Subj : Why most companies shouldnt build their own AI solutions To : All From : TechnologyDaily Date : Fri Jun 06 2025 08:00:07 Why most companies shouldnt build their own AI solutions Date: Fri, 06 Jun 2025 06:54:54 +0000 Description: The real challenge isnt building a model - its adding the connective layer that drives impact. FULL STORY ====================================================================== AI isnt hype anymoreits real. IDC predicts that by 2028 AI spending could hit $623 billion by 2028. That kind of investment doesnt come from buzz. It comes from companies seeing real value. AI tools are already cutting costs, speeding up work, and - lets be honest - making jobs more enjoyable. Nobody misses the repetitive stuff. Instead, were doing more of what were actually good at: strategy, creativity, and problem-solving. So now that companies have tasted that value, many want to go further. Not just use AIbut build entire internal AI-powered solutions themselves. Stitch together some models, build an app, launch it to their teams. The thinking goes: if off-the-shelf tools work, imagine how great itll be if we control the whole thing. Heres the reality: for most companies, especially non-tech companies, building in-house AI solutions is a bad bet. They take too long, cost too much, and rarely deliver what the business actually needs. Lets talk about why. Its not about the model. Its about the missing link Between tech and impact. Companies are already experimenting with models. Theyre using GPTs, building copilots, testing agents. Thats not the problem. The problem is believing the solution is just about picking a model or wiring one together. Thats not where most projects fail. They fail because the solutionhow it fits into your workflows, your systems, your peopleisnt well thought out. Its fragmented. Its not scalable. It doesnt stick. The model might be powerful, but the experience around it doesnt work. And without that, the value never materializes. This is why the connective layer matters. The interface. The orchestration. The automation . The safeguards. Its what turns "we have a model" into "were driving results." And most companies dont have the internal expertise to build that layer right. Going solo comes with hidden costs Trying to build your own AI-powered solution might feel brave. But unless your company is a product and engineering company, the odds are stacked against you. Heres where most organizations get it wrong: 1. You Dont Have the UX Muscle AI only delivers value when people actually use it. That means seamless, intuitive, trustworthy interfaces. Most enterprises dont have the product design and UX software and development capabilities to build interfaces that users actually want to engage with. Internal tools often lookand performlike science experiments. 2. Youre Flying Blind Vendors bring learning from hundreds of deployments. You dont. If youre rolling out a custom AI solution based on a few internal tests and gut instinct, youre guessing. You dont have enough data to know what good looks likeor what real adoption takes. 3. Youre Not Budgeting for What Comes Next AI isnt static. Models evolve. Interfaces break. User needs change. If youre not committing budget and headcount for constant iteration, retraining, and support, that in-house solution will be outdated in under a year. And it will sit unused, no matter how promising it looked at launch. 4. Security Concerns Are Overblown Yes, protecting data is critical. But assuming vendor AI tools are inherently less secure? Thats a flawed take. The best AI providers build with security and compliance at the core. If you trust cloud infrastructure, you can trust enterprise-grade AI vendors. 5. "Only We Know Our Business" Misses the Point Your internal team knows your business better. Thats not in question. But they likely dont know how to build scalable, production-ready AI. Vendors do. Theyve already solved the engineering challenges, the data problems, the deployment mess. Why start from scratch? If youre not a tech company, stop trying to be one. Theres no shame in partnering with expertsits how the winners win faster. Agentic AI is comingand its even harder to build right The next phase is agentic AI. These systems dont just generatethey act. They make decisions. They learn. They execute. Its already revolutionizing workstreams like customer service, reporting, and document creation. But these arent lightweight features. Theyre full systemsrequiring real orchestration, context awareness, governance, and maintenance. Trying to build them internally without the right foundation? Thats not just inefficient. Its risky. You dont need to build these things. You need to leverage the companies that already have. AI is a team sport, play with the pros AI feels like its getting easier. And in some ways, it is. Open-source models. No-code platforms . Accessible APIs. But building an AI solution that actually moves the needle? Thats still hard. Really hard. And if you think your internal team can replicate what vendors have spent years perfecting, youre wasting timeand likely money. The smartest companies arent trying to do it all themselves. Theyre focusing on what they do best and partnering for the rest. AI is a team sport. Play with the pros. Thats how you win. LINK! This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro ====================================================================== Link to news story: https://www.techradar.com/pro/why-most-companies-shouldnt-build-their-own-ai-s olutions --- Mystic BBS v1.12 A47 (Linux/64) * Origin: tqwNet Technology News (1337:1/100) .