SAP and Reejig on re-engineering roles with Work Intelligence

"The train has left the station."

That's how Reejig CEO Siobhan Savage captures the shift confronting HR and business leaders today.

Skills still matter. But the real conversation now is about work. What is work? Who should be doing it? How do you redesign it for a world where AI rewrites the rules faster than ever?

In a recent webinar, Amy Wilson (former Head of Product at SAP SuccessFactors, now Product Advisor at Reejig), Josh Gosliner (VP of Product Strategy at SAP SuccessFactors), and Siobhan Savage came together. They unpacked the changes reshaping workforce strategy.

Together, they made one thing clear. The future isn't just skills-based. It's work-based.

Here's what stood out.

Why the skills hype has hit reality

Josh kicked things off with rare honesty:

"I'm a little bit of a skills-based skeptic."

Not because skills are irrelevant. Because the reality inside most organizations doesn't match the hype.

He described a maturity curve most companies face:

• Skills Implied: Organizations still job-centric. They rely on resumes and job titles to infer skills.
• Skills Included: Companies capture credentials but haven't woven them into how they work.
• Skills Led: Some organizations extract skills data from across systems. They still translate it manually into decisions.
• Skills Based: The theoretical future where jobs dissolve into fluid skill-based assignments. Josh calls this "still science fiction."

"There's a real dichotomy in our customer base. Some bought a bunch of systems but don't have the data. Others have ambition but can't execute."

It's not that skills are unimportant. It's that skills alone aren't enough.

Why the conversation has shifted to work

Siobhan laid out a fundamentally different lens:

"People have skills. Jobs don't have skills. Jobs have tasks."

Early in Reejig's journey, they invested $40 million building skills models. But something wasn't working:

• Matching people to jobs was hit-or-miss. Skills were too abstract.
• Customers had no common language to describe how work happened.
• AI doesn't automate skills. It automates tasks.

So Reejig rebuilt the model around Work Context instead.

Why does this matter?

• Work is made of tasks.
• Tasks require skills.
• AI changes which tasks exist.

Without knowing the tasks, you can't manage AI's impact. You can't prepare people for what's next.

From Job Architecture to Work Architecture.

The data problem

Both Josh and Siobhan agree. The real barrier isn't technology. It's data.

Many organizations have job architectures so outdated they might as well be on stone tablets. Josh joked that job descriptions are:

"Like a piece of chewing gum from the 1980s. Super stale."

Here's what companies face:

• Job architectures live in spreadsheets. They're instantly out-of-date.
• Learning and development often trains people for skills the business doesn't need.
• Companies lack a unified "language of work" to connect talent acquisition, learning, workforce planning, and operational design.

Siobhan's assessment:

"We waste people's time training them for things that don't matter because we're guessing what the business needs."

The AI wake-up call

Pre-pandemic, the HR world was obsessed with retention and work movement. Post-COVID, and with the rise of GenAI, the conversation flipped.

"We've gone from skills-based orgs to CEOs asking how to build an AI-powered workforce." — Siobhan Savage

AI isn't just about automating tasks. It's redefining work itself:

• Every time you deploy AI, you remove old tasks. You also create new ones.
• AI forces organizations to rethink job architectures entirely.
• Transformation can't be a one-off project. It's a continuous evolution.

Siobhan warned:

"If you create a static skills taxonomy on a spreadsheet, it's out of date the moment you save it."

Organizations need living systems that update in real time as work evolves.

How SAP SuccessFactors and Reejig fit together

Josh emphasized SAP's unique strength. SAP has data spanning the entire enterprise. From supply chains to sales to finance. That means SAP can:

• Forecast labor demand based on business changes.
• Tie workforce planning to operational realities.
• Model AI's impact across the whole organization, not just HR.

SAP doesn't try to solve everything alone. That's why they built an open ecosystem connecting different partners, including Reejig.

Amy Wilson summed it up:

"Reejig creates a skills ontology, but that's a byproduct of their Work Intelligence. Work Intelligence is the tip of the spear for workforce transformation."

Josh explained that bringing Reejig into the ecosystem gives SAP customers:

• A unified language of skills and work.
• Alignment of learning, recruiting, and workforce planning on a single source of truth.
• A move beyond static job architectures to dynamic work design.

From automation to responsible reinvention

Both Siobhan and Josh were clear. AI will transform work. But it must not leave people behind.

Siobhan's rallying cry:

"We collectively have a responsibility to reinvent work. But not leave people behind."

Here's how Reejig approaches this responsibly:

• Identify which tasks AI can take over.
• Predict new tasks that will emerge.
• Redeploy people into adjacent roles based on skill and task similarity.
• Integrate learning directly into new pathways so employees pivot successfully.

It's not enough to cut jobs. Businesses need to engineer reinvention pathways. Otherwise they risk creating gaps and eroding trust.

The roadmap to reinvention

A major highlight of the session was Siobhan's demonstration of Reejig's capabilities:

• AI Potential: Quantifies how much of each role is automatable and the ROI potential.
• Emerging Task Insights: Identifies new tasks appearing as AI changes workflows.
• Reengineering Agent: Maps individuals to adjacent roles based on shared skills and tasks.
• Agent Orchestrator: Connects specific AI agents (like Microsoft Copilot) to automate defined tasks.

All this data feeds back into SAP's ecosystem. The whole enterprise stays aligned.

The takeaway

This wasn't just another webinar about skills taxonomies or AI buzzwords. It was a glimpse into how real organizations tackle the changes AI is forcing on work.

• Skills matter. But understanding work is the real breakthrough.
• Static job architectures are obsolete in the AI era.
• Workforce redesign must be people-centric. Otherwise it becomes pure cost-cutting.

SAP SuccessFactors and Reejig offer companies a practical path forward. They combine deep enterprise data with granular task-level insights.

If your CEO is asking how to build an AI-powered workforce, this is the blueprint.

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