Which AI ideas will actually pay back here, and in what order?
We map where the hours and risk sit, check which data is ready, cost the use cases, and set the order to do them in.
How it works: five steps, one Principal Engineer
One Principal Engineer leads it from the first call until your team has adopted the work and trusts it. Each step shows what it means for this engagement.
- Step 01
Discovery Call
30 minutes with the Principal Engineer who would lead this engagement. Free, and no sales handoff.
- Step 02
Direct Assessment
InterviewTalk to the people who do the work, not just leadership.
ScoreRank use cases by value, risk and readiness.
- Step 03
Shared Roadmap
CostBusiness case for the top candidates.
RoadmapAn order of work and draft guardrails.
- Step 04
PE-Led Delivery
Your PE owns the engagement, end to end.
- Step 05
Outcome Assurance
Your PE stays with you until the work is adopted and trusted.
In their words
Clients' own words, shared with their permission. Some chose to name their company.
Vizius joined CRAG, a leading MSSP/MSP, to directly interact with their clients, leading hands-on sessions that translated core AI concepts into practical use-cases. The result was a shared understanding of where AI fits, quicker path from idea to pilot and more concrete understanding of what AI can and can't do.
Brad HamlettPrincipal, CRAGReady when you are
Thirty minutes with the Principal Engineer who would lead the work. Pick a time on the spot, or email us.
Prefer email? Send us a note instead.
What you get
- A scored inventory of AI use cases, ranked by hours saved, risk and data readiness
- A data readiness check of the systems each use case depends on
- A business case and 12-month roadmap, with quick wins first
- Draft AI use guardrails
A Principal Engineer, not a salesperson
Likely lead: David Hyde-Volpe or Cameron Cromer. You talk to the engineer who does the work on AI Readiness Assessment & Roadmap.
Questions buyers ask
Do we need clean data first?
Not for everything. The assessment tells you which use cases your data can support today and which need work first.
Will you recommend a specific AI product?
Only if it fits. Often the best first step is automating re-keying between systems you already own.
What happens after the roadmap?
Usually one proof of concept on the top use case. You can also run the roadmap yourselves.
More detail
Find the processes worth automating before you buy anything. We map where the hours and the risk are, check which data is ready, cost the use cases, and set the order to do them in. You also get draft guardrails for the AI tools your people already use.
Related offerings
- Relevant
AI Proof of Concept
"Will this work on our data before we bet a budget on it?"
- Relevant
AI Governance & Guardrails
"What are our people already putting into AI tools, and what stops sensitive data going with it?"
- Relevant
Fractional Chief AI Officer
"Who is accountable for turning our AI ideas into results?"
Not sure this is the right fit?
Take the blind spot check. Four questions to start, then go deeper on one dimension.
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