๐๐ ๐ ๐จ๐ฏ๐๐ซ๐ง๐๐ง๐๐ ๐จ๐ง๐ฅ๐ฒ ๐ฐ๐จ๐ซ๐ค๐ฌ ๐ฐ๐ก๐๐ง ๐ข๐ญโ๐ฌ ๐๐๐ฌ๐ข๐ ๐ง๐๐ ๐๐ฌ ๐ญ๐ก๐ ๐ก๐๐ง๐๐ฌ๐ก๐๐ค๐ ๐๐๐ญ๐ฐ๐๐๐ง ๐ฉ๐จ๐ฅ๐ข๐๐ฒ ๐ข๐ง๐ญ๐๐ง๐ญ ๐๐ง๐ ๐๐๐ฅ๐ข๐ฏ๐๐ซ๐ฒ ๐ซ๐๐๐ฅ๐ข๐ญ๐ฒ.
It only really works when itโs treated as a delivery discipline, not just a policy exercise. When itโs embedded into how teams actually plan, build, and make decisions, not layered afterwards.
How should organisations bridge this gap in practice?
------ Sharing a longer reflection below ๐ ------
Iโve been reflecting on recent conversations.
Thereโs plenty of talk about AI governance principles, policies, and ethical intent.
Most organisations genuinely want to do the right thing.
The challenge is when AI governance becomes just a policy exercise, something to define, approve, and it's "done", instead of ๐ ๐๐๐ฅ๐ข๐ฏ๐๐ซ๐ฒ ๐๐ข๐ฌ๐๐ข๐ฉ๐ฅ๐ข๐ง๐ ๐ญ๐ก๐๐ญ ๐ก๐๐ฌ ๐ญ๐จ ๐ฐ๐จ๐ซ๐ค ๐ข๐ง ๐ซ๐๐๐ฅ ๐ฉ๐ซ๐จ๐ ๐ซ๐๐ฆ๐ฌ, ๐ฎ๐ง๐๐๐ซ ๐ซ๐๐๐ฅ ๐ฉ๐ซ๐๐ฌ๐ฌ๐ฎ๐ซ๐.
On paper, the policies look solid.
In practice, delivery teams are still asking:
- Who actually owns risk decisions as models evolve?
- What does โacceptable useโ mean when an agile team is delivering every two weeks?
- How do we use the speed of AI outputs and the learnings they generate to strengthen feedback loops, so governance evolves alongside delivery?
In practice, ๐ ๐จ๐ฏ๐๐ซ๐ง๐๐ง๐๐ ๐ฌ๐ญ๐๐ซ๐ญ๐ฌ ๐ญ๐จ ๐ก๐จ๐ฅ๐ ๐ฐ๐ก๐๐ง ๐ข๐ญโ๐ฌ ๐๐๐ฌ๐ข๐ ๐ง๐๐ ๐ข๐ง๐ญ๐จ ๐ก๐จ๐ฐ ๐๐๐ฅ๐ข๐ฏ๐๐ซ๐ฒ ๐๐๐ญ๐ฎ๐๐ฅ๐ฅ๐ฒ ๐ก๐๐ฉ๐ฉ๐๐ง๐ฌ.
Not controls over here and delivery over there, but an operating model that genuinely connects them.
Effective AI governance shows up as:
1. ๐๐ฅ๐๐๐ซ ๐๐๐๐ข๐ฌ๐ข๐จ๐ง ๐จ๐ฐ๐ง๐๐ซ๐ฌ๐ก๐ข๐ฉ across product, data, risk, legal, and delivery. Not โshared accountabilityโ in theory;
2. Governance checkpoints ๐๐ฆ๐๐๐๐๐๐ ๐ข๐ง๐ญ๐จ ๐๐๐ฅ๐ข๐ฏ๐๐ซ๐ฒ ๐ซ๐ก๐ฒ๐ญ๐ก๐ฆ๐ฌ;
3. ๐๐จ๐ง๐ญ๐ซ๐จ๐ฅ๐ฌ ๐ญ๐ก๐๐ญ ๐๐๐๐ฉ๐ญ ๐๐ฌ ๐ฆ๐จ๐๐๐ฅ๐ฌ ๐ฌ๐๐๐ฅ๐, ๐ซ๐๐ญ๐ซ๐๐ข๐ง, ๐๐ง๐ ๐๐ก๐๐ง๐ ๐, rather than one-off assessments frozen in time;
4. Teams enabled to make good decisions because they ๐ฎ๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐ ๐ก๐จ๐ฐ ๐ ๐จ๐ฏ๐๐ซ๐ง๐๐ง๐๐ ๐๐ฉ๐ฉ๐ฅ๐ข๐๐ฌ ๐ข๐ง ๐ฉ๐ซ๐๐๐ญ๐ข๐๐, not just that it exists.
This isnโt about adding more process.
Itโs about designing governance, so responsible decisions are the default, not something teams have to pause delivery to seek.
When governance is treated as a delivery discipline, organisations move faster and more confidently because ๐ฉ๐จ๐ฅ๐ข๐๐ฒ ๐ข๐ง๐ญ๐๐ง๐ญ, ๐จ๐ฉ๐๐ซ๐๐ญ๐ข๐จ๐ง๐๐ฅ ๐๐จ๐ง๐ญ๐ซ๐จ๐ฅ๐ฌ, ๐๐ง๐ ๐๐ฑ๐๐๐ฎ๐ญ๐ข๐จ๐ง ๐๐ซ๐ ๐๐ฅ๐ข๐ ๐ง๐๐ ๐๐ซ๐จ๐ฆ ๐ญ๐ก๐ ๐ฌ๐ญ๐๐ซ๐ญ.
In my experience, the real question isnโt:
โDo we have AI governance policies?โ
Itโs:
Is governance actually embedded in how your AI programs are planned, governed, and delivered day to day?
๐๐ก๐๐ซ๐โ๐ฌ ๐ง๐จ ๐จ๐ง๐-๐ฌ๐ข๐ณ๐-๐๐ข๐ญ๐ฌ-๐๐ฅ๐ฅ, ๐๐ฎ๐ญ ๐ ๐๐ญ๐ญ๐ข๐ง๐ ๐ ๐จ๐ฏ๐๐ซ๐ง๐๐ง๐๐ ๐๐ง๐ ๐๐๐ฅ๐ข๐ฏ๐๐ซ๐ฒ ๐๐ฅ๐ข๐ ๐ง๐๐ ๐๐๐ซ๐ฅ๐ฒ ๐ฐ๐ข๐ฅ๐ฅ ๐๐ ๐ ๐ฉ๐จ๐ฐ๐๐ซ๐๐ฎ๐ฅ ๐ฐ๐๐ฒ ๐๐จ๐ซ๐ฐ๐๐ซ๐.
