{"id":247642,"date":"2026-07-03T07:31:07","date_gmt":"2026-07-03T07:31:07","guid":{"rendered":"https:\/\/businesnewswire.com\/?p=202391"},"modified":"2026-07-03T07:31:07","modified_gmt":"2026-07-03T07:31:07","slug":"why-companies-lose-more-on-ai","status":"publish","type":"post","link":"https:\/\/ipsnews.net\/business\/2026\/07\/03\/why-companies-lose-more-on-ai\/","title":{"rendered":"Why companies lose more on AI?"},"content":{"rendered":"

Some AI pilots save companies money. Others just teach people how much a pilot can cost.<\/p>\n

Here\u2019s the full story: companies waste money on AI not because the tech is ineffective. They overspend because they focus it on the wrong work, layer it onto broken processes, skimp on training, underestimate review workload, measure activity instead of value, and more.<\/p>\n

AI can help a company reduce costs. It can accelerate research, automate admin tasks, summarize messy data, and jumpstart employees\u2019 first drafts. But it isn\u2019t a free coupon on poor processes. Applied thoughtlessly, AI amplifies your existing problems – quickly, loudly, and to your credit card\u2019s dismay.<\/p>\n

Choose a problem before buying a tool<\/h2>\n

The first place companies waste money on AI is by choosing a tool before they choose a problem. They buy a piece of software, announce an internal pilot, then scramble to find work that seems like something AI can do. Pilots become expensive guided missiles without a clear target.<\/p>\n

AI pilots should always be driven by a problem you want to solve. Faster research? Less manual summarization? Better support triage? Cleaner document creation? Less admin work? Start with a clear pain point, because vague problems will lead to vague results.<\/p>\n

When you can\u2019t point to something improved, every interaction with AI will feel experimental. Training stalls. Leads seem gimmicky. Money keeps ticking. But eventually the pilot ends and people wonder what exactly changed. Focus on clear problems first, and your AI pilots will have clear goals too.<\/p>\n

Solve the root problem instead of automating it<\/h2>\n

The problem area you pick should also help you identify process issues. Too often, companies pick a problem then fail to automate anything. They automate around the pain point instead of improving the pain point itself.<\/p>\n

Bad approval processes don\u2019t become great with AI. They become fast, digital approval processes. Instead of fixing the root problem, your employees can now approve things more quickly.<\/p>\n

If your team uses AI to automate a process that needs work, take time to map out the end-to-end workflow. Where do things get stuck? Where do people repeat steps? Where are the handoffs that cause problems? Trim that workflow down, then let AI fill in the useful gaps.<\/p>\n

Leaders know their weak processes. Employees complain about them daily. AI doesn\u2019t hide these problems, it exposes them faster. Your research was slow before AI. Files were impossible to find before AI. Teams argued about who owned the final decision before AI. Try fixing these problems without AI first. In most cases, you\u2019ll save more money upfront.<\/p>\n

If you do need AI, employees need practical training<\/h2>\n

Okay, but what if you do need AI? What if you\u2019ve picked a great use case that can benefit from automation? You still run the risk of overspending if employees don\u2019t learn how to use the tool properly.<\/p>\n

Companies waste money by treating AI training like an afterthought. AI isn\u2019t intuitive for everyone. If you don\u2019t train employees on what to automate, how to review AI work, and where AI can go wrong – you risk inconsistent use (or risky overuse). Some employees will learn on their own. Many won\u2019t. Leave training to chance and you leave your money up for grabs. Treat training as an indispensable part of a business process and it becomes your company\u2019s secret advantage<\/a>.<\/p>\n

Learning how to use AI should mimic how you\u2019ll use AI. Broad sessions about AI are fun and exciting. A week later, nobody remembers them. AI training needs to be job-specific. Show employees how AI can automate their jobs by walking through examples: drafting customer replies, summarizing policies, reviewing reports, cleaning up meeting notes, and identifying errors generated by AI.<\/p>\n

Measure whether AI created value, not if employees are using it<\/h2>\n

And finally, how do you know if AI is actually helping? As with anything employee-driven, companies love to measure usage. How many prompts did employees send? How many documents did AI generate? How many employees attended the AI workshop?<\/p>\n

Don\u2019t. Just\u2026don\u2019t.<\/p>\n

Companies waste money using activity as a measure of value. You can have a group of employees flooding the AI tool with work and still lose money. Busy doesn\u2019t equal better.<\/p>\n

So how should you measure success? Did AI reduce the cost of a particular job? Did it save time? Improve quality? Lower risk? Did it make your customers happy? Can you point to one or more business results driven by AI? If not, your goals need work. AI should be measured by its impact on the business, not by how busy it keeps your employees.<\/p>\n

As always, discipline is critical. Not every pilot project is worthy of expansion. Not every AI demo deserves to see the light of day. If your employees can\u2019t improve a key metric with AI, figure out why, then stop funding it. Pilots should evolve (or die) based on their ability to fund themselves. If your company can\u2019t say \u201cno\u201d to a failed experiment, AI pilots will eat away at your budget.<\/p>\n","protected":false},"excerpt":{"rendered":"

Some AI pilots save companies money. Others just teach people how much a pilot can cost. Here\u2019s the full story: companies waste money on AI not because the tech is ineffective. They overspend because they focus it on the wrong work, layer it onto broken processes, skimp on training, underestimate review workload, measure activity instead… Continue Reading →<\/span><\/a><\/p>\n","protected":false},"author":344,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[374],"tags":[],"class_list":["post-247642","post","type-post","status-publish","format-standard","hentry","category-ipsnews"],"yoast_head":"\nWhy companies lose more on AI? - Business<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ipsnews.net\/business\/2026\/07\/03\/why-companies-lose-more-on-ai\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Why companies lose more on AI? - Business\" \/>\n<meta property=\"og:description\" content=\"Some AI pilots save companies money. 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