Why The Skills Revolution Stalled … And What Task Intelligence Changes
HR teams today face an extremely challenging brief. In a rapidly changing regulatory and technological landscape, they’re expected to make defensible, data-backed decisions about their workforce – from headcount planning and hiring, to redeployment to large-scale transformation – with constrained budgets, and a huge insights gap.
Most organizations lack reliable, up-to-date data about their people. What they do have is patchy at best: job titles, incomplete employee profiles, and inconsistent job descriptions: fragments of information that rarely connect or tell the full story.
64% of HR leaders now say high-quality data and analytics are critical to the future success of their operations (Fosway) but only 18% of CHROs say their organization consistently uses data analytics to make better people decisions (Korn Ferry).
Without solid workforce data, it’s almost impossible to plan with confidence. The result is a cycle of reactive decision-making: lost capability, disengaged employees, and inequitable outcomes that erode trust.
This isn’t a new problem. For years, HR has struggled to operationalize People data in a scalable, sustainable way. There was simply no system for capturing “who’s good at what” in a usable format, or for keeping that information current.
The great hope: skills data
Around 2019, organizations turned to “skills” as the solution. As well as offering up a fairer approach to assigning talent to jobs, skills-based strategies promised agility, precision and business growth. If you know what skills people have, you can assign them to work as needed.
“77% of business and HR executives believe that flexibly deploying skills is critical for future success,” claimed Deloitte in 2022. Today, 81% of leaders globally say skills-based strategies drive growth – with over half of organizations already implementing them (Workday).
The challenge is truly understanding “skills”. Many companies invested heavily, launching 18-month skills projects, hiring consultants, and even deploying AI to infer skills across roles. What skills does a Chief of Staff need in a tech company? What about a Business Analyst in a marketing department?
The exercise was important: skills insights are indeed useful. But, on its own, skills data faces two related problems: firstly, it mainly functions at a micro level – for managers to have individual conversations about development, growth and redeployment, or for recruiters to fill a specific role. Secondly, given the scrutiny around these micro interactions, the data needs to be incredibly accurate. The bar for accuracy is high when exposing skills data to employees, creating reputational risk if it’s wrong.
The problem? Validating skills is really difficult. Employees struggle to articulate their own skills; self-assessments are inconsistent. Résumés are patchy; if you use AI to infer skills from a CV or job description, you will get something directionally strong, but it might not be 100% accurate. It’s not easy to work out the skills someone learned from a previous job, or where they are less proficient, or where their potential is.
The result for a lot of businesses has been data paralysis. Many organizations have ended up with no usable data at all. Considerable time, money, and effort have been spent on “skills” – yet many teams end up with data that can’t meaningfully inform large-scale workforce decisions. Pilots in “skills-based transformation” stall because skills data alone isn’t enough.
The shift: from skills to tasks
Skills intelligence is powerful. At the macro level, however, it rarely answers the questions that really matter. Knowing that 15,000 employees rate 4 out of 5 for “communication” does little to help us make big decisions, like how do we staff a new AI hub, or plan a market expansion.
A simpler, more actionable approach is emerging: generating task-level insight. Instead of asking “what are your skills?”, leaders can ask: “what do you do each week?” Tasks are concrete, observable, and relatable.
Employees can answer the question (or AI can infer the information from job descriptions and résumés) and managers can validate the answer. Leaders can quickly review task data and confirm: “Yes, that looks roughly right.” Validation becomes faster, simpler, and more scalable.
Using task intelligence for workforce planning brings several advantages:
- Directional accuracy beats perfection: at a macro level, you don’t need every data point to be exact. Even small adjustments improve decision-making.
- Lower reputational risk: task data is less personal, so employees are less likely to feel like they have been misrepresented.
- More equitable decisions: inferring what people do, rather than guessing, reduces bias.
- Scalable validation: senior leaders can sense-check a sample quickly, creating confidence without exhaustive review.
Task data gives organizations some usable workforce data. And, for many enterprises, the baseline is zero: they currently have no accurate visibility into what work is being done. Moving from nothing to “something directionally right” is already transformative.
In a world where work is being fundamentally redesigned thanks to AI, it’s important to base decisions on data. Consider this: while 39% of companies laid off staff due to automation, 55% now regret those decisions (Orgvue).
The future: combining skills and tasks
Skills data remains essential … but only when considered alongside tasks: the work people actually do. Together, skills and task data give leaders a clear view of supply (the capabilities available today) and demand (the work that needs to be done). This insight makes it possible to identify gaps, redeploy talent more effectively, and plan for future skills requirements with confidence.
AI-powered workforce intelligence transforms this picture from static to dynamic. Skills and task data can be continuously updated, validated, and connected across the organization, giving leaders a living view of their workforce. Decisions about hiring, redeployment, or upskilling can evolve alongside the business, grounded in real-time, evidence-based insight rather than guesswork.
The combination of skills and task intelligence also makes workforce planning fairer and more equitable. By focusing on what people do rather than what managers assume they can do, organizations reduce bias, uncover hidden capabilities, and ensure that opportunities are aligned with both talent supply and strategic priorities.
In this way, the skills revolution truly restarts. It’s no longer a one-off project or a static inventory of capabilities, but a continuous cycle of understanding, action, and adaptation: enabling organizations to build a workforce that is flexible, capable, and ready for the future.