For leadership
Executive readout
A short presentation of the findings, the overall readiness score and a clear recommendation on where to start.
- Readiness score by dimension
- Top risks
- Recommended first move
ASC Digital
AI audits
Assess maturity, risk, data readiness, and stack fit — then leave with ranked quick wins and a clear go / no-go on pilots.
Book a callAI & Automation
Know what is ready — before you scale AI
An ASC AI audit surfaces where automation and agents will succeed, where data or process will break them, and which investments deliver the fastest credible return.
Sample output
A readout you can act on
Every audit ends with a scored view across six dimensions, each backed by the evidence we found. This is what a typical readout looks like.
AI readiness — sample company
0.0/ 5Overall readiness
- Data qualityGap
2 / 5. Duplicate accounts and empty key fields would mislead any model.
- Process clarityNeeds work
3 / 5. Sales process documented; service triage rules live in people's heads.
- Knowledge sourcesGap
2 / 5. Policies scattered across drives, with no owner or review date.
- Systems & integrationsReady
4 / 5. CRM and ticketing connected; APIs available for target use cases.
- Governance & riskNeeds work
3 / 5. A data policy exists, but no review process for AI outputs yet.
- Skills & adoptionReady
4 / 5. Strong CRM habits and an internal champion already experimenting.
Audit scope
What we review
We look at evidence, not slideware. Each area below gets checked, scored and documented.
Business goals and candidate use cases
Where leadership expects AI to help, and whether those ideas are specific enough to test.
Data quality and access
Completeness, duplicates, ownership — and whether the right data can actually be reached.
Processes and handoffs
How work really flows today, including the workarounds AI would need to respect.
Knowledge sources
The policies, documentation and FAQs that assistants or agents would rely on.
Systems and integrations
CRM, ticketing, collaboration tools and the APIs between them.
Security, privacy and compliance
Data residency, access controls, vendor terms and regulatory exposure.
Current AI usage
Tools already in use — sanctioned or not — and what people actually use them for.
Skills and change capacity
Who can own AI day to day, and how much change teams can absorb right now.
Deliverables
What you walk away with
Three documents, each written for a different reader — and for decisions, not shelves.
For delivery teams
Ranked action backlog
Quick wins, foundational fixes and pilot candidates, each with an effort and impact estimate.
- Quick wins
- Foundational fixes
- Pilot candidates
For risk and IT
Risk register
Every risk we found, its likely impact, and the control or mitigation we recommend.
- Data and privacy risks
- Vendor exposure
- Recommended controls
Who we serve
Audits sized to the decision you need
For SMBs
A focused review of one domain (sales, service, or ops) with a short report and a recommended first pilot.
Typical: 2–4 week audit with stakeholder sessions and a ranked backlog.
For enterprises
Multi-domain assessment with risk, architecture, and business stakeholders — suitable for steering committees and budget cycles.
Typical: structured interviews, artifact review, and executive readout with portfolio recommendations.
FAQ
Common questions
How is an AI audit different from a CRM audit?
CRM audits focus on platform health and adoption. AI audits add model/use-case readiness, knowledge quality, agent risk, and automation fitness on top of that foundation.
Will you recommend pausing AI projects?
When data, process, or governance would make a pilot fail publicly — yes. A stopped bad pilot is a successful audit.
Do you need production access?
Read-only access and demos usually suffice. We escalate only when deeper technical validation is required for a recommendation.
Book an AI audit
Share the domain you care about most. We return with ranked findings and a recommended next move.