The problem this solves
Most companies approach AI backwards: a tool gets bought, a pilot gets launched, and six months later nobody can say what it changed. Use cases get picked by novelty rather than value, effort gets underestimated because data readiness is ignored, and finance rejects further budget because the first wave produced demos instead of outcomes. The result is AI fatigue before AI value.
How we work
We inventory candidate use cases systematically across marketing, sales, service, and operations: where hours are spent, which decisions repeat, what content gets produced, where response times hurt. The candidates come from your team's actual work, not from a vendor's feature list.
Each use case gets scored on impact, effort, and risk, with data readiness assessed honestly, because that is where most AI estimates collapse. For the top candidates we build business cases: assumptions stated, costs including licensing, expected effect anchored in your own baseline numbers, and how the result will be measured.
The output ends with a sequencing recommendation: what to do first, what waits, and what not to do at all, with reasoning attached to each.
Deliverables
- AI use case inventory across go-to-market functions
- Prioritization matrix scored on impact, effort, risk, and data readiness
- Business cases for the top-priority use cases
- Explicit do-not-do list with reasoning
- Sequencing recommendation
- Measurement approach per use case
What buyers ask before scoping.
Is this limited to HubSpot AI features?
No, the inventory covers your whole go-to-market stack. Where HubSpot Breeze features already cover a use case, we say so, because capability included in a license you already pay for is usually the cheapest path to value. Where they do not, we scope external tooling or custom builds on equal footing.
We already ran some AI pilots. Does that work feed in?
Directly. Pilots are evidence, even failed ones, and often especially failed ones. We assess why each pilot did or did not stick, which usually reveals more about your data readiness and process fit than any scoring workshop would.
How do you estimate benefits without making numbers up?
Conservative ranges anchored in your own baseline data: current hours spent, volumes handled, conversion rates. Every assumption is written down and challengeable, so finance can stress-test the case instead of taking a vendor ROI slide on faith. If a benefit cannot be grounded, the business case says so.
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