Hiring AI and data science experts is proving harder than advertised. In a recent global survey of HR and learning leaders conducted by Skillsoft, only 10% said their organizations have the skills necessary to achieve business goals over the next two years. The World Economic Forum’s Future of Jobs research offers some clues as to why: almost half of core skills will change over the next five years, and AI is hastening that journey.
Yet as companies pump money into AI platforms, the returns have remained elusive. According to MIT Sloan Management Review and BCG, very few organizations are realizing substantial business value from AI at scale. The chasm is not the technology — it’s the people, the workflows, and whether learning is seen as a strategic capability or perceived as compliance.
Here are four practical actions that leaders can take today to close the gap and equip their workforce for the age of artificial intelligence.
1) Create a trustworthy skills intelligence system
Most firms, in fact, are clueless about what their people can actually do. 91% of HR leaders feel employees exaggerate their skill levels — particularly in leadership, AI, and technical roles — while only 18% regularly measure skills over time (Source: Skillsoft). That is the setup for wasted spend and mired AI pilots.
Begin with a shared, transparent skills taxonomy (frameworks like SFIA or O*NET can help). Combine self-assessments with pre-tested diagnostics, hands-on labs, code reviews, and case study assignments. Lean on AI to infer skills from real work artifacts — pull requests, dashboards, product docs — and verify the evidence.
Measure a small number of outcome metrics: progression along the role-proficiency continuum, time to proficiency for key roles, application rates on the job, and business KPIs for AI-enabled projects.
Some companies, like AT&T, have demonstrated that a living skills inventory, refreshed by assessments and project data, is the basis for targeted upskilling at scale.
2) Integrate learning into the job, not as a side quest
Slide decks are not how you close skill gaps. They end up getting in the way of work. Think of learning as a product: design for user needs, shorten feedback loops and bake practice into every aspect of everyday life. Ditch “check-the-box” courses for microlearning tied to real deliverables, peer coaching, and time-boxed challenges that resemble production issues.
Protect time for growth — many high-performing businesses formalize 5-10 percent of learning time — and hold managers to account for skill outcomes, not course completions. Experience matters: sandboxes to build agents, promptathons to fine-tune use cases, and labs that match your tech stack. Companies from DBS Bank to Bosch have set up these academies with an emphasis on practice and portfolio rather than certificates.
Widen the aperture, crucially, beyond engineers. Operations, finance, marketing, and HR are all going to leverage copilots and automation — design paths for every function to build AI literacy and confidence.
3) Unleash safe AI sandboxes to turn curiosity into value
Resistance to change is the number one obstacle in AI adoption. According to research undertaken by Skillsoft, 41% of HR leaders cite resistance to change as their biggest hurdle in adopting AI. The antidote is structured exploration. Set up standing, controlled environments with approved models, private connections, and data loss prevention. Offer starter kits — jump-start libraries, templates, and guardrails — so teams can move fast with little chance of violating compliance.
Use experiments to measure against business outcomes, not vanity metrics. Good sequence: identify painful processes; do a 1-month pilot; capture baseline cycle time and error rates; compare post-AI assist. Capture the patterns that work and fold them into SOPs. This is how curiosity translates into reusable capability and, eventually, scale.
First, organizations go for productivity wins — summarization, drafting, QA — but the game-changer is rethinking workflows. According to IBM’s research, about 40 percent of the workforce needs retraining in order to learn new skills and stay relevant as AI becomes more prevalent; the biggest gains from AI are achieved when humans work in concert with machines instead of trying to compete against them.
4) Link upskilling directly to strategy and mobility
Just 20% of HR leaders report that their development programs are in line with the organization’s business goals. Remedy that by valuing skills. Isolate the handful of capabilities that either make money or endanger companies (e.g., data engineering for customer analytics, AI-driven service enhancements, model governance) and fund learning paths that ladder up into these priorities.
Design it so people can move up and around internally. Talent marketplaces and skills-based staffing technologies dramatically accelerate the deployment of newly trained employees to high-impact work. Unilever and other multinationals have demonstrated how internal gigs that stretch employees and reduce the time to hire someone can similarly retain vital talent by providing stretch opportunities without an exit letter.
Finally, update incentives. Acknowledge the managers who grow talent, not ship features. Monitor internal fill rates for AI-heavy roles, lateral moves made possible by adjacency of skills and the percentage of AI projects staffed by internal learners vs. external hires. Those signals demonstrate that the learning engine is plugged into business.
The bottom line on building AI skills and strategy alignment
The AI race will not be won by the company with the most models — it will be won by the company with the clearest view of its capabilities, the best learning culture, a way to experiment, and the tightest link between upskilling and strategy. Get those four things right and technology finally has something to run on: a workforce ready, able, and willing to use it.
Sources: Skillsoft Global Skills Intelligence Survey; World Economic Forum Future of Jobs; MIT Sloan Management Review and BCG research on AI value; IBM Institute for Business Value.