The question boards ask about AI investments has changed. "Are we using it?" has been replaced by "What has this investment added to profit?" McKinsey's "The state of AI in 2026" survey, published on August 25, 2026, clearly shows why the answer to that question is still unclear: employees say they benefit from AI, but that benefit is not reflected at the same pace in the company's financial statements.
In this article, we look at the survey's findings on the gap between productivity and profit, what the small number of organizations that have managed to close this gap do differently, and the lessons that can be drawn for executives in Türkiye.
About the survey
The online survey ran from May 4 to June 8, 2026, and 1,719 participants from 97 countries responded. Of the participants, 36% work at organizations with annual revenue above $1 billion. To offset differences in response rates across countries, McKinsey states that it weighted the data by each country's share of global GDP. The figures are therefore not the organizations' measured financial results, but the participants' own statements.
Strong impact for individuals, a flat line for the organization
At the individual level, the picture is positive. Among participants, 80% say AI has increased their own productivity, and 50% say it has helped them make better decisions. According to McKinsey, these effects are strikingly similar across management and specialist levels. Still, the experience is not positive for everyone: while 47% of mid-level managers and specialists report at least one negative effect linked to AI, the figure among senior executives is 31%.
At the organizational level, however, progress is limited. The share of respondents reporting that AI has made a positive contribution to earnings before interest and taxes (EBIT) is 37%, and this figure has effectively not changed since last year. Moreover, this stagnation is occurring while the share of organizations scaling AI across the enterprise is rising. The share reporting enterprise-wide scaling rose from 38% to 44% in a year, and the share using AI in three or more business functions rose from 51% to 56%.
In short, AI is spreading to more units and more employees, but the profit line is not moving at the same pace.
Where does the financial impact show up?
Participants who report no enterprise-wide EBIT impact also say they see financial impact in specific functions. Cost reductions are most often reported in supply chain management, service operations and manufacturing, while revenue increases are most often reported in marketing and sales, followed by product and service development and software engineering.
This finding offers an important clue: value is created concretely in specific processes, but when it stays scattered, it does not translate into a meaningful profit impact at the company level.
Cost is now a design question
One of the survey's new emphases this year is operating costs. Roughly one in five participants report that AI operating costs (including token costs) limit its use in their organizations. Even so, the appetite for investment continues: 28% of participants say they allocate more than 10% of their information and communication technology budget to AI, and 60% expect investment to increase next year.
Another notable finding is that software development agents are influencing purchasing decisions. In the survey, 32% of participants say their organizations gave up buying at least one software product or feature because they could build it in-house with agent-based coding tools. This shows that AI is not just a cost item but a factor that changes how the technology budget is allocated.
What do high performers do differently?
McKinsey defines as "high performers" those organizations that attribute at least 5% of their EBIT to AI and say they derive "significant" value from it. This group's share is about 6%, the same as in 2025. The survey points to several clear differences that set this group apart from the others:
- The goal is not efficiency alone. In both groups, about 80% of participants target efficiency; however, most high performers add growth and/or innovation to it. They are 3.3 times more likely to aim to fundamentally transform their business with AI over the next three years.
- They redesign the process instead of adding to the existing one. About three-quarters of high performers say they have fundamentally redesigned their workflows because of AI; last year this figure was 55%. Among other participants, the figure is only about one-quarter.
- Leadership ownership and measurement. They are twice as likely as the others to say that senior management is committed to AI initiatives and that they have defined processes for measuring impact.
- They manage risk actively. They are markedly more likely to take precautions against risks such as the exploitation of technical vulnerabilities and unauthorized or unintended actions.
In McKinsey's assessment, this group's advantage cannot be explained by budget; what matters is the consistency of the approach. According to the report's interpretation, the real constraint is increasingly not technology but the organization's capacity to absorb change. Rather than accumulating tools or pilots, organizations are advised to build a repeatable capability that can turn these efforts into new ways of working.
Workforce expectations did not materialize
The gap between productivity and profit also shows up in workforce forecasts. In last year's survey, 32% of participants expected AI to reduce the workforce; this year, only 14% report an actual reduction over the past 12 months. Two-thirds of participants say there has been either no change or very little change in total employment attributable to AI. This suggests that a productivity gain does not automatically turn into a change in capacity and cost, and that this is possible only through the deliberate reorganization of roles and workflows.
Takeaways for executives in Türkiye
The findings come from a global sample and rely on participants' own statements; even so, they raise guiding questions for mid-sized and large organizations in Türkiye:
- Define the metrics from the start. A sense of individual productivity is valuable, but to see a profit impact, measurable indicators such as time, error rate, cost or revenue must be recorded before implementation.
- Address the workflow, not the tool. The difference between adding AI to the existing process as one more step and redesigning the process from scratch is one of the key factors separating performance in the survey.
- Plan where the time saved will go. When a productivity gain is not matched by decisions on roles, capacity and work allocation, it may not show up in financial results.
- Include operating cost in the investment decision. As usage grows, token and infrastructure costs grow too; cost-benefit tracking at the use-case level should be set up from the start.
- Make risk management part of scaling. Risks such as unauthorized actions and security vulnerabilities become more visible as the scope widens.
Conclusion
McKinsey's 2026 survey shows that AI creates value at the employee level, but that turning it into profit at the organizational level is not automatic. What makes the difference is taking the goal beyond efficiency, redesigning workflows and measuring impact with discipline.
From Orient Global's perspective, the practical advice is this: before adding a new tool, select one or two end-to-end processes where a profit impact is expected, and record the current cost and time values of those processes. Reviewing AI investments against these baseline values at regular intervals allows for better decisions about what to scale and what to stop.
A note from the logistics side. Supply chain management is one of the functions where respondents most often report cost reductions, and it is familiar ground for one of Orient Global's consultants. Over more than 30 years in operations, logistics and supply chain, he has run logistics, warehousing, technical service and administrative operations end to end at a technology distribution company of roughly $1 billion in scale, managed nationwide warehouse and transport operations, and led teams of 100+ people through KPI-driven programs. Logistics builds the very habits this article recommends: record a process's baseline cost, time and error rate before changing it, redesign the flow instead of bolting on a step, and decide in advance where the freed-up capacity will go. Meet our consultants.
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