Future AI Target Operating Model (AI‑TOM)

Businesses must begin developing their future AI Target Operating Model (AI‑TOM) today to navigate the next wave of transformation, with urgency and purpose akin to when the shift to a digital operating model reshaped enterprises. Below is an in‑depth look at why this matters, how it parallels past change, and how the Jobs‑to‑be‑Done framework anchors success in an agentic‑first world.

1. From Digital TOM to AI‑TOM: A Historical Parallel

During the last major transformation wave, digital transformation, organizations rewired themselves to operate with technology at scale. McKinsey describes it as “the rewiring of an organization, with the goal of creating value by continuously deploying tech at scale” McKinsey & Company. That transformation required completely reimagining people, process, systems, supplier models, and governance; often formalized in a digital Target Operating Model (TOM) that specified capabilities, organization structure, and technology enablers.

Companies gaining advantage aligned strategy to capabilities, restructured functions, standardized processes, integrated data, organized partnerships, and revamped management systems. The result: increased efficiency, faster product cycles, and better customer satisfaction.

Now, with agentic AI, AI agents making autonomous decisions and acting on behalf of users, businesses face a similar inflection point Just as digital transformation reshaped operations, AI agents will reshape capabilities, governance, workforce, and customer interaction.

2. Why Organizations Need an AI Target Operating Model Now

Shift from deploying tools to orchestrating agents

The new frontier isn’t merely deploying generative AI tools but building AI agents that autonomously discover, decide, deliver; across business flows. A well‑defined AI‑TOM provides the blueprint for how these capabilities align with strategy.

Managing complexity & governance

Unlike traditional tools, agentic AI introduces autonomy, and risk. Enterprises will need new oversight, compliance layers, and ModelOps practices to govern models and agents consistently across the org. Without a coherent AI‑TOM, chaos and inefficiencies are likely.

Restructuring workforce and roles

As Deloitte and others are already piloting agentic use cases, enterprises must redesign roles; from business analysts to AI‑ops and digital strategists to-orchestrate-human‑AI collaboration effectively.

3. The Jobs‑to‑be‑Done Framework: Foundation for AI Operating Models

The Jobs‑to‑be‑Done (JTBD) framework helps define why AI is adopted, not just what tools are deployed.

Understand core jobs

JTBD forces organizations to define the real “jobs” users or business functions are trying to achieve, e.g. “expedite customer onboarding,” “detect anomalies in supply chain inventory,” or “automate candidate sourcing and scheduling.”

Mapping jobs to agentic capabilities

Once jobs are defined, the AI‑TOM can clarify which jobs are fully automatable, which require human-in-the‑loop oversight, and how agents and humans must collaborate end‑to‑end.

Inform capability design

This approach helps identify the processes, roles, governance, supplier integrations, data and system needs necessary to support agentic workflows. Essentially, JTBD becomes the key to articulating the Target Operating Model components (P-O-L-I-S-M) for AI.

4. Anatomy of an AI‑Target Operating Model (AI‑TOM)

A robust AI‑TOM should include:

• Processes & Capabilities: Define agentic workflows aligned to specific JTBD outcomes.
• Organization & Roles: New roles like Agent Manager, ModelOps Analyst, AI Strategy Lead; incentives and culture that reward human–AI collaboration.
• Locations & Infrastructure: Cloud platforms supporting agent orchestration (e.g. AWS Agentforce), integration protocols like Model Context Protocol (MCP) for interoperable agents.
• Information Systems: Data pipelines, observability, drift detection, compliance dashboards via ModelOps frameworks.
• Suppliers & Partners: Ecosystems including cloud providers, agent platform vendors, risk/governance consultancies.
• Management Systems: Governance processes, performance metrics aligned to business value (outcome‑based pricing, ROI by job, continuous agent performance monitoring).

This structure echoes the classic TOM template but is tailored for agentic AI capabilities, with JTBD anchoring decisions across all dimensions.

5. Jobs‑to‑be‑Done: The Key to Agentic‑First Success

By centering JTBD in the AI‑TOM:

• Strategy stays outcome‑focused: organizations avoid adopting AI just for novelty, instead targeting complaints, inefficiencies or unmet user needs.
• Capability design becomes disciplined: translated from job maps into role profiles, process flows, tech requirements, governance checks.
• Governance & ROI become clearer: because each agentic capability can be tied to delivery of a job, metrics and compliance flows are easier to define.
• Agility is retained: jobs evolve over time; the AI‑TOM can adapt by adding new agents or human agent combinations without re‑architecting the whole model.

6. Urgency & Next Steps for Business Leaders

Digital transformation taught us that operating in the digital era requires rearchitecting business models, organization, and processes to support scale, agility, and customer‑centricity. Agentic AI is the next transformation wave, and it demands a new operating model built for autonomy, agility, and continuous learning.

Organizations should take these steps now:

1. Articulate strategic priorities, mapping core JTBD across the value chain.
2. Develop an AI‑TOM blueprint, covering people, processes, technology, governance, partners, and management systems.
3. Pilot agentic use cases aligned to JTBD, with proper monitoring and ModelOps frameworks.
4. Create new roles and governance mechanisms to oversee agentic workflows.
5. Iterate and scale, using feedback loops from live jobs to refine the operating model.

Conclusion

As with the rise of digital and cloud, agentic AI represents a transformational opportunity, and risk, for businesses. Defining a clear AI Target Operating Model anchored in the Jobs‑to‑be‑Done framework ensures strategy drives capabilities, not the other way around. It’s time for organizations to begin shaping their AI‑TOM, before opportunity passes them by.