Perspectives on AI governance, adoption, and the operational conditions that determine whether technology investments produce measurable results.
AI Adoption
Access to AI tools has never been easier. But widespread access is not the same as widespread impact. Most organizations are still running pilots, accumulating subscriptions, and waiting for value that has not materialized. This piece examines why awareness and adoption remain stubbornly disconnected, and what it actually takes to move from experimentation to measurable business outcomes.
AI Governance
Organizations launch pilots to test AI, not to deploy it. But when the pilot ends, the hard work begins: scaling requires governance, operational change, accountability structures, and a clear view of who owns the outcome. Without that foundation, pilots sit in perpetual review. This piece explains the structural reasons AI pilots fail to graduate and what organizations can do differently.
AI Readiness
Many organizations commit significant capital to AI before they understand whether the underlying conditions for success are in place. Data quality, process discipline, governance clarity, and change readiness all matter before a dollar is spent on deployment. This piece makes the case for readiness assessment as a prerequisite, not an afterthought, in any responsible AI investment strategy.
Regulatory
Regulators are paying attention to how financial services firms manage AI risk. SEC and FINRA examination priorities for 2026 signal increasing scrutiny of model risk, data governance, and third-party AI vendor oversight. This piece summarizes what firms should expect and what governance structures are most likely to hold up under examination.
The FCG AI Readiness Assessment takes about 5 minutes and gives you a scored view of where your organization stands across 7 governance domains.
Start with a Readiness Assessment