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There is no denying the fact that Artificial Intelligence is the way forward and Sachin Prasad epitmosies just.
The Programme Director for Technical Product Management at IBM Data and AI, the US-based Sachin who is from Bengaluru, was awarded the prestigious 2025 Global Recognition Award for his transformative contributions to the IT and computer industry.
His pivotal role in advancing IBM’s Cloud Pak for Data platform has set new benchmarks for operational efficiency, AI-readiness, and regulatory compliance, empowering Fortune 500 clients across diverse sectors to achieve data-driven transformation.
Sachin Prasad
In a freewheeling chat with DH Web Desk, Sachin talks about the growing influence of AI.
Q) How much has AI evolved over the years?
A) In the twenty years I've worked with enterprise systems and the tech world, AI has changed in three different ways. The first generation (2000s–2015) was mostly about automating tasks based on rules and using basic machine learning to do simple things like find fraud or make predictions.
The second phase (2015–2022) saw the introduction of deep learning and neural networks, which made big strides in natural language processing and computer vision. But it was still hard to deploy and took a lot of resources. Developers still needed to know how to build and train Machine Learning Models so that business users could use them with ease.
We're now in the third phase, which I call "democratisation of AI on an enterprise scale." The coming together of foundational models, cloud-native architectures, and integrated data platforms has changed what is possible at its core. As someone who has worked on putting AI into banks, telecommunications, defense, and healthcare, I've seen companies go from testing AI pilots to using it in production systems that handle millions of transactions every day.
The biggest change isn't just in technical ability; it's in governance maturity. Early AI implementations frequently prioritized performance over explainability. Enterprise AI today needs to be accurate and follow rules like the GDPR. Five years ago, there weren't any architectural improvements in model transparency, data lineage tracking, or compliance automation that are needed now.
The difference between today's AI and earlier versions is that it has gone from being a set of specialized tools that only PhDs could use to a set of platforms that make it possible for business users to use AI in weeks instead of years.
Q) How significant is AI’s impact across industries?
A) AI's impact varies dramatically by sector, and understanding these nuances is critical for responsible deployment. In regulated industries like banking and healthcare where I've led implementations, AI has fundamentally transformed risk assessment and diagnostic accuracy while introducing new governance challenges.
For telecommunications providers serving millions of subscribers, AI-driven predictive analytics have reduced network downtime by 30-40% and enabled personalized service optimization at scales impossible with traditional methods. In defense applications, AI systems process sensor data to identify patterns human analysts would miss, though these deployments demand unprecedented security and explainability requirements.
Critical gaps remain between AI's theoretical potential and practical deployment, particularly around data quality, organizational change management, and skills development. The industry needs practitioners who understand both cutting-edge techniques and real-world constraints of regulated enterprises.
Q) How much will manpower be affected by AI's growing impact?
A) To be honest, this question needs a more nuanced answer based on what I've seen when i advocate AI in different businesses. AI is not a way to replace workers; it is a way to add/augment to the workforce. However, the transition needs to be planned carefully.
In my experience with large-scale enterprise AI deployments, the impact follows a clear pattern: routine, repetitive tasks become automated, freeing professionals for higher-value work requiring judgment, empathy and human interaction. Now, Data analysts will spend less time cleaning datasets and more time interpreting insights. Engineers focus on architecture rather than manual configuration. Customer service representatives handle complex issues while AI manages routine inquiries.
So in essence there are three types of transformations that I see would occur due to advancement & infusion of AI in corporate world -
Displaced Role - Mundane tasks would be automated easily. Purely transactional tasks which required limited decision making would be automated. Organizations must invest in reskilling programs - This is both and ethical imperative and business necessity
Enhanced Roles- Most existing positions evolve to incorporate AI. Engineers, Developers, Marketers, Sellers, Designers, Architects - from all walks to life would use AI tools to get jobs done quickly. AI fluency would be one of the skills that all traditional workers would embrace to stay productive.
Emergent Roles: Finally, Roles like AI governance officers, prompt engineers, AI Ethics Advisors which were non-existent 5 years back would be critical for enterprise success.