I wasted $40M on skills strategies so you don't have to

At Reejig, we're obsessed with workforce redesign. But before we nailed the formula, we made plenty of mistakes. $40 million worth, to be exact. My journey was an expensive education in what doesn't work when building skills strategies. Here's the reality check you need to avoid the same pitfalls.

The costly mistakes organizations are making

Despite the best intentions, many organizations fall into these traps.

• Poor data quality. Extracting or inferring skills from job descriptions and adverts led to irrelevant and misleading insights. These sources don't reflect the actual work being done.
• Inconsistent and messy data. Skills extracted from multiple systems created a chaotic, ever-changing dataset. AI dynamically updating records and pushing them into systems like Workday or SuccessFactors without governance made it worse.
• Flawed data sources. Job adverts are one of the worst data sources for inferring skills. The same job appears in multiple locations. This skews demand signals. It creates false insights about "emerging skills."
• Unharmonized data. Different systems and teams use their own language for skills. This results in fragmented datasets. No alignment across the business. Confusion and inefficiencies as each system operates in silos.
• Large language models (LLMs) issues. LLMs provided inconsistent and unreliable outputs. They offered different recommendations for the same job depending on how it was described.
• Job architectures. Using job architecture to match work was ineffective. They were rigid. Out of date by the time projects completed. They didn't align with how businesses actually describe or think about work.
• No skills governance. Without governance, duplications, inaccuracies, and inconsistencies compromised data quality. Maintaining a reliable skills dataset became impossible.
• AI automates tasks, not skills. AI and automation handle specific tasks. Like analyzing data or generating reports. They don't "have skills" the way humans do. Recognizing that AI is task-driven makes integration more effective.

Trust me. I tried all of this, and none of it worked. If you want a better outcome, you need a smarter approach.

If I automate chaos, I just scale the chaos.

What works and lessons learned

1. Focus on the work being done. Most organizations don't have a clear view of actual work. That's why we built Work Context, built on 25 industry-specific Work Ontologies. It fills the gaps and uncovers real insights.

2. People have skills. Jobs have tasks. Tasks need skills. Once you see work this way, you can't go back.

3. Create one common language of work. Workforce strategy and business leadership need a unified structure. Start with the work being done. Then layer in skills. This reduces friction and builds buy-in for change.

4. Harmonize your data. Pick one system to standardize skills across all use cases. Hiring, learning, work movement, and more. This ensures consistency and better alignment.

5. Put a governance layer in place. Don't allow your skills system to constantly update jobs in your systems of record. Agree on a governance timeline to approve updates.

6. Build strong data foundations. A reliable dataset on your people's skills and the work being done is critical. This lets you solve real problems. It supports meaningful strategy work.

7. Match work to workers based on real insights. Go beyond job titles. Understand the tasks being done. Align people with what they're best at.

Why this matters

The old methods don't just waste money. They prevent you from realizing your team's potential. Here's how our approach keeps you competitive:

• Uncover AI opportunities and build clear roadmaps.
• Eliminate inefficiencies with precise task mapping.
• Design meaningful career pathways that engage your workforce.
• Create a marketplace of opportunity, fueled by accurate data.

From Job Architecture to Work Architecture.

Take action

We've learned the hard way so you don't have to. If you're serious about future-proofing your workforce strategy, let us show you how to get it right.

Book a demo

Change starts with the right data. Together, we build the way the world works.