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Business-led AI Management Canvas

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Business Frame [Oversee]

1. Operating AI & Job for Impact

Define the Operating AI identity and core impact job.

1. Operating AI & Job for Impact — Guidance

In this block, you will provide
  • Operating AI name
  • Job for impact
  • What it handles
What to do

Name the Operating AI and define its job in one business-readable sentence.

Why it matters

This anchors scope and impact from the start.

Example
  • Operating AI Name: Claims Resolution Assistant
  • Job: Handles claim status questions and next-step guidance
Deep dive (Info box)

Describe responsibility, not implementation.

  • Good: Handles refund requests
  • Avoid: Uses NLP to classify intents

2. Business Purpose & Innovation Priority

Capture strategic intent and urgency.

2. Business Purpose & Innovation Priority — Guidance

In this block, you will provide
  • Business purpose
  • Named strategy
  • Why now
What to do

Define why this AI exists and the priority it supports.

Why it matters

Keeps effort tied to business value.

Example
  • Business Purpose: Customer Relationship
  • Priority: Improve response time
Deep dive (Info box)

Link purpose to a real initiative.

  • Customer Relationship
  • Economic Offering
  • Operational Excellence

3. Target Impacts

Describe outcomes that define success.

3. Target Impacts — Guidance

In this block, you will provide
  • Business impact
  • Human impact
  • Responsible AI impact
  • Time to impact
What to do

Set business, human, and responsible AI outcomes.

Why it matters

Impact is the success metric.

Example
  • Business: Reduce support cost by 20%
  • Human: Faster, clearer responses
  • Responsible AI: Transparent escalation
Deep dive (Info box)

Define all three dimensions.

  • Business value
  • Human experience
  • Trust and safety

4. Operating Areas

Identify where the AI starts and scales.

4. Operating Areas — Guidance

In this block, you will provide
  • Pilot areas
  • Rollout areas
  • Local differences
What to do

List where the AI runs now and later.

Why it matters

Context changes behavior and risk.

Example
  • Website chatbot
  • Call center
  • Mobile app
Deep dive (Info box)

Start small, then scale.

  • Location
  • System
  • Team/role

5. Ownership & Team

Map accountable roles and support network.

5. Ownership & Team — Guidance

In this block, you will provide
  • Sponsor
  • PM
  • Designer
  • Engineer
  • Expert
  • Operator
  • Advisors
What to do

Assign sponsor and accountable delivery roles.

Why it matters

Ownership prevents drift and gaps.

Example
  • Sponsor: VP CX
  • PM: Product Manager
  • Team: Designer, Engineer, Operator
Deep dive (Info box)

Make accountability explicit.

  • PM leads
  • Designer shapes behavior
  • Engineer builds
  • Operator verifies

Behavior Design [Tailor]

6. Named Ideal Behaviors

Define business-readable behavior expectations.

6. Named Ideal Behaviors — Guidance

In this block, you will provide
  • Business-readable behaviors
  • What the AI must do to achieve the target impact
What to do

List the business-readable behaviors the AI must perform to achieve the target impact.

Why it matters

Behavior is testable and business-readable.

Example
  • Verifies identity
  • Provides accurate status
  • Gives clear next steps
Deep dive (Info box)

Behavior statements should be specific.

  • Short
  • Action-oriented
  • Readable by non-technical teams

7. Scenario Coverage

Document task, user, context, and scenarios.

7. Scenario Coverage — Guidance

In this block, you will provide
  • Task
  • User
  • Thing
  • Location
  • Scenarios
What to do

Define task, user, thing, and location scenarios.

Why it matters

Coverage ensures real-world readiness.

Example
  • Task: Claim status
  • User: First-time customer
  • Thing: Policy
  • Location: Mobile app
Deep dive (Info box)

Use all four lenses.

  • Task
  • User
  • Thing
  • Location

8. Sensitive / High-Stakes Scenarios

Prioritize where failures matter most.

8. Sensitive / High-Stakes Scenarios — Guidance

In this block, you will provide
  • Where failure matters most
  • Trust
  • Safety
  • Operational exposure
What to do

Identify scenarios where failure has high consequences.

Why it matters

High-stakes cases need stronger controls.

Example
  • Incorrect claim denial
  • Misleading advice
  • Privacy-sensitive requests
Deep dive (Info box)

Prioritize by downside impact.

  • Legal risk
  • Financial impact
  • Trust damage

9. Interface & Human Guidance

Specify user guidance and fallback paths.

9. Interface & Human Guidance — Guidance

In this block, you will provide
  • Explanation
  • Confidence
  • Escalation
  • Recourse
What to do

Define user guidance and human handoff.

Why it matters

Guidance builds confidence and control.

Example
  • Show confidence
  • Explain answer
  • Enable human escalation
Deep dive (Info box)

Design for transparency and recourse.

  • Transparency
  • Control
  • Recourse

10. Validation Criteria

Define validation thresholds per scenario.

10. Validation Criteria — Guidance

In this block, you will provide
  • What must be true to count as validated
  • Success by scenario
What to do

Set pass/fail criteria by scenario.

Why it matters

Clear criteria enable validation and iteration.

Example
  • 90% correct responses
  • Clear next steps
  • Positive feedback
Deep dive (Info box)

Define before deployment.

  • Measurable thresholds
  • Scenario-specific checks

Operation & Evolution [Guide]

11. Operating Areas

Capture where the AI operates across business locations, cultures, customers, operations, and systems.

11. Operating Areas — Guidance

In this block, you will provide
  • Business locations
  • Country and culture norms
  • Real Life, Work, World situations
  • Local customers
  • Local operations
  • Local systems
What to do

Describe where the AI will operate and the local realities that may change behavior.

Why it matters

Country, culture, customer, operation, and system differences shape what works in practice.

Example
  • Business locations: Northeast branches
  • Culture norms: Escalate billing disputes by phone
  • Local systems: Regional CRM instance
Deep dive (Info box)

Treat operating context as part of the design.

  • Business locations
  • Country and culture norms
  • Real Life, Work, World situations
  • Local customers, operations, and systems

12. Verification

Explain evidence and verification practices.

12. Verification — Guidance

In this block, you will provide
  • Operator verification
  • Scenario verification
  • Live evidence
What to do

Confirm behavior in real operations.

Why it matters

Lab results are not enough.

Example
  • Operator runs live scenarios
  • Confirms expected behavior
Deep dive (Info box)

Verification is operational evidence.

  • Live checks
  • Evidence capture
  • Expectation match

13. Impact Measurement

Track outcomes observed in real operation.

13. Impact Measurement — Guidance

In this block, you will provide
  • How impact is measured in reality
  • Business outcomes
What to do

Track real business and user outcomes.

Why it matters

Outcomes prove value.

Example
  • Reduced call volume
  • Faster resolution
  • Improved satisfaction
Deep dive (Info box)

Measure outcomes, not just model metrics.

  • Business metrics
  • User outcomes

14. Guidance & Improvement Loop

Define feedback and iteration rhythm.

14. Guidance & Improvement Loop — Guidance

In this block, you will provide
  • Operator feedback
  • Expert feedback
  • Iteration path
What to do

Capture feedback and define iteration cadence.

Why it matters

Continuous improvement sustains performance.

Example
  • Operator flags issue
  • Designer updates behavior
  • Engineer updates system
Deep dive (Info box)

Run a repeatable learning loop.

  • Observe
  • Learn
  • Improve

15. Directed Evolution

Define how the AI should evolve through new behaviors and local scenarios.

15. Directed Evolution — Guidance

In this block, you will provide
  • New behaviors
  • Local scenarios
What to do

Name the new behaviors and local scenarios that should guide the AI next.

Why it matters

Directed evolution keeps improvement tied to observed business needs.

Example
  • New behavior: Proactively explains missing documentation
  • Local scenario: Spanish-language claim intake
Deep dive (Info box)

Evolve from real operation.

  • New behaviors
  • Local scenarios

Data, Datasets & Models [Reuse]

16. Key Data Hypothesis

Declare signals needed and rationale.

16. Key Data Hypothesis — Guidance

In this block, you will provide
  • What signals are needed
  • Why they matter
What to do

List required signals and why each matters.

Why it matters

Signal quality determines output quality.

Example
  • Customer ID -> identity verification
  • Policy data -> coverage logic
Deep dive (Info box)

Every signal needs a purpose.

  • What does it signal?
  • Why is it needed?

17. Data Sources

Identify source systems and practical constraints.

17. Data Sources — Guidance

In this block, you will provide
  • Potential sources
  • Owners
  • Access
  • Feasibility
What to do

Name source systems and ownership.

Why it matters

Access and quality determine feasibility.

Example
  • CRM
  • Claims DB
  • Profile service
Deep dive (Info box)

Evaluate practical readiness.

  • Availability
  • Quality
  • Ownership

18. Named Datasets

Outline training, test, and monitoring datasets.

18. Named Datasets — Guidance

In this block, you will provide
  • Training
  • Testing
  • Monitoring
  • Tailored vs existing
What to do

Define named training, testing, and monitoring datasets.

Why it matters

Named datasets become reusable assets.

Example
  • Claims History Dataset
  • Customer Interaction Dataset
Deep dive (Info box)

Separate datasets by lifecycle use.

  • Training
  • Testing
  • Monitoring

19. Models (Base + Tailored)

Document model stack and transparency layers.

19. Models (Base + Tailored) — Guidance

In this block, you will provide
  • Base models
  • Task models
  • Explanation / transparency layers
What to do

Define base and tailored model stack.

Why it matters

Most systems are multi-model.

Example
  • Base: GPT
  • Tailored: Claims fine-tuned model
Deep dive (Info box)

Describe model roles.

  • Task model
  • Data prep model
  • Explanation layer

20. Tailored Asset Reuse

Plan reuse of tailored models, scenarios, and behaviors from AIs already achieving impact.

20. Tailored Asset Reuse — Guidance

In this block, you will provide
  • Reuse tailored models, scenarios & behaviors
  • Start from AIs achieving impact
What to do

Identify reusable tailored models, scenarios, and behaviors from AIs already achieving impact.

Why it matters

Starting from proven assets reduces duplication and speeds the path to business value.

Example
  • Reuse tailored refund scenarios
  • Start from the support AI behaviors validated in pilot
Deep dive (Info box)

Reuse assets with evidence behind them.

  • Tailored models
  • Validated scenarios
  • Named behaviors
  • AIs already achieving impact