Something interesting is changing in my conversations with senior business leaders.
Until recently, discussions around the cost of enterprise AI often revolved around tokens, model selection, API charges, and the uncertainty of monthly bills. There was considerable enthusiasm about what AI could do, but equally significant anxiety about what it might eventually cost.
Over the past few months, however, I have noticed a shift. In recent interactions with leaders from engineering and manufacturing organisations, particularly around AI-assisted product design, the questions have started changing.
“How many tokens will this solution consume?”
“How much will it cost us to complete a design using AI, and how does that compare with our existing process?”
That is a far more meaningful conversation. And I believe it signals an important evolution in enterprise AI adoption.
The Token Cost Conversation Has Its Limitations
Token pricing has become the familiar language of Generative AI economics. Technology teams compare model prices, estimate input and output tokens, evaluate context windows, and calculate inference costs. All of this is necessary when designing an AI solution.
But imagine explaining to the head of an engineering business that processing a customer drawing will consume a certain number of tokens. What does that tell the business leader? Very little.
The business leader wants to know how much engineering effort can be saved, whether design turnaround can improve, how accurately specifications can be generated, and whether customer quotations can be delivered faster. These are business outcomes, not technology consumption metrics.
Repeated Attempts
A low-cost model requiring multiple prompt retries or failed extraction passes multiplies token volume while delaying output.
Human Correction
If senior engineers must spend hours correcting hallucinated dimensions or specs, the labor rework dwarfs any API savings.
Downstream Processing
True engineering automation requires computer vision, geometry parsing, and deterministic rule engines alongside LLMs.
A low-cost model requiring repeated attempts, extensive human correction, or additional processing may ultimately be more expensive than a seemingly costlier alternative. This is why the conversation needs to move beyond tokens.
What Does a Completed Task Actually Cost?
Consider an AI-assisted engineering design workflow. A customer submits a 2D engineering drawing. The system extracts relevant information, interprets geometry, identifies dimensions, develops product specifications, and assists in generating the final drawing and costing.
Traditionally, several of these activities require manual engineering effort, multiple checks, and coordination across teams.
Cost per Completed & Validated Engineering Design
The Economic Argument: These are illustrative numbers, but the logic is straightforward. The organisation is not saving money because tokens have become cheaper. It is saving money because the cost of completing a validated engineering task has reduced by 70%.
Of course, the comparison must also account for design accuracy, rework, implementation costs, and the quality of the final output. This is what I would call task economics. And this is where AI starts becoming a serious business proposition.
Subscription Pricing May Be the Practical Way Forward
These discussions have also strengthened my thinking about how enterprise AI solutions should be commercially offered. Traditionally, enterprise technology projects often involve substantial upfront investment, followed by implementation, integration, maintenance, and support costs.
For organisations still discovering the potential of AI, this can create hesitation: Why commit significant capital before the benefits are fully established? A well-structured subscription model offers a practical alternative across three pillars:
Modest Initial Customization Fee
The customer pays a modest initial fee strictly for necessary domain customization, rule configuration, and workflow integration.
Defined Monthly Task Volumes
Instead of unlimited usage at a fixed price, subscriptions link to a defined monthly volume of engineering tasks with transparent overage bands.
Provider Owns Tech Economics
The solution provider manages underlying model consumption, infrastructure, and ongoing optimisation so both parties focus on completed work.
The customer gains expenditure visibility. The provider gains recurring revenue and a direct incentive to improve efficiency. More importantly, both parties begin focusing on the same outcome: completing business work more economically.
Predictability Matters as Much as Savings
There is another dimension that deserves attention. Businesses plan around budgets, volumes, margins, and returns. They cannot comfortably plan around unpredictable token consumption.
Why Predictable Task Bands Win CFO Approval
A CFO may be comfortable approving a solution that costs ₹3 lakh per month if the organisation can reliably demonstrate that it replaces ₹10 lakh worth of existing processing effort. The same CFO will hesitate over a seemingly cheap token rate with no credible estimate of monthly expenditure.
Subscription pricing, supported by clearly defined usage bands, performance expectations, and cost controls, bridges this gap. It also encourages a more disciplined approach to measuring AI performance across five operational questions:
Tasks Completed
How many engineering tasks were completed end-to-end?
Human Correction
How many outputs required human rework or correction?
Turnaround Time
What was the cycle time from drawing input to final output?
Cost per Task
What did each successful, validated task actually cost?
Baseline Variance
How did that unit cost compare with the manual baseline?
These become the metrics that matter.
The Real Opportunity Is to Align Technology Economics with Business Economics
Token optimisation will remain important. Model selection, prompt efficiency, caching, and smaller specialized models can materially influence operating costs. But these should increasingly become engineering responsibilities within the solution rather than the center of every commercial discussion.
The customer should be buying a measurable improvement in a business workflow, not merely access to an AI model. And solution providers must be willing to demonstrate that improvement with evidence.
We may not see token pricing disappear anytime soon. It will continue to operate behind the scenes, much like computing and electricity costs do today. What should change is how enterprises evaluate the investment.
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