“Corporate spending on artificial intelligence is surging as executives bank on major efficiency gains. So far, they report little effect to the bottom line,” wrote NY Times technology and business reporter Steve Lohr in a recent article, “Companies Are Pouring Billions Into A.I. It Has Yet to Pay Off.”
“Nearly four decades ago, when the personal computer boom was in full swing, a phenomenon known as the productivity paradox emerged,” Lohr added. “It was a reference to how, despite companies’ huge investments in new technology, there was scant evidence of a corresponding gain in workers’ efficiency.”
“Today, the same paradox is appearing, but with generative artificial intelligence. According to recent research from McKinsey & Company, nearly eight in 10 companies have reported using generative A.I., but just as many have reported ‘no significant bottom-line impact.’”
After growing at an average annual rate of close to 3% in the decades following WW2, US labor productivity significantly slowed in the 1970s and 1980s. This period of slow productivity coincided with the rapid growth in the use of IT in business and the advent of personal computing, giving rise to the so-called Solow productivity paradox, a reference to Nobel Prize MIT economist Robert Solow’s 1987 quip: “You can see the computer age everywhere but in the productivity statistics.” Then starting in the mid 1990s a decade of faster growth returned as the internet revolution and business process re-engineering helped to spread productivity-enhancing innovations across the economy.
Subsequent analysis helped explain this seeming productivity paradox by looking at historical parallels. Companies and industries also took decades to learn how to reap the productivity benefits of previous transformative technologies, like the steam engine and electricity. For example, productivity growth did not increase until 40 years after the introduction of electric power in the early 1880s. It took until the 1920s for companies to figure out how to restructure their factories to take advantage of electric power with new manufacturing processes like the assembly line.
A similar productivity paradox has now appeared in the emerging age of AI. As explained by economists Erik Brynjolfsson, Daniel Rock, and Chad Syverson in a 2017 working paper, AI and the Modern Productivity Paradox,” the paradox is primarily due to the time lag between technology advances and their impact on the economy. While technologies may advance rapidly, humans and our institutions change slowly. Moreover, the more transformative the technologies, the longer it takes for them to be embraced by companies and industries across the economy. AI is likely to become one of the most important technologies of the 21st centuries, but we’re still in the early stages of AI’s deployment. Translating technological advances into productivity gains requires major transformations in business processes, organization and culture, — and these take time.
“A.I. technology has been racing ahead with chatbots like ChatGPT, fueled by a high-stakes arms race among tech giants and superrich start-ups and prompting an expectation that everything from back-office accounting to customer service will be revolutionized,” wrote Lohr. “But the payoff for businesses outside the tech sector is lagging behind, plagued by issues including an irritating tendency by chatbots to make stuff up.”
Lohr’s article referenced “Seizing the agentic AI advantage,” a research report published by McKinsey’s AI unit Quantum Black in June of 2025. The report is a very good introduction to the benefits, as well as to the serious challenges involved in implementing a new agentic AI architecture. McKinsey describes the report as “a CEO playbook to solve the gen AI paradox and unlock scalable impact with AI agents.” Here are the report’s key points:
- Nearly eight in ten companies report using gen AI — yet just as many report no significant bottom-line impact. Think of it as the “gen AI paradox.”
- At the heart of this paradox is an imbalance between horizontal (enterprise-wide) copilots and chatbots — which have scaled quickly but deliver diffuse, hard-to-measure gains — and more transformative vertical (function-specific) use cases — about 90 percent of which remain stuck in pilot mode.
- AI agents offer a way to break out of the gen AI paradox. That’s because agents have the potential to automate complex business processes — combining autonomy, planning, memory, and integration — to shift gen AI from a reactive tool to a proactive, goal-driven virtual collaborator.
- This shift enables far more than efficiency. Agents supercharge operational agility and create new revenue opportunities.
- But unlocking the full potential of agentic AI requires more than plugging agents into existing workflows. It calls for reimagining those workflows from the ground up — with agents at the core.
- A new AI architecture paradigm — the agentic AI mesh — is needed to govern the rapidly evolving organizational AI landscape and enable teams to blend custom-built and off-the-shelf agents while managing mounting technical debt and new classes of risk.
- But the bigger challenge won’t be technical. It will be human: earning trust, driving adoption, and establishing the right governance to manage agent autonomy and prevent uncontrolled sprawl.
- To scale impact in the agentic era, organizations must reset their AI transformation approaches from scattered initiatives to strategic programs; from use cases to business processes; from siloed AI teams to cross-functional transformation squads; and from experimentation to industrialized, scalable delivery.
- Organizations will also need to set up the foundation to effectively operate in the agentic era. They will need to upskill the workforce, adapt the technology infrastructure, accelerate data productization, and deploy agent-specific governance mechanisms. The moment has come to bring the gen AI experimentation chapter to a close — a pivot only the CEO can make.
“Like any truly disruptive technology, AI agents have the power to reshuffle the deck,” said the McKinsey report in its Conclusion section. “Done right, they offer laggards a leapfrog opportunity to rewire their competitiveness. Done wrong — or not at all — they risk accelerating the decline of today’s market leaders. This is a moment of strategic divergence.”
But the McKinsey report warns that the change won’t be easy. “This is a structural move toward a new kind of enterprise. Agentic AI is not an incremental step — it is the foundation of the next-generation operating model. CEOs who act now won’t just gain a performance edge. They will redefine how their organizations think, decide, and execute.”
Lohr’s article further references a recent survey by S&P Global, that found that “the percentage of companies abandoning most of their A.I. pilot projects soared to 42 percent by the end of 2024, up from 17 percent the previous year.” In addition, AI Agents are now at the Peak of Inflated Expectations in Gartner’s 2025 Hype Cycle for AI. AI is now sliding toward the stage Gartner calls the trough of disillusionment before eventually becoming a proven productivity tool.
“That was the pattern with past technologies like personal computers and the internet — early exuberance, the hard slog of mastering a technology, followed by a transformation of industries and work,” adds Lohr. “Whether that type of revolutionary change occurs, and how soon, depends on the real-world testing ground of many businesses.”
“That means that businesses will have to continue to invest billions to avoid falling behind — but it could be years before the technology delivers an economywide payoff, as companies gradually figure out what works best.”