The modern-day AI product team is not a place of lack of talent. The best data scientists, software engineers, and research minds will help you build the most accurate model, launch the prototype in days, and understand how things work underneath.
However, the problem lies in the fact that these teams may lack a solid product development approach that would be able to connect technical possibilities and actual user needs.
It is called product thinking. It is the process of taking a step back from technical possibilities and making sure whether an idea truly solves a problem for a certain user in a trustworthy way. This might sound too easy and straightforward. However, the problem is that in many cases, AI product teams give more importance to technical possibilities compared to the user's problems and difficulties of adoption and implementation.
Everything works so good that everyone becomes so enthusiastic about all the features of the product that the first question should have been answered long ago is being raised after release when the product fails.
The Common Pattern
It seems that we are witnessing yet another tale in AI teams. An enthusiastic team creates something new, an innovative demo appears, and leadership likes it and gives the green light. The product gets launched, and after some time it is simply turned off or ignored by the target audience.
It is not usually the technical problem, but rather the lack of basic product questions that were not asked in the beginning:
Who is it built for and what problem does he have?
What is the user trying to accomplish?
How is it going to react to mistakes made by the AI?
Is there any point of using AI at all or was it just a fancy technology to get passionate about?
Technical teams are capable of telling whether something can be created. Product thinking is responsible for deciding whether it should be built and would be actually used afterwards.
Why AI Makes This Gap Bigger
With traditional software, a wrong feature is just a bug in the roadmap, annoying but fixable. AI products raise the stakes in a few important ways.
AI is probabilistic in nature, not deterministic. The button works, or it does not work, but a model generates outputs with variations of which some are wrong. In product thinking, one has to assess the extent to which a system can fail without destroying users' trust in it, and if the error can be rectified at all.
Data is also a component of the product, not only its fuel. It's not sufficient to consider features and interfaces. It's necessary to ask oneself if the data that the product operates with is good enough, where it comes from, and if the product will improve itself once it is launched.
Trust should be designed, not taken for granted. Users do not necessarily trust answers generated by AI, like they trust search results. Trust in the answers needs to be designed in some way.
Skip these questions, and the result is a technically impressive system that nobody actually adopts.
What Product Thinking Looks Like in Practice
Product thinking is not a role in the AI company. It is about knowing the right questions to ask before any engineering takes place.
Focus on solving the problem, not deploying the technology. Begin with the user pain point and then think about "we could solve it using an LLM."
Evaluate the viability. Consider if you have enough quality data, if the task itself is learnable by the model, and if an erroneous output would cause a problem for the user.
Think about failure modes. Determine ahead of time what the product will do in case of hallucinations, confusion, or lack of knowledge of the model.
Consider the business case. Take into account the full cost to run the product in production, including computational, token, and human review costs.
Define "good" outputs. Design evaluation measures based on real-world outcomes rather than high benchmark scores.
This does not require any understanding on how to train a model. This requires discipline in formulating proper questions and making proper compromises prior to investing any engineering effort into an idea.
What AI Product Thinking Adds Beyond Traditional Product Management
While traditional PMs maximize features, AI PMs maximize behavior and results.
While traditional products adhere to standard paths, AI products need evaluations and feedback cycles.
Whereas traditional roadmaps are all about features, AI roadmaps include data, models, evaluation, and trust.
Why This Skill Is in Short Supply
Most of the Product Managers were trained on conventional software issues and require additional skills to work with probabilistic AI systems.
Most of the technical AI hires are trained in optimizing models rather than questioning their existence in the first place.
Few people have been trained in sitting on the point of convergence of both, which is precisely the problem.
This programme is designed to help professionals build the product judgement required for AI-native systems.
The programme covers:
Determining if an issue really is a suitable candidate for AI
Building around uncertainty and failure
Testing and red-teaming AI systems before their release to actual users
AI product business case development that includes feasibility, economics, and pricing among other things
This course is meant for people who have some prior knowledge, either because they currently work as Product Managers, engineers who want to shift careers into products, or entrepreneurs who don't want to spend a year building the wrong product.
Why IIT Kharagpur
It is India’s first IIT started in 1951 and ranks at 5th in Engineering by NIRF 2025.
The university is known around the world due to its expertise in AI, machine learning, and computing research.
The program is delivered live by professors from IIT Kharagpur along with AI experts from the industry.
Graduates of the program will be able to get IIT Kharagpur Executive Education alumni designation, as well as an on-campus graduation ceremony and access to the alumni network.
Capstone of the program will not be some sort of theoretical project. Graduates will have to defend an actual AI product they created in front of IIT Kharagpur professors.
This program is designed for professionals who seek a designation from a prestigious institution for their abilities in AI product development and not just another online course.
Conclusion
AI projects often fail, not due to a lack of technological capabilities, but because validation of the right problem, need, and business case isn’t done properly.
Product thinking is what bridges the gap. It may not be glamorous, and you will never see it on a demo video, but it’s the line between an AI feature that fails to make it in the first six months and an AI feature that makes its mark in someone’s everyday routine.
The EPGC in Building AI Products, Systems & Services at IIT Kharagpur is aimed at helping professionals gain this skillset via proper framework, product creation, evaluation, and decision making in AI products. This is a critical capability that anyone building AI products needs to have.