Achievers (High Strategy, High Execution): These leaders are performing well across all five readiness pillars.

Visionaries (High Strategy, Low Execution): Excelling in strategic thinking but weaker in execution pillars.

Operators (Low Strategy, High Execution): Perform well on execution-focused areas but score lower on strategic alignment.

Discoverers (Low Strategy, Low Execution): Consistently report the lowest readiness levels across all pillars.

Connect initiatives for agents to corporate KPIs, focusing on achieving revenue growth or cost savings, as part of an enterprise-wide roadmap for AI/agent technologies.

Funding should prioritize use cases that are critical to the business and measurable, employing standardized frameworks to gauge Return on Investment.

Form an AI/Agentic Center of Excellence that is cross-functional to oversee and magnify impact across the enterprise.

Put in place a responsible AI framework and embed risk-management practices (such as human-oversight guardrails and auditing mechanisms) throughout the complete lifecycle of every agent.

Concentrate on high-volume, high-value workflows, thoroughly documenting each step, dependency, and rule.

Document the process’s underlying rationale, including desired goals, SLAs, and compliance requirements, to help agents align with corporate objectives.

Treat documentation as a dynamic asset, continuously updating processes based on performance data and received feedback.

Consolidate AI capabilities onto centralized enterprise platforms to enable secure development, governance, reuse, and scaling.

Construct modular, API-first architectures that allow agents to interact across diverse applications and systems seamlessly.

Implement a data fabric or lakehouse structure to guarantee that both structured and unstructured data are consistent, synchronized, and readily available for AI applications.

Establish clear ownership and defined refresh schedules for datasets

Clearly communicate how agents will support employees and contribute to the business.

Recognize and reward pioneers who responsibly create new workflows and share effective methods.

Invest in structured training and upskilling programs to expand agentic AI proficiency across the entire workforce.

Develop a flexible, phased approach to change management to prepare employees for role adjustments and build trust.

Incorporate governance into the design phase, including impact assessments for potential bias, security flaws, and privacy concerns.

Utilize tiered governance models to strike a balance between innovation and control.

Customize the intensity of monitoring, reviews, and protective safeguards based on each agent’s autonomy level.

Regularly audit agent performance and retrain models to reduce risk and ensure ongoing compliance.