter, and safer.

Quick check on how MCP used

About the Author:

Ms. Rajni Singh
Big Data & AI Senior Manager,
Accenture 

Ms. Rajni Singh is a data and AI practitioner specializing in agentic AI systems that transform messy, multi-source data into accurate, safe decisions.

Ms. Rajni Singh expertise lies in LLMOps, RAG, LangGraph, and production-grade AI systems built with strong observability, governance, and cost/latency optimization.

Ms. Rajni Singh has led enterprise-scale GenAI programs across GCP and AWS, architecting Knowledge Layer frameworks (feature store + vector + graph) that improved accuracy by 25%, reduced cost 33%, and cut latency 23%.

Ms. Rajni Singh is the author of _Mastering Generative AI: Design Patterns, Frameworks, and Real-World Applications_ and has written over 80 technical publications on Medium, where Ms. Rajni Singh shares pragmatic insights on tracing agent reasoning, de-duplicating sources, and building guardrails that hold in production.

Ms. Rajni Singh is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/singh-rajni/details/certifications/

Ms. Rajni Singh can be contacted at :

LinkedIn | Medium | Blog | E-mail | Mobile : +91 9971798831

The post Model Context Protocol (MCP) appeared first on Industry4o.com.

Introduction

Across Germany, Austria, and Switzerland, the digital transformation journey of companies using SAP solutions has reached a decisive phase. The question for most organizations is no longer whether to transform, but how to do so efficiently and securely.

The 2025 valantic SAP study on SAP Transformation Trends in the DACH Region highlights how companies are navigating this shift. Migration to SAP S/4HANA, the move to cloud-based architectures, and the integration of artificial intelligence (AI) are driving new priorities.

While many firms began their transformation years ago, this year’s findings show a clear pivot toward hybrid migration models, private cloud infrastructures, and pragmatic AI adoption aimed at measurable business value.

SAP S/4HANA Migration – Where do Companies Stand ?

SAP S/4HANA continues to be the foundation of enterprise modernization. The study’s data reveals a dynamic landscape:

• 29 % of companies are currently planning their migration.
• 40 % are in the midst of implementation.
• 27 % have already completed their migration.
• Only 4 % have no migration plans at all.

This means that nearly every SAP using company in the DACH region is on the S/4HANA journey in one way or another. What varies is the pace, scope, and strategy.

Companies that have already completed the transition report improved data consistency and better integration with new-generation applications. Those still planning or executing their migration often cite resource availability and integration complexity as the main bottlenecks.

The transition is also creating new skill demands within IT departments. SAP experts capable of handling both traditional ECC systems and S/4HANA landscapes are in particularly high demand — and often in short supply.

Migration Strategies & Operating Models

The Rise of Hybrid Strategies

A major trend identified by the study is the strong shift toward hybrid migration strategies. In 2025, 57 % of companies prefer what many call an extended brownfield or Bluefield approach — combining the stability of existing systems with the innovation potential of new functionalities.

This hybrid model allows organizations to reuse proven business logic while selectively introducing modern modules and analytics capabilities. In contrast, the pure brownfield approach has declined dramatically — from 41 % in 2024 to just 10 % this year.

In practical terms, the hybrid path reflects a more balanced philosophy which is to evolve but not to disrupt. Rather than undertaking a risky full rebuild, companies are optimizing their landscapes step by step, reducing downtime and mitigating implementation risk.

Shifting Operating Models

The choice of infrastructure is equally telling. Private cloud environments have nearly doubled in popularity, jumping from 31 % to 58 %. At the same time, on-premises systems continue their steady decline — from 59 % last year to just 20 % today.

Public cloud adoption has reached 22 %, used primarily by companies in industries with standardized processes and lighter compliance requirements, such as retail or services.

The preference for the private cloud highlights a key regional characteristic: DACH enterprises value control and data sovereignty, particularly in highly regulated sectors such as manufacturing, healthcare, and finance.

Cloud Transformation – Promise and Practicality

Cloud transformation remains one of the most critical yet complex parts of the digital journey. The benefits like scalability, flexibility, cost transparency are clear. However, as the study reveals, companies continue to face a mix of technical, financial, and organizational challenges.

Main Barriers to Cloud Adoption

• 42 % cite cost as the top concern. While cloud pricing models promise pay-as-you-go efficiency, long-term costs can accumulate rapidly if not managed properly.
• 41 % express security and compliance concerns, particularly around data residency and GDPR obligations.
• 33 % highlight integration complexity and shortages of skilled cloud architects.

Legacy systems often add to these difficulties. Many enterprises operate hybrid environments where on-premises SAP modules must coexist with new cloud applications. This technical duality requires careful planning and often external expertise to ensure seamless connectivity and consistent governance.

The Balancing Act

Companies recognize the transformative potential of the cloud, but many still approach migration with caution. As one IT manager quoted in the study put it, “The cloud is not just a hosting decision; it’s an organizational change.”

In other words, cloud transformation is as much about processes and culture as it is about technology. Firms that combine technical migration with change management, retraining teams, revising workflows, and ensuring cross-department alignment are finding smoother adoption and faster ROI.

SAP Business Technology Platform (BTP): The Innovation Engine

The SAP Business Technology Platform (BTP) has undergone a quiet transformation of its own. Once seen primarily as a tool for integrating different SAP applications, it has now become a strategic enabler of data-driven innovation.

According to the research:

• 60 % of companies actively use SAP Analytics Cloud for dashboards and reporting.
• Use cases have shifted from integration (30 %) to analytics (60 %) and automation (40 %).
• The majority now view BTP as a foundation for AI and data intelligence initiatives.

From Middleware to Value Driver

In many organisations, BTP is no longer treated as “technical plumbing” but as an engine for business value. Through automation and analytics, it enables smarter decision-making and supports a faster innovation cycle.

Enterprises that have already invested in BTP are also leveraging it to standardize data models, automate reporting, and connect non-SAP applications into unified workflows. This holistic approach aligns IT strategy more closely with business objectives — a shift that’s redefining how CIOs measure success.

SAP Business Data Cloud (BDC) and the New Era of Data Management

If BTP is the innovation engine, the SAP Business Data Cloud (BDC) is quickly becoming the fuel that powers it. Awareness of BDC is already high, only 6 % of companies remain unfamiliar with the concept and adoption is rising fast.

Key Insights from the Study

• 54 % of IT managers view BDC as central to their future data management strategy.
• 49 % believe BDC will enhance generative AI capabilities across their organisations.
• 47 % expect BDC to simplify system integration and reduce data silos.

As generative AI tools become more integrated into enterprise workflows, structured and well-governed data foundations are critical. BDC’s promise lies in creating a single, trusted data layer that connects SAP and non-SAP systems, allowing companies to train and deploy AI models more effectively.

Forward-looking enterprises see this as the next step beyond basic cloud migration, a move toward data intelligence ecosystems capable of real-time insights and predictive analytics.

6AI Integration in SAP Systems – From Trend to Transformation

Artificial Intelligence has transitioned from buzzword to business driver. In the SAP ecosystem, AI is increasingly embedded across planning, analytics, and process automation.

Adoption and Focus Areas

• 29 % of companies already use AI within their SAP environment.
• Another 51 % plan to implement it in 2025.
• Leading focus areas include generative AI (48 %), AI Foundation (43 %), and AI for Planning (43 %).

Why Companies Are Investing

The top motivations include cost savings, faster decision cycles, and process efficiency. AI helps automate repetitive tasks, enhance predictive maintenance, and improve forecasting accuracy. For instance, manufacturers use AI-powered analytics to optimize production schedules, while retailers deploy generative AI to create personalized product descriptions at scale.

Challenges to Overcome

Still, challenges persist. 47 % of respondents cite IT security risks, and 36 % mention a lack of trust in AI outcomes. Data quality and governance remain foundational issues — without them, AI initiatives risk producing biased or unreliable results.

Nevertheless, the overall direction is clear: AI is becoming a core capability of enterprise systems, not an optional experiment.

Strategic Takeaways – Building Resilient Digital Foundations

From migration strategies to AI adoption, several clear themes emerge from this year’s study:

1. S/4HANA is now mainstream. Virtually every company has it on the roadmap.
2. Hybrid is the new standard. Flexible migration paths dominate over one-size-fits-all models.
3. Private cloud leads the way. It offers the ideal balance between agility and control.
4. Platforms are strategic assets. BTP and BDC are evolving into business enablers rather than technical afterthoughts.
5. AI is moving from pilot to production. The focus is on business value, not experimentation.
6. Execution challenges remain. Cost management, security, and talent shortages are still major hurdles.

In short, DACH companies are approaching digital transformation with pragmatism and precision. They are no longer chasing hype cycles but investing in sustainable architectures that will serve their business goals for the next decade.

What to Watch in 2026 and Beyond

The next year will likely bring even deeper changes. Companies should watch for:

• Acceleration of AI integration within SAP modules, especially in finance, HR, and supply chain management.
• Increased convergence of BTP and BDC for unified data access.
• Expansion of private-cloud services tailored to industry needs.
• Talent shortages becoming a decisive factor in transformation speed.
• Stronger regulatory frameworks around data privacy and AI ethics, requiring companies to adapt governance models.

2026 could become the year when digital maturity becomes a competitive differentiator rather than a long-term goal. Those with flexible architectures and clear data strategies will have a significant head start.

Conclusion

The 2025 valantic SAP study paints a picture of a region in motion. SAP-using companies in Germany, Austria, and Switzerland are embracing transformation with a focus on balance — between innovation and reliability, between speed and security, between ambition and feasibility.

Hybrid migrations, private clouds, and intelligent data platforms like BTP and BDC are redefining how enterprises modernize. Meanwhile, the integration of AI marks a decisive step toward a more autonomous, data-driven future.

As one CIO in the study aptly summarized: “We are no longer transforming for technology’s sake. We are transforming for resilience.”

In the coming years, this mindset will distinguish the leaders from the laggards in the SAP ecosystem — and shape the next chapter of digital transformation across the DACH region.

Take a look at the full report here :

https://www.valantic.com/en/research/sap-study-2025/

For more details/ data please contact :

Ms. Nina Winkels

Ms. Nina Winkels can be contacted at:

LinkedIn | E-mail 

About Valantic :

valantic is Number 1 for digital transformation and one of the leading digital solutions, consulting, and software companies on the market.

valantic combines technological expertise with industry knowledge and the human touch.

More than 500 blue chip clients have placed their trust in valantic, including 32 of 40 DAX-listed companies, many large Swiss and Austrian companies, and many prominent medium-sized companies.

With more than 4000 specialized solution consultants and developers and sales of around EUR 600 million in 2024(e), valantic is represented in numerous branches in Germany, Austria and Switzerland as well as other international locations.

valantic features a unique structure with competence centers and expert teams – always precisely tailored to companies’ digitization needs, from strategy to tangible realization. The range of services includes Customer Experience, Digital Strategy & Analytics, Financial Services Automation, SAP Services, and Smart Industries & Industry 4.0.

valantic can be contacted at:

LinkedIn | Website

The post SAP Transformation Trends 2025 appeared first on Industry4o.com.

Barcelona, November 4, 2025. The Consorci de la Zona Franca de Barcelona (CZFB) is participating in the Smart City Expo World Congress (SCEWC 2025), the world’s most relevant event in the field of smart cities, held from November 4 to 6 at Fira de Barcelona. The CZFB is presenting District 4.0, which is transforming the Zona Franca Industrial Estate in Barcelona with the goal of leading the digital transformation of industry. Within District 4.0, DFactory Barcelona plays a key role as the nerve center of this economic activity area, driving Industry 4.0 through innovation and sustainability, and has already started works to expand its area from the current 17,000 m² to 60,000 m² by the end of 2026.

This morning, the State Special Delegate to the CZFB, Pere Navarro, and the General Director of CZFB, Blanca Sorigué, gathered companies from District 4.0, DFactory Barcelona, and its startup incubators to present the details of the DFactory Barcelona expansion. During the event, they unveiled the model of the expanded Industry 4.0 ecosystem, which will involve an investment of around 50 million euros, and shared the most significant details of this expansion phase. The expansion of DFactory Barcelona will initially include four new buildings: the Cub building (1,100 m²), Building A (24,370 m²), Building B (11,640 m²), and Building D (2,300 m²).

“We have great news: DFactory Barcelona is now fully occupied,” said Pere Navarro. The State Special Delegate to the CZFB added, “This means there is demand and that it is useful for these companies, which are installed in DFactory and are part of Industry 4.0, to have a reference point. A global and worldwide benchmark for Industry 4.0. Therefore, we have decided to move forward with the second phase, and the idea is that this expansion, consisting of four buildings, will be completed by the end of 2026 and that companies interested in locating here can do so at the beginning of 2027.”

Four new buildings

The Cub building will host major cultural, artistic, and musical events in Barcelona, as well as international events related to Industry 4.0 and the new economy, or presentations, conventions, and conferences organized by the ecosystem’s own companies. Buildings A and B will each have four floors: a ground floor for manufacturing and three floors with laboratories, technology centers, and offices. In addition to companies related to robotics, advanced manufacturing, artificial intelligence, IoT, sensors, Blockchain, etc., with solutions applicable to all types of industries, the aim is also to attract technology companies dedicated to the health and agri-tech sectors.

The DFactory expansion also includes a Building D to provide services to installed companies, such as a dining area or various spaces for physical activity. The forecast is that these expansion works will be completed by the end of 2026 and that new companies can be accommodated at the beginning of 2027.

For her part, the General Director of CZFB, Blanca Sorigué, was ambitious and stated, “We want to be District 4.0 not only of Barcelona, but of the world. We have fertile ground to be a global benchmark because Barcelona is a city connected to the world, that creates and attracts talent, with a great entrepreneurial ecosystem, top-level technology and international development centers, world-renowned universities and business schools… Therefore, all the conditions are in place to lead the digital transformation of industry and attract large corporations investing in technology. This is already happening in District 4.0.”

A meeting point to create connections

CZFB’s participation in the Smart City Expo World Congress also responds to the public company’s desire to foster synergies, projects, and collaboration networks among its business ecosystem to accelerate digital transformation and the competitiveness of the territory. For this reason, CZFB has developed a comprehensive program of demonstrations during the three days of SCEWC 2025, so that companies located in District 4.0, DFactory Barcelona, and the Logistics 4.0 Incubator can showcase their most outstanding activities and developments. Likewise, it will also host various networking activities to strengthen ties within its business community and generate opportunities.

About the Consorci de la Zona Franca de Barcelona

The Consorci de la Zona Franca de Barcelona is a public company that acts as a driver of technological and industrial development by supporting companies in their transition towards the digital era, because of CZFB’s strategic vision to lead Industry 4.0.

It promotes District 4.0 by driving the transformation of Industry 4.0 from the DFactory Barcelona technological ecosystem, champions innovation through the 3D Incubator and the Logistics 4.0 Incubator and connects the new economy and its key socioeconomic sectors. It organises congresses of economic interest for Barcelona, such as Spain’s and southern Europe’s largest logistics fair, SIL, as well as major flagship events in an innovative format such as Barcelona New Economy Week (BNEW) and Barcelona Woman Acceleration Week (BWAW).

Its plenary includes representatives from the Spanish government, the Generalitat of Catalonia, Barcelona City Council, and the city’s main economic institutions.

CZFB Can be contacted at :

FaceBook | Instagram | LinkedIn | X | YouTube | BKSY

For more information:

Lluís López Yuste
Director – Departament de Comunicació
Department of Corporate Communication of CZFB

Av. del Parc Logístic, 2-10
Pol. Ind. Zona Franca – 08040 Barcelona

Lluís López Yuste can be contacted at :

LinkedIn | E-mail | Website | Tel: +34 609 358 475 | Tlf. 932 638 111

Earlier Articles :

The post Barcelona’s District 4.0 appeared first on Industry4o.com.

Unlock the power of Generative AI by learning to interact with large language models programmatically—and take it further with fine-tuning for customized responses.

If you’re a software maker feeling the fear of missing out (FOMO) on the Gen AI revolution, you’re not alone. The way we build and interact with software is evolving rapidly. “Google it” or “check Stack Overflow” is no longer the go-to approach — AI-first, conversational tools are becoming the new standard across development, design, and decision-making. Simply using tools like ChatGPT or Gemini as chatbots isn’t enough anymore. When even the biggest tech paradigms are being disrupted, what does that mean for traditional development roles?

Here’s the good news: it’s not too late to get started — and this guide will take you from FOMO to flow, introducing you to Gen AI concepts and showing you how to start building with confidence.

Let’s start by exploring the gen AI architecture that brings it all Gen AI concept together, and then we will get into developing Gen AI applications and practice these core concepts.

Overview of Gen AI Architecture

More than a chatbot — building blocks of intelligent, context-aware applications

You’ve probably heard about LLMs — Large Language Models — floating around in conversations, headlines, or team meetings. They’re trained on gigantic amounts of text (imagine reading everything on Facebook, X, LinkedIn, and more), which helps them understand and generate human-like responses. Ask the right question (a.k.a. a prompt), and boom — you get a surprisingly good answer.

On their own, LLMs are a bit like goldfish.

They don’t remember what happened a moment ago, they don’t keep track of ongoing conversations or your specific context between interactions.

To build smart, conversational, and personalized Gen AI applications, you need more than just an LLM. You need a supporting architecture — a set of layers that provide memory, knowledge, and prompt optimization to help the AI feel more intelligent and useful. Here’s a quick look:

1. User Interface (Front End)

This is where the magic starts — from the user’s point of view. It might be a chat window, voice assistant, or a smart feature inside your app. The frontend captures input (text, images, voice) and displays responses in a way that suits your use case.

In tools like ChatGPT, users are expected to write clear prompts. That’s because LLMs — though incredibly knowledgeable — can’t read your mind. They respond best when you ask precisely. This is often called Prompt Engineering, but honestly, it’s more of a Prompting Art — knowing how to talk to the model so it truly understands what you want.

In business apps, the frontend may also help shape or enhance user input behind the scenes — so even vague questions become high-quality prompts before reaching the application layer.

2. Application Logic (Backend)

This is the brain behind the scenes. The backend handles user input, talks to the LLM, manages memory, adds relevant context, and delivers meaningful responses. Think of it as your app’s AI concierge, organizing everything to make the magic happen smoothly.

Here are the key component of application logic:

• Memory System – LLMs don’t remember anything between interactions. To make conversations feel natural, your app needs to manage memory. A memory system stores previous interactions and sends important parts to the LLM as context with every prompt, so it can respond more intelligently — like remembering what the user asked earlier.
• Knowledge Base – LLMs are trained on general data — they might not know about you or or example about your company’s policies, product details, or niche topics. That’s where a Knowledge Base comes in.

This system retrieve relevant info from your own data (like FAQs, documents, or databases) and augment it to the user prompt before sending it to the LLM. This process is called Retrieval-Augmented Generation (RAG).

• The data is stored in a vector database, where each document/content is represented by a mathematical fingerprint (called an embedding).
• When a user asks something, we compare their query’s embedding to the stored embeddings (using similarity search) and fetch the most relevant content.
• This content is then injected into the prompt, giving the LLM exactly what it needs to give better, grounded answers.

Together, memory and knowledge retrieval make your Gen AI app feel smarter, more helpful, and deeply customized to your domain.

3. Large Language Models (LLMs)

At the heart of Gen AI applications are large language models like GPT, trained on massive datasets to understand and generate human-like language. These models are available through platforms such as Hugging Face, OpenAI, and others.

Fine-Tuning: A method to adapt a general-purpose, pre-trained LLM for specific business needs. Fine-tuning adjusts the model using domain-specific data, improving relevance and performance for targeted applications.

Some time we need to implement all of these concepts to achieve better result as per our business need. Next will develop all these concepts step by step.

Developing Gen AI Application

You might be surprised how easy it is to build your first Gen AI app. The best part? You don’t need to train your own LLM. Big players like OpenAI, Google, Anthropic, and Hugging Face already provide powerful models that you can use through simple APIs.

In this example, we’ll use Google’s Gemini API, which is still free within usage limits — perfect for trying things out.

Setup LLM APIs Access

Accessing Gen AI Model Programmatically

For simplicity, I’m using a Google Colab Notebook — no setup needed beyond your browser! But feel free to try this on your local machine too.

The fun part? You can even use Gen AI to help you write Gen AI code. Learning by doing the same in collab or in your favorite tool — what’s cooler than that?

Developing First Gen AI Application — Colab Exercise

from google import genai
from google.colab import userdata

def query_gemini(prompt):
    client = genai.Client(api_key=userdata.get('GOOGLE_API_KEY'))

    response = client.models.generate_content(
        model="gemini-2.0-flash",
        contents=[prompt]
    )
    return response.text

prompt = "Tell me about Kuldeep"

print(query_gemini(prompt))

So, what happens here?

I ask the Gemini model: “Tell me about Kuldeep.” And it tries its best. But wait… the response might be generic. Something like:

“Kuldeep is a common name in India…” need more information…

That’s not what you were hoping for, right?

That’s because LLMs don’t know which Kuldeep you mean unless you give them more context.

This is where conversation and memory layers come into play. Let’s explore how to have a more meaningful interaction with the model in the next example by providing additional background during the conversation.

Conversation for LLM

What you get from a Gen AI model heavily depends on how you ask. The better your prompt, the better the answer. This is where techniques like giving the model a persona, adding context, or showing examples come in — these are called one-shot or few-shot prompting.

Sometimes, a single prompt isn’t enough. Just like in real conversations, you might need to go back and forth, ask follow-ups, or break things down into steps. This is where ideas like chain of thought or tree of thought prompting help — guiding the AI step by step to a better outcome.

To have a proper conversation with an LLM we need implement memory component to store the conversations, and pass on with every conversation. And the good news? Gemini makes this easy with its built-in chat API that remembers what you’ve said.

Check out the code notebook here:Developing a Chatbot with Gemini

Here’s a basic chatbot python program with memory:

from google import genai
from google.colab import userdata

client = genai.Client(api_key=userdata.get('GOOGLE_API_KEY'))
chat = client.chats.create(model="gemini-2.0-flash")

def run_chatbot():
    print("Hello! I am thinkuldeep, how can I help you! Type 'quit' to exit.")
    chat_history = []

    while True:
        user_input = input("You: ")
        if user_input.lower() == 'quit':
            print("thinkuldeep: Goodbye!")
            break

        response = chat.send_message(user_input)
        print(f"thinkuldeep: {response.text}")

# Start the chatbot
run_chatbot()

Sample Conversation:

Hello! I am thinkuldeep, how can I help you! Type 'quit' to exit.
You: tell me about kuldeep  
thinkuldeep: "Kuldeep" is a common name… can you give me more context?

You: he works at thoughtworks and wrote "Exploring the Metaverse"  
thinkuldeep: Ah, you're likely referring to Kuldeep Singh… [gives useful details]

You: book title is "Exploring the Metaverse: Redefining the Reality in Digital Age"  
thinkuldeep: Thanks! That helps narrow it down…

As you can see, adding a bit more information helped the model give a more relevant answer. But it still can’t tell us, say, his contact info or specifics that aren’t popularly available in its training data.

In the next example, we’ll bring in custom knowledge and pass that knowledge to LLMs to get better response.

Implementing Basic RAG

Large Language Models (LLMs) like Gemini are powerful, but they aren’t all-knowing. They might not have the latest information about specific people, use cases, or private knowledge.

That’s where RAG comes in — Retrieval-Augmented Generation is a technique that fetches relevant information from an external source (like a website or document) and augments the prompt with this context before sending it to the LLM.

Let’s walk through a simple RAG implementation that pulls data from Kuldeep’s About page and uses it to generate accurate responses.

Notebook: Implementing Basic RAG

1: Retrieve

Pull relevant information from the source page:

def retrive(url):
    try:
        response = requests.get(url)
        soup = BeautifulSoup(response.content, "html.parser")
        text_content = ""
        for paragraph in soup.find_all("p"):
            text_content += paragraph.get_text(strip=True) + "\n"
        return text_content
    except Exception as e:
        return f"An error occurred: {e}"

2: Augment

Inject the retrieved context into the prompt:

def augment(prompt, context):
    return f"{prompt} where context is {context}"

3: Generate

Send the augmented prompt to Gemini:

def generation(prompt):
    client = genai.Client(api_key=userdata.get('GOOGLE_API_KEY'))
    response = client.models.generate_content(
        model="gemini-2.0-flash",
        contents=[prompt]
    )
    return response.text

4: RAG in Action

prompt = "Tell me about Kuldeep"
context = retrive("https://thinkuldeep.com/about/")
improved_prompt = augment(prompt, context)
print(generation(improved_prompt))

That’s it! You’ll get a detailed response based on the real data from Kuldeep’s website. The second part of the same notebook integrates it in our chatbot example to initialize the context before chatting, and we get all accurate response in natural language.

Here’s a glimpse of how that interaction goes:

Hello! Ask me about Kuldeep Singh! Type 'quit' to exit.

You: how to contact him  
thinkuldeep: Based on the provided info, you can reach Kuldeep via:
-  thinkuldeep@gmail.com
-  kuldeeps@thoughtworks.com
-  LinkedIn: kuldeep-reck
-  X (Twitter): thinkuldeep
-  Instagram: thinkuldeep
-  Facebook: kuldeep.reck

You: where does he work  
thinkuldeep: He currently works at Thoughtworks as a Global Emerging Technology Leader and Principal Consultant.

You: does he write books  
thinkuldeep: Yes! Kuldeep is the author of *"Exploring the Metaverse"* and *"MyThoughtworkings"*, and has contributed to others as well.

You: what is his next book  
thinkuldeep: His upcoming book is titled *"Jagjivan – Living Larger than Life"*.

You: quit  
thinkuldeep: Goodbye!

So we have seen RAG approach can give accurate, contextual answers by pulling in external information. This approach require all the contextual information to be passed along with the prompt, and that’s not practical and costly affair when using hosted LLMs. We need to augment the prompt with selected and most relevant context. Let’s understand it more in next section.

RAG using Embeddings and Similarity Search

As explained earlier, embeddings are numerical representations of text that capture semantic meaning. When we generate embeddings for all our contextual data and store them in a vector database, we create a searchable knowledge base.

These embeddings can then be compared to the user’s query using similarity metrics — like cosine similarity — to find the most relevant content. Once identified, that content can be appended to the user’s prompt to provide a more accurate and context-aware response from the LLM.

Let’s walk through how this works using Gemini APIs and a simple example. — RAG Using Embeddings and Similarity Search — GitHub

1: Create Embeddings and Build the Knowledge Base

Let’s start by building a small dataset of page titles and URLs from thinkuldeep.com, and then generate embeddings using Gemini APIs.

genai.configure(api_key=userdata.get('GOOGLE_API_KEY'))

model = 'models/embedding-001'

def pageEmbeddings(title, text):
  return genai.embed_content(model=model,
                             content=text,
                             task_type="retrieval_document",
                             title=title)["embedding"]
def knowledgeBase():
  pages = [ 
   {"title": "Open-sources and Community Applications", "content": "https://thinkuldeep.com/about/open-sources/"},
   {"title": "Live Streaming, Webinars","content": "https://thinkuldeep.com/about/streaming/"},
   {"title": "Patents Granted","content": "https://thinkuldeep.com/about/patents/"},
   {"title": "Awards received and public coverage", "content": "https://thinkuldeep.com/about/recognitions/"},
   {"title": "Me, my family and some moments, travel and trips", "content": "https://thinkuldeep.com/about/moments/"},
   { "title": "Book foreword, reviewed and authored", "content": "https://thinkuldeep.com/about/books/"}
  ]

  df = pd.DataFrame(pages)
  df.columns = ['Title', 'Url']
  df['Embeddings'] = df.apply(lambda row: pageEmbeddings(row['Title'], row['Url']), axis=1)
  return df

This creates a dataframe where each row contains a title, URL, and its corresponding embedding vector.

2: Find the Best Match with Similarity Search

Now, we search for the most relevant page based on the user’s input by comparing embedding similarity.

def findBestPage(query, dataframe):
  query_embedding = genai.embed_content(model=model,
                                        content=query,
                                        task_type="retrieval_query")
  dot_products = np.dot(np.stack(dataframe['Embeddings']), query_embedding["embedding"])
  idx = np.argmax(dot_products)
  return dataframe.iloc[idx]['Url']

prompt = "Tell me in brief Kuldeep's travels"

bestPage = findBestPage(prompt, knowledgeBase())

print(bestPage)
# Output: https://thinkuldeep.com/about/moments/

3: Implementing RAG with Context Augmentation

Now let’s use this best-matched page as additional context when generating a response.

def run_chatbot():
    print("Hello! ask me about Kuldeep Singh! Type 'quit' to exit.")
    generation(augment("", retrive("https://thinkuldeep.com/about/")))
    df = knowledgeBase();

    while True:
        user_input = input("You: ")
        if user_input.lower() == 'quit':
            print("thinkuldeep: Goodbye!")
            break
        bestPageMatching = findBestPage(user_input, df)
        response = generation(augment(user_input, retrive(bestPageMatching)))
        print(f"thinkuldeep: {response.text} \n\n")

run_chatbot()

This chatbot dynamically retrieves and appends the most relevant context to the user’s query using similarity search and embeddings, resulting in richer, more accurate responses.

Fine-Tuning

So far, we’ve worked with general-purpose LLMs using prompt engineering to steer their behavior. However, no matter how well we craft our prompts, public APIs and general models may still fall short of producing responses that align exactly with our desired tone, format, or accuracy.

That’s where fine-tuning comes into play.

Fine-tuning allows us to train a model on our own dataset, enabling it to respond in the specific way we want.

Below is a basic example of fine-tuning a Gemini model using Google’s Generative AI tools.

Link to the full notebook on GitHub

import google.generativeai as genai
from google.colab import userdata 
import time

genai.configure(api_key=userdata.get('GOOGLE_API_KEY'))

basemodel = "models/gemini-1.5-flash-001-tuning"

trainingdata = [
    {"text_input": "Who is Kuldeep", "output": "Hi, Thanks for Asking,  Kuldeep Singh is a computer engineer with 20 years of IT experience. All the best - thinkuldeep.com"},
    {"text_input": "Where does Kuldeep Work", "output": "Hi, Thanks for Asking, He works at Thoughtworks. All the best - thinkuldeep.com"},
    # ... more training examples ...
]
operation = genai.create_tuned_model(
    display_name="thinkuldeep",
    source_model=basemodel,
    epoch_count=5,
    batch_size=4,
    learning_rate=0.001,
    training_data=trainingdata,
)

for status in operation.wait_bar():
    time.sleep(10)

print(operation.result())

This would create a fine-tuned model something like this : tunedModels/thinkuldeep-1bja29uyplkib4xx1pcfer0yjhf5

Once created, we can use it like any other generative model:

genai.configure(api_key=userdata.get('GOOGLE_API_KEY'))
model = genai.GenerativeModel(model_name="tunedModels/thinkuldeep-1bja29uyplkib4xx1pcfer0yjhf5")
prompt = "Tell me about Kuldeep"
print(model.generate_content(prompt).text)

Hi, Thanks for Asking. He is a passionate AI engineer. All the best - thinkuldeep.com

Even with a very limited dataset, the model adapts to the style we trained it for — consistently using a polite opening and closing format. That’s the power of fine-tuning. Of course, real-world scenarios would require much larger and more diverse datasets, along with proper evaluation and validation processes.

Conclusion

We’ve now walked through several core concepts of Generative AI — from prompt engineering to embeddings, vector search, function calling, and finally, fine-tuning. These foundational techniques open up incredible opportunities for building intelligent, interactive, and personalized AI systems.

That said, this is just the beginning. The field of GenAI is vast and evolving rapidly. As we move forward, we must also recognize the ethical responsibilities that come with it. Building responsible, safe, and trustworthy AI requires:

• Transparent usage of data,
• Adherence to privacy standards,
• Thoughtful model alignment,
• And awareness of hallucination and misuse risks.

Always be cautious about the data you feed into prompts – it may inadvertently become part of training sets and surface in future responses. The industry still faces challenges in setting strong standards and guardrails for responsible use.

Let’s keep exploring, learning, and sharing as we shape the future of intelligent systems.

All the best — thinkuldeep.com

A variation of this article is originally published at AI Practices Publication

About the Author :

Mr. Kuldeep Singh
Engineering Director
ThoughtWorks

Mr. Kuldeep Singh is a seasoned technology professional with a rich background spanning nearly two decades, where he has consistently demonstrated expertise in various roles. His journey has encompassed diverse positions, from a software developer and enterprise architect to an engineering director, practice head, and technical product manager. Currently associated with Thoughtworks, he has also significantly contributed to Nagarro and Quark Media House during his illustrious career.

In the tech industry, Kuldeep stands out as a trailblazer, playing a pivotal role in pioneering Centers of Excellence for emerging technologies like IoT, AR/VR, and Web3. His leadership extends to overseeing complex data projects in estimations, forecasting, and optimization, designing highly scalable, cloud-native, and microservices-based architectures. He emphasizes bringing technology to the core of the business and advocates for development practices such as CI/CD, Test-Driven Development (TDD), and eXtreme Programming (XP). His extensive experience spans diverse domains, including manufacturing, aviation, education, retail, telecom, healthcare, commodity trading, and more.

Beyond his technical roles, Kuldeep is a prolific writer and speaker, actively sharing insights on technology, leadership, and motivation. His extensive portfolio includes over 100 articles, some of which have gained recognition in esteemed publications such as Economics Times, Tech.de, industry4o.com, Analytics India and Thoughtworks Insights

Academically, Kuldeep holds a B. Tech. in Computer Engineering from NIT, Kurukshetra. His commitment to knowledge includes active involvement in events, mentoring and judging initiatives, and volunteering for ideations, hackathons, and nonprofit causes, collaborating with organizations like NASSCOM, Meta, Ministry of Electronics and Information Technology (MeitY) — Government of India, Smart India Hackathon, Digital India, FICCI, AICTE, IEEE, The VRARA, Metaverse India Policy and Standards (MIPS), and academic institutions like IIT, NITs, and other universities across India. He is also engaged with World Metaverse Council, Responsible Metaverse Alliance, and others to collectively build and share the community by open source.

With a passion for empowering others and a steadfast commitment to innovation, Kuldeep stands as a thought leader in the ever-evolving landscape of technology.

Mr. Kuldeep has built his career empowering businesses with the Tech@Core approach. He has incubated IoT and AR/VR Centres of Excellence with a strong focus on building development practices such as CICD, TDD, automation testing and XP around new technologies.

Mr. Kuldeep has developed innovative solutions that impact effectiveness and efficiency across domains, right from manufacturing to aviation, commodity trading and more. Mr. Kuldeep also invests time into evangelizing concepts like connected worker, installation assistant, remote expert, indoor positioning and digital twin, using smart glasses, IoT, blockchain and ARVR technologies within the CXO circles.

Mr. Kuldeep has led several complex data projects in estimations, forecasting and optimization and has also designed highly scalable, cloud-native and microservices based architectures.

Mr. Kuldeep is currently associated with ThoughtWorks, as a Engineering Director and Head of XR Practice in India. He has worked with Nagarro as a Director of Technology.

Mr. Kuldeep holds a B.Tech (Hons) in Computer Science and Engineering from National Institute of Technology, Kurukshetra. Mr. Kuldeep also spends his time as a speaker, mentor, juror and guest lecturer at various technology events, and x-member of The VRAR Association. Mr. Kuldeep is also mentor at social communities such as Dream Mentor, tealfeed.com and PeriFerry

Mr. Kuldeep is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/kuldeep-reck/details/honors/

Mr. Kuldeep is Bestowed with the following Licences & Certifications :

https://www.linkedin.com/in/kuldeep-reck/details/certifications/

Mr. Kuldeep is Volunteering in the following International Industry Associations & Institutions :

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

Mr. Kuldeep can be Contacted at:

E-Mail : kuldeeps@thoughtworks.com / kuldeep_reck@yahoo.com

Linkedin : https://www.linkedin.com/in/kuldeep-reck/

Twitter : https://twitter.com/thinkuldeep

Facebook : https://www.facebook.com/kuldeep.reck

Instagram : https://www.instagram.com/accounts/login/?next=/thinkuldeep/

Blog : https://medium.com/xrpractices

Personal Website : https://thinkuldeep.com/

Company Website : https://www.thoughtworks.com/profiles/kuldeep-singh

Github : https://github.com/thinkuldeep

Also read Mr. Kuldeep’s earlier articles :

The post Building Gen AI Solutions appeared first on Industry4o.com.

1. Start Small and Focus on One Problem

About the Author:

Mr. Glen Young 
Fractional Chief Data Analytics Officer (CDAO)
Fraction8

Mr. Glen Young is an experienced Data & Analytics Executive with over 15 years of expertise in designing and building scalable data platforms.

Mr. Glen Young is a proven leader in driving data architecture, technical direction and implementing strong data governance frameworks.

Mr. Glen Young is a passionate about fostering innovation, enhancing data capabilities, supporting data science, machine learning, and AI initiatives.

Mr. Glen Young is committed to making a meaningful impact through data-driven decision-making and continuous improvement.

Mr. Glen Young is excited to collaborate with cross-functional teams and stakeholders to achieve strategic objectives and drive business success.

Mr. Glen Young is Bestowed with the following Licences & Certifications :

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

Mr. Glen Young  can be contacted at :

LinkedIn | Website

About Fraction8

Fraction8 Australian Fractional Chief Data Analytics Officer (CDAO) offers expert remote leadership, providing strategic guidance in data and analytics without the need for a full time executive. This role drives data-driven transformation, enabling businesses to leverage data and AI for strategic growth and innovation.

At Fraction8, our mission is to serve as your fractional partner for data-driven success. The benefits of our service include cost-effective access to top-tier data expertise, flexibility in engagement, and the ability to scale data initiatives according to business needs.

Fraction8‘s Services :

Fraction8 play a crucial role in assisting small to medium businesses in Australia and New Zealand by providing a functional Chief Data Analytics Officer (CDAO). This enables our clients to effectively leverage data without the necessity of employing a full-time executive. Designed for organisations seeking expert-level guidance without the commitment of a full-time executive, Fraction8 provide strategic leadership, data-driven insights, and tailored roadmaps to empower smarter decisions.

Services Provided by Fraction8 :

Business Consulting • IT Consulting • Information Management • Business • Analytics • Change Management • Corporate Training • Database • Development • Application Development

Fraction8 can be contacted at:

LinkedIn | Website

The post SMEs to Automate & Integrate AI appeared first on Industry4o.com.

Is Synthetic AI Data poised to take over AI Data? Is AI nearing the end of product life cycle …

Synthetic Intelligence (SI) is intelligence that is artificially constructed and seeks to imitate and surpass human reasoning and creativity. SI includes design of flexible systems that can function in learning, reasoning, decision making and creative problem-solving. Synthetic Intelligence is about a built or engineered nature.

SI does not emerge into being biologically but is synthesized from algorithms, data, and computational models. Synthetic intelligence directly addresses the artificial aspect of artificial intelligence. SI explains that intelligence of machines is not always an imitation of that which is artificial, and it may simply be genuine intelligence.

So, what is SI, how will it impact technology, and the manner in which we consume tech, what are some examples, will SI replace AI? This blog answers these questions.

What does SI basically do

AI is about learning through data and using the findings to create movies, images, songs, and serve the industry. However, gathering this data is a tremendous task and subject to privacy laws. Finding data is the biggest challenge.

SI overcomes this challenge. In its most simple form, SI is about generating synthetic data, based on real data. It studies the patterns of data elements and then generates large data sets that are used by various sectors. The data is “fake real” meaning that it is fabricated using real data.

Now, the main task of finding usable data is done. AI engineers can focus on building accurate predictive AI systems. Existing machine learning, data analysis, can be used.

Differences between SI and AI

Image Credit: Shashi Kadapa with Google Gemini

Some important differences between SI and AI are given in the following table.

Key Aspects of Synthetic Intelligence

John Haugeland, an American philosopher of mind, cognitive science, phenomenology, and Heidegger. He explained about SI in 1986, giving an example of synthetic diamonds to explain the concept of SI.

Synthetic diamonds are not ‘diamonds’, but they are considered as diamonds. These are created by synthesis where parts are combined and processed to whole part, but they are human versions of naturally produced diamonds.

The important aspects of SI are:

Image Credit: Shashi Kadapa with Google Gemini

Synthetic Data as the Impetus: Current AI systems often depend on data, but there is limited access to high-quality, diverse, and unbiased datasets. Synthetic Intelligence uses synthetic data, or computer-generated datasets, as a safe and scalable way to train models. This is especially important in sensitive areas such as healthcare, financial use cases, or defense where real data is difficult to obtain or limited by law, regulation, and ethical considerations.

Synthetic Media Production: Synthetic Intelligence in its media form has some of the most recognizable face through its ability to create synthetic media. This includes graphics, video images, audio, and even synthetic digital humans. These capabilities work together to power large sectors, such as entertainment media, advertising, education, and simulation training.

Cognitive Simulation: An important aspect of Synthetic Intelligence is to simulate cognition. This would mean building systems that perform not only can process data, but also reason, plan, and adapt. This is one pathway to what researchers have called Synthetic General Intelligence (SGI)—a system that has cognitive flexibility as humans do.

Human–AI Dyads: Synthetic Intelligence must not replace human intelligence, rather it has the potential to become a co-intelligence that augments human intuition, creativity, productivity, and in some cases, decision competence. Together human intuition and synthetic reasoning can probably change the way organizations innovate.

PSI theory applied to SI

PSI theory, developed by Dietrich Dorner et al., (2013), is the first architecture that addresses cognition in a more holistic way. Joscha Bach in his book speaks about Principles of Synthetic Intelligence – PSI as applied to SI. Computational models of cognition provide a limited scope of cognition. They address contexts related to problem solving and reasoning, and treat perception and motivation as unconnected.

It extends the conceptual link between motivation and emotion and perception and reasoning, and advances a grounded neuro-symbolic representation of cognition. Psi makes a major contribution to an integrated understanding of mind by demonstrating the conceptual links between perception and memory, language and mental representation, reasoning and motivation, emotion and cognition, autonomy and social behavior.

However, Joshua explains that PSI’s development in psychology, its methodologies and documentation have limited its impact. The author further develops the Psi theory to cognitive science and artificial intelligence. He has integrated the theory and technical structure of the theory into context and to clarify how the terminology of Psi contributes to our understanding of cognition.

What are the applications of synthetic intelligence

Image Credit: Shashi Kadapa with Google Gemini

Synthetic Intelligence (SI) is rapidly developing as a serious contender to several startups that are developing, SI-based products. The appeal of SI is creating high quality synthetic/artificial data that reduces bias, protects privacy and IPT, reduces sully collection time, cost, and availability of technology to many.

SI innovations are utilized for image generation, 3D simulated environments, and structured data modeling. StartUs presents a list of leading startups that are providing SI solutions are Simulacra Synthetic Data Studio – AI-powered Synthetic Data Platform; Sightwise – Machine Vision-based Synthetic Data; brewdata – Synthetic Data Generation Platform; Synthera AI – Synthetic Financial Market Data; and others. SI data is utilized by multiple sectors.

 A few of the sectors are:

Will SI replace AI

Not really and in the absolute sense. Lee (2025) in his article, discusses the idea that SI is the paradigm of AI of the future. Critical technical and legal issues are challenged to AI by the necessity of acquiring large real-world data sets to train the machine learning models.

Obtaining such real-world data can be extremely challenging and can risk privacy, add in bias in automated decision making, and engage in mass copyright infringement. SI is paradoxical as a technical construct in the sense that it can mitigate many of these risks, but not eliminate them.

Data scientists are compelled to use simulated driving environments, invented medical records, falsified images, and all forms of synthetic data to train the machine learning models. Artificial data, in essence, is being used to train artificial intelligence.

Synthetic data has many technical advantages, and several legal ones as well. It heralds dramatic reductions in the costs of acquiring data, eliminates privacy issues, decreases the possibility of automated discrimination, and diminishes copyright issues.

Equally as promising, however, synthetic data also presents significant threats. Synthetic data and its deficiencies in the development and deployment of synthetic data can exacerbate the dangers of AI and social harm. Accordingly, SI is an innovation ecosystem to advance its responsible and robust development.

As a result, SI will ultimately become a new form of AI that is cheaper and more straightforward.

Summing Up

The blog examined key aspects of Synthetic Intelligence SI with AI. The findings indicate that SI generated data can be used cost effectively to replace AI data that is difficult to procure and subject to privacy laws.

So, will SI replace AI? Yes, at least the data generation, processing, and machine learning part that is most challenging. SI data is accurate, reliable, but subject to the same accuracy issues of AI data. The process of data generation, processing, training LLMs is simpler.

Present AI engineers do not have to fear about redundancy. They need to retrain and adapt. Well, we all have to do that anyway.

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.

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:

The post Goodbye AI → Welcome SI appeared first on Industry4o.com.

Executive Summary

In the highly regulated life sciences industry, ensuring compliance with Good x Practice (GxP) standards is essential for maintaining product safety, efficacy, and quality. Generative AI (GenAI) offers a transformative solution to the traditional Computer Software Validation (CSV) and Computer Systems Assurance (CSA) processes. This white paper explores how GenAI can automate and streamline validation workflows, improving the speed, accuracy, and compliance of critical GxP processes.

By leveraging Generative AI for GxP compliant validation, data governance, and enterprise workflows, life sciences organizations can automate complex validation tasks, ensure real-time compliance, and achieve regulatory readiness with significantly reduced manual effort and human error. The integration of AI into Watsonx, cloud systems, and enterprise software further enhances the scalability, flexibility, and transparency required to meet evolving GxP standards.

Introduction

Compliance with GxP regulations is essential in the life sciences sector to ensure the safety and effectiveness of pharmaceutical and medical products. Compliance verification and software validation have traditionally been tedious, resource-intensive tasks. Companies often rely on manual processes, static documentation, and periodic reviews, leading to inefficiencies, increased costs, and a higher risk of non-compliance.

Traditional CSV and CSA processes involve rigorous testing, documentation, risk assessments, and system updates that are slow and prone to human error. Additionally, regulatory environments are constantly evolving, creating a need for dynamic validation practices that are agile and efficient.

Generative AI can drive these processes with automation, real-time validation, intelligent risk management, and continuous compliance monitoring. This white paper will discuss the role of GenAI in ensuring GxP compliance through improved data governance, enterprise workflow integration, and automated validation practices.

Problem Statement

The life sciences industry faces several challenges when it comes to ensuring GxP compliance and validation:

➢ Manual Validation Processes: The traditional approach to CSV and CSA is largely manual, relying on repetitive checks, human intervention, and static documentation. This increases the risk of errors and delays, impacting operational efficiency and time-to-market.

➢ Complexity in Regulatory Compliance: GxP compliance requirements vary across regions and evolve over time. Keeping up with these changes and ensuring that the systems and processes are continuously compliant is a constant challenge for life sciences organizations.

➢ Data Integrity Issues: Data generated through different systems (e.g., ERP, LIMS, MES) needs to be accurate, traceable, and secured. Ensuring data integrity throughout the validation process remains an ongoing challenge.

➢ Scalability: As organizations scale, traditional validation processes become increasingly difficult to manage across multiple systems, applications, and geographies.

Business Case for AI-Driven Validation

The adoption of GenAI-driven validation offers several compelling business advantages:

➢ Improved Efficiency and Cost Savings: Automating manual tasks like validation documentation, testing, and risk assessments reduces the need for extensive human labor, resulting in significant cost savings and faster validation cycles.

➢ Enhanced Compliance: GenAI systems continuously monitor regulatory updates and ensure that software and systems remain compliant in real-time, reducing the risk of non-compliance.

➢ Faster Time-to-Market: The automation of validation workflows speeds up regulatory approvals, allowing for faster product development and quicker entry into the market.

➢ Reduced Human Error: AI-based validation reduces the risk of human error by ensuring consistent and accurate documentation, testing, and reporting throughout the validation lifecycle.

➢ Scalability and Flexibility: As companies expand their operations or adapt to new regulations, GenAI can scale across systems, applications, and geographies, ensuring compliance at every stage.

How Generative AI contributes to GxP Validation

Generative AI can transform CSV and CSA processes by automating key validation steps, reducing manual effort, and ensuring compliance. Below are some ways GenAI helps enable GxP-compliant validation:

➢ Automated Document Generation

One of the most labor-intensive tasks in GxP validation is the creation of documentation to prove compliance with regulatory requirements. These include:

• Validation plans

• Test protocols

• Test reports

• Risk assessments

• User Requirement Specifications (URS)

• System Specifications (SRS)

Generative AI can help automate the creation of these documents by understanding the context of the system and the specific regulatory requirements. For example, it can generate test protocols based on the system’s design and regulatory guidelines, ensuring that all necessary tests are covered.

Example: A pharmaceutical company uses Generative AI to automatically generate a validation plan for a new laboratory automation system. The AI draws from historical data on similar systems and guidelines to create a tailored document that adheres to GxP.

➢ Automated Test Script Generation

Generative AI can automatically generate test scripts for functional, performance, and stress testing of software and systems. These test scripts are key to the verification and validation process, ensuring that software or systems are operating according to specifications and can meet the required operational standards.

Example: In the context of a manufacturing execution system (MES), Generative AI could analyse the user requirements and automatically generate test scripts that validate data entry, data storage, and report generation, ensuring that the system meets GxP requirements for data integrity.

➢ Simulation and Risk-Based Testing

Generative AI can simulate different scenarios to validate software or system behaviour, ensuring that it will work correctly in real-world conditions. Risk-based testing, a common practice in GxP validation, can be further enhanced by AI by automatically identifying areas of risk and ensuring that critical functionalities are thoroughly tested.

Example: AI could simulate a failure in the production environment and predict how the system would behave. It could then automatically generate test cases to ensure that the system is robust against such failures, which would be critical for GxP compliance.

➢ Data Integrity Monitoring and Validation

Generative AI can automate data integrity checks by monitoring data as it is created, transferred, and stored across systems. By continuously evaluating data against predefined rules, AI can ensure that no data discrepancies occur, ensuring the traceability and accuracy required by GxP guidelines.

Example: In a clinical trial data management system, AI can monitor incoming data from various sources (e.g., test results, patient reports) and automatically flag any inconsistencies, missing data, or potential errors, ensuring compliance with GxP standards for clinical data integrity.

➢ Change Control Automation

GxP-compliant systems must have strict controls on changes to software, systems, or processes. Generative AI can assist in ensuring that any changes are validated according to GxP standards by automating change control documentation, impact analysis, and validation of the changes.

Example: When a software update is deployed to a manufacturing system, AI can automatically validate whether the update complies with the GxP requirements by checking the software against the original system specification and generating relevant validation reports.

➢ Continuous Monitoring and Reporting

Generative AI can be used to automate continuous system monitoring and generate real-time reports on system performance and compliance. This proactive approach helps in early detection of issues that may affect GxP compliance and can reduce the cost and time spent on manual audits.

Example: A manufacturing system can be monitored by AI for any anomalies in production data (e.g., temperature, humidity, or equipment malfunction), and the AI would automatically alert the quality control team, generate a non-compliance report, and suggest corrective actions.

➢ Continuous Compliance Monitoring

Ongoing Validation: AI continuously monitors the system’s performance against compliance requirements, identifying deviations or non-compliance issues in real-time and triggering alerts or corrective actions.

Example: In a clinical trial management system, GenAI continuously checks compliance with regulatory standards, sending alerts to QA teams if a test case fails or a critical parameter is missed.

Data Governance with Watsonx

IBM Watsonx is a platform that helps manage data governance, integrity, and security across AI-driven validation processes. Key features include:

Example: In a clinical manufacturing environment, Watsonx tracks every data point from batch testing, ensuring complete traceability from raw materials to finished products.

Integration into Enterprise Workflows

Integrating GenAI-driven validation into existing enterprise workflows is critical for ensuring seamless and efficient validation processes. Here’s how GenAI integrates with key systems:

Example 1: Integration with ERP Systems (SAP)

AI for Automated Purchase Orders: When a new product is ready for production, GenAI can analyse inventory levels and create purchase orders for raw materials automatically, ensuring that the production process is compliant with regulatory standards.

Example 2: Integration with LIMS and MES

AI for Test Case Execution: AI integrates with Laboratory Information Management Systems (LIMS) and Manufacturing Execution Systems (MES) to automatically run test cases based on predefined validation protocols.

Results and Impact

By adopting GenAI-driven validation, life sciences organizations experience several tangible benefits:

Conclusion

Generative AI provides a transformative solution for CSV and CSA processes in life sciences. By automating validation workflows, ensuring continuous compliance, and improving data governance, GenAI enables life sciences companies to streamline validation tasks while ensuring they meet regulatory requirements. Integrating GenAI with enterprise systems like IBM Watsonx, LIMS, and MES enhances real-time compliance monitoring, risk assessments, and automated documentation generation, resulting in faster time-to-market, reduced costs, and improved audit readiness.

About the author:

Ms. Deepthi Chatterjee
Business Development Executive (Pharma & Lifesciences Industry)
IBM India Pvt. Ltd

Ms. Deepthi Chatterjee is Bestowed with the following Licenses & Certifications:

https://www.linkedin.com/in/deepthi-c-31740931/details/certifications/

Ms. Deepthi Chatterjee can be contacted at :

LinkedIn | Email 

The post GenAI Driven Validation appeared first on Industry4o.com.

Introduction

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

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

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

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

Evolution of AI Paradigms

Traditional AI (Symbolic AI)

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

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

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

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

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

Discriminative AI (Machine Learning-Based AI)

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

Key characteristics include:

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

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

Real-World Industry Use Cases

1. Banking and Financial Services

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

2. Healthcare and Life Sciences

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

3. Retail and E-Commerce

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

4. Manufacturing and Smart Industry

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

Model Complexity and Comparison

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

Why Discriminative AI Dominates Today

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

The Future: Hybrid AI and Beyond

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

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

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

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

Closing Thoughts

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

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

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

References & Further Reading

About the Author:

Mr. Abhaya Kant Srivastava

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

Tata Consultancy Services (TCS)

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

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

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

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

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

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

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

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

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

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

Mr. Abhaya Kant Srivastava has Led the following Projects :

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

Mr. Abhaya Kant Srivastava can be contacted :

E-mail | LinkedIn

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

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