Agentic AI Strategy - Designing an Operating Model for Agentic AI
- Beyond Team
- Oct 1
- 3 min read
One of the biggest misconceptions I hear in boardrooms is that AI agents can be “slotted in” like any other new technology. That’s rarely the case.
At Beyond: Putting Data to Work, we’re seeing that the moment an enterprise moves from piloting AI agents to deploying them at scale, the conversation quickly shifts. It’s no longer just about algorithms — it’s about operating models.
Why? Because agents don’t just perform tasks. They change how work is structured, how decisions are made, and who is accountable for outcomes. That means they demand a new approach to how organisations are designed and run.
Why Operating Models Matter in the Age of Agents
Every executive team knows that technology without the right operating model rarely delivers value. We saw it with ERP, we saw it with cloud, and we’re seeing it again with AI.
AI agents raise three critical operating model questions for leaders:
Where does autonomy sit? Which processes can be safely handed to agents, and which require human oversight?
Who owns outcomes? If an agent makes a mistake, is IT responsible, or the business function it serves?
How do we coordinate change? Agents cut across functions and silos — but organisations are rarely designed to support that.
The Building Blocks of an Agentic AI Operating Model
From our work with leadership teams, these are the foundations that make the difference:
1. Governance and Decision Rights
Clear rules for where agents can act autonomously, and where escalation to a human is mandatory. CEOs need confidence that agents are acting within defined boundaries.
2. Cross-Functional Orchestration
Agents don’t respect organisational silos — they connect marketing to operations, finance to supply chain. To manage that, many enterprises are standing up cross-functional AI councils or centres of excellence.
3. Roles and Responsibilities
New roles are emerging:
AI Product Owners to manage the lifecycle of agents.
AI Risk Officers to monitor performance and compliance.
Change Champions to prepare the workforce for new ways of working.
4. Data and Platform Foundations
Agents need consistent access to high-quality data across systems. That means aligning your data architecture and engineering with your operating model, so agents aren’t slowed down by integration gaps.
5. Cultural Readiness
This is often underestimated. We’ve seen employees either distrust agents or over-rely on them. Both are risky. The operating model must include training, communication, and clear guidelines for how people and agents collaborate.
Real Challenges Beyond Is Seeing
In practice, the sticking points are often less about technology and more about structure:
Unclear ownership: IT builds the agents, but business leaders feel the impact. Without clear lines of accountability, progress stalls.
Silos resisting change: Functions that operate in isolation struggle to adapt when agents cut across them.
Change fatigue: Leaders underestimate how disruptive agent-driven change feels for employees already juggling digital transformation.
These challenges are exactly why we work with C-suites to design operating models alongside AI deployments — because without structure, pilots don’t scale.
What the C-Suite Should Do Now
If you’re preparing to bring agents into your enterprise, don’t wait until the technology is live to think about operating models. Start asking:
Which parts of our business are most ready for autonomous workflows?
Who should set guardrails and monitor agent behaviour?
How do we build cross-functional mechanisms to oversee agents that cut across silos?
What new roles and skills do we need to support this shift?
These questions aren’t IT questions — they’re strategic questions that belong on the board agenda.
Final Thoughts on your Agentic AI Strategy
AI agents are not “just another tool.” They are catalysts for new ways of working.
At Beyond: Putting Data to Work, we believe that building an Agentic AI Operating Model is the difference between pilots that fizzle and agents that deliver enterprise-wide value.
For CEOs, that means the challenge isn’t just deploying agents. It’s reshaping the organisation to harness them responsibly, sustainably, and competitively.
Because in the end, technology will only take you so far. It’s the operating model that determines whether AI agents become a source of risk — or a source of transformation.
Get in touch to find out more.
