A Conversation With Bismit Pratapsingh, Technical Program Manager, Enterprise AI & Transformation Leader

As enterprise technology moves from traditional systems and automation toward increasingly intelligent and connected ecosystems, the role of technology leaders is evolving alongside it. With more than 23 years of experience across ERP, enterprise applications, solution architecture, digital transformation and large-scale technology programmes, Bismit Pratapsingh, Technical Program Manager and Enterprise AI & Transformation Leader, has experienced this evolution firsthand.

Having worked across India, Kenya and the United States, Bismit brings a global perspective to enterprise transformation. His professional journey has progressed from hands-on development and ERP implementation to technical programme management and enterprise transformation, while his academic work has expanded into artificial intelligence. He holds an MBA in Information Technology, an MS in Information Technology and an MS in Artificial Intelligence from the United States.

Today, his work sits at the intersection of enterprise technology and AI, with a particular interest in human-AI collaboration, intelligent decision-making, governance and responsible enterprise adoption. Through his research, writing and speaking engagements, including presentations at international conferences and contributions to technology publications, he explores how organisations can use AI to enhance decision-making while retaining human judgement and accountability.

In this conversation, Bismit discusses the evolution from ERP to the Intelligent Enterprise, the practical role of AI in large organisations, the importance of human oversight, and what the next phase of enterprise transformation could look like.

From ERP to Intelligent Enterprise: Shaping the Future Through Human-AI Collaboration

Q1. Tell us about your professional journey.

My professional journey spans more than 23 years in information technology, primarily across ERP, enterprise applications, digital transformation, and large technology programs. I started my career in India, later worked in Nairobi, Kenya, and subsequently continued my career in the United States. These experiences exposed me to different business environments and helped shape my understanding of enterprise transformation.

My career has evolved from hands-on development and ERP roles to solution architecture, program management, and enterprise transformation leadership. Alongside my professional career, I continued my education with an MBA in Information Technology, an MS in Information Technology, and most recently an MS in Artificial Intelligence in the United States. Today, my focus increasingly brings these two areas together, enterprise technology and AI.

Q2. You started your career when enterprise technology looked very different. How has your role evolved over the years?

When I started, enterprise technology was largely about developing applications, implementing ERP systems, and automating business transactions. Over time, my responsibilities expanded from hands-on technical and functional roles into solution architecture, business transformation, and technical program management.

That progression changed how I looked at technology. The question was no longer simply whether a system worked, but whether it solved the right business problem, could operate at enterprise scale, and delivered meaningful value. AI is now creating the next stage of that evolution.

Q3. Your career has largely been built around ERP and enterprise systems. What does the “Intelligent Enterprise” mean to you?

ERP created a common digital foundation by connecting business functions such as finance, procurement, supply chain, inventory, manufacturing, and sales. The Intelligent Enterprise builds on that foundation.

Instead of only recording what has happened, enterprise systems can increasingly help organizations understand what is happening, anticipate what may happen next, and recommend possible actions. AI, automation, analytics, cloud platforms, and enterprise applications are coming together to enable this shift. I see the Intelligent Enterprise as an evolution of ERP, not its replacement.

Q4. How do you see AI changing traditional ERP and enterprise operations?

One of the biggest changes will be the shift from transaction processing toward intelligent assistance and decision support.

Today, resolving a business exception may require an employee to search multiple systems, review historical transactions, identify the cause, and determine the next action. AI can potentially analyze that information much faster, recognize patterns, and recommend possible actions.

The real opportunity is not simply adding AI to an ERP screen. It is reducing operational friction and helping people make better and faster decisions.

Q5. Alongside your industry work, you have become increasingly involved in AI research, writing, and speaking. What led you in that direction?

After working with enterprise systems for many years, I wanted to understand AI not only as a technology but also in terms of how it could change enterprise decision-making and the role of people. That interest led me to pursue an MS in Artificial Intelligence and later became an important part of my research and writing.

My recent work has focused on enterprise AI, Human-AI collaboration, governance, and intelligent decision-making. I have presented research remotely at international conferences including IDFC 2026 and ICMRSD 2026, and I am scheduled to present at AIxHMI 2026. I was also invited to speak at the TALK AI Think Tank on the evolution from ERP to the Intelligent Enterprise.

I have also contributed research papers, review articles, and technology-focused writing, including work published by AFCEA SIGNAL Media. Across these activities, my central interest remains the same: how organizations can benefit from AI while preserving human judgment, accountability, and business context.

Q6. Where do you see the most practical opportunities for AI in large enterprises today?

There are opportunities across almost every business function. AI can support demand forecasting and supply-chain planning, identify unusual financial transactions, analyze procurement and supplier information, improve customer service, assist with operational exceptions, and help IT teams identify recurring issues faster.

However, organizations should not begin by asking, “Where can we put AI?” They should ask, “What business problem are we trying to solve?” Technology should follow the business need, and AI adoption should ultimately be measured by the value it creates.

Q7. As AI and AI agents become more capable, where should human judgment remain part of the process?

AI can analyze large amounts of information, identify patterns, and generate recommendations very quickly. But a recommendation is not the same as a decision.

Important business decisions often involve context, experience, customer relationships, ethics, risk, and consequences that may not be fully represented in the data. I believe AI should provide evidence, analysis, and options, while people interpret that information, challenge it when necessary, and remain accountable for important decisions.

That is why I see the future as Human-AI collaboration rather than humans versus AI.

Q8. After more than two decades of enterprise transformation, what have you learned about why technology initiatives succeed or fail?

One of my biggest lessons is that technology alone rarely determines the success of a transformation. A technically strong solution can still struggle if the business objective is unclear, the underlying data is poor, users are not involved, or stakeholders are not aligned.

The same applies to AI. Organizations need clear objectives, reliable data, governance, security, employee involvement, and measurable outcomes. I believe it is better to start with practical use cases, learn from them, demonstrate value, and then scale responsibly.

Q9. What advice would you give young professionals entering technology when AI is changing the industry so rapidly?

Learn AI, but do not forget the business.

Technologies and individual tools will continue to change. What remains valuable is the ability to understand problems, communicate effectively, learn continuously, and connect technology with business outcomes.

I would encourage young professionals to develop both technical understanding and domain expertise. They should learn not only how to use AI, but also how to question its output and understand its limitations.

Q10. Looking ahead, what do you think will distinguish a truly Intelligent Enterprise?

A truly Intelligent Enterprise will not simply be an organization that uses a lot of AI. It will be one where data, enterprise applications, automation, analytics, AI, and people work together effectively.

Enterprise systems will become more proactive, identifying emerging problems, explaining their potential impact, and recommending actions. AI agents may handle more routine activities, while people focus increasingly on exceptions, innovation, strategy, relationships, and decisions requiring judgment.

For me, the future is not about putting AI everywhere. It is about using AI where it creates real business value and combining it effectively with human intelligence.

Closing Note

The journey from ERP to the Intelligent Enterprise represents the next evolution of enterprise technology. ERP created the digital foundation that connected business processes and information; AI now creates an opportunity to make that foundation more intelligent and proactive.

Having experienced this evolution from hands-on development and ERP transformation to program leadership, and now exploring AI through research, writing, and professional engagement, I believe the next phase will be defined by collaboration rather than replacement.

Technology will continue to become more capable, but human judgment, experience, accountability, and leadership will remain essential. The organizations that succeed will be those that understand how to combine the strengths of both human and artificial intelligence.