AI Workflow Diagnostic
Identify the workflow worth pursuing and the blockers to address first.
Primary outcome Workflow inventory, fit score, risks, and first pilot recommendation
About 2 weeks Buyer-side AI workflow advisory
There are plenty of AI tools. Which workflow is actually worth changing?
Glasrocks helps companies identify the right workflow, design how people and AI share the work, and use a focused pilot to test time, quality, and operational risk.
You do not need to choose a tool first. Start with one repetitive, slow, or error-prone workflow.
Independent of a specific AI toolDiagnose before procurementStart with one workflowJudge results with operating metrics
Buyer-side perspective
We do not start with product features
Demos show what AI can do. Glasrocks addresses the next question: once that capability enters your company, will it be used, managed, and measured?
Typical tool-led approach Glasrocks workflow-led approach
Lead with model, agent, or platform features Find where delay, rework, and errors occur
Use a feature demo to judge feasibility Judge fit through data, risk, review, and ownership
Treat launch as completion Track time, quality, adoption, and exceptions after launch
Advisory services
Start at the decision your team needs to make
Every engagement has a clear question, deliverable, and stopping point. You do not need to commit to a large transformation program first.
Pilot Design Sprint
Turn a promising workflow into a pilot that can be tested in real work.
Primary outcome Pilot scope, human-AI roles, success metrics, and feedback plan
About 4–6 weeks AI Workflow Implementation
Build the sources, review points, interface, and handoffs required for daily use.
Primary outcome Workflow build, integration, exception handling, and rollout support
About 8–12 weeks AI Operating Model Review
Review an AI workflow stuck on quality, adoption, governance, or scale.
Primary outcome Health check, ownership design, quality monitoring, and scale recommendation
About 2–3 weeks Workflow patterns
Industries differ, but work often gets stuck in similar ways
We analyze how work enters, gets judged, retrieves knowledge, produces output, receives review, and moves to the next person.
Customer service
Repeated questions slow response
Classify → retrieve → draft → review → escalateInternal knowledge
Information exists but is hard to find or trust
Ask → retrieve → compose → cite → improveSales and intake
Requests arrive incomplete and routing is slow
Collect → infer intent → clarify → score → hand offDocuments and admin
People move data between email, forms, and systems
Receive → extract → check → route → recordGlasrocks Method
A method business leaders can use to decide
The method avoids model jargon and turns AI adoption into three manageable questions.
Read the full method- 1
Workflow Fit
Is the work repeated, measurable, grounded in reliable sources, and safe enough to improve?
- 2
Workflow Design
Should AI classify, retrieve, draft, or recommend, and where must people review and decide?
- 3
Operating Model
Who maintains knowledge, monitors quality, handles exceptions, and decides what happens next?
Practical insight for decision makers
Understand the organizational problem before chasing another AI term
Plain-language guidance on the decisions, costs, risks, and organizational work behind practical AI adoption.
01 Why AI pilots fail after the demo Read article 02 Should AI adoption start top-down or bottom-up? Read article 03 How to choose the first AI workflow Read article
Read all insights Start with one piece of work
Uncertainty is a reason to diagnose, not a reason to buy
Use the diagnostic to structure workflow fit, or describe a more complex situation directly by email.