AI Integration for Non-Technical Managers Course

Overview

AI Integration for Non-Technical Managers Course is a five-day foundation course for non-technical managers, business executives, functional leaders, transformation sponsors, and AI initiative owners, who leave with a Manager's AI Integration and Adoption Plan. Participants translate business problems into initiatives, assess readiness, choose sourcing and operating approaches, define decision boundaries, coordinate business and technical teams, set governance controls, plan adoption, and measure value. Agile Leaders Training Center provides training in AI integration for non-technical managers.

Who Should Attend

• Functional managers responsible for business processes and outcomes
• Executives responsible for sponsorship and investment decisions
• Transformation teams responsible for coordinating cross-functional change
• Initiative owners responsible for delivery, adoption, and value
• Risk and governance teams responsible for oversight and controls
• Business partners responsible for working with data and technology teams

The course assumes participants make or support business, transformation, investment, or governance decisions and leaves out coding, model development, technical architecture design, and vendor-product administration.

Departments and Industries

The course supports manager-led AI integration across business and public-service settings.

• Strategy, transformation, and business-unit leadership
• Operations, finance, marketing, people, and customer functions
• Technology, data, risk, and internal-control teams
• Financial services, retail, and professional-services organizations
• Healthcare, education, and public-service organizations
• Manufacturing, logistics, utilities, and infrastructure operators

Learning Objectives

By the end of this course, participants will be able to:

• Analyze business problems for suitable AI integration
• Evaluate process, data, people, and technology readiness
• Compare sourcing and operating approaches
• Design decision boundaries and cross-functional ownership
• Apply governance, adoption, and value controls
• Build a manager's AI integration and adoption plan

Course Agenda

Day 1: Business Problems and AI Fit

• Business Problem and Outcome Framing Canvas
• Process, Decision, and Evidence Flow Map
• AI Capability and Limitation Discussion Guide
• Human Judgment and Automation Boundary Matrix
• Manager AI Initiative Brief Template

Day 2: Readiness and Integration Choices

• Data, Process, People, and Technology Readiness Assessment
• Integration Dependency and Constraint Register
• Build, Buy, Partner, and Defer Decision Matrix
• Current and Target Operating Workflow Map
• AI Initiative Feasibility and Risk Scorecard

Day 3: Roles and Delivery Coordination

• Business, Data, Technology, and Risk Role Map
• Decision Rights and Accountability Charter
• Requirement, Assumption, and Acceptance Criteria Sheet
• Business-to-Technical Handoff Checklist
• AI Initiative Stage and Review Gate Board

Day 4: Governance and Adoption

• NIST AI RMF Manager Governance Canvas
• ISO/IEC 42001 Management Alignment Checklist
• Responsible AI Risk and Control Register
• Stakeholder Adoption and Capability Action Plan
• AI Benefit, Risk, and Performance Dashboard

Day 5: Manager Integration Practice

• Suggested Exercise: Frame a Business Problem and AI Fit
• Suggested Exercise: Assess Readiness and Integration Choices
• Suggested Exercise: Define Roles and Delivery Handoffs
• Suggested Exercise: Set Governance and Adoption Controls
• Capstone Exercise: Manager's AI Integration and Adoption Plan

Practical Exercises

The course uses suggested activities that turn business evidence into managed AI integration decisions.

• Suggested activity: frame problems, outcomes, processes, decisions, evidence, capabilities, and human boundaries
• Suggested activity: assess readiness, record dependencies, compare sourcing choices, and map target workflows
• Suggested activity: assign roles and decision rights, document assumptions, define acceptance, and plan handoffs
• Suggested activity: set governance controls, adoption actions, benefit measures, review gates, and the integration plan

FAQs

Who suits AI integration for non-technical managers training?

AI integration training suits managers, executives, functional leaders, transformation sponsors, initiative owners, and business partners who coordinate AI work. It assumes business decision experience and requires no AI programming.

How does manager AI integration differ from technical AI development training?

Manager AI integration focuses on business problems, readiness, sourcing, operating workflows, ownership, handoffs, governance, adoption, and value. Technical AI development focuses on data engineering, algorithms, coding, model testing, infrastructure, and deployment.

How should managers decide whether a business problem suits AI?

Managers should examine the decision, desired outcome, evidence, repetition, variability, uncertainty, data availability, human judgment, process constraints, risks, alternatives, and measurable value before proposing AI.

How can non-technical managers coordinate AI delivery?

Managers can define outcomes, owners, decision rights, assumptions, acceptance criteria, data responsibilities, human boundaries, review gates, risks, adoption actions, measures, and escalation while technical teams handle engineering choices.

What belongs in a Manager's AI Integration and Adoption Plan?

The plan includes problems, outcomes, use cases, readiness, dependencies, sourcing choices, target workflows, roles, decision rights, handoffs, controls, adoption actions, benefits, measures, gates, and review decisions.

Conclusion

Participants take back a Manager's AI Integration and Adoption Plan connecting business problems, readiness, integration choices, roles, governance, adoption, and value. The plan makes assumptions, dependencies, decision boundaries, handoffs, controls, ownership, and measures visible across teams. It supports coordinated AI initiatives while retaining accountable management judgment.