Workera

Big news: Workera is joining Pearson! Pearson has agreed to acquire Workera, bringing together the world's lifelong learning company and the AI-native platform that measures and verifies the capabilities of a workforce. We started Workera with a simple belief: in the age of AI, skills are the currency of work, and you can't build what you can't measure. So we designed and launched an AI-native platform that lets enterprises continuously measure and verify skills, then act on what they find to continuously improve their capabilities. Today, blue-chip enterprises and government agencies rely on Workera every day to see exactly where their workforce stands and where it needs to go next. Pearson has helped people around the world learn and grow for generations. Now we get to take verified skills to a scale we could only imagine on our own. Together, we share one ambition: to build the gold standard for workforce readiness, so every organization knows what its people can do and every individual has a clear vision for what's next. To our customers, our partners, Andrew Ng, and everyone who helped to build Workera with us: THANK YOU.  This isn't the finish line. We're just getting started! Full announcement → link in the comments. 👇 #Workera #Pearson #SkillsIntelligence #AIReadiness #FutureOfWork 

The future of work is changing fast. The ability to understand the skills people have today, identify what’s needed tomorrow, and build those capabilities will be critical for organisations and their people. With Workera joining Pearson, we’re bringing together AI-powered skills intelligence, assessment and learning to help organisations close skills gaps and help people keep moving forward. We’re excited to welcome Workera to Pearson!

Paris. London. Hoboken. What do these cities have in common? Last week, each one hosted a Workera executive dinner. On the same night and on both sides of the Atlantic, 36 HR, L&D and AI transformation leaders pulled up a chair with us. Paris brought a private salon with a century of literary history. London brought an Art Deco dining room tucked away in the City. Hoboken brought a cooking class, because the best way to prove a skill is to use it. You'll notice these photos were taken before anyone sat down. That's on purpose. What's said at the table stays at the table. What we can share: the questions were hard, the conversation was candid, and every guest left with a few new names in their phone. More cities are coming soon. If building an AI-ready workforce is on your plate this year, we'd love to see you at the next table. Thank you to everyone who joined us, and to the Workera team who made three cities happen in one night. Nick Pegram Katherine Bullock Viola T. Jim Hemgen Bernardo F. Nunes Sydney Di Bernardo Taylor Sullivan, Ph.D. Aran Moultrop #Workera #AIReadiness #FutureOfWork #SkillsIntelligence

Companies more than doubled AI training in a year. Most employees still get no time at work to build the skills. Our 2026 State of Skills Intelligence Report surveyed 1,000 employees at U.S. enterprises with 5,000+ people. ZDNET's Kayla Solino had the first look. → 58% were offered AI-specific training, up from 25% in 2025 → 56% say no hours are set aside during work to develop AI skills → Nearly 60% say AI skills carry equal weight or no priority in promotions → 43% say they've been passed over because someone misjudged their abilities 56%. That's the number to fix first. Training without time is a line item, not a capability. Swipe through the findings. Full report → Link in comments. #SkillsIntelligence #AIReadiness #FutureOfWork #LearningAndDevelopment

What if the strongest evidence of what someone can do isn't their resume, their title, or their last performance review? It's the work itself. Kian Katanforoosh joined Nirit Cohen 🔮 Cohen on The Future of Less Work podcast to talk about what changes when AI can see capability in the flow of work. Strengths and gaps in real-time. Coaching when it's useful. Verified skills profiles that travel with a person across roles. Plus the harder questions: can AI measure judgment? Who owns the data? 28 minutes. Link in comments. #AIReadiness #WorkforceAI #LearningAndDevelopment

You funded the tools. Can anyone tell you whether your teams are ready to use them? 80% of AI initiatives fail because leaders mistake tool access for capability (BCG). A license count cannot separate a team working well in a tool from a team working around it. Today we are expanding the Signature Catalog with tool-specific capabilities: verified measurement of proficiency in the named tools and frameworks your teams build in right now. Claude Code. Claude Agent Skills, Tool Use, and MCP. LangGraph. CrewAI. Mem0. Agent observability and evaluation tooling. Quantization toolkits. Same catalog. Same standard. Same program. Now moving at the speed of your tech stack. Built to stay current. When a framework ships a breaking change, the measurement keeps pace with it. Same bar, shorter shelf life by design. Seven capabilities to start, with more on the way. Read the announcement below 👇 #SkillsIntelligence #AIReadiness #AIAgents

Sixty engineers. Twelve weeks of work. Three points of improvement. The program lost its funding in the next planning cycle. What no one checked: the reassessment drew a different set of questions than the baseline. Same topic, assumed to be equally hard. That assumption was never verified. If the second form was tougher, the program worked and the measurement buried it. The sponsor lost standing over a result that was never real. Every improvement number is the distance between two measurements. It holds up only if both tests were equally demanding, and most assessment platforms cannot tell you whether they were. Across a large population, difficulty variance often washes out. It stops washing out at the moment that matters most: one function, one quarter, one number going to the board. Before your next AI readiness update, ask your team which growth figures came from a second assessment you can prove was as hard as the first. The full breakdown is in the comments. 👇 #AIReadiness #SkillsIntelligence #WorkforceTransformation #TalentDecisions #CHRO

By the time a new skill is stable enough to measure properly, your teams have been building on it for a year. No one plans that year. It is what happens when the business moves at the pace of the technology and measurement moves at the pace of rigor. Technical skill shelf life has fallen from about five years in 2020 to roughly two today. So the window where you hold verified evidence about a capability sits at the end of its useful life rather than the beginning. You get proof right around the time the capability stops mattering. The decisions get made anyway. Who staffs the next project. Where the development budget goes. What you tell the board when they ask how you are measuring AI readiness. New on the Workera blog: what that gap costs, and three ways to shorten it. Read the full article below. 👇 #AIReadiness #SkillsIntelligence #WorkforceTransformation

Every other industry deploys AI to be more productive. Professional services deploys it into the work it sells. A research task that took an associate two days is now a prompt. You cannot bill two days for it, and the client already knows. So the approach has to shift. Which is why what these firms are measuring is interesting. Across 10+ top firms, 101,000 employees, and 290 programs, the most widely assessed capability is Responsible AI Essentials. The sector that adopted generative AI fastest is the one placing the most weight on judgment. Data Storytelling and AI and Data Communication also made the top list. When the model does the analysis, the differentiated work is framing it for a client who has to act on it. Among 22,000+ employees reassessed, average scores rose 60.9 points on a 300-point scale. Read the story below in the comments ⬇️ #ProfessionalServices #AIReadiness #Consulting #SkillsIntelligence #FutureOfWork

Your skills taxonomy can finally be as specific as the work itself, but high granularity comes with a short shelf-life. Without a system to maintain it, your taxonomy inevitably fails in one of two ways: • It drifts into an outdated snapshot of past work. • It bloats by chasing every new tool and buzzword. In our latest post, Workera's VP of Product and Assessments, Taylor Sullivan, Ph.D. explains how to solve this with a two-track approach: combining a durable canonical core with a fast-moving edge for tool-specific skills. Read the full article to keep your skills data credible in the AI era below 👇 #SkillsTaxonomy #FutureOfWork #AI #TalentManagement #HRTech