Upskilling is urgent and strategic. The World Economic Forum’s Future of Jobs Report estimates that automation will displace 85 million jobs by 2025, and 40% of core skills will change for workers. By 2030, 77% of employers plan to reskill and upskill their workforce to work alongside AI. This isn’t just about avoiding obsolescence—it’s about unlocking new value, innovation, and resilience in the face of rapid technological change. The WEF emphasizes that the most successful organizations treat upskilling as a continuous, strategic investment. They create a culture of learning, experimentation, and internal mobility. This approach builds not only technical skills but also organizational agility and collaboration. 

Why Upskilling Internal AI Talent Matters (But External Solutions Still Play a Role)

Many companies try to “buy” AI talent, hiring external experts or relying on vendor-led training. While external partners and solutions companies can provide valuable expertise and accelerate progress, upskilling internal talent offers unique advantages: 

• Context Matters: Internal teams bring deep knowledge of your business, data, and processes. When upskilled, they can better align AI solutions to business needs.
• Retention & Engagement: Employees who are invested in and developed by their organization are more likely to stay, contribute, and champion change.
• Faster Adoption: Teams that build AI skills together move from experimentation to production faster, with fewer silos and less resistance.
• Cost Efficiency: Building internal talent can reduce the premium paid for scarce external experts and minimize the risk of “brain drain” when contractors leave. 

At the same time, vendor solutions and external expertise remain important options for organizations at different stages of their AI journey. The key is to find the right balance by leveraging both internal development and external support as needed. 

What Does “Building” Internal AI Talent Look Like? 

Recent research from IBM and Harvard Business Review highlights that effective AI upskilling is hands-on, project-based, and collaborative. For data science and software developer teams, this means: 

• Structured Enablement: Programs should teach teams to build production-ready GenAI apps and ML pipelines using real-world templates, CI/CD, and cloud-native tools.
• Collaborative Engineering: Upskilling should embed governance, peer reviews, and cross-functional teamwork—connecting GenAI workflows to enterprise systems like ServiceNow, GitHub Copilot, and MLOps platforms.
• Skills Assessment & Coaching: The best programs include toolkits to measure readiness and progress, peer coaching plans, and retrospectives to ensure learning sticks and evolves.
• Capstone Projects: Teams should deliver end-to-end AI solutions that demonstrate fluency and support long-term adoption through peer coaching and sustainment strategies.
• Sustainment & Continuous Learning: The most successful organizations embed sustainment planning—peer coaching circles, retrospectives, and continuous improvement—into their upskilling strategy. 

Recommendations for Impact

Effective AI upskilling for technical teams thrives on structured enablement strategies that reflect real-world systems, roles, and outcomes. Whether your organization is just beginning its AI journey, scaling existing efforts, or deepening specialized capabilities, the following approach can help ensure success:

For Software Development Teams: Empower cross-functional teams to transition from GenAI experimentation to enterprise-grade delivery. Focus on building production-ready GenAI applications using proven templates, CI/CD pipelines, and integrated tooling. Prioritize speed, security, and confidence in deployment.

For Data Science Teams: Accelerate the industrialization of ML workflows by adopting cloud-native tools and MLOps practices. Use live sessions, hands-on labs, peer reviews, and coaching to foster collaboration and reduce silos. Capstone projects and reusable toolkits can reinforce long-term value.

Key Design Principles:

• Tailored Enablement: Build hands-on learning journeys that reflect your enterprise architecture and use cases.
• Modular & Role-Based Curriculum: Ensure relevance for every team member by aligning content to specific roles and responsibilities.
• Outcome-Driven Success Metrics: Track adoption, capstone delivery, and peer coaching participation to measure impact.
• Embedded Sustainment Planning: Equip teams to continue growing and reusing what they’ve learned well beyond the initial program.

Every organization’s path is unique. Some may benefit from external expertise or vendor support, while others may focus on internal development. The key is to design your upskilling strategy to be adaptable, impactful, and aligned with long-term transformation goals.

Invest in Your People, Unlock AI’s Potential 

AI is not a plug-and-play solution. The organizations that will thrive are those that invest in their people, building data science and software teams that are not only AI-literate but AI-empowered. By focusing on internal upskilling, and leveraging the right partners and solutions, companies unlock innovation, accelerate adoption, and create a sustainable competitive edge. 

Ready to start building your internal AI talent? The future belongs to those who invest in their people—today. 

Heather Mackinnon-Miller, Artificial Intelligence Global Head, also contributed to this article.