Strategy & Planning
Turn a vague AI idea into a decision-ready initiative.
Most AI programs don’t fail because the model is weak. They fail because the use case was poorly framed, the value was unclear, the data assumptions were untested, or the risks were discovered too late. This lab helps teams define the business problem, score the opportunity, identify readiness gaps, and create a structured initiative brief before moving into data and build.
Frame the business problem
Turn a vague ambition into a sponsored, scoped use case with a real pain point and a target user.
Score value and readiness
A live 0–100 readiness score across value, data, feasibility, risk and adoption — with the gates you must clear.
Generate the initiative brief
A structured, board-ready brief you can save, copy, and hand off straight into the Data Lab.
Idea generator
Start from a vague ambition
Type a rough idea, turn the five knobs, and watch a sharpened framing and a scored spread of options appear. Pick one to seed the workshop.
User
Job
Pain
Data posture
Risk appetite
Change any knob to reshape the options in real time.
Your bet
Prioritize the support cases of questions that matter most to customers.
Sharpened framing
The clear version of "Use AI to improve customer support": start with the narrow, high value slice customers run into most often, lean on the answers you already have, so the waiting goes away. Prove it with a single number you can actually fail.
Generated options · value vs effort
GeneratedThe pulsing node is your recommended bet — click any node to switch, or hover for detail.
The shift this lab makes
From vague idea to decision-ready use case
Vague idea
Use AI to improve customer support.
Sharpened initiative
Deploy an internal knowledge assistant for Level 1 support agents to reduce average handle time, improve answer consistency, and increase first-contact resolution across policy and troubleshooting workflows.
Falsifiable target
Within 90 days, reduce average handle time from 8.5 minutes to 6.8 minutes for the top 25 support intents while maintaining answer accuracy above 90% and escalation quality above 95%.
Strategy workshop
Shape the initiative
Answer what you can — the readiness score and gates update live. Nothing here needs a real AI call.
Business context
Internal or customer-facing
AI ambition
AI pattern
Human review required?
Business value
Value driver
Time horizon
Data assumptions
Data sensitivity
Source freshness concern?
Structured / unstructured / mixed
Risk & governance
Impact if the AI is wrong
Regulatory exposure
Audit trail required?
Citation / evidence required?
Escalation path needed?
Delivery complexity
Workflow change level
Technical dependency level
Adoption complexity
Pilot timeline urgency
Strategy readiness
Do not proceed yetout of 100 · weighted across six categories
Required gates
- Business sponsor identified
- Baseline metric defined
- Target outcome defined
- Data source identified
- Risk level assigned
- Human review decision made
Not ready for Data Lab yet. Resolve required gates before proceeding.
Suggested next action
Resolve required gates: Business sponsor identified, Baseline metric defined…
AI Initiative Brief
AI assistant
Patterns
AI use-case archetypes
Six shapes most enterprise AI initiatives fall into — with the data they need, their common risks, and the controls that keep them safe.
Knowledge Assistant
- Best fit
- Internal search, policy lookup, support docs, employee enablement
- Data needed
- Curated documents, policies, FAQs — with clear ownership and freshness
- Common risks
- Stale sources, weak citations, hallucinated policy answers
- Controls
- Citation enforcement, source freshness, confidence threshold
- Success metric
- Handle time ↓, first-contact resolution ↑, answer consistency
Workflow Summarization
- Best fit
- Call notes, case & claim summaries, meeting recaps, research synthesis
- Data needed
- Transcripts, notes, source records — often with PII to redact
- Common risks
- Omitted details, incorrect emphasis, privacy leakage
- Controls
- Human review, redaction, summary quality checks
- Success metric
- Time saved per summary, reviewer edit rate
Decision Support
- Best fit
- Recommendations, prioritization, risk review, financial analysis
- Data needed
- Structured records + supporting evidence for each recommendation
- Common risks
- Unsupported recommendations, bias, unclear rationale
- Controls
- Explainability, evidence links, human approval
- Success metric
- Decision quality, cycle time, approval rate
Agentic Workflow
- Best fit
- Multi-step task execution, tool use, approvals, escalations
- Data needed
- APIs/tools with scoped permissions and a system of record
- Common risks
- Runaway actions, tool misuse, excessive cost
- Controls
- Permission boundaries, step logging, action approval
- Success metric
- Tasks completed autonomously, cost per task, error rate
Customer Experience Automation
- Best fit
- Contact center, IVR, chatbot, routing, omnichannel support
- Data needed
- Conversation logs, intents, knowledge base, routing rules
- Common risks
- Customer frustration, incorrect answers, compliance exposure
- Controls
- Escalation path, QA sampling, conversation monitoring
- Success metric
- Containment rate, CSAT, escalation quality
Finance / Operations Analytics
- Best fit
- Variance analysis, reporting automation, forecasting support, exception detection
- Data needed
- Trusted, lineage-tracked datasets and definitions
- Common risks
- Bad assumptions, stale data, overconfidence
- Controls
- Data lineage, variance thresholds, human sign-off
- Success metric
- Exceptions caught, reporting time ↓, forecast error
For reviewers
What this lab demonstrates
This lab demonstrates how an enterprise AI initiative should be shaped before build begins. It connects business value, user workflow, data readiness, risk exposure, adoption complexity, and delivery feasibility into a structured decision. The goal is not to approve every AI idea. The goal is to identify which ideas deserve investment, which need redesign, and which should not proceed yet.