What CAIOs Need to Know About Reskilling, Upskilling, and AI Training (Hint: Bring in Your CLO)

Stef Miller

The appointment of Chief AI Officers (CAIOs) across industries signals a new era of enterprise transformation. These executives aren’t just tasked with standing up AI labs, evaluating vendor models, or bringing in AI “tools”, they’re responsible for leading systemic change. But AI transformation isn’t powered by tech and algorithms alone, it’s powered by people.

If you’re a CAIO focused solely on models and infrastructure, you’re missing the bigger picture. Successful AI transformation depends on enabling the workforce with the right skills, at the right time, in the right parts of the business. And for that, you need to bring your Chief Learning Officer (CLO) to the table. Immediately.

Here’s what every CAIO needs to know about reskilling, how it intersects with AI-driven change, and why partnering with your CLO is one of the smartest strategic moves you can make.

AI strategy without the skills to back it up is just a thought experiment

The pace of AI innovation is outpacing most organizations’ ability to absorb it. While companies race to deploy GenAI pilots and build AI copilots, many are finding that their workforce isn’t prepared to adopt or optimize these tools. Why? Because they’ve overlooked the skills strategy required to operationalize AI at scale.

AI doesn’t just affect one team or function. It transforms how work gets done across the entire enterprise, how marketers analyze customer behavior, how finance leaders forecast risk, or how customer support teams respond to inquiries. With agentic AI, more proactive systems and models can act autonomously to achieve goals without oversight. CAIOs need to take a holistic approach to building the skills employees need to ensure that AI implementation actually drives the intended business outcomes, and as AI evolves for more sophisticated applications, this will only become more important.

That starts with understanding which functions need what skills and how urgently.

The new skills map: What functional teams need for AI readiness

There are some skills every employee needs. Soft skills are essential to make the most of what AI has to offer, like critical thinking, problem solving, and communication. It goes without saying that technical skills and function-specific skills are also essential.

Let’s break it down by function, because skill development isn’t one-size-fits-all.

1. Engineering & Data Teams

Required skills: Prompt engineering, model fine-tuning, data governance, MLOps, model risk management.
Why it matters: These teams are the custodians of AI infrastructure. Without deep technical training, even the best tools can’t be securely or efficiently deployed.

2. Marketing & Sales

Required skills: AI-assisted content creation, creative and design capabilities, performance forecasting, customer segmentation using ML models, ethical use of GenAI.
Why it matters: These teams are under pressure to do more with less and personalize at scale. GenAI can unlock productivity, but only if teams know how to use it effectively and responsibly.

3. Finance & Legal

Required skills: Risk modeling with AI, compliance with AI-related regulation, interpreting AI-driven analytics.
Why it matters: AI introduces new types of risk. Finance and legal teams need to evolve from gatekeepers to strategic partners who can anticipate and manage those risks.

4. HR & Talent

Required skills: AI-powered workforce planning, skill gap analysis, ethical AI use in hiring and performance reviews.
Why it matters: HR plays a critical role in both mitigating bias in AI systems and in orchestrating the upskilling and reskilling strategy that enables AI transformation.

5. Customer Support & Operations

Required skills: Human-in-the-loop design, AI-enhanced service workflows, understanding AI-generated outputs.

Why it matters: These are often the front lines of AI deployment (think AI chatbots or ticket routing). Training ensures that AI augments, not replaces,human capabilities.

The CLO is your transformation partner

Here’s the truth: most CAIOs don’t have a background in organizational development. But CLOs do. They bring the expertise to diagnose skill gaps, architect learning programs, and roll out upskilling, reskilling, and training that drive behavioral change.

Why the CAIO-CLO partnership matters

Business alignment: CLOs can connect AI skills development to real business outcomes, including higher productivity, better decision-making, lower operational costs.
Change management expertise: Rolling out AI is a change management challenge. CLOs understand how to construct learning journeys that build trust and reduce resistance.
Scalability: CLOs already have the systems in place to deploy skills development at scale, whether through learning and development (L&D) platforms, learning experience platforms (LXPs), or partnerships with providers.
Measurement: CLOs bring the tools to measure learning effectiveness and tie it back to business KPIs. That’s essential in a world where ROI on AI investments must be clear and defensible.

How to roll out AI skills development across the enterprise

So what does good look like? Here’s a step-by-step framework that CAIOs and CLOs can co-own to build an AI-ready workforce.

1. Conduct a skills inventory and gap analysis

Start by mapping the current skills of your workforce and comparing them against the future-state AI strategy. This reveals both immediate gaps and longer-term needs by function and level.

2. Prioritize roles for upskilling and reskilling

You won’t be able to train everyone at once. Focus first on:

Roles seeing the greatest impact from AI automation or augmentation
Teams piloting AI use cases
People leaders who will influence team adoption

3. Design role-based learning paths

Avoid generic “AI 101” content. Instead, tailor learning to the needs of each function:

Data-driven simulations for technical teams
Scenario-based ethics training for HR/legal
Copilot tutorials for marketers and sales reps

4. Deliver through integrated channels

Blend on-demand content with live instruction, coaching, and peer-to-peer learning. Use nudges, microlearning, and AI-driven personalization to embed skills in the flow of work. Where possible, enable skills development with hands-on learning. Employees can practice technical skills with assessments and labs. Soft skills are cemented with Role Play, so the employee has practiced the skills before applying them in a real-world scenario.

5. Track, iterate, and optimize

Establish KPIs such as training completion, behavior change, time-to-productivity, and business impact. What are the business’ goals and how can upskilling initiatives support outcomes like employee engagement and retention, cost savings, better business continuity, and reduced operational costs? Treat skills development like product development: ship, learn, and improve.

A final word: Your talent strategy is your AI strategy

If you’re a CAIO with a robust tech roadmap but no corresponding workforce roadmap, you’re planning for failure. The organizations that win with AI will be those that can adapt faster than the competition, and that means investing in people just as much as in platforms.

So bring in your CLO early. Make them your co-pilot in change. Because in the end, the AI transformation you lead won’t be measured by how many models you deployed, it will be measured by how much value your people created because of them.

TL;DR for Executives:

AI transformation depends on targeted skills development by function.
CLOs bring the expertise to build scalable, business-aligned training programs.
The CAIO – CLO partnership is essential to drive adoption, productivity, and trust in AI.
Don’t separate your tech and talent strategies, they must move in lockstep. A few companies, like Moderna, are going as far as merging the two roles because they see the relationship as so essential.

Learn more about how to align your talent and AI strategy with Udemy Business.