The rapid adoption of agentic AI is not automatically translating into higher productivity for enterprises. According to McKinsey’s Technology Trends Outlook 2026, productivity declined in nearly 30% of companies after teams began using agentic AI tools.
The findings highlight a growing gap between the ability of AI agents to generate more software and an organization’s ability to turn that output into measurable business value.
More Code Doesn’t Necessarily Mean More Products
McKinsey found that AI tools increased coding activity by 180% in one study, while shipped software releases increased by only 30%. The difference suggests that higher development activity does not necessarily translate into more completed products or business outcomes.
The report points to the importance of having a systematic approach when introducing autonomous software development tools. Without appropriate processes, organizations can experience unintended outcomes despite increased AI-assisted activity.
Developer Trust Remains a Challenge
Trust is another factor affecting adoption.
According to the report, 46% of developers globally said they actively distrust the accuracy of AI tools, compared with 33% who said they trust them. Only 3% reported high trust in AI-generated outputs.
This trust gap can influence how extensively development teams rely on autonomous AI tools and how much human review remains necessary.
Agentic AI Investment Continues to Grow
Despite the productivity challenges, investment in agentic software is expanding rapidly.
McKinsey’s findings cited equity investment rising from negligible levels through 2023 to approximately $5 billion in 2025, before exceeding $61 billion in the first half of 2026. The 2026 figure was heavily influenced by a reported $60 billion acquisition of Cursor.
Job postings related to agentic software also increased 221% between 2024 and 2025, indicating continued demand for skills around autonomous AI development.
From AI Adoption to AI Value
The findings arrive as enterprises move from AI experimentation toward broader deployment.
McKinsey’s separate 2026 State of AI survey found that 40% of respondents from organizations with more than $1 billion in annual revenue reported scaling AI agents, up from 27% the previous year. However, enterprise-level financial impact has not increased at the same pace: 37% of respondents said AI had contributed to organizational EBIT, essentially unchanged from the previous year.
This points to an important distinction: deploying AI agents and capturing measurable business value are two different challenges.
What Enterprises Need to Consider
The McKinsey findings suggest that organizations deploying agentic AI need to look beyond coding speed or the number of AI-generated outputs.
Key areas include:
- Clear development workflows for AI-assisted engineering
- Human review and quality controls
- Developer training and AI literacy
- Security and governance
- Measurement based on business outcomes rather than output volume
- Integration with existing technology and development processes
As agentic AI adoption accelerates, the focus is increasingly shifting from “How much can AI produce?” to “How much measurable value does AI create?”
