AI Proof-of-Concepts (PoC) are often one of the easiest things to get excited about and one of the hardest things to scale. A model can perform well against a narrow problem space but prove to be vulnerable in the chaos of fragmented data, ever-shifting business environments, and legacy systems.
The problem is that a lot of business and technology leaders still think that a PoC is proof of business readiness. It isn’t. A PoC usually just demonstrates that a technology can function in a lab environment. A production AI implementation has to show that it can generate value in a real operational process.
Clean Data Hides Real-World Complexity
The majority of AI PoCs are driven on a limited data set that has been scrubbed and prepped. It enables teams to build and validate a model in a short amount of time, but it provides a skewed perspective on how the solution will function at scale.
Dilraj Singh Gandhi, EVP Digital & AI at TAFE, says a conventional PoC often establishes only that AI can work under controlled conditions. “A PoC is usually built on a very small and exceptionally clean dataset,” he says. “During testing, training and cross-validation, the model may show 95% or 98% accuracy. However, once it is exposed to a larger and more diverse dataset, the quality of the data may be very different and the accuracy may fall to around 60%.”
The problem is not only messy data. Enterprise data carries business meaning that is rarely visible in a pilot. Product names change, customer requests are incomplete, commercial teams apply informal judgment, and local processes evolve faster than central systems and AI models are updated.
Jatinder Bansal, Group CIO at Aspire IIP, encountered this while working on a touchless customer-order intake process. A customer might place an order using the name of a discontinued product, while a salesperson knows from context which current product should be entered into the system.
“Until last year, Apple was selling the iPhone 15. Now it has launched iPhone 17. When a customer sends an order for iPhone 15, the salesperson knows it means iPhone 17 and punches 17 in the system. How does the system understand that iPhone 15 is no longer available and should be read as 17?” he questions.
That judgement may depend on stock availability, replacement rules, agreed pricing, the customer account and the employee’s reading of intent. These exceptions are manageable for people but hard for AI unless organizations deliberately model the business context and define how the system should handle uncertainty.
Value Lies in the Full Workflow
A successful PoC can demonstrate that a model can extract data, generate an answer or classify a document. Enterprise value appears only when that output can be used safely and reliably in the wider business workflow.
Kunal Dikshit, CTO at Fedbank Financial Services, says CIOs must avoid treating AI as a standalone technology project. “An AI document-extraction solution is useful only when the extracted information can actually flow into the lending process, be validated, audited and used by the relevant employees or systems,” he says.
This is the point at which the full set of enterprise requirements surfaces. The solution must integrate with core platforms, meet security and compliance needs, manage exceptions, remain available at scale and fit into the way employees work. It also needs monitoring, governance and a viable cost model.
In real terms, this means that CIOs need to evaluate an AI project using operational questions upfront: which downstream system will consume the output; who checks an output that has a low confidence score; what happens when business rules are altered; what auditing record needs to be maintained; who is liable if the AI’s recommendation is inaccurate? If these questions come later, after a PoC’s green light, the journey to production will be slow, costly, and risky.
Build an MVP, not a Showcase POC
The more effective approach is to replace a conventional PoC with a minimum viable product, or MVP, that is piloted in a limited but genuine business environment. It is a more demanding route, but it reveals the issues that matter before an organization invests in an enterprise-wide rollout.
Gandhi’s recommendation is to use representative data, or synthetic data that reflects the noise and imperfections of real enterprise information, and deploy the MVP in one or two carefully selected locations or business units. “The shift from POC to MVP requires a change in mindset,” he says. “An MVP demands more preparation, business commitment and accountability than a conventional POC, but it provides a much more realistic basis for scaling AI.”
For CIOs, an enterprise-ready AI program should have a use case tied to a material business problem rather than a technology demonstration, representative data including normal errors and variation, integration into the full workflow with validation and exception-handling, a named business owner responsible for adoption and outcomes, and measurable KPIs that cover business results such as cycle-time reduction, revenue conversion, risk reduction or cost savings, not only model accuracy.
The goal is not to eliminate experimentation. It is to ensure that experimentation is designed to answer the question that matters: can this AI system work reliably, economically and accountably in the real enterprise?
