As we enter 2026, the position of artificial intelligence (AI) in business has undergone dramatic changes. The fanatic boom of “just adopt AI anyway” that swept the market until recently has come to an end.
Currently, companies have entered a “judgment phase” demanding strict accountability and clear return on investment (ROI) from AI. This article details the harsh reality of AI investment facing companies and strategies for winning in future business, based on data from the latest domestic and international survey reports.
End of the AI Boom and the “ROI Crisis”
Globalization Partners (G-P)’s 3rd Annual “AI at Work Report” reveals a very harsh reality regarding AI investment.
73% of the executives surveyed reported that “at least some of their AI investments over the past 12 months resulted in outcomes below expectations.”
Currently, 100% of the surveyed companies are said to be using AI in some form, meaning technology penetration itself is complete. However, the percentage answering that they “actively use AI to drive innovation” reportedly decreased significantly from 60% the previous year to 42%.
This background involves tightening budget management. Approximately 70% of executives in the same survey explicitly stated they “are prepared to reduce AI budgets if goals are not achieved this year,” so it’s safe to say the blind experimental phase has ended.
Actually Increasing? “Hidden Costs” of AI Adoption
AI promised “automation and efficiency of operations,” but ironically, there are many cases where on-site burden is increasing. The reality of these “hidden costs” emerges from G-P’s survey:
- Increased verification work: 69% of executives report “increased time spent monitoring and correcting AI-generated content.” Only 23% of executives place full trust in AI output, resulting in management costs from double-checking.
- Employees “performing productivity”: 88% of leaders express concern about “productivity theater” where employees use AI simply to appear busy without creating substantial value.
- Proliferation of shadow AI: The same report also states that about one-third of employees use unauthorized AI tools not known to the IT department weekly, pointing out this creates security risks and obstacles to measuring ROI across the entire organization.
The Structure of “74:20 Profit Concentration” Separating Winners and Losers
Even under these harsh conditions, companies that are certainly achieving results exist. PwC’s “AI Performance Study” published in April 2026 quantitatively demonstrated the serious AI gap between companies.
According to the survey, a structure is confirmed where just 20% of companies (AI leaders) account for a full 74% of the economic value brought by AI. The remaining 80% of companies (laggards) are said to be sharing just 26% of the profits.
This can be described as an extreme manifestation of “Pareto’s law (80:20 rule)” in the business AI domain. This serious gap arises not from differences in investment amounts but from “strategic approach.”
Four Characteristics Seen in AI Leader Companies
According to PwC’s analysis, the top 20% of companies that capture the majority of profits share the following commonalities:
- Focus on reinventing business models: Using AI not just as a cost reduction tool but to reinvent business models themselves and enter new domains.
- Fundamental workflow redesign: Not “adding (addition)” AI tools to existing processes, but “redesigning (multiplication)” the entire workflow around AI.
- Promoting autonomous decision-making: Expanding AI decision-making areas without human intervention at about 3 times (2.8x) the speed of other companies.
- Strong governance: Establishing “responsible AI” frameworks and governance committees to ensure reliability.
The True Factor Determining the Gap: “Organizational Learning Speed”
PwC’s report warns that if this approach continues, the performance gap between leaders and laggards will continue to expand due to “differences in learning speed.”
What must be noted here is that this “learning speed” refers not only to “system (machine) learning speed” where AI reads data and becomes smarter. What truly determines winners and losers is “organizational learning speed.”
- System learning (AI evolution): As data increases, AI model accuracy improves.
- Organizational learning (human evolution): How quickly can business processes, governance, and decision-making mechanisms be updated based on AI experiment results?
The bottom 80% of companies are satisfied with just introducing AI tools, and this “organizational learning” stagnates. Meanwhile, the top 20% of companies are rapidly transforming organizational structure and strategy in line with technological evolution. The difference in the speed of turning these “two wheels of AI and organization” manifests as an insurmountable performance gap over time.
Human Value in the AI Era: Declining Skills and Soaring Skills
As AI and organizational transformation progress, evaluation criteria for humans are also changing significantly.
In G-P’s report, 82% of executives acknowledge that “the value placed on general talent has decreased due to AI.” On the other hand, the battle for “top-tier AI talent” who can control AI and drive innovation is intensifying, with 82% of executives responding they “want to hire excellent talent even if the location is overseas.” Extreme polarization in the talent market is progressing.
Meanwhile, a survey by the National Bureau of Economic Research (NBER) highlights gaps in internal perceptions regarding future employment. While management (CEO/CFO) anticipates “personnel reduction (0.7% decrease),” employees predict “employment increase (0.5% increase),” and this expectation gap may create future organizational friction.
Expectations for “Agentic AI” Holding CEO’s Fate
According to Boston Consulting Group’s (BCG) “AI Radar 2026” report, the success or failure of AI strategy now directly impacts CEO careers. In the same survey, half of CEOs believe “AI strategy success or failure will determine their own jobs,” and 72% of CEOs are reported to be directly in charge as primary AI decision-makers.
Under such pressure, many CEOs are pinning hopes on “agentic AI (autonomous agents)” as the 2026 breakthrough.
According to the same report, 90% of CEOs believe “agents that plan, act, and learn autonomously will generate measurable ROI within the year.” As a result, companies are allocating over 30% of their AI budgets to this field, with pioneering companies reportedly exceeding 50%.
Summary: Creating Metrics to Survive Future AI Strategies
The survey result that “73% of companies are disappointed with ROI” does not demonstrate the limitations of AI technology itself. It suggests that “the definition of AI value was insufficient” in the adoption process so far, and above all, that “organizational learning speed” was not accompanied.
As proposed by Katie George, Microsoft’s CVP of Workforce Transformation, the true value of AI lies in “capability addition” that brings deep insights and predictive power, not just cost reduction.
To break out of the endless “trial loop” of AI investment and achieve true transformation, the following perspectives are important:
- Outcome-based definition: Set metrics based on business outcomes like “sales improvement” or “accelerated product development,” not “how many people are using the tool.”
- Identifying leading indicators: Evaluate “improved decision-making” or “reduced rework” before final profits emerge as legitimate value.
- Visualizing transformation processes: Visualize how AI changed work processes and build mechanisms to rotate “learning cycles” across the entire organization.
The 2026 judgment will further accelerate the selection between the 20% of companies that can master AI as a driver of organizational transformation and the 80% of companies that simply added AI to existing operations and were swallowed by the wave.
Reference Source Reports
This article was created by integrating and referencing the following survey data and insights.
- Globalization Partners (G-P): “3rd Annual AI at Work Report” (Published 2026)
- Boston Consulting Group (BCG): “AI Radar 2026” (Published 2026)
- PwC: “AI Performance Study” (Published April 2026)
- National Bureau of Economic Research (NBER): Survey report on employment and productivity (2026)
- Microsoft: Insights from Katie George, CVP of Workforce Transformation