Restaurants Abandon Early AI Bets as ROI Fails to Materialize Across Sectors

A broad retreat from artificial intelligence investments is underway across the restaurant and hospitality sectors as organizations confront a widening gap between expenditure and measurable business outcomes. Industry analysts now describe the final quarter of last year as the "Great Decoupling," a period when capital markets stopped rewarding AI ambition and began penalizing firms unable to demonstrate concrete revenue or efficiency gains.

Gartner moved generative AI into its "Trough of Disillusionment" category, signaling that the early excitement phase had ended and businesses were entering a more difficult, reality-driven chapter of adoption. By mid-2025, fewer than one-third of AI leaders reported that their CEOs were satisfied with the returns generated by AI initiatives, even as companies were spending close to $2 million per project on average. Integration challenges, particularly when layering AI onto legacy systems, proved far more difficult than many organizations had anticipated.

A report from MIT found that roughly 95 percent of investments in generative AI had yet to deliver meaningful returns, becoming a rallying point for a growing cohort of skeptics questioning whether the technology's promise was outpacing its practical value. The findings resonated across industries, from Czech enterprises—where only about one-third of organizations that attempted to scale AI initiatives succeeded—to consulting firms now pivoting toward specialists as generalist strategy roles face projected declines of 10 to 25 percent.

"Decision intelligence is that next layer up," says one business intelligence executive quoted in restaurant industry analysis. "I like to say it's a business intelligence tool with artificial intelligence and decision intelligence." The advice from practitioners is straightforward: before diving into advanced tools, organizations must ensure their business intelligence and data infrastructure are solid. Without that groundwork, even the most sophisticated AI systems will struggle to deliver value.

Yet demand for AI talent remains robust. Nearly half of business leaders surveyed by KPMG said they would pay 11 to 15 percent more for talent with strong artificial intelligence skills, with respondents projecting average spending of $207 million over the next 12 months—nearly double year-earlier levels. When asked to report primary return-on-investment barriers, 65 percent cited difficulty scaling AI use cases, up from 33 percent the previous quarter, while 62 percent pointed to skills gaps, up from 25 percent.

"AI outcomes increasingly depend on workforce readiness," says Rahsaan Shears, aIQ program lead at KPMG. "The limiting factor isn't the technology—it's whether people have the skills to direct AI, apply judgment and take responsibility for results." Most business leaders—87 percent—are prioritizing upskilling and reskilling to build an AI-ready organization, while 68 percent are focused on hiring new talent.

(KPMG polled 237 U.S.-based C-suite and business leaders representing organizations with annual revenue of $1 billion or more between mid-February and mid-March. Adastra surveyed 80 senior executives from major Czech enterprises, finding that 51 percent of organizations are already working with AI, though most fall between early pilots and more mature adoption.)

The workforce management sector is positioning itself as a counterpoint to the broader disillusionment. Sona, a workforce management firm, raised $45 million in a Series B funding round and claims its labour AI platform takes real-time data about bookings, revenue, weather, and every shift ever worked to build models that evolve in real-time. "Every other enterprise software category has been transformed by AI, but the tools managing the world's largest workforce are still fundamentally the same systems that were built 20 years ago," says Steffen Wulff Petersen, co-founder and CEO at Sona. The company's pitch reflects a broader industry narrative: that AI represents a unique opportunity to uproot entrenched, outdated frontline tools.

Meanwhile, security concerns are mounting. AI adoption is outpacing security controls across enterprises, with AI traffic increasing by as much as 80 to 90 percent year over year and thousands of AI applications now in use, according to Zscaler. Organizations are seeing significant data movement across these tools, including unsanctioned usage known as shadow AI. "AI is in your environment" whether you know it or not, security analysts warn, underscoring the visibility gap many security teams face.

The tension between technical capability and human judgment is reshaping workforce strategy. Anthony Salcito, senior vice-president and general manager of enterprise at Coursera, observes that while Singapore leads the Asia-Pacific region in technical AI upskilling with over 169,000 enrollments, this interest is mirrored by a 185 percent year-over-year increase in enrollments for critical thinking. "This is a human-based transformation, less of a technology-based transformation," Salcito says. The parallel growth indicates that the workforce recognizes a critical truth: technical proficiency is only half of the equation.

The consulting industry is experiencing a structural shift as AI reduces demand for generalist strategy consultants across the $400 billion management consulting market. Recruiting analysis from Revelio Labs and industry groups shows specialist hiring rose 20 to 35 percent recently, with forecasts suggesting up to 60 percent growth over five years while generalist roles may decline. Firms are favoring specialists with expertise in AI, data, cybersecurity, and supply chains as clients demand demonstrable technical depth rather than broad strategic frameworks.