When everyone has AI, your operating model will set you apart

Most operating models still treat tasks as the unit of value creation. As execution becomes more automated, a greater share of value creation instead depends on the decisions that set direction, allocate resources and resolve tradeoffs. As AI takes on more execution, the differentiator becomes the decisions that determine what gets done, as well as the proprietary data and human judgment behind them.

The decisions that matter most are choices that directly affect customer and business outcomes: what price to offer, how much inventory to position or which clinical trial site to select. Getting one of these decisions right can have a material impact on the outcome, often disproportionate to the value available from optimizing the work around it. This makes the decision itself a particularly important source of value in an AI-enabled organization.

Companies, therefore, need to identify the relatively small number of decisions that matter most and decide how people and AI should make them. Today, operating models are largely designed around how work gets done. This shift gives the decisions that direct that work greater weight in operating model design.

For each decision, leaders should specify the outcome it serves, who is accountable and how AI should contribute. As Figure 1 shows, the right decision mode (that is, how people and AI share the work of making a decision) depends on its level of risk and complexity. Leaders should also consider where people need to retain control for reasons such as ethics, accountability, legitimacy or talent development. Clear guardrails help keep decisions traceable and auditable.

Figure 1: The decision mode framework

The right balance of human and AI roles depends on the decision.

Most companies have spent years improving performance within individual functions. Now, with decisions better defined, the bigger opportunity lies across the organization, where handoffs create delay, duplication and customer frustration. Companies can capture that value by organizing work around outcomes that cut across functions.

Start by identifying the five to eight end-to-end value streams or mega-processes that matter most to customers and drive the biggest customer and business outcomes; for example, signal to shelf, quote to cash, claim to settlement or patient to outcome. Then map the work and the key decisions inside each one. These decisions usually sit at the seams of the organization, which is why functions rarely see them end to end. What follows is the harder part because value streams only carry real authority when accountability, decision rights and budget move with them.

The matrix does not disappear, and how far companies go is a deliberate choice. Some give value streams greater accountability for outcomes, with functions providing strategy, standards and expertise. Others rebalance more incrementally, with accountability shared. Either way, the change reaches past reporting lines into the operating model itself, including structure, processes, talent and incentives.

Within each value stream, work is organized around a shared outcome rather than the priorities of individual functions. As Figure 2 illustrates, people and AI can play different roles in the decisions along the way, while more of the execution becomes automated.

Figure 2: Inside the value stream

In AI-enabled operating models, value streams cut across functions while functions keep their core responsibilities

As work is redesigned around AI, companies also need to rethink the distribution of work. For example, AI agents already perform meaningful work across many enterprises, resolving customer issues and managing supply chain tasks and processes. In fact, almost every large company deployed at least one agent last year.¹ Yet most agents have no named owner, clear line of accountability or place in the workforce plan. As agents take on more work and a greater share of routine decisions, gaps in ownership make it harder to govern their performance, resolve exceptions and determine who ultimately owns the outcome.

Those gaps will be harder to sustain as companies redistribute more work between people and AI. In our recent survey of 3,000 C-suite leaders at some of the world’s largest companies, 58% of executives said their organizations plan to change how work is distributed between people and AI in the next 12 months, more than double the share who said the same at the start of 2026.²

Managing people and AI as one workforce begins with accountability. Every agent should have a named human owner who is ultimately accountable for what it does. Policies and guardrails should also apply consistently across people and machines, with a clear record of how authority is delegated. And agents need to appear in the workforce plan alongside people, with the same visibility into capability and performance, so leaders can plan and manage the workforce as a whole. That reaches into talent and into the systems that support it, neither of which was built to hold both.

Beyond accountability, planning changes too. The team has always been the real unit of performance, and its composition has always mattered more than the capabilities of any one member. With AI, the team is now a mix of people and agents, and that mix has to be designed rather than assumed. Once leaders know the outcomes and decisions a team owns, they can set the balance of human judgment and machine capacity to deliver at scale. Within the team, human work concentrates where it is irreplaceable: knowing when to trust the system, when to step in and when empathy matters most.

Running that combined workforce at scale also raises a question of where responsibility for it sits.

As human and machine capacity are managed together, companies also need a better way to measure what that capacity costs. Yet most financial and workforce systems still budget capacity in two columns: payroll and contractors.

While AI agents have become a third source of productive capacity, their costs behave differently from those of employees and contractors and are often managed separately through technology rather than workforce budgets. Employee labor is largely a committed cost, while machine capacity is consumption-based: it can expand and contract with use. Its economics resemble cloud infrastructure more than payroll. AI spending also does not scale predictably with headcount. In a recent Accenture study of AI deployments, fewer than 10% of users and workflows drove the vast majority of AI spending.⁴ As a result, two similarly sized workforces can have very different AI costs depending on where and how extensively AI is used. It also means matching AI capability to the work because using more capable models than the task requires adds unnecessary cost.

As more work is organized around critical decisions and horizontal value streams, cost should be measured against the outcomes that those value streams deliver. The right measure is cost per outcome: What did the organization achieve, and what did it cost to achieve it? A practical starting point is to set a baseline for a single value stream, with people, agents and inference on common terms and the full cost tied to what that stream delivers.

How companies use this freed capacity is a strategic choice. It can be taken as cost savings or redirected into growth, service or innovation. A cost leader and a premium player may make different choices. Making that choice in advance helps ensure the capacity is directed toward the intended business outcome.

Measuring cost per outcome requires people, agents and inference to sit on more comparable terms in planning systems. More broadly, all five shifts described in this article depend on something few enterprises have today: a connected view of the people and agents doing the work, the decisions that shape it, what that work costs and the governance around it.

Without that connection, leaders can’t reliably determine the cost of an outcome or make informed choices about the mix of human and machine capacity behind it. This is both an operating model issue and a finance issue, so the CHRO and CTO need to address it with the CFO. Until they have a common view, the real economics of the operating model remain invisible.

Once companies can connect the decisions that matter with the people and agents doing the work and what that work costs, they have the foundation for something more valuable: learning from every decision. Most organizations record outcomes but rarely capture the context behind them, including what managers knew, what they expected, what AI recommended, why people accepted or overrode that recommendation, and what happened as a result.

Institutional knowledge lives in the heads of experienced people, not in the systems around them. AI can change the economics of organizational learning by making that knowledge easier to capture, share and apply. Indeed, management thinkers have long argued that organizations improve by learning continuously and correcting errors: Peter Senge popularized the idea of the “learning organization,”⁸ while philosopher Karl Popper emphasized progress through testing ideas and learning from mistakes.⁹

AI can make those principles more practical at scale by helping organizations retain what they learn and apply it to future decisions and work. “Decision history,” the accumulated record of the context, recommendations, judgments, actions and outcomes associated with pivotal decisions, can inform both future decision makers and future AI systems. Building that record across the decisions within each value stream is what turns learning into a capability rather than an archive. That changes the systems that hold the record and the processes that close the loop, and it changes what is asked of people. Their judgment now needs to be captured as part of the record so it can inform future decisions.

While most large companies will eventually have access to similar foundation models, agent platforms and computing infrastructure, they won’t have identical histories of customer interactions, operating exceptions, management choices and outcomes. A company’s decision history is a source of advantage that grows stronger with every decision made. Because that advantage compounds, the gap between those who start now and those who wait widens with every cycle. The longer companies wait, the harder that gap may be to close.