Beyond Skills: Reengineering Work — A Conversation with SAP SuccessFactors

“The train has left the station.”

That’s how Reejig CEO Siobhan Savage captures the seismic 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? And how do you redesign it for a world where AI is rewriting the rules faster than ever?

In our recent webinar, Amy Wilson (former Head of Product at SAP SuccessFactors and now Product Advisor at Reejig), Josh Gosliner (VP of Product Strategy at SAP SuccessFactors), and Siobhan Savage came together to unpack 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 honesty you rarely hear from vendors:

“I’m a little bit of a skills-based skeptic.”

Not because he thinks skills are irrelevant—but because the reality inside most organizations doesn’t match the hype.

He described a maturity curve most companies are grappling with:

• Skills Implied: Organizations still job-centric, relying on resumes and job titles to infer skills.
• Skills Included: Companies (often in regulated sectors) capture credentials but haven’t woven them into how they work.
• Skills Led: Some organizations extract skills data from across systems—but they’re still translating it manually into talent decisions.
  
  
• Skills Based: The theoretical future where jobs dissolve entirely 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 software, 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 because skills were too abstract.
• Customers had no common language to describe how work happened.
• AI doesn’t automate skills—it automates tasks.
  
  

So Reejig threw away their skills model and built a Work Ontology instead.

Why does this matter?

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

If you don’t know the tasks, you can’t manage the impact of AI—or prepare people for what’s next.

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 written 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 and are instantly out-of-date.
  
  
• Learning and development often trains people for skills the business doesn’t actually 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 internal mobility. But post-COVID—and with the rise of GenAI—the conversation has 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 eliminate old tasks—but 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: it 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 the impact of AI across not just HR but the whole organization.
  
  

But SAP doesn’t try to solve everything alone. That’s why they built the Talent Intelligence Hub—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 Talent Intelligence Hub helps SAP customers:

• Get a unified language of skills and work.
• Align learning, recruiting, and workforce planning on a single source of truth.
• 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 is helping companies do it responsibly:

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

It’s not enough to cut jobs. Businesses need to engineer reinvention pathways—or risk creating talent gaps and eroding trust.

The Roadmap to Reinvention

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

•  AI Impact Index → 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 tools (like Microsoft Copilot) to automate defined tasks.

And crucially, all this data feeds back into SAP’s Talent Intelligence Hub—ensuring 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 can tackle the seismic changes AI is forcing on work itself.

• Skills matter. But understanding work is the true unlock.
• Static job architectures are obsolete in the AI era.
• Workforce transformation must be people-centric to avoid becoming pure cost-cutting.

SAP SuccessFactors and Reejig are offering companies a practical path forward—combining deep enterprise data with granular task-level insights.

If your CEO is asking how to build an AI-powered workforce—and yours almost certainly will—this is the blueprint.

Ready to explore what this looks like for your organization? Talk to a Work Strategist.