Is your operating model adaptable enough for AI?

Deloitte’s 2026 Human Capital Trends found that while 85% of leaders say adaptability at speed is critical, only 7% say they are leading in enabling continuous workforce growth and adaptation. As AI capabilities evolve, organizations will find that operating model decisions and directives that once lasted years now need to be revisited more frequently because of how rapidly AI is reshaping the assumptions those operating models were built upon.

AI is already accelerating the pace at which organizations must make decisions about work, governance, talent, and technology. Leaders face growing pressure to capture value from AI investment while navigating rapidly changing AI capabilities and workforce implications that are not always fully understood. At the same time, the workforce is growing tired of large-scale transformation, leaving few organizations able to absorb a full operating model redesign every time assumptions change.

The solution is an adaptable operating model. Rather than relying on one-off, large-scale redesigns, adaptable operating models create the conditions for continuous, smaller adjustments. This allows organizations to adapt faster to future changes in a market where those changes cannot yet be known.

Adaptability is hardly a new concept: we wrote about it in Deloitte Adaptable Organization 2.0, which was an expansion of earlier insights from 2018. But adaptability’s relevance is only increasing as AI accelerates the pace of organizational change.

Building an adaptable operating model for an agentic future  

Organizations can’t respond quickly enough to market changes with static operating models. Instead, any transformation, especially AI transformation, is a series of strategic operating model choices that embed adaptability in five layers.

1. Ecosystem

AI demands a higher level of ecosystem awareness to keep pace with change fueled by AI innovation. In the past, assumptions about the external environment (customers, competitors, partners, economic forces, etc.) might have remained valid for years. AI compresses that cycle. Now, adaptability at the ecosystem layer depends on an organization’s ability to continuously sense and respond to change in its external environment. Organizations must maintain a constant understanding of how these external forces are evolving and what they mean for their role within the broader ecosystem.

Yet the same forces creating this challenge can also help address it. AI is creating new ways for companies to both monitor and engage with their ecosystem. It makes it possible for organizations to identify emerging trends earlier and test response scenarios more quickly, including:

• Supporting market scans in real time with AI agents
• Gathering real-time customer usage and feedback data from products and services
• Modelling strategic shifts to test viability before implementing
• Identifying new markets and growth opportunities

Outcomes

The result is an organization that is prepared to evolve alongside its ecosystem. Organizations that use AI to continuously sense their ecosystems can anticipate and respond to changing customer preferences earlier, embed emerging technologies faster through partnerships, and play a more active role in advancing—instead of reacting to—the social and economic well-being of the communities in which they operate.  

2. Organization

In a world with agents and powerful automation, expectations about how organizations scale are changing. Growth used to be constrained by the number of people in that organization. Now, growth depends on how effectively an organization combines human judgment and AI capability. A flexibly allocated team of 100 people, equipped with AI agents, automation, and intelligent workflows, might be able to produce what previously required 120, 150, or 200 people.

But more capacity does not automatically create more value if organizational governance can’t keep up. The most adaptable organizations will prioritize the flexibility to quickly focus human and AI capacity on the work that matters most, while preserving accountability and trust. That requires a shared vision for what the priorities are, clear decision governance for what gets escalated, and well-defined guardrails to spot what level of risk is acceptable.

AI also exposes the limits of functional silos. Take functions such as HR, technology, and finance for example: to scale AI, these areas must share ownership for redesigning work, managing human-agent workforce costs, and maintaining agent performance standards. Organizations that make these decisions separately, behind functional boundaries, will struggle to scale AI responsibly. In an AI-enabled operating model, adaptability comes from the ability to continuously move together and coordinate these interconnected choices as priorities, technologies, and market conditions evolve.

Outcomes

At the organization level, the right combination of humans and AI can create a direct path to operating scale and profitability. Meanwhile, proper execution of guardrails—like governance, human override authority, decision accountability, and portfolio discipline—creates the conditions for healthy organizational growth without the risks of unconstraint.  

3. Leadership

As AI reduces traditional capacity constraints, organizational performance becomes less dependent on headcount and more dependent on leadership judgment. Leaders become architects of adaptive systems, responsible for creating the conditions for collective success in increasingly complex human-agent work environments.

Adaptable organizations will continue to rely on leaders who can energize, empower, and connect people through uncertainty. But in an AI-enabled operating model, leaders must do this while orchestrating a system in which humans and agents work together effectively. The leaders who do this well will be the ones who can continuously translate changing business priorities into clear direction for both humans and agents, and make deliberate choices about where AI should be applied, where human judgment remains critical, and how investments in people, agents, and token consumption should be balanced.

As we noted in Why C-suite leaders need to rethink organization design for the agentic era, the importance of middle management in particular increases in an AI-enabled adaptable organization—their proximity to the work and relevant experience in judging output makes them critical to deciding where human experience adds value, where agents can be trusted, and when intervention is required.

Adaptability in this environment requires leaders to have the ability to:

• Set outcomes and guardrails for agents
• Exercise override authority on agent outputs
• Coach people through the judgment-heavy work AI can't do
• Calibrate trust, accountability, and purpose
• Evaluate the cost-benefit of AI-enabled versus human work
• Develop people for what’s next

Outcomes

Adaptable leadership turns AI from a productivity tool into a scalable organizational capability. Leaders create the conditions for responsible autonomy, make sure human and AI capacity is directed toward the highest-value opportunities, and balance productivity, quality, risk, and workforce development.  

4. Team

AI is changing the scale and composition of teams. A single team is now a network of employees, agents, and sometimes external partners, all working together. But the foundations of adaptable teams remain true—organizations that respond quickly to change are good at organizing work around outcomes rather than traditional organizational boundaries. The bottleneck becomes how rapidly an organization can mobilize, empower, and reshape human/agent teams around priorities without losing accountability.

In the agentic era, fixed team structures defined by reporting relationships and stable membership can’t respond fast enough to changing priorities. Instead, work should begin with a strategic outcome. With a clear outcome, the organization can bring together the right combination of human expertise and AI capability (the human-agent team) to deliver that outcome. When priorities inevitably shift, the composition or capacity of those human-agent teams can be reshaped without requiring broader organizational redesign.

But adaptability requires more than assembling teams quickly. Adaptable teams must also be empowered to act. That empowerment comes from a clear definition of success and the authority to make decisions within defined guardrails. In human-agent teams, those guardrails become even more important because organizations must be explicit about expectations for how humans and agents collaborate, including where people intervene and who remains accountable for the results. Without that clarity, teams stall, or AI goes ungoverned.

Outcomes

Adaptable teams move away from process-first execution to outcome-first performance, focusing scarce human and agentic capacity on the work that creates the greatest value. By organizing teams around clear outcomes, organizations can pivot more quickly to emerging strategic priorities without requiring a full organizational redesign.  

5. Individual

The shift from fixed job descriptions to fluid roles accelerates with AI. Roles should be defined by the outcomes they own rather than a list of static responsibilities, and characterized by how the work is divided between humans and AI. As routine tasks become more automated, context, relationship-building, storytelling, creativity, empathy, negotiation, and coaching become increasingly important sources of human value for individuals working with AI.

Individual adaptability is therefore deeper than learning new AI tools. For many employees it may mean a dismantling and rebuilding of beliefs, behaviours and habits as they adjust to new ways of working and evolving professional identities. Organizations must help people navigate this transition by strengthening resilience, building confidence working with AI, reinforcing trust, and embedding the everyday behaviours that enable people to apply judgment, exercise accountability, collaborate effectively, and continuously adapt as work evolves.

Outcomes

At the individual level, adaptability helps people adjust alongside changing work. Organizations that encourage experimentation, learning, and trust in human-AI collaboration can create the conditions for individual employees to adapt to new ways of working while continuing to grow and contribute.  

How Deloitte can help

Adaptability can bring confidence to organizations in this time of immense change, but it requires expertise to implement. Deloitte’s deep capabilities across human capital, strategy, operations, and technology allow us to help you think holistically about infusing adaptability into your operating model.

Ready to move forward to an AI-enabled operating model designed for an adaptable future? Let’s continue the conversation.