The Executive’s Guide to Upskilling Your Workforce for AI

Recent research by McKinsey shows that 31% of the workforce will require retraining or reskilling within the next three years. With companies rushing to become AI-first, I’m not surprised. In fact, I think that number should be higher.

Much like digital literacy became essential in the early 2000s, AI literacy is the new baseline for workforce competence. Organizations that fail to develop AI skills will fall behind competitors who leverage AI to enhance productivity, drive innovation, and deliver superior customer experiences.

This guide offers a comprehensive roadmap for executives seeking to transform their workforce for the AI era. We’ll examine practical strategies for conducting skills gap analyses, developing talent through multiple channels, creating a learning culture, empowering change champions, and addressing AI anxiety.

Each section provides actionable frameworks backed by research and case studies, enabling you to immediately apply these approaches within your organization.

Section 1: Conducting an AI Skills Gap Analysis

Where Do you Want to be?

Before launching any training initiative, you must first understand the specific AI-related skills your organization requires. When working with my clients, I’ve identified three categories of AI skills that companies need:

Foundational AI Literacy (All Employees)

In my opinion, this is table-stakes. Every employee in your company needs to have basic AI literacy, the same way they need to have basic computer literacy.

• Understanding basic AI concepts and terminology
• Recognizing appropriate use cases for AI tools
• Effective prompt engineering and interaction with AI assistants
• Critical evaluation of AI outputs and limitations
• Awareness of ethical considerations and responsible AI use

Intermediate AI Skills (Domain Specialists)

As you go deeper into your AI transformation, you’ll want to start automating processes and integrating AI deeper into workflows. This means training a percentage of your workforce on AI automation and AI agents.

Ideally, these are domain specialists who understand the workflows well enough to design automations for them.

• Ability to identify automation opportunities within specific workflows
• Data preparation and quality assessment
• Collaboration with technical teams on AI solution development
• Integration of AI tools into existing processes
• Performance monitoring and feedback provision

Advanced AI Expertise (Technical Specialists)

Finally, for organizations that are building AI products and features, the following skills are absolutely necessary.

• AI ethics implementation and compliance
• AI system design and implementation
• Model selection, training, and fine-tuning
• AI infrastructure management and optimization
• Data architecture and governance for AI

Where are you now?

The next step is understanding your organization’s current AI capabilities. When working with clients, I often start with a survey to leadership and employees.

My Leadership Capability Assessment evaluates executive understanding of AI potential and limitations, and assesses their ability to develop and execute AI strategy.

My Workforce Literacy Survey measures baseline understanding of AI concepts across the organization, and assesses comfort levels with AI tools and applications.

For organizations that are building AI products and features, create a Technical Skills Inventory to document existing data science, machine learning, and AI engineering capabilities, map current technical skills against future needs, and identify training needs for different technical roles.

I also recommend an overall Organizational Readiness Assessment to evaluate data infrastructure and governance maturity, assess cross-functional collaboration capabilities, and review change management processes and effectiveness.

At this point, it becomes fairly obvious where the gaps are in where you are right now and where you want to be.

Create A development plan

I then create a custom skills development plan to close the gap. Here’s a sample timeline I draw up for clients, although this depends heavily on how fast you move and how big your organization is.

Time Horizon	Priority Skills	Target Audience	Business Impact
0-3 months	AI literacy, foundational concepts, AI tool usage	All employees	Improved AI adoption, reduced resistance
3-6 months	Role-specific AI applications, workflow integration	Department leaders, domain experts	Process optimization, efficiency gains
6-12 months	Advanced AI development, AI system design, AI ethics implementation	Technical specialists, innovation teams	New product/service development, competitive differentiation
12+ months	Emerging AI capabilities, human-AI collaboration, AI governance	Executive leadership, strategic roles	Business model transformation, market leadership

I suggest running the skills gap analysis every quarter and re-evaluating. The pace at which AI is developing requires continuous up-skilling at training in the latest technologies.

Section 2: The Build, Buy, Bot, Borrow Model for AI Talent

As your organization develops its AI capabilities, you’ll need a multi-pronged approach to talent acquisition and development. The “Build, Buy, Bot, Borrow” framework offers a comprehensive strategy for addressing AI talent needs. This model provides flexibility while ensuring you have the right capabilities at the right time.

Building Internal Talent Through Training and Development

Internal talent development should be your cornerstone strategy, as it leverages existing institutional knowledge while adding new capabilities. Develop an organizational learning strategy that includes:

Tiered Learning Programs

• Level 1: AI Fundamentals – Basic AI literacy for all employees
• Level 2: AI Applications – Role-specific training on using AI tools
• Level 3: AI Development – Specialized technical training for selected roles
• Level 4: AI Leadership – Strategic AI implementation for executives and managers

Experiential Learning Opportunities

• AI hackathons and innovation challenges
• Rotation programs with AI-focused teams
• Mentorship from AI experts
• Applied learning projects with measurable outcomes

Learning Ecosystems

• On-demand microlearning resources
• Self-paced online courses and certifications
• Cohort-based intensive bootcamps
• Executive education partnerships

Many organizations are finding that the “build” strategy offers the best long-term return on investment. I’ll dive deeper into how to build AI talent in later sections.

Strategic Hiring for Specialized AI Roles

Despite your best efforts to build internal talent, some specialized AI capabilities may need to be acquired through strategic hiring. This includes AI/ML engineers, data scientists, and AI integration specialists.

To develop an effective hiring strategy for AI roles:

1. Focus on specialized competencies rather than general AI knowledge
   • Identify the specific AI capabilities required for your business objectives (from skills gap above)
   • Create detailed skill profiles for each specialized role
   • Develop targeted assessment methods to evaluate candidates
2. Look beyond traditional sources of talent
   • Partner with universities and research institutions with strong AI programs
   • Engage with AI communities and open-source projects
   • Consider talent from adjacent fields with transferable skills
3. Create an AI-friendly work environment
   • Provide access to high-performance computing resources
   • Establish clear AI ethics and governance frameworks
   • Support ongoing professional development in rapidly evolving AI domains
   • Build a culture that values AI innovation and experimentation
4. Develop competitive compensation strategies
   • Create flexible compensation packages that reflect the premium value of AI expertise
   • Consider equity or profit-sharing for roles that directly impact business outcomes
   • Offer unique perks valued by the AI community, such as conference attendance or research time

Using AI to Augment Existing Workforce Capabilities

The “bot” aspect of the framework involves strategic deployment of AI tools, automations, and agents to amplify the capabilities of your existing workforce. This approach offers several advantages:

1. AI agents can handle routine tasks, freeing employees to focus on higher-value work
2. AI tools can provide just-in-time knowledge, enabling employees to access specialized information when needed
3. AI can augment decision-making, helping employees make more informed choices

Implement these strategies to effectively leverage AI for workforce augmentation:

AI Agents

• Map existing processes to identify routine, time-consuming tasks suitable for AI automation
• Deploy AI agents for common tasks like scheduling, report generation, and data summarization
• Create seamless handoffs between AI and human components of workflows

Knowledge Augmentation

• Implement AI-powered knowledge bases that can answer domain-specific questions
• Deploy contextual AI assistants that provide relevant information during decision-making processes
• Create AI-guided learning paths that help employees develop new skills

Decision Support

• Develop AI models that can analyze complex data and provide recommendations
• Implement scenario-planning tools that help employees visualize potential outcomes
• Create AI-powered dashboards that provide real-time insights into business performance

I highly recommend developing AI automations and agents in parallel with employee up-skilling programs. Low hanging automations can be deployed in weeks and provide immediate benefits.

This is why so many major tech companies are going all in on agents and have paused hiring. If you’re interested in how to find opportunities to do this in your organization and design effective agents, read my guide here.

Borrowing Talent through Strategic Partnerships

The final component of the talent strategy involves “borrowing” specialized AI capabilities through strategic partnerships. This approach is particularly valuable for accessing scarce expertise or handling short-term needs.

Strategic Vendor Relationships

• Evaluate AI platform providers based on their domain expertise, not just their technology
• Develop deep partnerships with key vendors that include knowledge transfer components
• Create joint innovation initiatives with strategic technology partners

Consulting and Professional Services

• Engage specialized AI consultants for specific, high-value projects
• Use professional services firms to accelerate implementation of AI initiatives
• Partner with boutique AI firms that have deep expertise in your industry

Academic and Research Partnerships

• Collaborate with university research labs on cutting-edge AI applications
• Sponsor academic research in areas aligned with your strategic priorities
• Participate in industry consortia focused on AI standards and best practices

Talent Exchanges

• Create temporary talent exchange programs with non-competing organizations
• Develop rotational programs with technology partners
• Participate in open innovation challenges to access diverse talent pools

The borrowed talent approach offers several advantages:

1. Access to specialized expertise that would be difficult or expensive to develop internally
2. Flexibility to scale AI capabilities up or down based on business needs
3. Exposure to diverse perspectives and industry best practices
4. Reduced risk in exploring emerging AI technologies

By strategically combining the build, buy, bot, and borrow approaches, organizations can develop a comprehensive AI talent strategy that provides both depth in critical areas and breadth across the organization.

Section 3: Creating an AI Learning Culture

Let’s dive into how you can up-skill employees and build AI talent internally, as I mentioned above.

AI training cannot follow a one-size-fits-all approach. Different roles require different types and levels of AI knowledge and skills. From my client work, I have identified three primary audience segments:

Executive Leadership

• Focus Areas: Strategic AI applications, ethical considerations, governance, ROI measurement
• Format Preferences: Executive briefings, peer discussions, case studies
• Key Outcomes: Ability to set AI strategy, evaluate AI investments, and lead organizational change

Managers and Team Leaders

• Focus Areas: Identifying AI use cases, managing AI-enabled teams, process redesign
• Format Preferences: Applied workshops, collaborative problem-solving, peer learning
• Key Outcomes: Ability to identify AI opportunities, guide implementation, and support team adoption

Individual Contributors

• Focus Areas: Hands-on AI tools, domain-specific applications, ethical use of AI
• Format Preferences: Interactive tutorials, practical exercises, on-the-job application
• Key Outcomes: Proficiency with relevant AI tools, ability to integrate AI into daily workflows

For each segment, design targeted learning experiences that address their specific needs and preferences. Here’s an example of what I recommend to clients:

Level	Executive Leadership	Managers / Team Leaders	Individual Contributors
Basic	AI Strategy Overview (2 hours)	AI for Team Leaders (2 hours)	AI Fundamentals (2 hours)
Intermediate	AI Governance Workshop (2 hours)	AI Use Case Design (4 hours)	AI Tools Bootcamp (8 hours)
Advanced	AI Investment Roundtable (2 hours)	AI-Enabled Transformation (8 hours)	Domain-Specific AI Training (8 hours)

But AI training does not stop there. AI is always evolving so a one-time training program is insufficient. Many organizations struggle with the pace of changes in AI, with capabilities evolving faster than organizations can adapt.

This means you need to foster a continuous learning mindset:

Leadership Modeling

• Executives should openly share their own AI learning journeys
• Leaders should participate in AI training alongside team members
• Management should recognize and reward ongoing skill development

Learning Infrastructure

• Create dedicated time for AI learning (e.g., “Learning Fridays”)
• Develop peer learning communities around AI topics
• Establish AI learning hubs that curate and share relevant resources

Growth Mindset Development

• Promote the belief that AI capabilities can be developed through effort
• Encourage experimentation and learning from failures
• Recognize improvement and progress, not just achievement

I’ve found it’s a lot easier to create and maintain an AI learning culture when there are champions and go-to experts in the organization driving this culture.

I often advise clients to identify these AI champions and empower them by creating AI leadership roles, providing them with advanced training and resources, and creating a clear mandate that defines their responsibility for driving AI adoption.

These AI champions should be included in AI strategy development, use case and implementation approaches, and vendor selection and evaluation processes.

Other ways to sustain this learning culture and increase AI adoption that have worked well for my clients are:

1. Incentivizing AI adoption through recognition programs, and financial incentives
2. Creating mentorship programs and group learning cohorts within the company
3. Establish communities based on specific business functions (marketing AI, HR AI, etc.)
4. Implement hackathons and innovation challenges
5. Create knowledge repositories for AI use cases and lessons learned

Section 4: Addressing AI Anxiety and Resistance

Despite growing enthusiasm for AI, 41% of employees remain apprehensive about its implementation. Understanding these concerns is essential for effective intervention.

Key factors driving AI anxiety include:

• Fear of Job Displacement – Concerns about automation replacing human rolesand uncertainty about future career paths
• Security and Privacy Concerns – Worries about data protection and cybersecurity risks
• Performance and Reliability Issues – Skepticism about AI accuracy and reliability and fears of over-reliance on imperfect systems
• Skills and Competency Gaps – Concerns about keeping pace with change

One of the most effective ways to allay these fears is to demonstrate how the technology augments human capabilities rather than replacing them. This approach shifts the narrative from job displacement to job enhancement.

Pilot Projects with Visible Benefits

• Implement AI solutions that address known pain points
• Focus initial applications on automating tedious, low-value tasks
• Showcase how AI frees up time for more meaningful work

Skills Enhancement Programs

• Develop training that shows how AI can enhance professional capabilities
• Create clear pathways for employees to develop new, AI-complementary skills
• Emphasize the increased value of human judgment and creativity in an AI-enabled environment

Role Evolution Roadmaps

• Work with employees to envision how their roles will evolve with AI
• Create transition plans that map current skills to future requirements
• Provide examples of how similar roles have been enhanced by AI in other organizations

Shared Success Metrics

• Develop metrics that track both AI performance and human success
• Share how AI implementation impacts team and individual objectives
• Create incentives that reward effective human-AI collaboration

A common pitfall is focusing too narrowly on productivity gains. The McKinsey report notes that “If CEOs only talk about productivity they’ve lost the plot,” suggesting that organizations should emphasize broader benefits like improved customer experience, new growth opportunities, and enhanced decision-making.

Conclusion: Implementing an Enterprise-Wide Upskilling Initiative

Timeline for Implementation

Creating an AI-ready workforce requires a structured, phased approach. Here’s a sample timeline I’ve implemented for my clients:

Phase 1: Assessment and Planning (1 months)

• Conduct an AI skills gap analysis across the organization
• Develop a comprehensive upskilling strategy aligned with business objectives
• Build executive sponsorship and secure necessary resources
• Establish baseline metrics for measuring progress

Phase 2: Infrastructure and Pilot Programs (2-3 months)

• Develop learning infrastructure (platforms, content, delivery mechanisms)
• Identify and train initial AI champions across departments
• Launch pilot training programs with high-potential teams
• Collect feedback and refine approach based on early learnings

Phase 3: Scaled Implementation (3-6 months)

• Roll out tiered training programs across the organization
• Activate formal mentorship programs and communities of practice
• Implement recognition systems for AI skill development
• Begin integration of AI skills into performance management processes

Phase 4: Sustainability and Evolution (6+ months)

• Establish continuous learning mechanisms for emerging AI capabilities
• Develop advanced specialization tracks for technical experts
• Create innovation programs to apply AI skills to business challenges
• Regularly refresh content and approaches based on technological evolution

This phased approach allows organizations to learn and adapt as they go, starting with focused efforts and expanding based on successful outcomes. The timeline above is very aggressive and may need adjustment based on organizational size, industry complexity, and the current state of AI readiness.

Key Performance Indicators for Measuring Workforce Readiness

To evaluate the effectiveness of AI upskilling initiatives, organizations should establish a balanced set of metrics that capture both learning outcomes and business impact. Based on my client work, I’ve found that KPIs should include:

Learning and Adoption Metrics

• Percentage of employees completing AI training by role/level
• AI tool adoption rates across departments
• Number of AI use cases identified and implemented by teams
• Employee self-reported confidence with AI tools

Operational Metrics

• Productivity improvements in AI-augmented workflows
• Reduction in time spent on routine tasks
• Quality improvements in AI-assisted processes
• Decrease in AI-related support requests over time

Business Impact Metrics

• Revenue generated from AI-enabled products or services
• Cost savings from AI-enabled process improvements
• Customer experience improvements from AI implementation
• Innovation metrics (number of new AI-enabled offerings)

Cultural and Organizational Metrics

• Employee sentiment toward AI (measured through surveys)
• Retention rates for employees with AI skills
• Internal mobility of employees with AI expertise
• Percentage of roles with updated AI skill requirements

Organizations should establish baseline measurements before launching upskilling initiatives and track progress at regular intervals.

Long-term Talent Strategy Considerations

As organizations look beyond immediate upskilling needs, several strategic considerations emerge for long-term AI talent management:

Evolving Skill Requirements

• Regularly reassess AI skill requirements as technology evolves
• Develop capabilities to forecast emerging skills needs
• Create flexible learning systems that can quickly incorporate new content

Talent Acquisition Strategy

• Redefine job descriptions and requirements to attract AI-savvy talent
• Develop AI skills assessment methods for hiring processes
• Create compelling employee value propositions for technical talent

Career Path Evolution

• Design new career paths that incorporate AI expertise
• Create advancement opportunities for AI specialists
• Develop hybrid roles that combine domain expertise with AI capabilities

Organizational Structure Adaptation

• Evaluate how AI impacts traditional reporting relationships
• Consider new organizational models that optimize human-AI collaboration
• Develop governance structures for AI development and deployment

Cultural Transformation

• Foster a culture that values continuous learning and adaptation
• Promote cross-functional collaboration around AI initiatives
• Build ethical frameworks for responsible AI use

Final Thoughts

AI is going to shock the system in an even bigger way than computers or the internet. So creating an AI-ready workforce requires a comprehensive organizational transformation.

By conducting thorough skills gap analyses, implementing the “build, buy, bot, borrow” model for talent development, creating a continuous learning culture, and addressing AI anxiety with empathy and transparency, organizations can position themselves for success in the AI era.

I’ve worked with dozens of organizations to help them with this. Book me for a free consultation call and I can help you too.