Opportunity portfolio
Keep finding and reprioritising the next workflows worth changing.
Keep your AI agents useful as the business changes. Identify can review their work, refine training and instructions, support your people and improve workflows across departments, with clear human oversight.
The Embedded AI Partner model gives leadership an ongoing transformation capability without creating a large permanent internal team before the demand justifies it.
Keep finding and reprioritising the next workflows worth changing.
Run one or two live Map / Build / Activate workstreams at a time.
Review outputs, update approved knowledge and instructions, and retest agents as your departments, systems and industry requirements change.
Review access, vendors, controls, exceptions and emerging AI risk on a practical cadence.
Track adoption and business performance against the baseline agreed before Build.
Give the leadership team a consistent view of what is working, what changed and what deserves investment next.
Delivery stand-up, blockers, decisions, user feedback and next build actions.
What people are using, where exceptions appear and what needs to change in the operating design.
Active outcomes, next opportunities, investment decisions, risk and value realised.
Re-score opportunities as the business, AI capability and underlying technology change.
Typical initial commitment is three to six months. The right level depends on the number of active workflows, technical complexity, internal capability and how quickly leadership wants to move. Speak to the team to shape the right engagement.
One primary workstream, leadership rhythm, opportunity portfolio, governance and outcome tracking.
One to two active workstreams, broader functional coverage and a stronger ongoing optimisation rhythm.
Multiple sites, business units or complex implementation environments with a larger embedded pod.
You already have priority AI opportunities or at least one Build underway.
Leadership wants a continuing portfolio, not disconnected projects.
Internal teams need senior product / architecture / implementation support.
The business wants adoption and outcomes measured after go-live.
There are enough repeat opportunities to justify a standing capability.
Leadership has not agreed why AI matters to the business.
There is no meaningful workflow or problem ready to work on.
The organisation primarily wants generic AI training.
There is not yet internal ownership for the change.
Every quarter updates the opportunity portfolio, workflow library, governance record and outcome scoreboard. The ongoing relationship should create more leverage, not simply more consulting hours.
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