1. Prioritize outcome-centric use cases

Start with high-volume, measurable services like onboarding, support triage, finance ops or compliance, where AI agents can clearly demonstrate ROI both in short and long term. Target areas with processes with defined inputs, predictable workflows and quantifiable outcomes. 

Success indicators: Business begins to request AI agents rather than a push from technology teams.

2. Shift to AI-orchestrated outcomes from IT enablement

Move from managing systems and applications to managing outcomes though AI agents. Drive KPIs around business outcomes instead of system uptime, rearchitecting tech stack to allow AI agents to work seamlessly while managing risks, reprioritizing tech investments. 

Success indicators: Technology teams are spending significantly less of their time on technical issues and more time on solutioning for business needs.

3. Deploy forward-deployed engineers (FDEs)

Execution quality becomes the competitive advantage in the agent-driven world. Embedding FDEs with business teams accelerates identification of new use cases, prompt tuning, edge-case resolution and rollout quality, turning each deployment into a reusable asset.

Success indicators: Business begins to develop its own agent use cases with FDE support, creating a pipeline of transformation opportunities.

4. Build the enterprise agent layer

Design enterprise architecture to facilitate AI agent operations across functions and data domains. Deploy task-specific agents trained on enterprise data, integrated with core systems (e.g., SAP). Build capabilities for agentic AI ops (including feedback, telemetry and observability). 

Success indicators: Agents can autonomously execute end-to-end workflows while escalating exceptions appropriately.

5. AI as a fabric

Treat AI as a strategic lever and embed it across the enterprise, from finance to customer to ops to services. Provide enterprise-wide AI capabilities to drive benefit realization. Create the right scalable and reusable agentic approach (marketplace or library), standardize data and knowledge, and move from feature-led AI to capability-led design.

Success indicators: Business teams independently identify and implement AI solutions using standardized enterprise capabilities.

6. Govern by design

As AI agents make decisions, enterprises must hardwire trust, traceability and compliance. Bake explainability, ethics, auditability and fallback mechanisms into agent design. Governance isn’t an afterthought; it’s the foundation of trust and risk mitigation.

Success indicators: Stakeholders trust agent decisions for regulatory audit validation.

7. Redesign pricing around results

Move beyond traditional technology pricing models to usage-based pricing to value-based models, charging for outcomes like closed deals, resolved tickets or reconciled accounts.

Success indicators: Technology spending is correlated with business outcomes rather than infrastructure consumption.

8. Redesign op model for human-AI collaboration

Transition human roles from operators to supervisors — such as AI auditors, escalation managers and governance stewards — enabling more strategic, leaner teams. Enable right skill building, incentive alignment and AI brand positioning in the enterprise, in addition to reimagining legacy org structures and roles to scale and sustain new model.

Success indicators: Significant portion of employees are trained on or are familiar with AI agents, and teams are seeing a stronger level of output/productivity per employee.