By Vidisha Suman, Partner, and Shiv Raj, Principal, Kearney India
During the initial phase of enterprise AI adoption, the underlying usage economics remained relatively obscured. Vendors absorbed inference costs, innovation budgets reduced pressure to demonstrate returns, and bundled pricing masked the true consumption profile of AI-enabled applications.
This environment encouraged rapid experimentation. Organisations deployed copilots, summarization tools, AI assistants, and workflow automation capabilities without fully accounting for the long-term operating costs of large-scale deployment.
That environment is now changing.
This progression is not unusual. Most technology waves initially prioritize accessibility and deployment speed before governance, cost visibility, and operating discipline mature. AI may be entering this phase earlier because usage economics are more directly linked to consumption patterns.
At the same time, enterprise demand for AI continues to expand. Organisations are embedding AI into software development, customer operations, internal workflows, and productivity tools at increasing scale. The opportunity remains significant, but scaling AI sustainably now requires stronger economic discipline.
Across the AI market, pricing models are becoming increasingly consumption based. Advanced reasoning capabilities are being priced separately from lightweight models, and enterprises are introducing tighter controls around access, governance, and usage allocation as AI spending becomes more visible. Several developments illustrate this shift: GitHub moving Copilot toward usage-based pricing, OpenAI differentiating pricing for advanced reasoning models, and the market segmenting between lower-cost and advanced models.
Costs are increasingly linked to workload intensity and model selection. This shift matters because AI spending is moving from experimentation budgets into recurring operating expenditure. This should not be interpreted as slowing AI adoption. If anything, broader deployment is likely to increase consumption over next several years.
The commercial implication is straightforward: enterprises will increasingly need to align model capability with workload value and business impact. For many organisations, the challenge is no longer whether to expand AI adoption, but how to scale it sustainably
At the same time, enterprises are beginning to confront a second challenge: AI inefficiency is materially more difficult to identify than traditional infrastructure inefficiency. In cloud environments, inefficiency is typically visible through idle infrastructure, overprovisioned capacity, or underutilised resources. AI inefficiency is harder to detect. It often appears as duplicated review, escalation to higher-cost models, weak output quality, compliance exposure, or misplaced confidence in AI-generated recommendations.
As AI adoption scales, these issues become operational and financial concerns rather than isolated technical problems.
Lessons from Previous Technology Cycles
Earlier enterprise technology cycles followed a similar progression.
Early adoption prioritised experimentation, accessibility, and rapid deployment. As usage scaled, organisations introduced governance disciplines, architectural standards, financial controls, and operating models capable of supporting enterprise-wide deployment sustainably.
Cloud computing followed this pattern most visibly, with early adoption focused on speed and flexibility. Over time, enterprises developed FinOps capabilities to manage allocation, workload efficiency, usage visibility, and operating economics at scale.
Personal computing improved productivity but introduced support overhead, training complexity, downtime risk, and endpoint security costs.
SaaS accelerated adoption but created seat sprawl, overlapping applications, and identity-management challenges.
Mobile technologies entered through employee demand before enterprises implemented formal device management and governance controls.
Cloud computing improved flexibility and scalability but ultimately required FinOps disciplines around allocation, rightsizing, and unit-cost optimization.
Enterprise AI combines elements from all these cycles: It introduces hidden support and review costs comparable to earlier PC environments, governance/sprawl risks like SaaS, consumerisation dynamics like mobile technologies, and usage-based economics like cloud.
The Likely Direction of Enterprise AI Economics
There are four plausible scenarios for the next phase of enterprise AI economics:
1. AI becomes low-cost enterprise infrastructure if unit economics improve rapidly
2. AI becomes inexpensive but commercially limited if model costs decline more quickly than measurable enterprise value expands.
3. AI becomes selectively governed and tiered if business value remains meaningful while advanced reasoning capability continues to carry material cost.
4. AI adoption slows materially if costs remain elevated and measurable value remains difficult to demonstrate.
Current market evidence points toward the third outcome.
Enterprise demand for AI remains strong, but advanced reasoning models are not becoming economically insignificant at-scale. The market is increasingly segmenting between lightweight models for routine tasks, advanced models for complex reasoning, and governance layers that determine which workloads justify premium intelligence.
India May Reach the Operating-Scale Phase Faster Than Expected
India may experience this transition earlier than many global markets. Across enterprises, GCCs, and technology services firms, AI adoption is already moving from experimentation toward scaled deployment. As usage expands across large employee bases, AI economics become materially more important. At the same time, Indian models such as Sarvam could enable lower-cost, locally optimized alternatives for select workloads, creating a future enterprise stack that combines frontier global models with smaller, domain-specific local models.
Going ahead, the likely enterprise model is therefore selective deployment based on workload economics.