The rise of the agentic product operating model: What will change in a world of agents?

As organisations adapt, the key organisational constraint will shift from human capacity to human judgment. Unlike before, where scale and resources set incumbents apart from startups, agentic AI technologies are narrowing this gap, making execution abundant and judgment the differentiator. While agentic AI has the potential to deliver unprecedented speed, efficiency, growth, and customer outcomes, most organisations still use it to automate existing ways of working, rather than as a way to rethink the approach to better, faster delivery.

Realising the value of agentic AI requires a fundamental rethink of operating models: how decisions are made, teams are structured, and work is divided. Mature organisations lead the way by establishing strong foundations, and by anticipating how the environment and operating model will evolve.

Establishing the right foundations – what must be true before an organisation evolves its product operating model with agentic AI?

1. Define decision-making boundaries

To develop effective agentic product operating models, organisations must understand and leverage the strengths of both agents and humans. Agents are well-suited for repetitive, high-volume tasks, and while they are becoming better at handling ambiguity and context, humans remain necessary to provide ethical and value-based decision-making. Understanding the intersect of where agent optimisation ends and human judgment begins is key. Leading organisations make these boundaries explicit by using decision-making frameworks that determine if a task should be handled by agentic AI or humans based on:

• Capability maturity: Can the agent match or surpass human quality?
• Judgement intensity: Are there complex or ethical trade-offs?
• Context sensitivity: Does the task require deep organisational knowledge or risk management?
• Emotional intelligence: Does the work demand trust, empathy, or persuasion?

2. Double-down on foundational operating practices

Mastering foundational product operating practices is crucial before adopting agentic AI, as its introduction can dramatically amplify existing weaknesses.

For example, if teams are organised around functions rather than value streams, agents optimise isolated activities at the expense of end‑to‑end outcomes. Weak strategic traceability from executives to the work of teams would mean agent output increases cost and risk rather than impact, while extensive top-down governance rather than a lightweight iterative model, would create bottlenecks at machine speed.

Without strong foundations, agentic AI accelerates failure. Leading organisations therefore must explicitly assess their end-to-end readiness and only embed agentic AI into the operating model when the organisation can support it effectively.

3. Strengthen culture before scaling agentic AI

As agentic AI makes execution fast and cheaper, organisational advantage shifts from output to how decisions are made. Without clear principles, rules, and guardrails, organisations risk producing undifferentiated products. Those investing in a values-driven culture integrate ethics and purpose into both human and AI decisions, gaining strategic relevance. The most AI-mature organisations use culture to shape how they:

• Make decisions among many AI-generated solutions, empowering leaders to choose paths aligned with the organisation’s purpose Navigate ethical trade-offs quickly and consistently
• Build trust with customers by ensuring agentic AI is transparent
• Keep employees motivated by shifting focus to judgment, creativity, and ownership.

Mature organisations prioritise by assessing and strengthening cultural elements, such as empowerment, accountability, risk appetite, and customer centricity before scaling agentic AI adoption.

Evolving the operating model to realise the value of agentic AI

1. Redesign cross functional teams to flat networks of hybrid teams

Digital companies have cross-functional product teams but are still constrained by human communications, giving rise to the two-pizza team concept popularised by Jeff Bezos, where effective teams can be fed by two pizzas, reducing coordination overhead and increasing speed, ownership and innovation. In agentic product operating models these give way to one-pizza‑ hybrid teams; small (two-to-five person) human teams working hand-in-hand with dynamic networks of agents. Additionally, hybrid team membership becomes more fluid: teams will maintain stable missions aligned to outcomes, but with flexible agent compositions that adapt as needs change.

Winning operating models will rely on empowered agentic AI teams, supported by more robust systems of record, so leaders can see which teams own which capabilities, how AI agents are deployed, and where dependencies or shared services exist. This makes orchestration a critical leadership capability central to value creation.

Early learnings from implementing AI assistants (such as PR Builder and PR Review Assistant) for a European Bank highlight the importance of effective orchestration and monitoring to prevent quality issues. Key lessons include avoiding the release of agents without thorough testing, properly training engineers, and allowing sufficient time for teams to adopt these new tools.

2. Recalibrate narrow-skilled roles to broad roles and M shaped skills profile

While some roles disappear, many are reshaped. Product owners remain responsible for judgement, with agents handling preparation and execution. Scrum Masters evolve into orchestrators of complex interactions between humans and agents who manage systemic risks, while new specialisms emerge around training, monitoring, and governing agents.

Leaders must redesign operating models for these hybrid teams, clarifying when agents or humans should take the lead. Success increasingly favours T-shaped experts, deep specialists who can reimagine workflows and interpret data, and M-shaped leaders capable of orchestrating hybrid teams. Agility as a competency will become essential: people will need to be able to work quickly, observe what agents do, and manage rapid test-and-learn experimentation.

Working with a Nordics airline on agentic adoption across the product lifecycle, we have seen the rise of T-shaped engineers, combining deep technical/orchestration skills (vertical) with broad contextual and customer understanding (horizontal). The closer humans working with agents are to customer needs, the better the agents perform, highlighting the importance of a strong product operating model that tightly aligns technology with the business.

3. Shift from agile governance meetings to hybrid workflows driven by ROI decisions

In hybrid teams, agents become active participants in planning, execution and monitoring. Stand‑ups and planning shift from reviewing human output to managing agent‑generated options, risks and insights, with humans retaining accountability.

Beyond amending their ways of working, leaders will be able to amend the metrics that indicate success. Traditional speed and throughput metrics will become less meaningful. Instead, organisations must focus on customer impact, value flow, agent effectiveness, and the ROI of decisions to ensure meaningful outcomes move to what truly matters: customer impact, flow of value, agent effectiveness, decision ROI, and employee Net Promoter Scores. As part of our work with a global fintech provider, we introduced a balanced scorecard of metrics in all business lines to monitor health across the end-to-end product lifecycle covering value, experience, flow and quality.

What leaders can do to thrive in the new era

Leadership will increasingly be measured by their ability to define and lead a blended human-agent workforce, actively shaping adaptive systems and determining when agents should augment or replace human roles. They must prioritise decision quality over sheer activity, taking responsibility for ensuring choices align with strategy, values, and risk.

Leaders of the future will move away from utilisation metrics to instead measuring success by customer impact, value flow, and learning agility. Only those leaders who embrace these shifts will unlock the full promise of human-agent collaboration.