By Vidisha Suman, Senior Partner, and Shiv Raj, Principal, Kearney
In the first phase of enterprise AI adoption, the focus was primarily on experimentation, accessibility, and deployment speed. As unit economics kicks-in, AI will rapidly evolve from an innovation initiative into a recurring operating expense, forcing enterprises to rethink how intelligence is allocated, governed, and scaled across workflows.
Hence, the central enterprise question is increasingly practical rather than conceptual: what is the cost required to achieve a successful business outcome?
The relevant framework is workload economics.
Two dimensions matter most.
The first is cost intensity, which represents the total cost required to deliver an AI-enabled outcome, including inference, orchestration, governance, remediation, or supporting infrastructure.
The second is value density, which reflects the measurable business value generated by a use case, including revenue uplift, cycle-time reduction, quality improvement, labour savings, or risk reduction.
|
Low Value Density |
High Value Density |
|
|
Low-Cost Intensity |
Allow with governance guardrails |
Industrialize and scale |
|
High-Cost Intensity |
Redesign, down-tier, or retire |
Invest selectively with explicit ROI controls |
This framework matters because enterprise AI economics will ultimately be shaped by two competing forces:
On one side, unit costs may decline through smaller models, improved infrastructure efficiency, caching strategies, and increased competition from open models. On the other side, overall enterprise AI consumption may rise materially if agentic workflows increase model calls, reasoning models require more compute per task, context windows continue to expand, or demand grows faster than efficiency improvements.
Lower model costs may ultimately support broader AI deployment. However, expanding enterprise usage may still increase total expenditure materially as AI becomes embedded across workflows and operational processes.
The Emerging Operating Model for Enterprise AI
Most enterprises currently govern AI through procurement and software-management processes originally designed for traditional SaaS applications. That approach is unlikely to remain effective under increasingly usage-based AI economics.
AI increasingly needs to be managed as an operating portfolio with continuous oversight of usage, value creation, allocation, and efficiency. The FinOps Foundation’s AI guidance reflects this shift by recommending formal governance structures, tooling for cost and usage visibility, anomaly detection capabilities, ROI tracking, etc.
For CIOs, the implications are primarily architectural.
Key priorities are likely to include workload segmentation, model routing, caching strategies, tiered model catalogues, and governance policies. Frontier models should be reserved for high-value reasoning tasks while routine or repetitive workloads should be routed toward lower-cost models. Without effective workload routing, enterprises risk inverted economics. These capabilities are increasingly becoming prerequisites for scaling AI deployment across the enterprise efficiently rather than mechanisms for restricting adoption.
For CFOs, the role extends well beyond initial investment approval.
AI programs built around broad productivity assumptions and loosely defined budgets are likely to face increasing scrutiny as expenditure scales. Programmes linked to measurable workload economics, operational accountability, and explicit value tracking are more likely to sustain investment support.
Finance functions should therefore play an active role in allocation logic, budget governance, variance reviews, show-back and chargeback structures, ROI measurement, and consumption oversight. Many enterprises initially assumed that declining model costs would naturally improve AI economics over time. The opposite outcome is also possible. Organisations that establish clearer workload governance and economic visibility early may ultimately be better positioned to scale AI deployment across functions and workflows.
India’s AI Governance Moment
For Indian enterprises, this transition may become particularly important over the next 12-18 months. AI investment decisions have so far been driven largely by CIOs, CTOs, and digital leaders. As AI spending becomes more visible within operating budgets, CFO scrutiny is likely to increase, especially in an environment where macroeconomic conditions remain uneven and enterprises are under pressure to improve operating efficiency while sustaining growth investments.
Parallelly, finance leaders will increasingly need visibility into token economics, workload-level ROI, and the long-term operating cost of enterprise AI deployment. This means that CFOs will need to become more deeply conversant with AI and its operating economics.
The End of the Subsidized Adoption Phase
The first phase of enterprise AI adoption prioritised experimentation and access. The next phase is likely to focus on sustainable scaling, clearer operating economics, and workload-level governance.
As AI becomes a visible operating expense, trade-offs become more difficult to avoid. If enterprises absorb the full cost burden, many are likely to reduce routine usage and introduce tighter governance controls. If AI providers continue subsidising usage, margin pressure is likely to increase. If vendors pass through the full cost of advanced reasoning models, adoption rates may slow and financial scrutiny from CFOs is likely to intensify.
The commercial challenge for enterprises is scaling AI adoption in a way that remains operationally sustainable as usage expands across the organisation. That will require clearer workload economics, stronger governance disciplines, and operating models designed for continuous AI deployment rather than isolated experimentation.