An AI pilot can be deceptively economical. It serves a limited group of users, handles a narrow set of tasks and usually operates under the close supervision of the team that built it. In that environment, using a powerful model for every interaction, passing large amounts of context and allowing repeated attempts to get the best possible response may be entirely reasonable. The objective is to establish whether the idea has merit.
Once an application becomes part of everyday work, usage becomes harder to predict and costs that barely registered during experimentation begin to multiply. More users generate more model calls. Longer conversations accumulate context. Agentic AI applications, for example, may invoke a model several times before completing what appears to the employee to be a single task. Retrieval, validation, retries, and human review add their own overheads.
The journey from prototype to production for AI solutions follows a well-established methodology of solution design, deployment, and operational practices. AI, machine learning, and computer vision systems have been successfully deployed in production environments for many years. Over the past two years, GenAI solutions have likewise matured, with many progressing beyond proof-of-concepts (POCs) and minimum viable products (MVPs) into production deployments. More recently, Agentic AI solutions, including multi-agent systems, have also begun moving into production across industries and business functions, both in India and globally.
The economics of moving beyond the pilot
This is the shift enterprises need to recognise as they move beyond experimentation. AI economics cannot be treated as something finance examines after a successful pilot has already been approved for wider deployment. Much of the eventual cost profile is determined much earlier, through decisions about architecture, model selection, workflow design and how the application consumes information.
Token economics is becoming an important part of that conversation. Tokens are the units language models process when they read and generate information, but for enterprises the real question is what they receive in return for consuming them.
Driving token usage down indiscriminately is no more sensible than reducing cloud consumption without considering what the underlying application is doing. A low-cost AI interaction that creates no useful outcome is still waste. A more expensive interaction may be commercially sound if it removes a costly bottleneck, accelerates an important decision or completes work that would otherwise require substantial human effort.
The useful measure, therefore, is the relationship between AI consumption and a recognisable unit of business activity. What does it cost to use AI to resolve a customer request, review a contract, support a sales opportunity or process a transaction? So choosing the right AI business use case with measurable outcomes is key, and the AI solution design, including scalingneeds to align with that.
Matching model capability to the task
Looking at AI through that lens also exposes one of the most common inefficiencies in early deployments – treating the most capable model as the default model.
That approach makes sense during experimentation. It makes far less sense when the application is processing routine work at scale. Many enterprise tasks are constrained and repetitive. Classification, extraction, straightforward summarisation and structured responses may not require the same level of reasoning as complex research, planning or decision support.
Production architecture should reflect those differences.
Instead of choosing one model for an entire application, enterprises increasingly need to think in terms of a model portfolio. Smaller or more specialised models can handle predictable tasks, while more capable models are reserved for work where additional reasoning genuinely changes the quality of the outcome. Some workflows may use several models behind the scenes, with each performing a different role.
This is not purely a cost-reduction exercise. Smaller models can also offer advantages in latency, deployment flexibility and consistency for narrowly defined tasks. The relevant question is not whether a large or small model is inherently better. It is whether the capability being paid for is actually required by the task. This could also be extended to which type of AI shouldbe used for a use case – AI ML/NLP, Computer Vision, GenAI, Agentic AI and Physical AI e.g. analysis of a 2-page document can be done using NLP and does not need Gen AI.
That makes model routing an increasingly important production discipline. Some applications can route requests based on known task types, while more sophisticated systems can assess complexity and direct work accordingly. Either way, enterprises should move away from consuming premium model capability by default and towards using it deliberately.
Like any enterprise technology initiative, the success of an AI solution depends on getting the fundamentals right. This includes selecting the appropriate use cases and technology stack, designing the solution well, deploying it in phases on a use case-by-use case basis, and establishing robust operational and monitoring practices. For example, agents should implement effective caching to optimise external API calls, models should be tuned for low latency and efficient token usage, components should be designed for reuse across deployment phases to accelerate future rollouts, and model performance and token costs should be monitored continuously and diligently.
Optimising how AI applications consume information
There is also considerable room to improve the economics of an application without changing the model. Enterprise prompts often contain repeated material such as system instructions, policy documents, reference information and conversation history. Sending the same information for processing again and again can add cost without adding value. Better retrieval design, disciplined context management and prompt caching can reduce unnecessary consumption.
Making the hidden cost of AI agents visible
The same scrutiny should be applied to AI agents. Agentic systems can make the economics of AI less visible because one user action may trigger a chain of model interactions, tool calls and validation steps. The employee sees a completed task. The underlying system may have performed far more work than that single interaction suggests.
This is where basic cost monitoring stops being enough. An enterprise needs visibility into the full computational path of the business process. Where are model calls being made? Which models are being used? Where do retries occur? Are multiple calls materially improving the result, or has inefficiency simply become embedded in the workflow?
That visibility should be built into production from the outset. AI expenditure needs to be understood by application, use case and business function, and wherever possible connected to a business outcome. The objective is not merely to count tokens, but to understand the cost of completing a meaningful AI-assisted activity.
Building governance for AI at scale
Governance has to evolve alongside the economics.
Most enterprises already understand the need to consider privacy, security, compliance and model risk. The production challenge is broader. As different business teams begin using different models and providers, organisations also need control over access, usage and spending.
Without a common layer of visibility, AI can quickly become fragmented. One team connects directly to one provider, another adopts a different model, and a third builds its own approach to logging and access management. Each decision may be reasonable in isolation. Collectively, they create an environment that is expensive to operate and difficult to govern.
A common AI gateway or control layer can help by providing consistency around authentication, logging, cost controls and routing policies. The objective is not to force every use case onto the same model or platform. It is to establish enough common infrastructure that each application team does not have to reinvent production governance independently.
Good governance should make responsible scaling easier, not turn the organisation into an approval queue. Standards for data handling, approved model access, security, evaluation and spending can be established centrally, while teams retain flexibility in how they solve individual business problems within those boundaries.
There is a commercial reason to preserve that flexibility. The model landscape will continue to change. A model that is the best fit for a workload today may not remain so. Enterprises should therefore avoid architectures that make switching models unnecessarily difficult. The ability to test alternatives and move workloads when the economics justify it is part of protecting the long-term business case.
A different management mindset for production AI
Moving from pilot to production therefore requires a different management mindset. The enterprise is no longer asking only whether AI can perform a task. It is deciding how much intelligence that task deserves, which model can provide it efficiently, how consumption will be controlled and whether the result justifies the full cost of operating the system.
A pilot proves that the technology can do something useful. Production has a harder job. It must prove that the enterprise can keep doing it reliably, responsibly and at an economics that still makes sense when usage scales.However the good news is that there are proven ways to effectively scale AI solutions across all areas of AI into production.