Overview

Lecture URL: https://skill.re/learn/manager/mapping-workflows-for-ai-integration.php

AI FOR MANAGERS CERTIFICATION

Organizational AI Integration (Level 4) | Workflow Design and Integration

LECTURE: Mapping Workflows for AI Integration

Lesson 1.1 | Estimated Duration: ~25 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 Workflow Design and Integration module: Mapping Workflows for AI Integration.

This is Lesson 1.1 in Level 4, the Organizational AI Integration 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 Maintaining Authenticity and Trust. 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 1.1: Mapping Workflows for AI Integration

Title

Mapping Workflows for AI Integration: Systematically Identifying Where AI Delivers Genuine Value

Purpose

This lesson equips managers to systematically analyze their team's workflows to identify where AI integration can create genuine value. Rather than asking "Where can we use AI?" you'll learn to ask "Where does AI solve a real problem in our workflow?" You'll map processes, identify bottlenecks, assess AI-readiness, and make data-driven decisions about integration points. This moves you from personal AI adoption to strategic team-level deployment.

Why This Matters for Managers

At Level 3, you learned to use AI effectively for individual tasks. At Level 4, the stakes change. When you integrate AI into team workflows, you're:

• Committing organizational resources to new tools, training, and process change
  - Creating dependencies on AI systems that must function reliably
  - Affecting multiple people whose work flows through these processes
  - Accountable for outcomes when AI-augmented processes fail or don't deliver promised benefits

Poor workflow analysis leads to costly mistakes: implementing AI tools that duplicate effort, creating dependencies on capabilities that become obsolete, or redesigning processes in ways that actually reduce quality or employee satisfaction. Systematic mapping prevents wasted investment and identifies the high-leverage integration points where AI creates genuine competitive advantage.

Managers who master workflow mapping gain a framework for continuously improving team effectiveness as AI capabilities evolve. You move from "we should try AI here" to "this is where AI delivers measurable value within our workflow."

Core Concepts

Workflow Mapping Fundamentals

A workflow is the sequence of steps, decisions, and handoffs that transform input into output. Mapping means making this sequence explicit--understanding current state (as-is), identifying problems, and designing improved state (to-be).

Key workflow elements:

• Inputs: What the process needs to start (data, requests, requests from customers)
  - Steps: Sequential actions team members perform (research, analysis, creation, review)
  - Decision points: Where humans make judgments that determine next steps
  - Handoffs: Where work transfers between people or systems
  - Outputs: The results the workflow produces
  - Constraints: Regulatory requirements, quality standards, compliance rules
  - Resources: Tools, technology, expertise required

AI Integration Points

Not all workflow steps are equally suited for AI. You're looking for steps where AI:

1. Handles repetitive, pattern-matching work (research, data organization, initial drafting, categorization)
2. Augments human judgment (providing options, generating alternatives, highlighting patterns)
3. Accelerates high-volume activities (responding to routine inquiries, initial processing, sorting)
4. Ensures consistency (applying standards, formatting, checking completeness)
5. Creates decision support (analyzing data, summarizing information, flagging exceptions)

Poor fit for AI: Steps requiring genuine creativity, novel judgment in unique contexts, managing sensitive relationships, or complex stakeholder navigation.

Bottleneck Identification

Bottlenecks are workflow steps that constrain overall team performance. They might be:

• Time bottlenecks: Steps that take disproportionate time (research that takes 2 hours per output)
  - Resource bottlenecks: Steps that require expensive or scarce expertise
  - Quality bottlenecks: Steps where errors cascade downstream
  - Capacity bottlenecks: Steps that limit how much work the team can handle
  - Dependency bottlenecks: Steps that block others from progressing

AI often delivers most value attacking bottlenecks that are:

• Repetitive (same type of work repeatedly)
  - Pattern-based (require recognizing patterns rather than creating novel solutions)
  - Data-heavy (involve processing large volumes of information)
  - Highly procedural (follow defined rules or standards)

AI-Readiness Assessment

Not every bottleneck is ready for AI. Before investing in integration, assess readiness:

Data readiness: Do you have sufficient, high-quality data for AI to learn from? (If you're asking AI to categorize documents, do you have examples of well-categorized documents?)

Process maturity: Is the current process stable and well-defined? (AI works best when it's augmenting a clear, repeatable process--not fixing a chaotic one)

Tool availability: Do suitable AI tools exist? (Some needs don't have good solutions yet)

Team capability: Can your team work effectively with AI tools? (Do they have required technical skills or learning capacity?)

Organizational readiness: Does the organization support this change? (Is there buy-in? Are there governance restrictions?)

Economic viability: Will the improvement justify the investment? (Tool costs, training, process redesign, ongoing management)

Moving from Personal to Team Workflows

Your personal AI use has taught you how individuals can work differently. Team workflows are more complex:

• Multiple perspectives: Different team members may have different needs and concerns
  - Quality accountability: You're responsible for output quality across all team members' work
  - Compliance and governance: Team processes must meet organizational requirements
  - Knowledge transfer: Workflows need to work even when specific people leave
  - Scalability: Processes should work as the team grows or circumstances change

This requires moving from "AI tools I like" to "AI tools that work reliably for the team."

Practical Managerial Use Cases

Use Case 1: Mapping a Customer Support Workflow

Your support team handles 200+ customer inquiries daily. Response time has been a complaint. You want to understand where AI could help.

Your mapping process:

1. Current workflow: Ticket arrives -> assigned to agent -> agent researches answer (30 min avg) -> agent drafts response -> supervisor reviews (spot-check basis) -> response sent
2. Bottleneck identification: Research takes 30 minutes per ticket. That's 100 hours/week of research across the team.
3. AI-readiness assessment:

• Data readiness: You have 2 years of resolved tickets with resolutions--good training data
  - Process maturity: Ticket system is stable; research process is defined
  - Tool availability: AI summarization and search tools exist
  - Team capability: Agents are tech-comfortable
  - Economic viability: Reducing research time by 50% would save 50 hours/week--significant value

1. Integration points identified:

• AI summarization of previous similar tickets (agents reference, not start from scratch)
  - AI search to quickly find relevant knowledge base articles
  - AI-generated draft response for routine inquiries (supervisor reviews before sending)

1. To-be workflow: Ticket arrives -> AI surfaces similar previous cases and relevant KB articles (2 min) -> agent customizes response based on this material (15 min) -> supervisor reviews (spot-check) -> response sent

Use Case 2: Mapping a Sales Proposal Process

Your sales team spends extensive time creating custom proposals for enterprise deals. Win rates are good, but cycle time is long.

Your mapping process:

1. Current workflow: Deal qualifies -> sales person conducts discovery (several meetings) -> sales person spends 4-6 hours building proposal -> legal reviews (2-3 days) -> proposal sent -> customer reviews -> negotiation/revision
2. Bottleneck identification: Proposal creation takes 4-6 hours per deal. With 20+ active deals, this is significant. Also, proposals often require revision because they don't address specific customer concerns.
3. AI-readiness assessment:

• Data readiness: You have templates and 50+ previous winning proposals
  - Process maturity: Proposal structure is defined; discovery questions are consistent
  - Tool availability: AI writing and personalization tools exist
  - Team capability: Sales team is comfortable with tools
  - Economic viability: Reducing time by 30% (1.5 hours/deal) would free up significant sales person time; better proposals could improve win rates

1. Integration points identified:

• AI generation of proposal first draft based on deal details and company materials
  - AI analysis of customer discovery notes to identify key concerns to address
  - AI personalization to incorporate customer's specific language and priorities

1. To-be workflow: Deal qualifies -> discovery conducted -> AI generates proposal draft incorporating customer details (30 min) -> sales person refines, ensures accuracy, makes final customizations (90 min) -> legal reviews -> proposal sent

Use Case 3: Mapping an Onboarding Workflow

Your organization brings on new employees frequently. Onboarding is inconsistent--some new hires feel well-integrated after 2 weeks; others struggle for 3 months.

Your mapping process:

1. Current workflow: New hire starts -> assigned to random mentor -> mentor informally shares information -> new hire reads documentation -> new hire shadows colleagues -> questions to multiple people -> gradually figures things out
2. Bottleneck identification: Information transfer is inconsistent. Critical context is in people's heads, not documentation. New hires make preventable mistakes because they don't know key procedures.
3. AI-readiness assessment:

• Data readiness: You have documentation, past onboarding materials, colleague expertise
  - Process maturity: Current process is not mature; it's informal
  - Tool availability: AI tutoring and Q&A tools exist
  - Team capability: New hires vary in technical skill
  - Economic viability: Better onboarding reduces mistakes, accelerates productivity, improves retention

1. Integration points identified:

• AI-powered Q&A system that answers new hire questions based on company documentation
  - AI summary of key procedures and organizational norms in digestible format
  - AI-generated personalized learning plan based on new hire's role and background

1. To-be workflow: New hire starts -> AI provides welcome pack with personalized learning plan -> mentor is available for guidance, not primary information source -> new hire uses AI Q&A for routine questions -> regular check-ins with manager -> structured progress assessment at 1, 2, 4 weeks

Examples

Example 1: Marketing Analytics Team Workflow Map

Current State Analysis:

• Weekly reporting (15 hours/person)
  - Data gathering from 6 platforms (3 hours)
  - Manual spreadsheet consolidation (4 hours)
  - Insight analysis and interpretation (5 hours)
  - Report writing and formatting (3 hours)

Bottlenecks Identified:

• Data gathering is time-consuming and error-prone (different analysts find slightly different numbers)
  - Spreadsheet consolidation is repetitive and slow
  - Insight generation takes significant time but requires human judgment

AI-Readiness Assessment:

• Data readiness: High (structured data from platforms, historical records)
  - Process maturity: Stable (same report weekly)
  - Tool availability: High (AI data integration and visualization tools)
  - Team capability: Medium (analysts understand data, learning curve with new tools)
  - Economic viability: High (freeing 10+ hours/week per person)

Integration Decision:

Implement AI for data gathering and consolidation; maintain human interpretation of insights.

To-Be Workflow:

• AI aggregates data from all platforms automatically (eliminates 4+ hours)
  - AI generates draft report with standard visualizations (eliminates 3+ hours formatting)
  - Analysts focus on interpreting patterns, identifying what's changed, recommending actions (maintain 5 hours human-focused work)

Expected Impact: 50% reduction in reporting time; improved consistency; analysts focus on higher-value insight work.

Example 2: Legal Document Review Workflow Map

Current State Analysis:

• Contract reviews (highly variable, 2-40 hours per contract)
  - Initial reading and annotation (0.5-5 hours)
  - Cross-reference to master agreement and policies (1-10 hours)
  - Flagging deviations and risks (1-5 hours)
  - Drafting revision suggestions (1-20 hours)

Bottlenecks Identified:

• Cross-referencing is tedious and time-consuming
  - Junior attorneys spend significant time on routine flagging
  - Variation in approach across attorneys reduces consistency

AI-Readiness Assessment:

• Data readiness: High (master agreements, policy documents, past reviews)
  - Process maturity: Stable (clear review criteria)
  - Tool availability: High (AI contract analysis tools exist)
  - Team capability: Medium (attorneys are analytical but may be skeptical of AI legal work)
  - Economic viability: High (contract review is expensive; time reduction is significant)

Integration Decision:

Implement AI for routine flagging and cross-referencing; senior attorney reviews AI output and handles novel issues.

To-Be Workflow:

• AI reviews contract against master agreement and policies, flags standard deviations (reduces 5+ hours)
  - Senior attorney reviews AI-flagged items, adjusts as needed, handles novel issues (2-10 hours instead of 10-40)
  - Junior attorneys assist with complex custom negotiation (focus on learning and client communication)

Expected Impact: 40-60% reduction in review time; more consistent flagging; junior attorneys develop expertise faster.

Example 3: Product Management Prioritization Workflow Map

Current State Analysis:

• Quarterly prioritization process (80+ hours across PM team)
  - Gathering feature requests from sales, customers, support (10 hours)
  - Analyzing each request (research, impact assessment, technical feasibility) (30 hours)
  - Consolidating into recommendation (10 hours)
  - Leadership alignment and final decision (30 hours)

Bottlenecks Identified:

• Analysis is time-consuming and inconsistent (some features analyzed deeply, others superficially)
  - Information gathering from multiple sources is inefficient
  - Analysis quality varies by who does it

AI-Readiness Assessment:

• Data readiness: Medium (feature requests exist, but customer impact data is scattered)
  - Process maturity: ? Medium (process is defined but fairly ad-hoc)
  - Tool availability: Medium (AI analysis tools exist; customer feedback tools are evolving)
  - Team capability: High (PMs are analytically skilled)
  - Economic viability: High (better prioritization drives product strategy effectiveness)

Integration Decision:

Implement AI for request aggregation and initial analysis; human PMs focus on nuanced judgment and leadership communication.

To-Be Workflow:

• AI aggregates feature requests from all sources, identifies duplicates (eliminates 5+ hours)
  - AI analyzes each request for technical requirements and customer impact patterns (reduces 20 hours to 8 hours)
  - PMs review AI analysis, conduct deep dives on strategic priorities, make final recommendations (maintain 30 hours high-value work)
  - Leadership alignment remains human-driven (30 hours unchanged)

Expected Impact: 25-30% reduction in total process time; more consistent analysis; PMs focus on strategy rather than research.

Anti-Patterns/Misuse Risks

Anti-Pattern 1: "We Have a Tool, Now Find Problems"

The problem: You acquire an AI tool (because it's trendy or a vendor convinced you) and then search for places to use it. This leads to forcing AI into workflows where it doesn't fit.

Why it fails: Workflows are optimized for different constraints and values. Forcing AI creates complexity without benefit. Your team resists using AI for the wrong reasons.

Right approach: Always start with workflow analysis. Identify problems first, then evaluate whether AI is the right solution.

Anti-Pattern 2: "Let's Automate Everything"

The problem: You identify that AI could handle 80% of a workflow and eliminate the human step. You implement full automation without considering what you're losing.

Why it fails: Workflows have valuable human judgment steps. Full automation removes quality control, context adaptation, and creative problem-solving. When automation fails, there's no human backup.

Right approach: Map where human judgment is essential. Design AI to augment humans, not replace the judgment step. Maintain human checkpoints.

Anti-Pattern 3: "Don't Worry About Data Quality"

The problem: You want to use AI to process customer feedback but your feedback data is messy, inconsistent, and incomplete. You assume AI will handle it.

Why it fails: AI learns from examples. Poor data quality creates poor AI performance. Your team stops trusting the AI. You waste time correcting AI errors instead of improving the original workflow.

Right approach: Assess data readiness before implementation. Plan data cleanup if needed. Start with high-quality subsets if full dataset isn't ready.

Anti-Pattern 4: "One Size Fits All"

The problem: You implement an AI solution that works well for 70% of your workflow but the remaining 30% requires manual override. You force all work through the AI process anyway.

Why it fails: Your team develops workarounds. The "standard" process becomes slower than the previous manual approach because of exceptions. Compliance risk increases.

Right approach: Design workflows that route exceptions appropriately. AI handles standard cases; humans handle exceptions. Make the exception path explicit and sustainable.

Anti-Pattern 5: "Skip the Mapping, Just Ask the Team"

The problem: Instead of mapping workflows systematically, you ask your team "Where should we use AI?" and implement their suggestions.

Why it fails: Teams often identify personal pain points rather than team bottlenecks. You miss leveraging AI in areas where it would create most value. You don't get a complete picture of how changes affect the whole workflow.

Right approach: Use systematic mapping. Include team input, but don't let it replace analysis. You'll often see opportunities the team doesn't surface.

Human Judgment Checkpoints

Before you commit to AI integration in a workflow, pause at these judgment checkpoints:

Checkpoint 1: Is This a Real Bottleneck?

Ask yourself: "If we eliminated this step entirely, would the team's overall productivity improve meaningfully?" If the answer is "maybe" or "it depends," you may be addressing a minor issue. Invest in addressing major bottlenecks first.

Checkpoint 2: Is the Problem Really Solved by AI?

Some bottlenecks are solved by better process design, not AI. Example: "We spend 2 hours per week manually consolidating data from systems that don't integrate" might be better solved by system integration than AI. Evaluate alternatives.

Checkpoint 3: Can the Team Sustain This?

Will your team be comfortable with this workflow change? Do they have the technical capability? Do they understand the change? If resistance is high, even a good AI integration can fail. Factor in change management difficulty.

Checkpoint 4: What Happens When AI Fails?

What's the fallback if the AI component breaks or produces poor output? Can the team revert to the old process? Is there manual review? If there's no reasonable fallback, you have a risk management problem.

Checkpoint 5: Are We Measuring the Right Thing?

You plan to measure "time saved." But what if the real value is "fewer errors" or "more consistent quality"? What if time saved goes to more work, not less? Define the success metric before implementation.

Responsible AI Considerations

Consideration 1: Bias in Historical Workflow Data

When you map current workflows, you're documenting how work is currently done. If current workflows reflect biased decisions (e.g., certain types of customers get handled differently), mapping that process and then having AI learn from it perpetuates and scales the bias.

Action: When analyzing workflows, ask: "Are we treating all customers/cases consistently?" If workflows vary by customer type or other protected characteristics, address the bias before adding AI.

Consideration 2: Transparency About AI Steps

If you embed AI into a workflow, team members need to understand where AI is making decisions and where humans are responsible. Lack of clarity creates accountability problems when things go wrong.

Action: In your workflow maps, make AI steps explicit. Document: "AI generates three options; human chooses." Not: "AI handles customer segmentation."

Consideration 3: Job Impact and Redeployment

Workflow changes often reduce time spent on certain tasks. You need to plan what people do with freed-up time. If the answer is "we'll lay people off," that's a real consequence that affects team morale, retention, and your ability to attract talent.

Action: When identifying bottlenecks, think about redeployment. Where can freed-up capacity go? Faster turnaround? New capabilities? Better customer service? More strategic work?

Consideration 4: Accuracy and Error Rates

When you assess AI-readiness, consider: What's the acceptable error rate for this workflow? If AI is 95% accurate but your current manual process is 99% accurate, is that a step backward? If AI is 99% accurate but one error causes major customer impact, is that acceptable?

Action: Establish error thresholds before implementation. Define what errors are acceptable and what require human escalation.

Practice/Reflection Prompts

Prompt 1: Map Your Highest-Value Workflow

Choose one workflow your team performs regularly that you think could benefit from AI. Using the mapping format from this lesson:

1. Document current state: What are all the steps? Who does them? How long does each take?
2. Identify bottlenecks: Which steps take disproportionate time or create quality issues?
3. Assess AI-readiness: For each bottleneck, evaluate data quality, process maturity, tool availability, team capability, and economic viability.
4. Identify integration points: Where would AI add value without eliminating important human judgment?
5. Document to-be state: What would the workflow look like with AI integration?

Write a brief summary (1-2 pages) of your findings.

Prompt 2: The AI Readiness Conversation

Identify a workflow bottleneck you could address with AI. Interview three team members who perform this work:

1. What do they find most time-consuming about this workflow?
2. What judgment call do they make that feels most important?
3. What worries them about automating any part of this workflow?
4. What data or tools would actually help them work better?

Synthesize their perspectives. Does it match your analysis? What did you miss?

Prompt 3: Comparative Workflow Analysis

Map two very different workflows in your team (e.g., "client onboarding" and "project status reporting"). Compare:

1. Which has higher AI-readiness?
2. Which has greater bottleneck impact?
3. Which would be simpler to implement first?
4. What does the easier implementation teach you that would help the harder one?

Prompt 4: Tool Evaluation Preparation

For your identified integration opportunity, research two different AI tools that could help:

1. What data does each require?
2. What level of technical setup is needed?
3. What does each cost (per user, per month)?
4. What are users saying about ease of use and reliability?

Which would be better for your team and why?

Prompt 5: Resistance Anticipation

For your identified workflow integration, think about potential team member concerns:

1. Who on your team might worry about job security or changing role?
2. What quality risks might they worry about?
3. What practical challenges might they anticipate?
4. For each concern, what information or commitment from you would address it?

Key Takeaways

1. Map before implementing: Systematic workflow analysis prevents wasted AI investments. Know your bottlenecks, not just your opportunities.
2. Assess AI-readiness across multiple dimensions: Data quality, process maturity, tool availability, team capability, and economic viability all matter. No single dimension is sufficient.
3. Identify high-leverage integration points: AI creates most value in repetitive, pattern-based steps with clear data and quality standards. Not all bottlenecks are AI-ready.
4. Maintain human judgment in critical areas: The best workflows combine AI's pattern-matching and consistency with human judgment on novel cases and relationship-critical decisions.
5. Plan for change management: Technology integration is as much about people and process as it is about tools. Map that too.
6. Establish success metrics before implementation: Define what "improved workflow" means for your team. Time saved? Quality improvement? Consistency? Customer satisfaction? Different integrations optimize for different outcomes.
7. Consider consequences beyond efficiency: Workflow changes affect job satisfaction, career development, team dynamics, and organizational culture. Factor these into your decision-making.

Glossary Items

Bottleneck: A workflow step that constrains overall team productivity. Bottlenecks can be time-based (take disproportionate time), resource-based (require scarce expertise), capacity-based (limit how much work can be handled), or quality-based (errors here cascade downstream).

Data Readiness: Whether sufficient, high-quality data exists to train or implement AI. High data readiness means you have clean, well-structured, representative examples. Low readiness means data is sparse, dirty, or non-representative.

Handoff: The point in a workflow where work transfers from one person or system to another. Handoffs often introduce delays, information loss, and quality variation.

Integration Point: A specific step in a workflow where AI can add value--typically by handling pattern-matching work, augmenting human judgment, or accelerating routine tasks.

Process Maturity: Whether a workflow is stable, repeatable, and well-documented. Mature processes have clear steps, consistent outcomes, and good documentation. Immature processes are ad-hoc and vary based on who's doing the work.

Workflow: The sequence of steps, decisions, and handoffs that transforms input into output. Workflows can be formal (documented procedures) or informal (how people actually work).

AI-Readiness: Whether a particular workflow or organization is prepared for AI integration. Readiness includes data quality, process clarity, technical infrastructure, team capability, and organizational support.

Related Lessons

• Lesson 1.2: Designing AI-Augmented Processes--Once you've mapped workflows and identified integration points, you'll design the detailed AI-augmented processes.
  - Lesson 1.4: Measuring Workflow Improvement--You'll establish metrics to demonstrate that your mapped workflow actually delivers expected benefits.
  - Lesson 2.2: Building Team AI Capability--Workflow mapping often reveals capability gaps your team needs to address.
  - Lesson 4.2: Monitoring and Feedback Systems--The feedback loops you establish will monitor whether your mapped workflows deliver expected value.

Length: ~450 lines

Reading Time: 35-40 minutes

[SYNTHESIS AND APPLICATION]

Let us step back and look at the bigger picture of what we have covered in this session on Mapping Workflows for AI Integration.

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 mapping workflows for ai integration 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 Designing AI Augmented Processes, 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 1.1: Mapping Workflows for AI Integration, part of the Workflow Design and Integration module in Level 4: Organizational AI Integration 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 4: Organizational AI Integration | Workflow Design and Integration | Lesson 1.1

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

Duration: ~25 minutes | Word Count: ~3852