Ask an hourly worker to choose between a raise and a schedule they can count on, and many take the schedule.
The hourly labor market, otherwise known as the Labor Economy, doesn’t have a wage problem. It has a schedule volatility problem.
WorkWhile CEO Simon Khalaf said matching technology and artificial intelligence can cut that volatility for workers and employers alike by treating labor allocation as a math problem, not a staffing exercise.
“Put all the labor supply in one pool, all the labor demand in another, and match between them,” Khalaf told PYMNTS CEO Karen Webster. “With AI, you can dramatically cut the cost of managing a workforce and spread volatility across a much larger base. So, what would be a 20% hit for 10 workers becomes a 1% adjustment across 200. It’s math more than staffing.”
For decades, the debate over hourly work has centered on pay. The latest Wage to Wallet Index pointed to a variable that workers rank higher: whether they know how many hours they’ll get next week. Without that, they can’t plan. A paycheck you can’t predict is one you can’t budget around.
Webster called it “this hidden transfer of risk,” adding that millions of hourly workers carry uncertainty that wouldn’t be tolerated anywhere else in the economy.
Joint research from PYMNTS Intelligence and WorkWhile found that 45% of hourly workers report schedules that are hard to plan around, with many schedules swinging by five hours or more a week. That’s roughly $20 billion in annual wage volatility that’s gone for good because, as Webster put it, “you can’t manufacture lost time.”
The worker preference is simple, Khalaf said. Given the trade, they want certainty first.
“Give me predictability over growth,” he said.
Reliable income lets households plan and lean less on high-cost borrowing.
The fallout reaches beyond payroll. An unpredictable paycheck changes how families budget, borrow, save and spend.
A $7 Trillion Economy Built on Uncertain Income
The scale of the problem is badly underestimated, Khalaf said.
“We’re talking about an economy that is almost double that of Germany,” he said. “If the hourly Labor Economy was a country, it’d be the third largest country in the world.”
That view reflects his background. Before WorkWhile, Khalaf spent years building data-driven businesses at Yahoo and Verizon, where forecasting demand ran on predictive models, not intuition. He’s applying the same mindset to labor.
His argument is that instability isn’t baked into hourly work. It’s a design problem.
Webster drove the point home with an analogy executives know.
“If you said to your board, next week’s revenue or next month’s revenue could be up 20%, down 20% … you may be fired,” she said. Yet that’s exactly what many hourly workers face week to week.
Khalaf pointed to payday loans nearing 64% APR and said killing that dependency is a top goal. When a paycheck swings, workers fill the gap with borrowed money, and the cost of borrowing only deepens the hole.
Nearly two-thirds of hourly workers were hit by financial disruption in the past 90 days, a sign that irregular schedules spark recurring crises even when pay holds steady.
So, the debate shifts from hourly pay to income certainty. A worker who knows next week’s earnings can make choices that one who doesn’t simply can’t.
How AI Fills the Gaps and Keeps Paychecks Coming
Khalaf’s fix isn’t to make every employer guarantee hours. It’s to pool enough demand in one place that when one employer’s hours dry up, another’s open up, and the worker’s week stays full. Pool demand at scale, and the swings spread across many workers instead of landing on a few.
WorkWhile’s platform pools labor supply and demand across hundreds of job categories, then uses AI to match workers to shifts, forecast their earnings and surface extra work before bills come due. It now spans 265 job types, giving workers a mobility no single employer could match. When one source of hours thins, the platform routes them to another, and the paycheck keeps coming.
The goal is to cut volatility through scale. If demand dips, spreading fewer hours across hundreds of workers hurts less than cutting shifts for a small team.
The same tech predicts earnings and nudges workers to add shifts instead of taking on unsecured debt.
Employers Are Fighting Their Own Technology Problem
Many workforce systems still run on decades-old assumptions, making forecasting and staffing needlessly clunky, Khalaf said. Hiring often means manual approvals, compliance reviews and a tangle of software before anyone gets filled. Event staffing and hospitality, with their high turnover, trap managers in endless recruiting instead of real planning.
So WorkWhile has started splitting scheduling, payroll and compliance into standalone products, rather than making customers adopt the whole platform at once.
It’s adding free telehealth and expanding AI forecasting tools that tie projected income to upcoming bills. Instead of treating workers as throwaway labor, the model offers benefits usually reserved for permanent jobs while keeping mobility intact.
“I will not rest until 80 million American workers are thriving,” Khalaf said, defining success as a labor market where workers no longer depend on interest-bearing debt and “the job comes to them.”
Watch the full interview with Karen Webster and Simon Khalaf to learn more about:
- Why hourly workers will trade a raise for a schedule they can plan around.
- How an AI-driven marketplace fills gaps in demand to keep paychecks steady.
- Why employer scheduling technology has become a competitive weakness.
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