The Skeptics Missed The Turn In AI

Walk the halls of a typical Fortune 1000 and you’ll see AI-generated drafts, forecasts, and code reviews sliding into workflows with little fanfare. That pattern shows up in the year-three executive summary of Wharton Human-AI Research and GBK Collective, which tracks mainstream use, tighter guardrails, and budgets moving to proven programs in 2025. External benchmarks echo the turn, from McKinsey’s 2024 global survey documenting regular generative AI use to Stanford’s 2025 AI Index showing broadening business engagement.

Enterprise Adoption Is Now A Fact 

Three years of Wharton-GBK data trace a clear arc from exploration to everyday work. The study’s 2025 findings describe daily use as common, with IT and procurement leading while adoption spreads across HR, legal, finance, and operations. The same report notes a U.S. executive sample of roughly 800 leaders surveyed between June 26 and July 11, 2025, reinforcing that these are decision makers inside large enterprises. This year, the report found 82% use Gen AI at least weekly (+10pp YoY), and 46% (+17pp YoY) daily. Moreover, three out of four leaders described positive returns on Gen AI investments.

This picture holds beyond this survey. IBM’s Global AI Adoption Index found that by December 2023, 42% of enterprise respondents had actively deployed AI and another cohort was in active exploration, a pattern consistent with mainstreaming rather than novelty. McKinsey’s 2024 State of AI reported regular generative AI use by 65% of organizations and cited value concentrations in software, customer operations, and marketing. Stanford’s 2025 AI Indexadds macro context on rising use and falling inference costs, which helps explain why adoption keeps climbing inside firms.

ROI Discipline Is Taking Hold 

Executives are not treating AI like a toy anymore; they are instrumenting it. Wharton’s 2025 study highlights a decisive turn toward measurement, with most firms tying initiatives to business metrics and roughly three in four already seeing positive returns. Budgets follow accountability: 88% expect to increase generative AI spend over the next 12 months, and 62% anticipate double-digit increases, with more dollars shifting from pilots to programs that clear performance thresholds. KPMG’s August 2024 survey of billion-dollar enterprises backs this confidence, with 78% of leaders expecting positive ROI within one to three years.

The strongest evidence for usefulness comes from field data. A large-scale study across more than five thousand support agents found that access to a generative assistant increased resolved issues per hour by about 15% on average, with the biggest lifts for less-experienced workers. In software, a controlled experiment showed developers with an AI pair programmer finished a coding task 55.8% faster than the control group. These results align with where Wharton’s study sees repeated wins: analysis, summarization, document workflows, and coding. The practical lesson is simple. Measure throughput, quality, and cycle time; scale what clears your hurdle rate; and stop what doesn’t.

This discipline is reshaping how companies build. Wharton reports rising allocations to internal R&D and a shift toward domain-tuned capabilities designed for a firm’s data, guardrails, and processes in 2025’s “accountable acceleration” phase. The pattern matches Deloitte’s ongoing State of Generative AI, which ties outperformance to reworked workflows, strong governance, and targeted skilling rather than tool nostalgia. Leaders who treat AI as an operating improvement, not a stunt, are already harvesting repeatable gains.

Skepticism Flags Risks, Not Futility 

Skeptics are not wrong to warn about poor execution. Boston Consulting Group’s 2025 analysis finds only 5% of firms achieving material value at scale while 60% report little impact, a reminder that buying tools is not the same as redesigning work. Gartner expects that more than 40% of agentic AI projects will be canceled by 2027 for weak business cases, high costs, or poor risk management. These cautions sharpen priorities: fewer vanity demos, more outcome-based design.

Read alongside those warnings, the Wharton study undercuts blanket pessimism. It documents a cohort of leaders moving past hype: as I tell my clients who I help adopt AI, success depends on embedding ROI metrics, tightening guardrails, and funding internal capabilities where they matter most. The OECD’s cross-country portrait shows that adoption concentrates in larger and data-mature firms and in sectors like ICT and professional services, where users tend to be more productive. That nuance reconciles the headlines: value arrives first where workflows are digital, measurable, and well-informed by data. McKinsey’s 2024 report and Deloitte’s enterprise series both show that when organizations pair tools with process redesign, training, and risk management, returns show up quickly in specific functions.

The path forward is concrete. Start where the evidence is strongest: support desks and developer workflows already show durable lift. Use Wharton’s playbook to formalize ROI tracking, push budgets toward the use cases that clear your thresholds, and invest in internal capabilities that align with your data and controls. Add the macro signal from Stanford’s AI Index and IBM’s adoption index, and the direction is hard to miss: the market is sorting disciplined operators from dabblers, not proving AI useless.

Conclusion 

Enterprise AI is past the novelty stage. Leaders are using it frequently, measuring it against business metrics, and backing the winners with bigger budgets. Skeptical reports serve a purpose by spotlighting waste and weak governance, but they describe the cost of poor execution rather than a dead end. The Wharton analysis supplies a usable blueprint: set ownership, measure outcomes, align people and processes, and fund the capabilities that move the needle. Pair that with external evidence from real workplaces, and the signal is clear. Treat AI as an operating system for work and you will bank the advantage while others debate the headlines.