ORGANIZATIONAL AI READINESS
Technology can create an advantage. Organizational capability determines how effectively you realize, extend and sustain it.
For CEOs, CHROs, CIOs, COOs and other C-suite leaders, readiness means connecting AI to a business outcome—and preparing the knowledge, judgment, accountability and ways of working needed to deliver it.
01 · IS YOUR ORGANIZATION READY?
Leadership must adaptively orchestrate these capabilities as a system: adjusting priorities, decision rights, coordination and learning as the technology and business needs change.
THINK TOGETHER
Connect AI use to strategic priorities and trusted knowledge. Establish ownership, access and maintenance of the information people and systems rely on.
Do we know which information is authoritative—and who keeps it current?
DECIDE CLOSE TO THE WORK
Give people the competence and authority to challenge outputs. Specify what may be delegated, what needs review and when to escalate.
Who can approve, override or stop an AI-assisted action?
COMMIT TO SHARED OUTCOMES
Bring business, technology and people leaders together around a shared outcome. Involve affected employees and address how roles, expectations and incentives will change.
Are we redesigning the whole workflow—or optimizing isolated tasks?
ACT, LEARN AND ADAPT
Test against a baseline, examine failures and share lessons. Revisit the approach when models, information or working conditions change.
Can we show better outcomes after review effort, errors and cost are included?
02 · USEFUL CAPABILITY, CLEAR BOUNDARIES
These examples focus on generative AI in knowledge work. Use them as candidates to test, not guarantees: performance depends on the task, model, information and workflow.
Develop first drafts, alternative explanations, proposals and ideas. Give people more options to consider and less blank-page work.
Keep a person responsible for factual accuracy, suitability and what is ultimately communicated.
Help people navigate approved material, compare documents and assemble relevant context.
Check sources and omissions. A fluent answer is not proof, and missing organizational knowledge cannot be assumed.
Explore explanations, structure comparisons and assist analytical work where outputs can be checked against evidence.
Validate calculations, assumptions and conclusions. Success on one task does not establish reliability on the next.
Trial assistance with bounded tasks such as classifying requests or preparing routine responses, using representative examples and clear acceptance criteria.
Define permissions, exceptions and escalation before allowing a system to act. Keep accountable ownership with people.
Research with consultants found that AI assistance improved some tasks and worsened others. It supports testing particular workflows, not assuming universal productivity gains. Dell’Acqua and colleagues · Navigating the Jagged Technological Frontier ↗
NIST identifies risks including confidently incorrect content, privacy exposure and over-reliance on AI. Verification and oversight must match the consequences of an error. NIST · Generative AI Profile ↗
03 · AGILITY IS A FOUNDATION, NOT THE WHOLE ANSWER
Agile brought useful disciplines: short feedback cycles, collaboration and adaptation. Its manifesto favours working software over comprehensive documentation; it does not reject documentation.
Where an organization interpreted agility as permission to leave knowledge undocumented, it may depend on individuals remembering why decisions were made, which exceptions matter and how work crosses team boundaries.
AI readiness requires knowledge, judgment and accountability that extend beyond individual teams.
Harry’s view: AI makes that organizational work more urgent. Establish authoritative information, capture decision rationale, keep guidance current and make responsibility explicit. The answer is useful, maintained knowledge—not a return to paperwork for its own sake.
This is a challenge to incomplete implementations of agile, not a claim that agile prevents maturity. CMMI distinguishes project-level management from organization-wide defined practices; documentation volume alone does not establish a maturity rating. CMMI maturity is not an AI-readiness certification.
Agile Manifesto ↗ · CMMI maturity definitions ↗ · SEI · CMMI and Agile ↗
04 · START WITH THE WORK
01 · DEFINE
Choose a workflow where improvement matters. Map the current decisions, handoffs, knowledge gaps and baseline performance.
02 · DESIGN
Agree responsibilities, approved information, review requirements and how people will work differently. Involve technology, security and risk specialists where needed.
03 · LEARN
Compare quality, cycle time, rework and total cost. Decide what to change, stop or extend, then carry the learning into the organization.
For example, faster customer-response drafting is useful only if accuracy, resolution quality and total handling effort also meet the business need. This is an illustrative test, not a promised client result.
BUILD THE ADVANTAGE
Harry focuses on the leadership and organizational conditions for AI adoption: aligning sponsors, resolving competing priorities, clarifying decision authority and building commitment to changed ways of working. He works alongside technical specialists; his role is leadership, stakeholder alignment and organizational change rather than system configuration or engineering.
The diagnostic is a starting point for discussing the four organizational capabilities, not a technical AI audit or certification.