Joanna Riley, CEO & cofounder of Censia. Striving for a more just and efficient global economy through better talent data and technology.
As the concept of work shifts, access to a contextual workforce infrastructure will become the differentiator.
There’s one roadblock keeping businesses from realizing the full potential of AI transformation: Their workforce supply chain is broken.
Think about what Walmart did as it set out to become one of the world’s largest retailers. Its leaders focused not only on what customers wanted to buy, but also on how Walmart could source, move and replenish those products efficiently at scale. Rather than relying entirely on conventional retail distribution, Walmart built its own distribution centers and transportation capabilities and developed practices such as cross-docking to move merchandise quickly from suppliers to stores.
In doing that, one input mattered more than anything else—the data the supply chain ran on. Walmart could access thousands of options for toothpaste, for example. But knowing how to put the right toothpaste in front of the right audience at the exact right time came down to having precise data about its consumers and markets.
Without accurate, self-sustaining data, the chain collapses.
Today, businesses similarly need to reinvent their workforce supply chain. And to do it, they need accurate, reliable access to the vastly complex data of human capability.
What changes in the era of work redesign?
Existing systems built to handle how we work have been upended by the promise—and challenge—of integrating AI. Everyone is racing to adapt, from the CFO modeling headcount and the COO deciding which roles to automate to the CEO guiding the organization toward what comes next.
Boston Consulting Group estimates that 50% to 55% of jobs in the U.S. will be reshaped by AI in the next three years. This profound shift necessitates a cohesive overhaul for our ways of working and a more refined understanding of our talent pipeline.
At the same time, Bain & Company estimates that “weak management systems and poor deployment of human capital drain companies of up to 40% of their productive power.” As the workforce changes, the pressure will be to leverage new technologies to significantly reduce this number. The opportunity on the table is the chance to reclaim that productivity.
Work redesign doesn’t come with a playbook. It does, however, come with a timer.
Businesses are racing to restructure their workforce and need to answer the same supply chain question about work that Walmart did about retail: What do we need, what capabilities do we have and what do we need to close the gap?
But the issue is, work redesign has a data bottleneck.
Across industries, people decisions need to run on accurate, comprehensive talent data. Currently, very few businesses can access that information reliably and at scale.
Without the correct context, integrating AI into existing systems doesn’t eliminate blind spots. It just automates them.
The right foundation fuels transformation.
As Nancy Hauge, chief people officer of Automation Anywhere, puts it, “The future of work will not be defined by how sophisticated AI becomes. It will be defined by how intentionally leaders redesign work around it.”
When businesses can access the contextual workforce infrastructure that powers everything, it changes what’s possible. Operational people decisions can be made on real data, not assumptions. Work intelligence is poised to become a trillion-dollar advantage that fuels the next stage of work redesign.
The organizations that move fastest won’t be the ones with the most tools or pilots. They’ll be the ones who treat work like a supply chain challenge and can make decisions based on accurate, dynamic data, turning workforce decisions into operational impact.
The shift required is strategic, not purely technological.
Start with outcomes, not the org chart.
Legacy workforce wisdom examines the talent available to a business and asks: How can we leverage our existing resources to achieve our goals? It’s an attempt to reverse engineer a solution with the resources at hand.
The future of work inverts that paradigm.
Now organizations must ask the question that changes everything: What outcomes are we building, and do we have what it takes to get there?
Then, they can determine what resources are needed to get there and assemble the precise combination of teams, agents and systems needed to succeed. With the right contextual workforce infrastructure, that suddenly becomes not just possible but seamless. That’s the difference between a workforce strategy and work intelligence that actually designs how work gets done
Walmart understood this inversion. They didn’t ask what they had on the shelves. They asked what customers wanted to buy, and built their supply chain from there.
Leverage the importance of context.
When businesses can access the contextual infrastructure to power their workforce, planning stops being a reactive exercise and can become a strategic capability. AI investments become defensible because they’re built on intelligence, not just inferences, and can live natively within processes designed to support their success.
“In some sense, models are becoming commodity,” says Satya Nadella, Microsoft’s CEO. “The performance of your AI application is directly correlated to how well your data is brought into context.”
In essence, having a robust and accurate contextual workforce infrastructure powering frontier models will be the difference between success and failure.
The timer on work redesign is already running.
The organizations that build the right foundation now will have something the others don’t: a clear vision of what they need, the right data on what they have and the confidence to close the delta faster than the competition.
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