You’ve invested in AI. What’s holding back the return?
Arist interviews people across your organization and connects what they say to adoption and operating data, to understand where AI initiatives are getting stuck.
Tool usage doesn’t tell you why AI is or isn’t working.
A dashboard can count sessions. It cannot tell you whether people trust the output, see the value for their role, or stopped using the tool because policy, access, or workflow gets in the way.
Those problems call for different decisions. Arist helps leaders tell them apart before choosing an intervention.
Get past the adoption dashboard.
- 01
Define meaningful use
Establish what success looks like for the roles and workflows in scope.
- 02
Interview across the business
Ask people about their actual work, incentives, obstacles, and experience with the tools, across roles and regions.
- 03
Analyze the differences
Connect what people say with usage and organizational data. See the causes by group and the value at stake.
- 04
Find the barriers
Identify where policy, access, workflow, management, or skills are getting in the way, so leaders know which to address first.
- About $5M less AI spend, within 8 weeks
At one of the largest grocery chains in the US, Arist diagnosed what had been blocking effective long-term AI adoption.
What leaders get
A role-by-role view of AI use
See where value, trust, access, and workflow break down.
Root-cause clarity
Know whether the answer is a product change, a policy change, a process change, more support, or a combination.
A focused next investment
Know where the next round of AI investment should go.
A defensible baseline
Track adoption and value from a clear starting point.
Evidence of change
Repeat the research and show leaders where use and value improved.
Know what’s blocking AI value before spending more.
Bring us your AI adoption goal. We’ll show you how Arist would find what’s getting in the way.