A vendor-neutral assessment that turns scattered AI experiments into a governed roadmap of use cases that actually move business KPIs — built by advisors who’ve implemented AI in enterprise environments, not sold it.
Every board meeting includes a question about AI. Every vendor in your ecosystem has a pitch ready. And internally, teams are launching pilots faster than anyone can evaluate whether they’ll ever reach production. The pressure to show progress is real. But progress without structure isn’t a strategy — it’s expensive activity.
Most organizations aren’t short on AI ambition. They have pilots running, vendor demos happening, teams experimenting. What they don’t have is a clear picture of where they actually stand — across data foundations, governance, talent, and infrastructure — that would tell them which of those investments have a viable path to production and which ones don’t.
Without that picture, every AI investment is a bet made without enough information. And the bets are adding up. 73% of AI pilots in the mid-market never reach production. Not because the technology failed, but because nobody assessed whether the organization was ready to support it.
The result is what looks like momentum from the outside but feels like pilot chaos from the inside. Dozens of experiments running in parallel. No clear governance. No prioritization framework. And a board that’s moving from “what’s our AI strategy?” to “where’s the return on all this AI spending?”
Vendor-led assessments don’t solve this. They arrive at conclusions that lead to their platform. Large consulting firms staff AI engagements with generalists who understand the technology but not your operating environment. And internal teams, no matter how talented, rarely have the mandate or the objectivity to tell the board what isn’t working.
What makes this assessment different
Our advisors are former CIOs and CTOs who’ve had to make AI work inside real organizations with real constraints — not technologists building models, but operators who understand both the potential and the organizational reality. We know the difference between AI that demos well and AI that runs in production.
We have no platform to sell, no partnership commissions, no incentive to steer you toward a specific technology stack. When we recommend a path, it’s because it fits your data, your talent, and your infrastructure — not because it serves a revenue model that isn’t yours.
Most AI engagements produce assessments. This one produces a governed roadmap with a systematic progression from pilot to proof to scale. The difference between $1M in wasted pilots and $1M redirected to high-impact use cases is a framework for deciding which is which.
We launch the AIMM-360 assessment — a 9-dimension maturity framework that scores your AI readiness across Strategy, Use Cases, Data, Technology, Operating Model, Governance, Talent, Change, and Value. 12–20 stakeholder interviews. Current state inventory. Pilot and experiment audit. Data landscape mapping. The output is a clear picture of where you are, where your peers are, and where investment creates the highest leverage.
We identify 50–100 AI use cases across your organization and score each one by business value, implementation feasibility, and risk. Quick wins are separated from strategic bets. The use cases that look exciting but can’t be supported by your current data or talent infrastructure get flagged before they consume budget.
Governance framework design. Operating model recommendations. Technology architecture gap analysis. Talent and skill planning. Change management approach. This is the structural work that determines whether AI initiatives scale or stall — and it’s the part most organizations skip on the way to their next pilot.
A phased 12–18 month implementation plan. Investment business case. Board-ready narrative. Success metrics definition. Governance playbook. Everything your board needs to approve and track the AI agenda — in their language, not yours.
Typical engagement: 6–8 weeks / $200K–$350K
A newly appointed CIO inherited a sprawling infrastructure with no clear ownership model. Within weeks, the board demanded a modernization roadmap. We mapped the landscape, identified the real constraints, and delivered a phased approach that protected near-term stability while building toward the future.
A CIO stepping into a regional healthcare system found the technical debt far exceeded what the previous leadership had disclosed. We conducted a 100-day diagnostic that surfaced the real state of infrastructure, security posture, and team capability.
A CIO stepping into a regional healthcare system found the technical debt far exceeded what the previous leadership had disclosed. We conducted a 100-day diagnostic that surfaced the real state of infrastructure, security posture, and team capability.
A CIO stepping into a regional healthcare system found the technical debt far exceeded what the previous leadership had disclosed. We conducted a 100-day diagnostic that surfaced the real state of infrastructure, security posture, and team capability.
A CIO stepping into a regional healthcare system found the technical debt far exceeded what the previous leadership had disclosed. We conducted a 100-day diagnostic that surfaced the real state of infrastructure, security posture, and team capability.
A CIO stepping into a regional healthcare system found the technical debt far exceeded what the previous leadership had disclosed. We conducted a 100-day diagnostic that surfaced the real state of infrastructure, security posture, and team capability.
Most conversations start with the same question — “We’re doing AI, but we can’t tell you if it’s actually working.” If that sounds familiar, a 20-minute conversation with one of our advisors will give you a clear sense of whether a structured assessment would change the trajectory. No pitch. No vendor agenda. Just an honest read from someone who’s built AI programs inside organizations like yours.
