{"slug": "ais-problems-arent-what-you-think", "title": "AI’s problems aren’t what you think", "summary": "AI is not eliminating jobs as widely feared but is instead creating a costly problem of AI sprawl inside enterprises, according to an industry analysis. Developers who used AI most heavily produced about twice the output of moderate users but consumed roughly ten times the compute, and at one financial firm multiple business units built overlapping AI capabilities without coordination. The piece argues that more AI does not automatically produce more value and that organizations must fit AI into a growth strategy and operating model to avoid redundancy and escalating costs.", "body_md": "The biggest and loudest prediction about AI is that it will [eliminate](https://www.axios.com/2025/05/28/ai-jobs-white-collar-unemployment-anthropic) millions of jobs. It is dramatic and easy to repeat. But from what I’ve seen, inside most enterprises the more immediate problem has turned out to be something else entirely: a growing mass of tools, agents, models and usage costs spreading faster than most organizations can govern or connect to real business value, also known as [AI sprawl.](https://www.ibm.com/think/topics/ai-agent-sprawl)\n\nNone of that invalidates the initial fear. Indeed, AI can clear backlogs, speed up analysis, draft usable content and reduce time spent on repetitive work. In my opinion, what goes wrong is the assumption that those gains will scale seamlessly, and that more AI will automatically produce more value.\n\nWhat really matters is not only how much AI a company can deploy, but whether its use fits inside a growth strategy, an operating model and an organization that can use it well.\n\nThe early results of AI use made the logical progression feel obvious, even a foregone conclusion. If it could already improve output in narrow use cases, then broader deployment should produce broader gains. Simple! Better models were expected to deliver better results. More agents were expected to drive more automation. For many companies, this logic held for long enough to encourage overexpansion.\n\nBut this logic has started to break down as usage continues to scale. I’ve seen returns diminish much quicker than expected. To illustrate, one [industry analysis](https://www.businessinsider.com/ai-tokenmaxxing-fails-as-productivity-strategy-jellyfish-2026-5?utm) found that developers who used AI most heavily produced about twice the output of moderate users, but consumed roughly ten times the compute.\n\nAt a certain point, more AI does not create proportionally more value – it simply becomes more expensive. But where, exactly?\n\nExperimentation played a key role in this downturn, but it’s not the culprit. As AI continues to sprawl, the problem continues that AI is spreading faster than most companies can coordinate. Teams often solve the same problem in parallel, paying for overlapping capabilities and layering new tools atop existing ones without any clear inventory of what ‘s already in place. What can appear as momentum is really turning into redundancy.\n\nI’ve seen versions of this play out repeatedly. At one financial firm, several business units were pursuing AI projects aimed at automating research and reporting. Each team moved independently; selecting their own tools, building their own workflows and creating separate data pipelines, with little to no coordination between teams. In some cases, different groups were developing nearly identical capabilities without realizing it, solving the same problems twice without any shared visibility into each other’s work.\n\nIndividually, the projects showed real promise. Collectively, the projects created duplication, fragmented data and inconsistent standards business and enterprise wide.\n\nBy the time leadership stepped back to assess, the company found itself paying for overlapping capabilities, maintaining multiple versions of the same underlying data, and struggling to determine which solutions were actually delivering value versus which were simply consuming budget and eating at engineering time.\n\nPerhaps most troubling: nobody at the enterprise level had a complete view of what was being built, by whom or why. What began as healthy, well-intentioned experimentation had, without anyone deciding it should, evolved into full-blown AI sprawl, creating a patchwork of disconnected initiatives that was difficult to govern, harder to secure and far more expensive than a coordinated approach could and should be.\n\nEarly wins encourage a still wider rollout, but many organizations expand usage before they put real controls in place. Experimentation becomes sprawl. Budgets grow quickly, and few leaders have a reliable view of who is using what or why.\n\nThis is where I see many companies still get the issue wrong. They treat AI and growth strategy as two separate efforts, then wonder how adoption gets so messy. A business cannot drop AI into its operations and expect momentum to take over. The technology has to support a clear path to growth, whether that means improving margin, speed, service, capacity or decision-making. At the same time, growth plans cannot assume AI changes nothing about delivery, design or operating leverage. The real challenge is in ensuring the two work together.\n\nPersonally, I’ve seen better results when AI initiatives are tied to a specific business objective from the beginning, rather than launched as broad, abstract or transformative effort. One mattress retailer I’ve worked with took this approach, starting with a single, focused and well-defined use case rather than trying to transform or overhaul the entire organization at once. The company introduced an AI-powered training platform for store associates, giving employees a low-pressure way to practice sales conversations and product recommendations before applying them to external situations with customers on the floor.\n\nBecause employees experienced immediate and tangible value from the tool, adoption spread quickly across locations, with minimal need for top-down mandates. Early, visible success helped to build internal credibility and generate momentum, which leadership then leveraged to expand into more complex AI initiatives across areas such as inventory management, demand forecasting and replenishment planning.\n\nUltimately, the technology succeeded not because it was innovative for its own sake, but because it was connected to a larger growth strategy: improving sales effectiveness on the floor, driving operational efficiency behind the scenes and strengthening workforce capability at entry level. A major lesson we walked away with here was that starting small and specific, with a clear throughline to business value creates a strong foundation for sustainable and scalable AI use.\n\nAll this takes more than a few easy guardrails. It takes strategy. Leaders need a real inventory of the tools, agents and assistants already in use across the business, who owns them, what data they can access and everything that they support.\n\nThey also need financial controls that match the economics of token-based usage, including role-based access, thresholds and review processes that make spend visible before it becomes a surprise. Similarly, they need metrics that go beyond mere activity. More prompts do not mean more value. If a deployment cannot be tied to throughput, margin, quality, cycle time or another tangible result, it is still unfinished.\n\nThis is also why blunt shutdowns rarely work. If leaders clamp down too hard, employees often move to unsanctioned tools and create a larger [shadow AI](https://www.paloaltonetworks.com/cyberpedia/what-is-shadow-ai) problem, or the unauthorized use of artificial intelligence tools, models or chatbots by employees, without the knowledge or approval of IT and security teams, with even less visibility and more risk. The better answer is disciplined adoption: clear ownership, rules, metrics and enough flexibility for teams to use AI where it works.\n\nThat matters for the people as much as it does for the budget. Those that modernize well end up with *better* work – not just less of it.\n\nThe story of the moment isn’t about AI replacing people – or even AI in general. It’s about whether companies know their own businesses well enough to keep incorporating powerful new tools without mistaking activity for progress. As technological capabilities continue to appear, the winners will be the organizations that understand where it belongs, what it can improve and how to turn each new wave into something permanent.\n\n**This article is published as part of the Foundry Expert Contributor Network.****Want to join?**", "url": "https://wpnews.pro/news/ais-problems-arent-what-you-think", "canonical_source": "https://www.cio.com/article/4198475/ais-problems-arent-what-you-think.html", "published_at": "2026-07-20 13:00:00+00:00", "updated_at": "2026-07-20 13:48:34.434162+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-agents", "ai-infrastructure", "ai-ethics", "ai-policy"], "entities": ["Anthropic", "IBM", "Business Insider", "Jellyfish"], "alternates": {"html": "https://wpnews.pro/news/ais-problems-arent-what-you-think", "markdown": "https://wpnews.pro/news/ais-problems-arent-what-you-think.md", "text": "https://wpnews.pro/news/ais-problems-arent-what-you-think.txt", "jsonld": "https://wpnews.pro/news/ais-problems-arent-what-you-think.jsonld"}}