{"slug": "ai-agents-high-effort-low-return", "title": "AI agents: High effort, low return", "summary": "Only 37% of companies attribute any positive EBIT impact from their AI programs, according to McKinsey's Technology Trends Outlook 2026, even though 89% of companies now use AI regularly. McKinsey calculates that a single agent workflow requires five to 30 times more computing resources than a typical chatbot query, and 93% of survey participants said this caused them to exceed their AI budgets. METR researcher Sydney Von Arx told MIT Technology Review that McKinsey misinterpreted METR's data: the twelve-hour 50% time horizon for Claude Opus 4.6 on software engineering tasks refers to the time a human would need on average for the task, not the AI's processing time.", "body_md": "AI has arrived in the business world — and the results are thus far underwhelming.\n\n[According to McKinsey](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-top-trends-in-tech), 89% of companies now use AI regularly, and the majority are at least experimenting with AI agents. However, a significant gap remains between usage and economic success.\n\n“We find that only 37% of companies attribute any positive EBIT impact from their AI programs, let alone from newer agentic deployments,” writes the consulting firm in the [McKinsey Technology Trends Outlook 2026.](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-top-trends-in-tech) \n\nThis is particularly problematic because AI agents not only open up new possibilities but also increase the complexity and costs of IT. McKinsey points out in its study that upgrades to the existing tech stack are necessary for agents to function reliably.\n\nIn addition, there is the resource requirement during ongoing operation, as agent-based workflows are significantly more computationally intensive than simple chatbot interactions, because they are based on repeated inference loops, tool calls, retrieval steps, and cross-system coordination.\n\nAccording to McKinsey’s calculations, a single agent workflow requires five to 30 times more computing resources than a typical chatbot query. In a recent survey, 93% of participants stated that this caused them to exceed their AI budgets, according to the firm.\n\nMcKinsey concedes, however, that this poor track record doesn’t have to be permanent. Agentic AI is still in its infancy, and the benefits companies derive from it will likely increase once the technology is more mature and deeply embedded in the enterprise.\n\nFurthermore, current agent development tools from Anthropic and OpenAI have improved tool usage and interaction with computers, according to the report, while frameworks such as LangGraph from LangChain have added the orchestration and “human-in-the-loop” controls needed to guide agents through longer workflows.\n\nOne indicator of progress is the growing ability of AI agents to handle longer, more complex tasks from start to finish. McKinsey refers to the findings of nonprofit Model Evaluation and Threat Research (METR), which benchmarked the timeframe over which AI models can successfully complete tasks independently. For Claude Opus 4.6, METR estimates the 50% time horizon for software engineering tasks at approximately twelve hours. This means that for tasks of this size, successful autonomous completion is expected in half of the cases.\n\nHowever, McKinsey appears to have misinterpreted the data: As METR researcher Sydney Von Arx explained in an [interview with MIT Technology Review](https://www.technologyreview.com/2026/02/05/1132254/this-is-the-most-misunderstood-graph-in-ai), the value — in this example, the twelve hours — refers to the time a human would need on average for the task, not the AI. It is therefore more about performance than processing time.\n\nVon Arx explained that she, too, had initially been skeptical about whether the time horizon was the right measure. However, she and her colleagues had observed that the time horizons of top-performing models had increased over time — and that the pace of this development had also accelerated.\n\n“I can speculate all I want about whether this makes sense or not, but the trend is there,” the AI expert stated.\n\nMETR\n\nAI agents’ potential is particularly great in the field of software development. If successfully deployed, they could unlock almost a trillion dollars in added value for companies, [estimates McKinsey](https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/beyond-the-copilot-scaling-the-agentic-product-development-life-cycle). The potential benefits extend far beyond faster code generation. Agents could analyze requirements, generate code, conduct tests, find bugs, or even handle parts of the deployment process. The analysts see the goal not just as a more productive individual developer, but as a redesign of the entire product development lifecycle.\n\nIn practice, however, only a minority have achieved this so far, McKinsey has discovered. [A study](https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/beyond-the-copilot-scaling-the-agentic-product-development-life-cycle) revealed that only a quarter of companies using AI agents for software development were able to significantly accelerate their development cycles. McKinsey considers at least a doubling of productivity for more than a quarter of teams to be significant progress.\n\nSignificant differences also exist between individual developers: According to the study, around 80% of developers using AI tools achieved productivity increases of only about 3%. In contrast, the top 20% saw increases of 55%.\n\nBut that’s not all: The study revealed that productivity actually declined in 30% of companies after development teams started using coding agents. It seems almost everyone is programming by “feel” — but that doesn’t always lead to added value, McKinsey writes.\n\nOne possible reason is that while AI increases the amount of code produced, this doesn’t automatically translate into more usable software. In one study, programming activity increased by 180% thanks to AI tools. But the number of published releases increased only by 30%.\n\nThis shifts the key metric: Economic benefit is determined not by the amount of code generated or the number of AI interactions, but by the outcome of the entire development process.\n\nAdded to this is a trust issue. According to McKinsey, around 46% of developers worldwide actively distrust the accuracy of AI tools. A third trust them in principle, while only 3% have great confidence in the results.\n\nDespite these initial hurdles, capital investment is increasing rapidly. According to McKinsey, investments in providers of agent-based software development solutions rose from a negligible level in 2023 to around $5 billion in 2025.\n\nIn the first half of 2026, they then exceeded the $61 billion mark, primarily due to SpaceX’s acquisition of Cursor for $60 billion (in stock). The number of related job postings also increased by 221% between 2024 and 2025.", "url": "https://wpnews.pro/news/ai-agents-high-effort-low-return", "canonical_source": "https://www.cio.com/article/4226418/ai-agents-a-lot-of-effort-but-still-little-return.html", "published_at": "2026-10-02 10:01:00+00:00", "updated_at": "2026-10-02 10:07:58.399646+00:00", "lang": "en", "topics": ["ai-agents", "artificial-intelligence", "ai-research", "ai-infrastructure"], "entities": ["McKinsey", "Anthropic", "OpenAI", "LangChain", "LangGraph", "Model Evaluation and Threat Research", "Claude Opus 4.6", "Sydney Von Arx"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/ai-agents-high-effort-low-return", "markdown": "https://wpnews.pro/news/ai-agents-high-effort-low-return.md", "text": "https://wpnews.pro/news/ai-agents-high-effort-low-return.txt", "jsonld": "https://wpnews.pro/news/ai-agents-high-effort-low-return.jsonld"}}