Run the AI Opportunity Score
- 01
Workflow friction
Repeated handoffs slow down work every week.
Think intake, routing, approvals, follow-up, status updates, and document processing.
- 02
Workflow friction
People copy the same information across tools or documents.
AI often helps when the work is repeated, text-heavy, and reviewable.
- 03
Workflow friction
Exceptions and blockers are discovered too late.
Useful AI workflows often surface exceptions earlier for human review.
- 04
Data readiness
The company has usable documents, tickets, records, or knowledge sources.
AI is stronger when approved sources already exist, even if they need cleanup.
- 05
Data readiness
Important knowledge sources have clear owners.
Unowned knowledge creates stale answers and weak adoption.
- 06
Data readiness
Core systems can export, connect, or share data safely.
Spreadsheets, CRMs, helpdesks, drives, and project tools can be starting points.
- 07
Customer response
Customer or internal support questions repeat often.
Repeated questions create good candidates for triage, knowledge retrieval, and drafted responses.
- 08
Customer response
Escalations, urgency, or customer sentiment are hard to spot.
AI can help flag risk, but people should own escalation decisions.
- 09
Revenue follow-up
Sales or customer follow-up quality varies by person.
AI can support account briefs, follow-up drafts, and CRM hygiene.
- 10
Revenue follow-up
CRM, lead, or customer records are incomplete or stale.
A useful AI workflow can suggest cleanup and next actions.
- 11
Revenue follow-up
Marketing or proposal work is slower than the business needs.
AI can turn source expertise into reviewable drafts and reusable briefs.
- 12
Back-office opportunity
Weekly reports take too long to prepare.
AI can gather inputs, draft summaries, and flag missing owners.
- 13
Back-office opportunity
Invoices, approvals, collections, or admin requests wait in inboxes.
Classification, summaries, and routing are common first workflow candidates.
- 14
Team adoption
The team is willing to change the workflow, not just try a tool.
Adoption improves when AI is built into the way work already happens.
- 15
Team adoption
Each candidate workflow has a business owner who can review output.
AI workflows fail when nobody owns quality and exceptions.
- 16
Governance risk
There are clear rules for what data employees can put into AI tools.
If this is low, governance should come before broad implementation.
- 17
Governance risk
Customer-facing, financial, employee, or sensitive outputs get human review.
Human review is required for high-impact or trust-sensitive work.
- 18
Governance risk
Leadership wants practical controls rather than uncontrolled AI experimentation.
Governance is a conversion accelerator when it lets safe work move faster.
- 19
First-90-day feasibility
A first workflow can be scoped narrowly enough for 30-90 days.
The best first project is important, bounded, and measurable.
- 20
First-90-day feasibility
The business can measure whether the workflow improves.
Useful metrics include response time, cycle time, rework, quality, revenue response, and reporting effort.
Current score
0/100 · Curious
The opportunity is real, but the safest next step is to clarify workflows, owners, and risk boundaries before committing to a build.
Answer every question to use this as the final result.
Top opportunity zones
- Workflow friction0/100
Manual steps, repeated handoffs, and work queues where AI could reduce drag.
- Data readiness0/100
Whether documents, records, and knowledge sources are usable enough for AI workflows.
- Customer response0/100
Support, service, and customer-response work where speed and consistency matter.
Controls to inspect
- Data readiness0/100
Whether documents, records, and knowledge sources are usable enough for AI workflows.
- Team adoption0/100
Whether the team is ready to use AI with training, owners, and review standards.
Recommended next step
QuickStart AI Audit
Start with focus before implementation.