01
Inputs
Where context enters the workflow.
- Requests
- Notes
- Systems
- Conversation notes
- Reports
AI Workflow Audit
Find where AI actually belongs.
NORTIQ maps how the work runs today, where signal gets lost, and where AI can create practical leverage.
The audit starts with the work, not the tool.
Map the current workflow before deciding what AI should do.
The AI Workflow Audit maps inputs, workflow steps, decision points, friction, AI support opportunities, and human review points before recommending what should change.
NORTIQ audit canvas
Map the current state before deciding what AI should do.
02
Workflow
How work actually moves today.
- Handoffs
- Manual steps
- Follow-up
- Approvals
03
Decisions
Where judgment, approval, or prioritization happens.
- Escalation
- Approval
- Prioritization
04
Friction
Where signal gets lost or work slows down.
- Rework
- Delays
- Missing context
05
AI support
Where AI may support the work.
- Synthesis
- Drafting
- Triage
- Preparation
06
Human review
Where judgment stays accountable.
- Owner
- Manager
- Founder
- Operator
Diagnostic lenses applied to the workflow
How the work happens today.
Who decides, where judgment is required, and where decisions slow down.
Where context enters, duplicates, disappears, or becomes useful.
What systems, files, reports, and AI experiments are already in play.
Where AI can support the workflow, and where structure needs to come first.
What ownership, cadence, review, and implementation sequence are needed.
The audit is not a tool-shopping exercise.
The goal is to understand the work well enough to decide what should be redesigned, what should be supported by AI, what should stay human, and what should wait.
Current workflow → AI fit → first operating move.
The audit creates enough clarity to choose the next practical test.
The audit path
Current state
How the work runs today.
AI fit
Where AI can and cannot help.
Operating design
What workflow, cadence, and ownership need to change.
First build path
What to test, build, or implement next.
What you leave with.
Four executive outputs, not a theoretical AI strategy deck.
Workflow map
Where the work slows down and who owns it.
AI fit map
Where AI can support synthesis, triage, drafting, coaching, or review.
Adoption risk view
What needs ownership, cadence, data, or human review.
First operating move
The workflow, agent, GTM OS path, or implementation step worth testing.
Workflow examples and candidate logic.
Use these accordions when you want the detail behind the diagnostic.
Workflow examples the audit can inspectLeadership, GTM, operations, customer, and adoption workflows.
Founder and leadership workflows
Useful when the founder or senior operator is still the point where too many decisions, approvals, and context checks converge.
GTM and revenue workflows
Useful when pipeline, customer conversations, CRM context, or deal execution creates scattered signal and founder-dependent decisions.
Operations and customer workflows
Useful when customer work, support, onboarding, internal operations, or delivery depends on repeated manual coordination.
Team adoption and operating cadence
Useful when AI tools exist but the team does not have the rituals, ownership, or feedback loops required to use them consistently.
What makes a workflow a good AI candidate?Repeating work, usable inputs, clear decisions, cadence, and human review.
Good candidate signals
The workflow repeats often.
AI is more useful when the same kind of work happens repeatedly and can be structured.
Inputs are available or can be captured.
The workflow has usable source material such as notes, system records, forms, documents, reports, or structured team inputs.
The decision path is clear.
The team can explain what decisions are made, who makes them, and where review is needed.
The output will be used in a cadence.
The work connects to meetings, reviews, handoffs, coaching, reporting, or recurring operating rituals.
Human judgment stays in the loop.
AI supports preparation, review, synthesis, routing, or drafting without replacing accountability.
The value is operational, not just novel.
The workflow becomes easier to run, easier to inspect, or easier to improve.
Poor candidate signals
- The workflow is rare or one-off.
- Inputs are unreliable or unavailable.
- No one owns the output.
- The team will not use the result.
- The risk is high and the review path is unclear.
- The problem is actually strategy, ownership, or process, not AI.
Book the AI Workflow Audit conversation.
Pick a 30-minute time in Calendly. Use the conversation to identify the workflow, decision path, or adoption problem worth mapping first.