Enterprise AI is entering a more demanding phase. A single successful deployment used to count as proof of value. Now the real test is whether that success becomes a template for the next one, at lower cost and greater impact.
Nisheeth Srivastava, chief technology and innovation officer for Capgemini in India, puts the standard plainly: “Scaling requires making the second use case cheaper than the first.”
Companies running multiple AI pilots often build a number of custom integrations for each project, such that every new deployment adds cost rather than reducing it.1
Why Costs Climb
Each new use case frequently requires rebuilding data pipelines, integrations, and governance from the ground up. Srivastava argues that technology is no longer the limiting factor for companies. Enterprise-wide AI readiness is: reliable data foundations, clear ownership of outcomes, and a shift toward treating AI as infrastructure instead of a series of isolated projects.
Srivastava suggests a simple test for every modernization decision: does it make AI deployment easier or harder twelve months from now? Treating modernization and AI as separate budgets and timelines forces enterprises to solve the same problem twice. Legacy technology often carries forward an old operating model, encoding outdated decisions, authorities, and data flows.
From Automating Tasks to Coordinating Them
The next phase of AI extends this challenge further. Srivastava sees the near-term value of agentic AI in reducing "coordination latency," the time and effort spent coordinating decisions across complex processes. Trade finance, claims, lending origination, and supply chain all show potential, particularly where delays stem from handoffs and approvals.
The larger opportunity is redesigning processes around continuous, intelligent decision-making rather than inserting AI into existing workflows. Over time, Srivastava expects this to evolve into institutional agents that compound organizational knowledge and context. Reaching that point requires a connected architecture linking data, processes, policies, decisions, and institutional knowledge, something most enterprises have yet to build.
Data as the Foundation for Repeatable AI
Scaling AI depends on data that is trusted, consistent, contextual, and usable across the business. Srivastava advocates federated ownership, where business domains hold responsibility for the data they generate, supported by centralized standards and shared platforms. A McKinsey case study illustrates what shared infrastructure can do to that cost curve. A financial services company built one reusable unstructured data pipeline to support 15 AI use cases. Deploying those use cases without a shared pipeline would have cost roughly $30 million. With one, the cost came in around $13 million. Every additional use case built on that same foundation, and the savings compounded with each one.
What It Takes to Get There
Making the second use case cheaper than the first requires specific capabilities:
- Reusable data pipelines designed to serve multiple use cases rather than one-off builds per project
- Consistent data ownership and governance standards applied across business domains
- Metadata, lineage, and classification that make data discoverable and trustworthy across systems
- Connected architecture linking data, processes, and institutional knowledge for agentic systems to draw on
- Modernization roadmaps evaluated for their specific impact on AI readiness
Where the Real Advantage Lies
Srivastava frames the competitive stakes clearly: “The winners will not necessarily be those with the most AI, but those that learn how to combine”; human cognition, AI-enabled execution, and institutional context most effectively.
That combination depends on infrastructure built for reuse, not on access to more models. Organizations that invest in reusable pipelines, consistent governance, and connected data now are positioned to see costs fall and value compound with every deployment that follows.
See how ZL Tech helps enterprises build the governed data foundation that makes every new AI use case cheaper than the last.
