A structured diagnostic for technology leaders who need an honest read of whether the organization is set up to run AI at scale — before committing to a direction – built by advisors who’ve implemented AI in enterprise environments, not sold it.
Every executive team is pushing the same question forward: what’s our AI strategy? The pressure to commit is already here. The board wants direction. Vendors are circling with platforms that solve problems you haven’t scoped yet.
Most maturity models assume consistency. The real story is usually in the imbalances.
You don’t need another AI strategy deck. You need a true picture of what your organization can actually run.
What you will receive
A structured read across nine dimensions: strategy, data, talent, governance, infrastructure, use case selection, operating model, risk, and adoption. Where you can move now. Where you’re emerging. Where you’re behind.
The dimension pairs that don’t add up. A strong data function with weak governance is a different problem than a flat mid-band profile. We name which imbalances will block your roadmap and what each costs.
The capabilities your current AI plan assumes, mapped against what’s actually in place. Where the plan is supported. Where it isn’t.
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.
Our advisors are former CIOs, CTOs, and senior technology leaders. They’ve inherited the same tangled landscapes, faced the same board pressure, and made the same calls you’re being asked to make now. At any given time, Acacia fields a team of 10–30 former operators working directly with clients — not as background advisors, but embedded in active engagements, driving outcomes alongside your team.
We have no product to sell, no platform to implement, no commission on what you choose. Every recommendation is based on your context alone. When we tell you something can wait, it’s because it can — not because we don’t have a solution to sell you.
This isn’t a general technology assessment repurposed for new leaders. The diagnostic was designed specifically for the first 100 days — the window where every decision carries disproportionate weight and the difference between inherited risk and self-created risk is hardest to see.
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.
