Title

AI cost management: How to govern growing spend without slowing adoption

Section
Guide
Summary

By Maxim Sankey, Morgan Book, Tiago Rodrigues

As AI adoption accelerates, organizations face a difficult balance between encouraging experimentation and maintaining financial discipline. Employees and developers need room to explore what AI can do, while Finance and technology leaders need confidence that rapidly growing consumption will not make spend unpredictable. 

Heavy-handed restrictions can undermine the experimentation organizations are trying to encourage, while leaving usage unmanaged creates a different set of risks. A more sustainable approach combines visibility into demand with targeted interventions, better profiling and forecasting, and commercial decisions based on how AI is being consumed across the organization.

  1. Make AI cost governance a shared responsibility

    Effective AI cost governance requires collaboration across functions because no single team has all the information or capabilities needed to manage it. 

    Finance needs the budgeting and forecasting perspective; IT and Engineering understand the technical choices driving consumption; business leaders understand the use cases and outcomes; and Procurement brings commercial and supplier expertise. 

    We are starting to see organizations bring these perspectives together through dedicated AI governance teams. The precise model will vary, but ownership needs to be clear, with a mechanism for connecting decisions about usage, budgets, and suppliers. Without that coordination, organizations risk managing each part of the problem in isolation. 

  2. Understand and segment demand 

    As we addressed in our earlier article on this topic, visibility into AI spend is the starting point. To act on that information, organizations need to understand the demand behind it: who is consuming AI, for what, through which tools, and how those patterns are changing. 

    That includes understanding which users, teams, and departments are driving consumption, how actual usage compares with budgets and supplier commitments, and where significant variations are occurring. 

    Generic per-user benchmarks can only go so far. A marketing team using AI intensively to develop materials may have a fundamentally different consumption profile from someone using a general-purpose assistant occasionally. Organizations therefore need to segment users and use cases, establish expected ranges, and identify meaningful exceptions rather than treating everyone alike. 

    This creates a much stronger basis for deciding where intervention is needed. 

  3. Intervene before you restrict 

    Unusually high consumption should prompt investigation rather than automatically triggering a cutoff. 

    A highly productive user may have a legitimate reason for consuming considerably more than their peers, while another user may simply be selecting an expensive model or feature for work that could be completed just as effectively another way. Understanding what is driving the spend is therefore essential. 

    Targeted intervention and education can address inefficient consumption without undermining adoption. Reviewing usage with individuals or teams can help identify more efficient tools or behaviors, while productive power users should be protected where higher demand is justified. Stronger controls can follow where necessary. 

    Training therefore becomes part of cost governance as well as an adoption tool. Helping high impact users understand the cost implications of different tools, models, and behaviors can improve efficiency while preserving the freedom to experiment. 

  4. Match the intervention to the source of spend 

    The appropriate response will also depend on where AI spend is coming from. The four areas outlined in our earlier article require different interventions. 

    • Employee productivity AI: Segment users and use cases, provide targeted education, and use expected spend ranges or alerts to shape behavior without unnecessarily restricting access. 
    • Developer productivity AI: Focus less on simple consumption limits and more on whether tools and models are appropriate for the engineering task and producing useful output. 
    • AI-enabled workflows and products: As usage scales, technical choices such as model selection and workflow design increasingly affect run-time costs, requiring close collaboration between technical and financial stakeholders. 
    • AI embedded in third-party software: Keep a central tracker of AI investments to prevent duplication. Assess the incremental cost and benefit of AI functionality and preserve commercial flexibility while supplier offerings and pricing models continue to evolve. 

    Across all four areas, the principle is consistent: understand what is driving demand before deciding how to respond. 

  5. Turn better demand intelligence into better forecasts and commercial decisions 

    Observed demand also provides a stronger basis for AI budgeting than generic assumptions about what an average user should consume. 

    Segmenting users, teams, and use cases helps organizations understand where consumption is relatively predictable and where greater volatility should be expected. Expected spend ranges can then be established and refined as adoption develops. 

    This will not eliminate uncertainty. AI usage, capabilities, and pricing are changing too quickly for forecasts to remain static. It can, however, give Finance a more credible view of where spend is heading, what is driving the change, and how much the organization should plan for. 

    The same intelligence can improve supplier decisions. Once demand is better understood, Procurement can make more informed choices about commitment volumes, discount structures, rollover provisions, overage protection, contract duration, supplier reporting, and flexibility. 

    The sequence matters. Increasing a supplier commitment before understanding demand can lock the organization into a commercial model that does not accurately reflect how AI is being used. A clearer baseline helps distinguish consumption that is predictable enough to commit against from areas where flexibility remains more valuable. 

    The same principle applies to AI embedded within existing software. Before accepting an AI premium or expanding a commitment, organizations should understand expected adoption, incremental cost, and the available alternatives. 

    Better demand intelligence ultimately gives Procurement a stronger basis for sizing commitments, preserving flexibility, and negotiating supplier terms. 

  6. Building a sustainable approach to AI cost governance 

    AI cost governance will need to evolve as models, tools, pricing, and usage patterns change. Regular reviews of consumption, exceptions, forecasts, budgets, and supplier commitments can help organizations respond as new patterns emerge, while continued feedback and education can embed more efficient behaviors over time. 

    The logic is straightforward: better visibility supports more targeted interventions; better-managed demand improves forecasts; and better forecasts lead to stronger commercial decisions. 

    Done well, this approach gives organizations a more sustainable foundation for AI adoption, with better-informed users, more realistic budgets, and commercial arrangements that evolve alongside demand. It also gives Finance and technology leaders greater confidence that growing AI investment is being actively managed as the organization scales its use of the technology.