Building Enterprise AI Capability: People, Skills, and Culture

Technology alone does not create enterprise AI capability. Sustainable outcomes depend on defined roles, coordinated delivery, and a culture that treats AI as a governed operational discipline. Teams need clear responsibilities, shared language, and a structured path from experimentation to production.

Define the operating model first

• AI steering committee. Cross-functional oversight to align investments, risk, and policy.
• Executive sponsorship. Clear decision authority for funding, priorities, and risk acceptance.
• Model inventory. A single view of use cases, owners, data sources, and risk classification.

Build the right skills mix

• Data science and ML engineering for model development and deployment.
• Security and compliance leads to align with ISO 27001 and regulatory expectations.
• Product and domain experts to keep models grounded in business outcomes.
• AI governance and risk specialists for auditability and policy enforcement.

Create a culture that scales responsibly

AI literacy cannot be limited to technical teams. Enterprises that scale responsibly invest in training, shared playbooks, and practical guardrails. Encourage experimentation with clear escalation paths, and normalize model reviews the same way you review security or compliance controls.

Operationalize learning and change management

• AI training paths for business stakeholders and delivery teams.
• Prompt and model review workflows integrated into delivery governance.
• Feedback loops for model performance, risk signals, and user trust.