Overview

Lecture URL: https://skill.re/learn/manager/building-an-ai-roadmap.php

AI FOR MANAGERS CERTIFICATION

Strategic AI Leadership (Level 5) | AI Strategy for Managers

LECTURE: Building an AI Roadmap

Lesson 1.2 | Estimated Duration: ~24 minutes

Welcome to the AI for Managers certification program. I am your instructor, and today we are covering one of the essential lessons in the AI Strategy for Managers module: Building an AI Roadmap.

This is Lesson 1.2 in Level 5, the Strategic AI Leadership track. Whether you are joining us as a new manager finding your footing, a seasoned director refining your approach, or a VP setting strategic direction for your organization, the material in this session is designed to meet you where you are and give you something immediately actionable.

In our previous lesson, we covered Developing an AI Vision for Your Domain. Today we build directly on that foundation. If any of those concepts feel uncertain, I would encourage you to revisit that material before we go further.

Before we begin, let me set expectations. This is not a passive lecture. I will ask you to think, to challenge assumptions, and to connect what we discuss to your own work. The managers who get the most out of this program are those who pause, reflect, and apply. So I encourage you to have a notepad ready, whether physical or digital, and to jot down ideas as they come to you.

Let us get started.

Lesson 02: Building an AI Roadmap

Title

Building an AI Roadmap: Translating Vision into Actionable Sequencing

Purpose

This lesson teaches you to translate a strategic vision into a detailed, realistic roadmap. A roadmap operationalizes your vision--it specifies what you'll do, when you'll do it, what it depends on, and how you'll manage dependencies and expectations. You'll learn practical roadmap construction that accounts for team capacity, organizational readiness, learning, and iterative refinement.

Why This Matters for Managers

A vision without a roadmap is inspirational but not actionable. Teams don't know what to prioritize. Executives don't know when to expect outcomes. Resources get allocated inconsistently. Without a roadmap:

• Teams feel overwhelmed (too many initiatives with no sequencing)
  - Executives get impatient ("When will this deliver value?")
  - Dependencies aren't managed (you attempt initiatives before prerequisites are ready)
  - Learning from early projects doesn't inform later ones
  - Budget and capacity planning remain ad hoc

A strong roadmap:

• Creates clarity on "what first, second, third"
  - Allows realistic timeline expectations
  - Identifies prerequisite investments (data, training, infrastructure)
  - Creates early wins that build momentum and confidence
  - Enables learning-based iteration (refining subsequent phases based on early results)
  - Allows managers to say "no" to things that aren't on the roadmap

For you as a manager: The roadmap is your primary planning and communication tool. It's how you move from "nice idea" to "actual execution."

Core Concepts

Roadmap vs. Plan: Key Distinctions

A roadmap is different from a detailed project plan:

| Aspect | Roadmap | Detailed Project Plan |

||||

| Timeframe | 18-36 months | 3-6 months (current phase) |

| Detail level | Quarters or 6-month phases; milestones and dependencies | Week-by-week tasks, assignments, resources |

| Certainty | Later phases are directional; early phases are specific | Should be quite specific |

| Purpose | Guide strategic sequencing and organizational readiness | Execute the current phase |

| Audience | Leadership, team, stakeholders | Team, project managers |

Your roadmap is strategic sequencing with high-level milestones. Detailed project plans happen for each phase as you execute.

The Five Elements of a Strong AI Roadmap

1. Phase Structure

Organize your roadmap into phases (typically 6 months to 1 year). Each phase should:

• Represent a cohesive set of related initiatives
  - Build toward your vision
  - Include some quick wins (confidence-building)
  - Include some capability-building work (training, infrastructure)
  - Be completable with realistic resources

Example phase structure:

• Phase 1 (Months 1-6): Foundation & Quick Wins
  - Phase 2 (Months 7-12): Building Capability & Expanding
  - Phase 3 (Months 13-18): Scaling & Sustaining

1. Initiative Prioritization

For each phase, identify which initiatives you'll pursue and the order you'll sequence them.

Prioritization criteria (use a framework, not just gut feeling):

• Business impact: How much does this move your business objectives?
  - Feasibility: How realistic is this with current team skills, data, and resources?
  - Dependencies: What prerequisites must be in place first?
  - Confidence: How certain are you this will work? (Reduces with unknowns)
  - Learning value: Does this teach you something that informs future initiatives?

Many teams use a scoring model: Rate each initiative on each criterion (1-5), weight the criteria, and rank by total score.

1. Dependency Management

Identify what must happen before each initiative can succeed. Common dependencies:

• Prerequisite work: "Before we can automate customer support, we need to standardize our response templates and train the team on AI basics"
  - Data readiness: "Before we can predict customer churn, we need 18 months of clean historical data"
  - Infrastructure: "Before we can do real-time AI predictions, we need to upgrade our data pipeline"
  - Team capability: "Before engineers can build AI features, they need training on prompt engineering and fine-tuning"
  - Organizational alignment: "Before we launch this customer-facing AI, we need legal and compliance review"

Make dependencies explicit in your roadmap. This is how you prevent "let's start initiative B before we're ready."

1. Resource Allocation

Estimate resource needs for each phase:

• Team time: How many people, for how long, at what skills level?
  - Budget: Training, tools, infrastructure, external expertise (consultants, vendors)?
  - Senior leader attention: How much of your time will this require?

Be realistic. Most teams under-estimate resource needs, leading to:

• Burnout (doing initiative work on top of existing jobs)
  - Incomplete initiatives (starting too many things, finishing none)
  - Poor quality (rushing)

If resource estimates reveal you can't do everything, that's valuable information. Your roadmap might shift from "do everything in 18 months" to "do the highest-impact initiatives in 18 months, phase 2 in months 19-36."

1. Success Metrics and Milestones

Each phase and major initiative should have clear success criteria:

• What outcome are we trying to achieve? (Should tie back to your vision)
  - How will we measure it?
  - What does success look like? (Specific, realistic targets)

Example:

• Initiative: AI-assisted customer support responses
  - Success metric: 40% of routine inquiries answered by AI draft (human-reviewed before sending)
  - Secondary metric: Response time reduced from 4 hours to 1.5 hours
  - Milestone: Launch to 10% of customer base (month 3), 50% (month 4), 100% (month 5)

Practical Managerial Use Cases

Use Case 1: Building a Data Analytics Roadmap

Scenario: You lead data and analytics for a mid-market retail company. You have a vision to use AI for demand forecasting, inventory optimization, and customer insights. You need to sequence this over 18 months with your team of 6.

Approach:

Phase 1 (Months 1-6): Foundation and Quick Wins

• Initiative 1.1: Team training on AI/ML concepts and your tools (2-3 people, light lift)
  - Initiative 1.2: AI-assisted data cleaning (medium impact, high feasibility; builds confidence)
  - Initiative 1.3: Assess data quality for forecasting (prerequisite to Phase 2)
  - Dependencies to manage: Might need better data governance before forecasting
  - Resources: 2 people full-time, 1 external trainer (contract), $25K for tools/training
  - Success metrics: Team trained and confident with 1 small AI project shipped; data quality assessment complete

Phase 2 (Months 7-12): Capability and Early Value

• Initiative 2.1: AI-powered demand forecasting (high impact, now feasible with trained team and clean data)
  - Initiative 2.2: Build reporting dashboard around forecasting insights (enables use)
  - Dependencies: Foundation phase must be complete; business stakeholders aligned on how they'll use forecasts
  - Resources: 3 people full-time, possible external ML specialist for 3 months, $40K budget
  - Success metrics: Forecast accuracy improves 15%; 80% of planners use it in decisions

Phase 3 (Months 13-18): Expansion and Scale

• Initiative 3.1: Inventory optimization AI (builds on forecasting; higher complexity)
  - Initiative 3.2: Customer segment insights (learning from forecasting phase informs this)
  - Dependencies: Phase 2 learnings about how business uses AI; team comfort with more sophisticated models
  - Resources: 2 people, external partner, $35K
  - Success metrics: Inventory carrying costs down 10%; time-to-decision on new product investments reduced 40%

This roadmap is specific, realistic about resources, manages dependencies, and sequences learning.

Use Case 2: AI in Manufacturing Operations

Scenario: You lead operations for a manufacturing plant. Vision: AI for quality inspection, predictive maintenance, and production optimization. You have 200 factory workers and a small engineering team.

Roadmap approach:

Phase 1 (Months 1-6): Pilot and Build Trust

• Initiative 1.1: AI-assisted visual quality inspection (pilot on one product line)
  - Initiative 1.2: Change management and worker communication (critical--anxiety is high)
  - Dependencies: Camera hardware, AI model trained on your historical defects
  - Resources: 1 engineer full-time, 1 change manager part-time, $60K (cameras + model development)
  - Success metrics: AI detects 85% of defects caught by human inspection; worker satisfaction with pilot is >70%

Phase 2 (Months 7-12): Expand Quality, Begin Predictive Maintenance

• Initiative 2.1: Scale quality AI to all product lines (based on Phase 1 learnings)
  - Initiative 2.2: Predictive maintenance pilot (sensor data collection, model development)
  - Dependencies: Scale quality AI (must work reliably); collect 6+ months of sensor data
  - Resources: 1.5 engineers, $40K
  - Success metrics: Quality AI scaled reliably; predictive maintenance model achieves 80% accuracy in predicting equipment failures; unplanned maintenance incidents down 25%

Phase 3 (Months 13-18): Optimization and Integration

• Initiative 3.1: AI-driven production scheduling (uses quality and maintenance insights)
  - Dependencies: Quality and maintenance systems stable and reliable
  - Resources: 1 engineer, $25K
  - Success metrics: Throughput improved 12%; schedule adjustments reduce downtime

Why this roadmap works:

• Phase 1 focuses on building trust with workers (change management is the real constraint)
  - Sequence allows learning (what you learn about quality informs predictive maintenance)
  - Resource allocation is realistic for a small engineering team
  - Each phase has clear, measurable outcomes

Examples

Example 1: A Realistic 18-Month AI Roadmap

Customer Success Organization, 80-person team

Vision: Use AI to accelerate customer onboarding, reduce escalations, and free team for strategic customer work.

Phase 1: Q1-Q2 (Months 1-6) -- Foundation & Quick Wins

• Implement AI-powered onboarding assistant (Q1 launch)
  - Team training on prompt engineering and AI tools
  - Assess knowledge base quality and standardize documentation
  - Resources: 1 CSM lead (50%), 1 technical person (100%), $15K (tools)
  - Dependencies: Documentation must be standardized first
  - Metrics: Onboarding time reduced 20%, team satisfaction with tool >70%, 95% of new customers use assistant

Phase 2: Q3-Q4 (Months 7-12) -- Expansion & Capability

• AI-assisted escalation triage (AI suggests resolution path, CSM decides)
  - Customer health scoring (AI identifies at-risk accounts)
  - Team deep-dive: Advanced prompting and workflow optimization
  - Resources: 1.5 CSMs, 1 engineer, $25K
  - Dependencies: Phase 1 tooling and training in place; data clean enough for health scoring
  - Metrics: Escalation resolution time down 30%, at-risk account identification accuracy >85%

Phase 3: Q1-Q2 Year 2 (Months 13-18) -- Scale & Strategic

• Customer health insights automated and embedded in dashboards
  - AI-powered contract renewal intelligence
  - Shift team roles: fewer routine escalations, more strategic account management
  - Resources: 0.5 engineer (maintenance), $10K
  - Dependencies: Phase 2 complete and stable
  - Metrics: Churn improved 5%, strategic time per CSM increased 25 hours/month, NPS trend up

Phase 4 (Months 19+) -- Sustained and Learning

• Maintain systems, monitor for improvements/issues
  - Quarterly learning retrospectives: what's working, what to adjust
  - New initiatives emerge from learnings (not predetermined)

Example 2: How Dependencies Shape a Roadmap

Finance & Planning Organization

Early drafts of the roadmap wanted to "do everything immediately":

• Build AI models for forecasting (requires clean historical data--18+ months of consistent definitions)
  - Automate close processes (requires documented procedures--currently inconsistent across departments)
  - Implement predictive budgeting (requires integration with multiple source systems--IT currently overloaded)

Reality check on dependencies:

Phase 1 focuses on prerequisites, not on "sexy" AI work:

• Months 1-3: Standardize general ledger definitions across all departments
  - Months 2-4: Document close procedures; automate the standard ones (AI isn't needed; just good process)
  - Months 1-6: Data governance: ensure 24 months of clean historical data

Only after those are done does Phase 2 begin:

• Months 7+: Build forecasting models (now have clean, consistent data)
  - Months 9+: Implement predictive budgeting (now have integration in place)

This feels slower, but it actually executes faster because you're not fighting with bad data or unclear processes later.

Anti-Patterns & Misuse Risks

Anti-Pattern 1: The "Do Everything Immediately" Roadmap

The problem: Listing every possible initiative without sequencing or considering resource constraints.

Example: "Months 1-6: Implement AI for customer support, product recommendations, internal process automation, fraud detection, and HR hiring--all simultaneously."

Why it fails:

• Team is overwhelmed and burning out
  - Nothing gets done well; everything is partially done
  - No time for learning from early initiatives
  - Dependencies aren't managed (you start initiatives before prerequisites are ready)
  - You get three partially completed projects instead of one complete one

Better approach: Ruthlessly prioritize. What are the 2-3 initiatives that will have the most impact in Phase 1? Do those well. Plan others for Phase 2.

Anti-Pattern 2: Ignoring Resource Constraints

The problem: Creating a roadmap assuming unlimited resources.

Example: "We'll implement 5 major initiatives with our existing team (which is already at capacity handling current work)."

Why it fails:

• Existing work doesn't pause. You're adding AI initiatives on top, and something breaks
  - Team burnout
  - Quality suffers
  - Initiatives don't get sufficient attention and fail
  - Manager credibility suffers ("We committed to these dates and didn't deliver")

Better approach: Be realistic about available capacity. "Our team has 20% available capacity this quarter. We can do 1 major AI initiative + supporting work. For 2 initiatives simultaneously, we need to either:

• Add 1 FTE, OR
  - Pause or reduce some existing work, OR
  - Extend timeline"

Make that explicit in the roadmap and conversation with leadership.

Anti-Pattern 3: No Clear Dependencies

The problem: Sequencing initiatives without identifying what must happen first.

Example: Roadmap shows "Launch AI inventory forecasting (Q2)" without noting "This requires clean supplier data, which depends on Q1 data cleanup project that's currently delayed."

Why it fails:

• You hit Q2 and realize the prerequisite isn't ready. The initiative stalls.
  - Blame follows ("Why didn't the data team finish on time?")
  - Credibility suffers
  - Learning doesn't happen

Anti-Pattern 4: Skipping the Learning Phase

The problem: Treating Phase 1 as just a "quick win" and racing to scale in Phase 2 without learning what actually works.

Example: AI customer service pilot goes live in Month 3, and by Month 6 you're deciding to scale to all 500 agents. But you've only observed 3 months of real usage with 10 agents.

Why it fails:

• You scale something that has fundamental problems you haven't discovered yet
  - Phase 2 fails expensively ("We spent 3 months rolling this out and it isn't working")
  - Team confidence collapses
  - Subsequent initiatives face resistance

Better approach: Build in explicit learning time. "Phase 1 (Months 1-6): Pilot and observe. Month 7: Full retrospective--what worked, what didn't, what should we adjust? Phase 2 (Months 8-12): Scale with adjustments." This feels slower upfront but accelerates overall because you're not fixing foundational problems later.

Anti-Pattern 5: Metrics That Don't Measure Anything

The problem: Success metrics that are vague, unmeasurable, or not connected to business objectives.

Example: "Success is that the team is more productive" (what does "more productive" mean? How do you measure it?).

Why it fails:

• You can't actually tell if the initiative succeeded
  - Leadership doesn't know if it's worth continuing investment
  - You can't learn what's working and what isn't
  - No accountability

Better approach: Specific, measurable metrics tied to business objectives.

• "Team productivity increases by 15% (measured by initiatives completed per person-month)"
  - "Response time decreases from 4 hours to 2.5 hours (measured by support ticket timestamps)"
  - "Quality defects decrease 20% (measured by defect rate per unit produced)"

Human Judgment Checkpoints

Checkpoint 1: The Capacity Reality Check

For each phase, list the team capacity required (person-months). Be honest: Can your team actually deliver this while continuing current work? If not, what changes?

Options:

• Extend timeline (realistic)
  - Get additional resources (possible)
  - Reduce or pause other work (often necessary)
  - Accept lower quality or incomplete initiatives (risky)

Checkpoint 2: The Dependency Chain

Walk backward from each initiative: "What must be true for this to succeed?"

• Data ready? /
  - Team trained? /
  - Infrastructure in place? /
  - Stakeholders aligned? /
  - Organizational process changed? /

Where you see , that's a Phase 1 prerequisite, not a Phase 2 initiative.

Checkpoint 3: The Learning Validation

Ask: "What are we uncertain about, and does Phase 1 help us learn?" If Phase 1 is just "execute the thing we already know," you might be missing the real risk (implementation challenges, team adoption, data quality issues).

Better: Build explicit learning into Phase 1. "We think customer support AI will reduce response time by 20%. We're piloting with 10% of customers for 6 weeks to validate that hypothesis before scaling."

Checkpoint 4: The Stakeholder Reality Check

Share the roadmap with key stakeholders (executive sponsor, affected team leaders, customers/users if applicable) and listen:

• "This is realistic" -> You've got it right
  - "This is too slow / too fast" -> Adjust timeline
  - "We need X to happen first" -> Discover a dependency you missed
  - "We don't have these resources" -> Adjust scope or timeline

Checkpoint 5: The Quarterly Reassessment

Your roadmap isn't frozen. Every quarter, ask:

• "Are we learning what we expected?"
  - "Have conditions changed (resources, priorities, competitive landscape)?"
  - "Should we adjust Phase 2 based on Phase 1 learnings?"
  - "What new information do we have that changes prioritization?"

A good roadmap evolves as you learn and conditions change. A bad roadmap is a prison you're locked into.

Responsible AI Considerations

Planning for Fairness and Inclusion

Your roadmap should include explicit initiatives for ensuring fair and inclusive AI:

• Phase 1 might include: Bias audits of existing data, team training on responsible AI
  - Initiative dependencies: Before deploying any AI to real work, we need bias testing and fairness assessment

Escalation and Learning Infrastructure

Your roadmap should include capacity for incident response:

• Time reserved for investigating issues: "We assume 10% of team time will go to investigating and learning from AI failures"
  - Escalation protocol development: Phase 1 should include defining how to escalate when AI produces bad outcomes
  - Blameless postmortems: Budget time for deep learning when things go wrong, not just blame and move on

Transparency and Communication

Your roadmap should include ongoing communication:

• Team communication: Regular updates on what's happening, why, and what changes ahead
  - Transparency about uncertainty: "We don't know if this will work; we're testing"
  - Customer communication (if applicable): What's changing from their perspective, and why

Practice & Reflection Prompts

Prompt 1: Phase Structuring

For your domain, sketch out 3 phases (6 months each, 18 months total) that would move your vision forward:

• What's Phase 1's focus? (Usually: foundation + quick win + learning)
  - What's Phase 2's focus? (Usually: capability building + expansion)
  - What's Phase 3's focus? (Usually: scale + advanced capability)

For each, list:

• 3-4 major initiatives you'd pursue
  - Dependencies that must be satisfied first
  - Success metrics for the phase

Prompt 2: Dependency Mapping

Pick your top 3 initiatives and map dependencies:

• What data/infrastructure/training/organizational changes must happen first?
  - How long does each prerequisite take?
  - When can the actual initiative start?

Prompt 3: Resource Estimation

For Phase 1, estimate:

• People: How many FTEs at what skills level? For how long?
  - Budget: Tools, training, external expertise?
  - Management time: How much of your time will this require?

Compare against available resources. Do you have capacity, or do you need to adjust scope/timeline?

Prompt 4: Metric Definition

For each phase, define 2-3 success metrics:

• Business outcome: How does this move your business objectives?
  - Adoption/usage: Are people actually using this?
  - Quality/reliability: Is it working as intended? Any problems emerging?

Make sure you can actually measure these. (If you can't measure something, you can't know if it's succeeding.)

Prompt 5: Roadmap Storytelling

Write a one-page narrative of your roadmap: "Over 18 months, here's how we're moving from where we are to our vision..." Include phases, key initiatives, dependencies, and expected outcomes. Could you present this to your leadership and have them understand and buy in?

Key Takeaways

1. A roadmap operationalizes vision. Vision says "what we're trying to achieve"; roadmap says "in what order, with what resources, managing what dependencies."
2. Sequencing is about learning and readiness, not just "what to do." Phase 1 builds foundation and early wins. Phase 2 builds on those learnings. Phase 3 scales. Each phase enables the next.
3. Dependencies matter more than you think. Many roadmap failures come from attempting initiatives before prerequisites are ready. Make dependencies explicit and manage them actively.
4. Resource estimation is hard and critical. Under-estimating capacity leads to overcommitment, burnout, and failure. Be realistic. If you don't have capacity, adjust scope or timeline.
5. Metrics matter for accountability and learning. You can't know if the roadmap is working without clear, measurable success criteria. Define them upfront.
6. A roadmap evolves as you learn. The first phase is most specific; later phases are directional and adjust based on learnings. That's healthy.
7. Communication is the roadmap's purpose. The document exists to create clarity and alignment. If stakeholders understand and support it, the roadmap is working.

Terms & Glossary

Roadmap: A strategic plan showing major initiatives, sequencing across phases, dependencies, and success metrics over 18-36 months.

Phase: A 6-12 month period that represents a cohesive set of related initiatives and a milestone in your journey toward vision.

Initiative: A discrete body of work with clear objectives, timeline, and success criteria (e.g., "implement AI customer support").

Dependency: A prerequisite that must be satisfied before an initiative can succeed (e.g., data must be clean, team must be trained, infrastructure must be in place).

Quick Win: An early initiative with high feasibility and tangible value, chosen to build team confidence and demonstrate progress.

Readiness: The organization's capability to execute (team skills, data quality, infrastructure, cultural openness).

Learning Phase: A period where an initiative is piloted at small scale specifically to validate assumptions and discover challenges before full scale.

Related Lessons

• Lesson 01: Developing an AI Vision for Your Domain - Creates the vision that the roadmap operationalizes
  - Lesson 03: Measuring AI Impact and ROI - Defines how to track whether roadmap initiatives are delivering promised value
  - Lesson 04: Communicating AI Strategy Upward - Uses the roadmap as a communication tool with leadership
  - Chapter 02, Lesson 02: Developing Team and Department Policies - Policies should be part of your roadmap (e.g., Phase 1: develop governance policies)
  - Chapter 03, Lesson 01: Leading AI Transformation - Roadmap is the structure through which change is managed

Next: Move to Lesson 03 to define metrics for measuring whether your roadmap initiatives are delivering the impact you planned.

[SYNTHESIS AND APPLICATION]

Let us step back and look at the bigger picture of what we have covered in this session on Building an AI Roadmap.

The concepts here are not abstract frameworks meant to sit in a binder on your shelf. They are practical tools for the decisions you make every day as a manager. Whether you are leading a small team or a large department, whether you work in technology, finance, healthcare, education, or any other sector, the principles we discussed apply to your work right now.

Here is what I want you to take away from this session:

First, the conceptual understanding. You now have a clearer mental model of building an ai roadmap and how it fits into the broader landscape of AI-augmented management. This mental model is what allows you to make good decisions rather than reactive ones.

Second, the practical application. We walked through specific scenarios, examples, and frameworks that you can apply in your work this week. Not next quarter. This week. I want you to identify one specific situation in your current work where you can apply what we discussed today.

Third, the judgment dimension. Perhaps most importantly, we discussed when and how to exercise human judgment. AI is a powerful tool, but it requires an informed, thoughtful manager at the helm. That is you. Your judgment, your context awareness, your understanding of your team and your organization, those are irreplaceable.

[REFLECTION EXERCISE]

Before we close, I would like you to spend two minutes, just two minutes, on this reflection:

Think about your work this past week. Identify one task, one decision, one communication where the concepts from today's lesson would have changed your approach. What would you have done differently? What would the outcome have been?

Write that down. That connection between concept and practice is where real learning happens.

[CLOSING REMARKS]

In our next lesson, we will explore Measuring AI Impact and ROI, which builds directly on what we have covered today. I would encourage you to complete the reflection exercises before moving on, as they will prepare you for the next set of concepts.

This has been Lesson 1.2: Building an AI Roadmap, part of the AI Strategy for Managers module in Level 5: Strategic AI Leadership of the AI for Managers certification.

Remember: the goal is not to know more about AI. The goal is to be a better manager because of how you use AI. Those are very different things, and this program is designed for the latter.

Thank you for your time, your attention, and your commitment to growing as a leader in an AI-transformed workplace. I look forward to our next session together.

END OF TRANSCRIPT

AI for Managers Certification Program

Level 5: Strategic AI Leadership | AI Strategy for Managers | Lesson 1.2

A SkillsClinic initiative by No Worker Left Behind and The Work Company.

Duration: ~24 minutes | Word Count: ~3712