AI strategy and governance
Move from scattered experimentation to responsible AI execution connected to business priorities.
Guardrails
Policy, risk, and accountability
Adoption
Use cases, workflows, and enablement
Value
Executive decisions and measurable progress
Client: David Macphee, SVP
Business situation: Rapid employee experimentation with AI tools created compliance risks and data governance vulnerabilities across operational units.
JLS role: Fractional CAIO
Outcome: Implemented an enterprise AI governance framework aligned with NIST and ISO 42001 standards, enabling secure, compliant AI deployment.
“JLS provided the executive foresight we needed to adopt generative AI safely. They cut through the noise and built a structured, secure path to real AI ROI.” — David Macphee, SVP, Blue Cross Blue Shield
Fractional CAIO leadership often works alongside AI governance, AI adoption strategy, cybersecurity, compliance, software development, and recruiting. We can help determine whether the immediate need is leadership, a specific use case, operating controls, or delivery capacity.
No. Many organizations need leadership for responsible AI adoption in internal operations, customer service, knowledge work, analytics, software delivery, and decision support.
Good governance should make responsible adoption easier by clarifying what is allowed, who decides, what evidence is required, and how risks are handled.
Start with the business outcome, process friction, available data, user need, risk, and ability to measure improvement.