The Enterprise AI Problem Is Not Finding More Use Cases
Enterprise AI adoption has spent considerable time in the pilot phase. The harder problem is identifying the right business problems and building systems that can reliably solve them.
A recent The Hindu article featuring Sindhu Gangadharan, Managing Director of SAP Labs India, points to the shift from AI pilots towards problem-solving.
Read the original source →Organisations have experimented with copilots, assistants, agents, automated workflows, and internal prototypes. These experiments have helped demonstrate what the technology can do, but they have also exposed a more fundamental challenge.
The difficult question is no longer whether an organisation can find something to do with AI. It is whether the organisation has identified a problem important enough to solve.
The distinction is important because experimentation and operational value are not the same thing.
A pilot can demonstrate technical feasibility. A production system has to operate within an existing business process, work with the right data, understand the relevant context, interact with other systems, and produce an outcome that can be measured.
From AI Pilot to Enterprise System
This changes where an organisation should begin.
Instead of starting with a model and searching for a use case, the starting point can be the business problem itself. What is creating friction? Where is time being lost? Which decisions require too much manual effort? Where does existing information fail to reach the person or process that needs it? Which outcomes could improve if the right intelligence were available at the right point in the workflow?
The technology then becomes part of the solution rather than the reason for building it.
This is particularly relevant as enterprises move from conversational applications towards systems that can take actions. An agent that can access data, use tools, interact with applications, and execute parts of a workflow has a very different relationship with the organisation than a chatbot answering questions.
The quality of the surrounding system therefore becomes increasingly important.
Context matters. Data matters. Architecture matters. Governance matters. The workflow matters. So does a clear definition of what success looks like.
This may be why the transition from AI pilots to enterprise AI is proving less straightforward than the rapid progress of the underlying models might suggest.
Building a demonstration is primarily a technology exercise.
Building something that reliably solves an important business problem is a systems exercise.
The next phase of enterprise AI may therefore depend less on how many experiments an organisation can run and more on how well it can identify, design, integrate, and measure the problems it chooses to solve.