AI Adoption Consulting

The Problem

Most enterprises now have AI pilots running in isolated pockets - a few analysts using it for drafting, a data team running a proof of concept - but no path from pilot to production adoption that a risk, compliance, or audit function will actually sign off on. The result is a portfolio of demos that never scale, while shadow use of consumer AI tools quietly outpaces anything the organization has sanctioned.

Our Approach

• Separate "can this work technically" from "can this be adopted safely at scale": most pilots answer the first question and stall on the second.
• Build the adoption path around existing risk and compliance functions rather than around them, so scaling doesn't require a fight with the second line of defense later.
• Prioritize use cases by adoption readiness as well as value: a high-value use case with unresolved data governance issues will stall regardless of its ROI case.
• Treat change management as a workstream with its own budget and owner, not an afterthought bolted onto the technical rollout.

Methodology

• Inventory: catalog current AI pilots, shadow usage, and use-case demand across functions, with an honest read on which are adoption-ready versus stuck.
• Assess: evaluate top candidate use cases against data governance, model risk, and change readiness criteria alongside expected value.
• Sequence: build a prioritized adoption roadmap that sequences quick, low-risk wins ahead of higher-value, higher-governance-burden use cases.
• Scale: support the first one to two use cases through production rollout, establishing the reusable adoption pattern for the rest of the roadmap.

Deliverables

• Current-state inventory of AI pilots, shadow usage, and demand.
• Use-case prioritization matrix scored on value and adoption readiness.
• Sequenced adoption roadmap with governance checkpoints built in.
• Change management plan for the first wave of production use cases.

Benefits

• A credible path from pilot to production that your risk function will actually approve.
• Fewer abandoned proofs of concept competing for the same executive attention.
• Faster time-to-value on the use cases that were always going to work, instead of stalling on the hardest one first.
• An adoption pattern your teams can repeat without consulting support each time.