AI Adoption

Find the AI use cases worth funding, choose an architecture that will not lock you in, and put them in production with a plan your team can run.

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Typical planWeeks
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Readiness assessment
Use case identification and prioritization
Technology selection and architecture
Implementation strategy and roadmap
Integration with existing systems
Training and change management
Indicative. The exact plan is set in your written scope.
Our approach

Six steps, each with a decision at the end.

  1. 01Readiness assessmentData quality, infrastructure and team skills, scored so you know what has to change first.
  2. 02Use case identification and prioritizationTen or more candidate use cases, ranked by value, feasibility and risk with RICE.
  3. 03Technology selection and architectureMulti-provider design across OpenAI, Anthropic, Azure, Vertex and Bedrock, with fallbacks and cost routing.
  4. 04Implementation strategy and roadmapA 4-week POC playbook, then a production plan with owners, milestones and success metrics.
  5. 05Integration with existing systemsVector search, caching and data pipelines connected to the tools your teams already use.
  6. 06Training and change managementEnablement for the people who will use and run the system, so adoption does not stall after launch.

What changes when it is done right.

Better productsCapabilities customers notice: search that answers, assistants that resolve.
Leaner operationsRoutine work automated, with humans on the exceptions.
Evidence-based decisionsEvery rollout backed by a controlled measurement.
DifferentiationFeatures competitors cannot copy by calling the same API.
Room to scaleAn architecture that absorbs new models without a rewrite.
In practice
Insurance · Fortune 500Neural search and generative AI cut searches per case by 31.3%.260 s → 26 stime per answer90%+answer accuracyRead case study →
Recommended engagements
AI Quick Win4 hours · Find 3 to 5 AI opportunities in one week.$800AI Implementation8 hours · From diagnosis to an integration plan with clear ROI.$2,400Enterprise programMulti-team rollout with a dedicated consultantCustom

Questions about AI Adoption

Do we need clean data before we start?
No. The readiness assessment tells you which use cases work with the data you have today and what to fix for the rest.
Which AI providers do you work with?
OpenAI, Anthropic, Azure OpenAI, AWS Bedrock and Google Vertex AI, behind a routing layer so you can switch.
Can you build it, or only advise?
Both. AI Implementation and the MVP Sprint end with working software in your repository.
How do you measure ROI?
With a controlled study: a treated group against comparable control groups, so the gain is attributable to the AI.

Bring one use case. Leave with a plan.

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