The New Executive Job: Allocating Intelligence
AI is changing executive decision-making. The next challenge is deciding where intelligence creates the greatest return.
Late last year I argued that the AI invoice was coming. My point wasn’t that Microsoft, OpenAI or Anthropic would eventually charge more. Valuable technology has always become commercialised. The more interesting question was what would happen once AI spending became visible enough to compete with every other investment inside the organisation.
That moment has arrived.
The market has shifted noticeably over the past six months. Microsoft has introduced usage-based billing for advanced Copilot capabilities such as Copilot Cowork alongside administrative budgets and spending controls. GitHub Copilot has moved to an AI credit model for premium usage. OpenAI and Anthropic continue to expand premium reasoning models that are priced according to consumption rather than simple subscription tiers.
This isn’t simply a pricing story. It’s a change in the economics of enterprise AI.
One of the better descriptions of what’s happening comes from Artefact, which describes the phenomenon as the “token cost illusion.” Individual token prices continue to fall, yet enterprise AI bills continue to grow because organisations are consuming far more compute than they were a year ago. Larger context windows, agentic workflows, multiple model calls and tool orchestration all increase consumption, often by more than the underlying price reductions offset.
That combination changes executive behaviour.
For the past two years success was measured by adoption. Organisations wanted employees experimenting with AI, learning new tools and incorporating them into everyday work. Today a different set of questions is appearing in executive meetings. Which teams genuinely need frontier models? Which work justifies autonomous agents? Which activities can be handled perfectly well by smaller, cheaper models?
Those questions aren’t about software procurement. They’re about investment.
From Universal Access to Deliberate Allocation
Enterprise technology eventually became standard equipment. Laptops, smartphones, collaboration platforms and productivity software all followed a similar pattern. Costs declined, deployment expanded and broad access became the obvious choice.
AI is developing differently because capability and cost remain tightly connected. A lightweight assistant, a frontier reasoning model and an autonomous agent deliver different levels of performance and consume very different amounts of compute. The assumption that everyone should receive the same capability is becoming much harder to defend.
I’ve started seeing this play out in conversations with industry peers. Premium AI licences are being prioritised rather than distributed automatically. Business units are being asked to explain the value they expect to create before receiving access to more capable models. Engineering, finance, legal, marketing and customer operations can all present persuasive cases. Few organisations have budgets that allow them to approve every request.
This isn’t evidence that organisations are losing confidence in AI. If anything, it demonstrates the opposite. Businesses apply governance to the capabilities they believe will matter most.
A Different Kind of Capital Allocation
Recent research from KPMG helps explain why these discussions are becoming more common. Only 35 per cent of organisations report having full visibility into their AI operating costs, yet organisations with strong cost visibility are five times more likely to report established returns from their AI investments.
Those numbers are revealing because they shift attention away from technology and towards management. Executives cannot make sensible investment decisions without understanding the economics of the resource they are allocating.
Grant Gross captured the same transition in a recent CIO article, writing that “the AI adoption spending spree is over. Time to focus on value.” The article also describes organisations introducing controls after AI budgets were consumed much faster than expected, including Uber placing limits on AI coding tools after annual budgets were exhausted in only a few months.
These examples point to the same conclusion. Enterprise AI is moving away from broad deployment and towards deliberate allocation.
AI Budgets Are Becoming Strategy Documents
Budgets have always reflected organisational priorities. AI budgets reveal something slightly different. They indicate where leadership believes better judgement, faster analysis and higher-quality decisions are likely to create the greatest commercial return.
Organisations can invest similar amounts in AI and produce very different outcomes because they strengthen different parts of the business. One may concentrate on software engineering. Another may focus on customer operations. A third may invest most heavily in finance, legal or research. The technology is broadly similar, but the pattern of investment reveals a fundamentally different strategy.
That observation leads to a simple conclusion. The organisations that gain the greatest advantage from AI are unlikely to be those spending the most. They are more likely to be those making better decisions about where advanced AI capability creates disproportionate value.
Executive Playbook: Allocating Intelligence
Recognising the shift is only the beginning. Executive teams also need a practical way to decide where additional AI capability belongs.
1. Start with business value
Begin with the outcomes that matter most to the organisation rather than the technology itself. Revenue growth, customer experience, operational efficiency, decision quality and risk reduction provide a much stronger starting point than discussions about models or licences. AI investment should support strategic priorities rather than create new ones.
2. Identify high-leverage work
Not every activity deserves the most capable AI available. The strongest business cases usually combine complex judgement, high frequency and meaningful commercial impact. Those activities generate returns that justify premium capability. Routine work often does not.
3. Match capability to the task
Different work requires different levels of intelligence. Many activities can be completed effectively using lightweight assistants or specialised models, while others benefit from frontier reasoning or autonomous agents. Organisations already apply this principle when assigning people with different experience and expertise to different kinds of work. AI should be managed in much the same way.
4. Measure outcomes, not activity
Usage statistics are easy to produce but rarely explain whether value has been created. Better measures include cycle time, customer outcomes, quality, financial performance and risk reduction. Those indicators provide a clearer basis for future investment decisions because they focus attention on business performance rather than technology adoption.
5. Review the allocation
Business priorities change. Technology changes. Markets change. AI investment should be reviewed with the same discipline applied to capital, talent and major programmes. Some teams will justify additional capability over time. Others may discover that less expensive tools produce comparable results.
Questions Worth Taking to the Executive Team
Every executive team should be able to answer five straightforward questions.
Which decisions create the greatest value in our organisation?
Where would better judgement or faster analysis produce the strongest commercial return?
Which roles genuinely require frontier AI capability?
How are we measuring business value rather than AI activity?
If our AI budget doubled, or halved, how would our allocation change?
The discussion that follows those questions is likely to be more valuable than another debate about vendors, models or feature releases. Technology will continue to improve, prices will continue to move and new capabilities will continue to emerge. Those developments matter, but they are no longer the central management challenge.
Every generation of executives inherits a new strategic resource. Earlier generations learned how to allocate financial capital, digital technology, data and cloud infrastructure. This generation will learn how to allocate intelligence. The organisations that approach that task with the same discipline they apply to every other strategic investment are likely to discover that competitive advantage comes less from owning better AI than from putting it in the right places.
References
Artefact. (2026). Is AI really getting cheaper? The token cost illusion. https://www.artefact.com/blog/is-ai-really-getting-cheaper-the-token-cost-illusion/
Gross, G. (2026). The AI adoption spending spree is over. Time to focus on value. CIO. https://www.cio.com/article/4183263/the-ai-adoption-spree-is-over-time-to-focus-on-value.html
KPMG. (2026). Growing adoption signals progress as cost visibility and accountability drive AI value. https://kpmg.com/xx/en/media/press-releases/2026/06/growing-adoption-signals-progress-as-cost-visibility-and-accountability-drive-ai-value.html
Rowe, J. (2025). 2026: The AI Invoice Arrives. For Every Scale. https://www.foreveryscale.com/p/2026-the-ai-invoice-arrives