Is Your Company Ready to Operate with the Intelligence It Is Buying?
Updated: Oct 10
How operating models connect AI investments, work transformation, and the evolution of the business value chain.
A company invests in artificial intelligence. It equips employees with copilots, experiments with agents, connects new tools to existing systems, and starts generating insights faster.
Yet approvals still move through the same management layers. Departments pursue competing objectives. The data needed to take action remains fragmented. And nobody owns the outcome from beginning to end.
The available intelligence has increased. The ability to turn that intelligence into action remains constrained by how the company operates.
This scenario raises a fundamental leadership question:
Is your company ready to operate with the intelligence it is buying?
1. The Operating Model Determines What AI Can Deliver
An operating model defines how an organization assigns responsibilities, makes decisions, allocates resources, coordinates people and technology, and measures performance.
These choices determine whether strategy can translate into execution.
A new technological capability changes what is possible. Capturing its value requires a corresponding review of how work happens and which outcomes matter.
An agent that analyzes a situation in seconds will have limited impact if the next action sits in an approval queue for days.
Recent research reinforces this connection. In September 2026, McKinsey linked AI value creation to operating model choices, including outcome-oriented teams and the redesign of end-to-end processes. That same month, Accenture emphasized how organizations combine technology, human judgment, and proprietary data in the way they operate.
For executives, the implication is practical: AI investment decisions must involve the people who can change processes, priorities, responsibilities, and incentives.
2. Individual Productivity Must Translate into Business Performance
Consider the process of preparing a commercial proposal.
AI can draft the document, assemble customer information, and recommend a product configuration. But the total turnaround time still depends on pricing, capacity assessment, contractual terms, and internal approvals.
If those activities remain disconnected, the benefit stays concentrated in document preparation.
Redesigning the entire workflow creates a broader opportunity. The company can establish decision thresholds, integrate information, and route only exceptions for specialist review.
Performance can then be measured through the time it takes to deliver a proposal, the quality of the offer, and the margin preserved.
This is how individual productivity can translate into organizational performance.
But the time saved needs a destination: serving more customers, improving service, reducing rework, or freeing capacity for new activities.
Turning that capacity into results requires deliberate management decisions.
3. Transformation Can Reshape the Value Chain
The ambition of transformation can extend beyond efficiency.
By connecting information and strengthening its ability to decide and act, a company can improve, expand, or reconfigure its value chain.
The value chain encompasses the activities through which a business creates and delivers value, along with their associated costs and margins. The operating model determines how the organization mobilizes its capabilities to perform those activities.
When a company changes the value it promises customers, it must adapt its ability to deliver on that promise.
Imagine an industrial equipment manufacturer. Technology could support three distinct strategic moves:
Improve the existing value chain. Data and AI help improve production planning, reduce defects, and make customer support more effective. The company strengthens the activities it already performs.
Expand the value chain. Data from equipment in operation enables monitoring, predictive maintenance, and recommendations for better performance. The manufacturer participates in a larger share of the customer journey, potentially creating recurring revenue.
Reconfigure the value chain.The manufacturer develops an offering tied to equipment availability, coordinating maintenance, spare parts, and services with partners. Activities, relationships, and revenue mechanisms change.
These are illustrative possibilities, dependent on demand, economic viability, and execution capabilities. They are not a mandatory sequence.
Expanding the value chain may be the wrong choice when the additional costs, risks, or complexity exceed the value customers recognize and are willing to pay for.
The strategic question is where the company can create additional value and capture a sustainable return.
4. A New Value Proposition Requires New Responsibilities and Incentives
The manufacturer example reveals the depth of the organizational change involved.
A company that promises equipment availability must coordinate sales, engineering, services, finance, and partners throughout the contract.
It needs to establish who monitors performance, who intervenes when a failure occurs, and who bears the cost of an unmet commitment.
It also needs to revisit incentives.
Sales compensation based entirely on the initial transaction may conflict with the profitability of a long-term service contract. A service team measured exclusively on expense reduction may undermine the availability the customer was promised.
The operating model must make these decisions consistent with the value proposition.
Organizing teams around products, services, or customer journeys can help establish ongoing accountability for results. Shared data, integration, and AI platforms can give those teams reusable capabilities, while specialist functions establish common standards and controls.
The right design depends on the business.
What matters is that autonomy comes with clear boundaries, resources, and accountability. Renaming teams will accomplish little if funding, prioritization, and approvals continue to prevent execution.
5. AI Agents Require Explicit Decision Rights
AI agents introduce additional decisions into the operating model:
Which activities can they perform independently?
When must they request approval?
Who monitors their quality and performance?
How can an inappropriate action be stopped?
Who is accountable for the outcome delivered to the customer?
The organization must assign those responsibilities to identifiable people and functions.
An agent’s autonomy should reflect the impact of its actions, their reversibility, and the quality demonstrated in practice.
Preparing a maintenance recommendation and authorizing a production shutdown require different levels of control.
As agents become part of operational workflows, their permissions, escalation paths, and oversight mechanisms become part of the organization’s management design.
Delegating execution to an agent leaves the company responsible for the commitments it makes and the outcomes it delivers.
6. Leadership Must Evolve Alongside the Work
As analysis and execution become faster, managers need to focus on decision quality, coordination across functions, and the development of necessary capabilities.
This includes resolving conflicting priorities, removing unnecessary dependencies, and helping teams use newly available capacity productively.
The people closest to the operation should participate in its redesign. They often understand the exceptions, dependencies, and informal workarounds that formal process descriptions fail to capture.
Their participation also helps make new responsibilities practical. A redesigned workflow needs people who understand when to trust an automated action, when to investigate, and when to intervene.
For leadership, the challenge is to connect technological capability with an organization that can learn, adapt, and remain accountable.
7. Start with a Business Journey and Measure the Complete Outcome
A practical starting point is a meaningful, clearly bounded customer or business journey.
The work can follow a straightforward sequence:
Establish the baseline. Understand current performance, costs, delays, and quality.
Identify where value is lost. Examine bottlenecks, repeated work, fragmented decisions, and unmet customer needs.
Define the desired outcome. Clarify what should improve for the customer and the business.
Assign end-to-end responsibility. Give an accountable owner the authority to coordinate the necessary changes.
Redesign and test the operation. Integrate people, technology, and controls, with explicit criteria for broader adoption.
Measurement must connect customer outcomes with business returns.
End-to-end cycle time, quality, retention, and service capacity should be assessed alongside the costs of technology, integration, supervision, and ongoing maintenance.
New services need to demonstrate demand and sustainable margins. Estimated benefits must be compared with realized results.
This discipline becomes especially important when technology enables a company to take on new responsibilities in its value chain. A broader offering can create more value for the customer while introducing obligations the business has never managed before.
8. The Leadership Decision Behind the AI Investment
Buying intelligence expands the range of possibilities.
Operating with it requires choices about how the company works, how decisions are made, and where it intends to participate in the value chain.
Those choices determine whether AI improves isolated tasks, strengthens the existing business, or enables a broader transformation of what the company offers and how it earns revenue.
Before the next investment, there is a question worth bringing to the leadership table:
Which decisions, processes, and responsibilities are we prepared to change to turn this capability into customer value and business results?
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