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 workforce and business leaders today.

Capabilities 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 capability-led. It's work-based.

Here's what stood out.

Why the capability hype has hit reality

Josh kicked things off with rare honesty:

"I'm a little bit of a skeptic."

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

He described a maturity curve most companies face:

• Capability implied: Organizations still job-centric. They rely on resumes and job titles.
• Capability included: Companies capture credentials but haven't woven them into how they work.
• Capability led: Some organizations extract data from across systems. They still translate it manually into decisions.
• Work-based: The theoretical future where jobs dissolve into fluid, work-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 capabilities are unimportant. It's that capabilities alone aren't enough.

Every enterprise is deploying AI. Almost none can see the work they're deploying it into.

Why the conversation has shifted to work

Siobhan laid out a fundamentally different lens:

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

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

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

So Reejig rebuilt the model around Work Context instead.

Why does this matter?

• Work is made of tasks.
• Tasks require capabilities.
• 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 capabilities the business doesn't need.
• Companies lack a unified "language of work." They can't connect hiring, 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 workforce world was obsessed with retention and work movement. Post-COVID, and with the rise of GenAI, the conversation flipped.

"We've gone from capability-led organizations 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.
• Redesign can't be a one-off project. It's a continuous evolution.

Siobhan warned:

"If you create a static 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.

AI capability is compounding. Work visibility is not.

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:

• Forecasts labor demand based on business changes.
• Ties workforce planning to operational realities.
• Models AI's impact across the whole organization.

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

Amy Wilson summed it up:

"Reejig creates a Work Context layer. But that's a byproduct of their Work Intelligence. Work Intelligence is the tip of the spear for workforce redesign."

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

• A unified language of work and capabilities.
• 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 redesign 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 takes over.
• Predict new tasks that will emerge.
• Redeploy people into adjacent roles. Based on capability and task similarity.
• Integrate learning directly into new pathways. 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 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 capabilities 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 taxonomies or AI buzzwords. It was a glimpse into how real organizations tackle change.

• Capabilities 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.

Book a demo.