As AI Becomes Standard, Operating Models Will Define Competitive Advantage

AI Shifts Focus From Technology Ownership to Execution

A perspective by Accenture illustrates that as artificial intelligence becomes more capable and accessible, business advantage may shift away from owning the underlying technology. Organizations increasingly have access to similar foundation models, agent platforms and cloud computing infrastructure. The difference may lie in how companies organize people, data, processes and governance around those tools.

An operating model is the system that turns strategy into performance. It includes organizational structure, decision rights, processes, leadership, workforce capabilities, incentives, metrics, data and technology. Changing only the technology without updating these connected elements can limit AI adoption results.

Many existing operating models were designed for a period when intelligence was scarce, work moved through sequential processes, and people carried out most tasks. AI alters those assumptions by automating parts of execution and expanding productive capacity without a proportional increase in employee headcount.

AI may reduce traditional tradeoffs between scale, agility, efficiency and differentiation. Companies may coordinate work across functions more quickly and cheaply, though they still need to decide where human judgment remains necessary.

The article identifies five changes organizations should consider: organizing around decisions, creating end-to-end value streams, managing people and AI agents as one workforce, measuring cost per outcome and capturing learning from decisions.

Organizations Should Focus on Major Decisions

The first proposed change is a shift from organizing around tasks to organizing around decisions. As AI automates more execution, the decisions that direct work may become more important sources of value.

Examples include setting prices, deciding inventory levels or selecting a clinical trial site. These decisions can directly affect customer and financial outcomes, and improving them may deliver more value than optimizing individual tasks around them.

Organizations should identify the relatively small number of high-impact decisions and establish how humans and AI should contribute to each. This includes defining the desired outcome, assigning accountability, setting decision rights and determining the appropriate level of automation.

A person may lead while AI provides advice; a person may establish direction while AI executes; AI may operate with human monitoring; or AI may operate within governance established by people. The appropriate model depends on the decision’s complexity and risk, as well as ethical, legal and accountability considerations.

AI agents cannot rely on informal organizational signals in the way employees often do. AI systems require documented guardrails, escalation triggers and audit trails to identify when an issue needs escalation, when authority is lacking or when a situation is unusual.

Faculty, an applied AI company acquired by Accenture, worked with NHS England during the COVID-19 pandemic on an early warning system that forecast hospital bed capacity roughly three weeks ahead. In one case, models indicated a rise in COVID-19 cases in a Midlands city. Further analysis found the increase was concentrated in one hospital rather than reflecting broader community transmission, prompting an investigation of potential hospital transmission rather than an immediate localized lockdown. The example illustrates AI providing decision support while human leaders retained authority.

Value Streams Can Replace Isolated Functional Priorities

The second proposed shift is from function-led work toward end-to-end value streams. Delays, duplication and customer problems often occur when work passes between functions.

Organizations can identify five to eight value streams important to customers and business performance, including signal to shelf, quote to cash, claim to settlement and patient to outcome. They can then map the work and important decisions within each stream.

A value-stream approach may move accountability, decision-making authority, and budget responsibility closer to the teams responsible for outcomes. Functional departments would continue to provide expertise, standards and strategy, while cross-functional streams would have greater authority over end-to-end results.

AI can reduce the effort required to coordinate information across organizational boundaries. AI agents operating across inefficient processes may also expose bottlenecks that were less visible under traditional functional reporting.

A global industrial solutions company introduced an enterprise operating model covering nine major processes, including lead to cash. The company is preparing to deploy more than 100 AI agents to coordinate decisions in that process. In cash application management, order-to-invoice work and master-data management, it reported about 70% touchless processing and reductions of up to three days in lag time.

Companies Face a Combined Human and AI Workforce Challenge

The third shift involves managing people and AI agents as a combined workforce. Many companies have deployed AI agents for customer service, supply chain work and other tasks, but agents often lack an identified owner, formal accountability or a defined place in workforce planning.

In a survey of 3,000 C-suite leaders at large companies, 58% said they planned to change how work is distributed between people and AI over the next year. That was more than twice the share reported at the beginning of 2026.

Companies should assign every agent a named human owner accountable for its performance and outcomes. Policies, performance standards and governance arrangements should apply to both people and machines, with clear records of delegated authority.

Organizations should include agents in workforce planning systems alongside employees and contractors. Leaders need visibility into the skills, capabilities, costs and performance of human and machine resources to determine the appropriate mix for a team or process.

Human work may increasingly center on contextual judgment, oversight, empathy and intervention when systems produce unreliable or inappropriate results. This raises questions about whether responsibility for the combined workforce belongs with human resources, technology leadership or a shared model.

Moderna combined its human resources and IT functions in 2025 under a chief people and digital technology officer. The move did not create a complete model for managing people and agents together, but reflected the growing overlap between workforce and technology planning.

Cost Measurement May Move From Headcount to Outcomes

The fourth change concerns the economics of AI-enabled work. Traditional budgeting systems generally treat payroll and contractors as the main sources of productive capacity. AI agents introduce a third category with different cost characteristics.

Employee expenses are generally committed costs, while AI capacity often varies with consumption. AI spending more closely resembles cloud infrastructure than payroll. Costs can differ widely between organizations with similar employee counts because expenses depend on where AI is deployed, which models are used and how often systems run.

An Accenture study found that fewer than 10% of users and workflows accounted for most AI spending in the examined deployments. Companies should match model capabilities to specific tasks rather than using more expensive systems when simpler technology would suffice.

Amazon’s robotics operations illustrate why headcount may be a less useful proxy for productive capacity. Packages handled per employee rose from about 175 in 2015 to 3,870 in 2024. Amazon had deployed more than 1 million robots by 2025. Its DeepFleet system coordinates robot movement, and the company has reported faster fulfillment and greater efficiency at newer facilities.

Companies can measure cost per outcome within individual value streams rather than assessing costs primarily per employee. This approach combines the costs of people, agents and AI inference with the business result delivered, such as improved settlement times, customer service outcomes or order-processing performance.

Chief human resources officers, chief technology officers and chief financial officers need a common view of these costs. Without connected workforce, technology and finance data, leaders may not be able to accurately evaluate the cost of an outcome or returns from AI investments.

Decision Histories Could Become a Source of Advantage

The final proposed shift is to make learning cumulative. Organizations often record outcomes but fail to capture the context behind decisions, including the information available at the time, AI recommendations, human judgment, reasons for overriding a recommendation and the eventual result.

The accumulated record is described as decision history. If organizations record and use it consistently, they may improve future human and AI decisions.

Mastercard’s fraud-decisioning system evaluates approximately 159 billion transactions annually. Each transaction and resulting outcome can provide data for future fraud assessments as fraud methods change. Mastercard has also said generative AI has improved the speed of compromised-card detection.

Companies may eventually use similar AI models and infrastructure, but they will not have identical records of customer interactions, operational problems, management choices and results. A decision history could become a proprietary asset that improves over time.

Recommended First Steps: Focus on Accountability and Measurement

Companies can start where potential value and operational friction are highest. Recommended actions include assigning accountable owners to a limited number of customer-focused value streams and giving them authority over relevant tradeoffs.

Organizations can also create a decision register that identifies key decisions, the appropriate human-AI decision model, named human stewards, guardrails and escalation conditions.

Companies should begin integrating AI agents into workforce views, including ownership, performance, capability and cost. They can establish a cost-per-outcome baseline for at least one value stream and test a learning loop by documenting the context, judgment and result for a recurring class of decisions and using that information in later decisions.

The five changes are interdependent. New decision rights require updated governance and incentives. Departments cannot be accountable for outcomes if they continue to be rewarded only for functional targets. Organizations cannot manage AI agents effectively without connected workforce, technology and financial data.

AI deployment alone will not determine long-term results. Organizations that redesign how work, decisions, accountability and learning operate may be better positioned to translate widely available AI technology into sustained business performance.

Discover more at Accenture.