It has never been more true that “skills pay the bills”.

Companies spend three times more on talent than on capital assets (1).

However, the way we manage people often doesn't match the attention we give to £$€. AI has the potential to solve long-standing productivity issues, but what do both leaders and employees say the biggest barrier to AI adoption? Lack of skills! (2)

In this context, it’s heartening to see the desire to be more ‘skills-powered’ continues strongly, with 78% of managers saying its important and 60% of employees wanting to work for one (3).

So, given we’re all thinking about AI, and working on building skills, we’ve got this all sorted out, right? Right?!?!?

Productivity gains have been hard to grasp

On AI, it’s early days:

Only 1% of leaders think their company is ‘mature' in AI (2)
92% plan to increase investment in AI (2)
86% of all business transformations will be driven by AI (5)

It is proven that talent and transformation outcomes are linked (6), but we're at risk of the same failure. We’re not doing enough to help build future skills for, and around, AI:

Only 1 in 3 orgs are reskilling for AI (7)
Over 50% of employees feel underwhelmed or unsupported (2)
There’s a 20-point gap between C-suite readiness and employee views (2)
Despite 46% of leaders saying skill gaps are the main adoption barrier (5)

If you’re leading transformation or AI initiatives and you’re not re-imagining your workforce strategy, you’re going to come unstuck. It’s a classic case of not realising your business problem is a skills problem (they pretty often are).

What can we learn from productivity leaders?

“Companies that give the same strategic thought to workforce investment that they do to capital investment can unleash operational excellence.” McKinsey, 2025

McKinsey identified 56 outperforming companies, across sectors delivered 4-11% higher Total Shareholder Return and Productivity - measured as revenue growth per full-time position - than average (1):

The common thread? They’re talent and skills magnets who prove that maximising employee value creates shareholder value.

What do they do?

✅ Shift mindsets: treat workforce spend with strategic importance. It’s about both risk and opportunities.

✅ Challenge conventional wisdom: rethink how work has been done, for example through novel shift patterns, remote working, job sharing, flexible work arrangements etc.

✅ Do proper workforce planning: use data and technology to align work with skills.

✅ Invest today: go beyond traditional on-the-job training to build targeted, rounded development programmes to reskill and upskill their people.

✅ Solve tomorrow: proactively address skill gaps and make bets on partnering with higher education and peers.

✅ Deploy technology: automate existing work AND imagine new possibilities.

✅ Engage widely: productivity enhancements must have cross-functional engagement; this is not an HR problem but can be the facilitator.

✅ Measure what happens: with tools and practices that transparently show how employees and teams contribute to business outcomes.

Okay... so how does skills help deliver AI productivity breakthroughs?

Skills transformation is a structured way to get the ‘right skills’ to achieve business goals, shifting the focus from cost to achieving targeted outcomes, just like our productivity leaders.

Done well, skills-based approaches:

✅ Prioritise critical capabilities

✅ Align roles, skills and work to business goals/ demand

✅ Use data to act with intent and precision

✅ Convert strategy into executable actions

In the case of generating business value from AI solutions, as a Skills Leader or HR pro here are 10 key questions you can use data and insight to help inform:

What outcomes are we seeking, and how might AI help?
What capabilities and skills do we need for these outcomes? (practical guidance here)
What skills do we have access to?
What should we own in-house vs buy-in? (is there value, and is it feasible, to?)
What complementary skills do people need for AI?
Which jobs will be affected, how much and when?
Which jobs and skills to prioritise for investment?
What are the best routes to reskill and upskill people?
How to enable faster learning and activation?
What are risks and opportunities for business and people outcomes?

Many of these build on the best practices identified in the McKinsey report (1).

They’re also often similar to call outs in our practical, skills-powered framework which is all about how to think through how skills can help you solve your business problem, AI-driven or not. Data and insight can bring objectivity into answering these questions, helping you make progress, faster.

The importance of complementary skills

AI adoption isn't just about technical skills. Complementary skills like analytical thinking, technical proficiency, and resilience are crucial. A recent report (4) examined skills change by job in the US. It shows that some skills are being substituted as AI grows in demand. Declining skills include data entry, summarising, reporting, administration and customer service.

At the same time, we see that some skills you might expect to be automated, aren’t—e.g. demand for information management, decision-making continue strongly. This is also reflected in Simply’s tracking of global labour market trends.

On complementary skills, the chart shows how complementary skills like Analytical Thinking, Technical proficiency and Resilience are notably more common in AI-enabled roles than non-AI roles.

This pattern is also true beyond AI-focussed jobs in IT or Data Science. When companies invest in AI strongly, they also demand 5% more complementary skills like analytical thinking and resilience. So, planning for AI is about planning for all skills and jobs across your org.

What’s more, AI jobs with this bundle of skills are advertised can receive higher pay, e.g. 5-10% higher pay for data scientists with the complementary skills.

It’s clear those ahead in AI know the value of human skills in unlocking productivity and are willing to pay more to secure that talent, but the impact is more nuanced – requiring greater thought – than marketing hype to date would suggest.

Get ready to execute your AI strategy

Driving value from AI depends on organizing and activating both technical and complementary skills. HR leaders can facilitate building a robust, agile workforce to support AI initiatives.

Driving productivity from AI will rest on how well you can organise and activate your skills: skills-powered approaches are for the win here.

If you're wrestling with the questions set out here, know you're not alone! Drop us a line and we can help think through your unique context and how AI and Skills dovetail to unlock value for you and your business, or connect you with peers in companies trying to do the same.

PS: Simply tracks 375 technical AI and related skills as part of our ‘AI’ capability and the complementary skills which exist in each job to activate them. If you need to understand how your company can be on the front-foot to unlock value from AI, get in touch.

Learn how Finastra are becoming a more skills-powered organisation. — Click to read about global fintech Finastra's skills journey.

Ready to dive deeper? Download our whitepaper, The truth about skills-based organisations, and discover how Finastra transformed their workforce with purpose and precision.

(Re)sources:

(1) McKinsey, The Missing Productivity Ingredient (2025)

(2) McKinsey, Superagency in the workplace: Empowering people to unlock AI’s full potential (2025)

(3) Oliver Wyman, Workforce Transformation in the AI Era (2025)

(4) Oxford Internet Institute. Mäkelä, Elina and Stephany, Fabian. Complement or substitute? How AI increases the demand for human skills (2025

(5) World Economic Forum, Future of Jobs Report (2025)

(6) Bain, Three Common Transformation Talent Mistakes (2024)

(7) Accenture, Pulse of Change (2025)