The real AI revolution will be boring.

Share this article Copy Link Share on X Share on Linkedin Share on Facebook The real AI revolution will be dull as dishwater, argues Appian’s Mark Talbot.(Photo: jaymast / Getty Images) Ask someone what the AI revolution looks like, and they’ll probably mention chatbots, copilots, or a questionable image they saw on social media. But the truth is that the AI that’s actually transforming industries is far less glamorous. It’s not built to entertain or impress — it’s built to work. Quietly, reliably, and at scale. I call it “boring AI,” and I’m here to champion it. With more and more analysts questioning whether or not we’re in an AI bubble, I believe that these less eye-catching use cases will survive any market correction. Because while most of the world is chasing the flashiest use cases or largest models, the organisations seeing real returns are the ones embedding AI into the places that matter most: the back-end processes, the admin load, the cross-system complexity. The places where work actually happens. Flashy AI fails. Boring AI delivers Right now, there’s a yawning gap between AI potential and AI performance, evidenced by MIT Nanda’s now-notorious finding that 95% of generative AI pilots fail to deliver measurable ROI. But look further into the data, and it’s clear that the root of this failure isn’t a technology problem. It’s a design and deployment problem. The research coined the “Gen AI Divide”: between the 5% who design for impact, embedding AI into complex, high-value processes where it can truly move the needle, and the 95% who bolt on generic AI tools with low stakes, and get low returns. Most companies are deploying AI where it’s most visible — sales, marketing, chat interfaces — instead of high-impact areas like back-end processes and procedures. Perhaps most damningly, the Financial Times recently reported that “fear of missing out” is currently driving AI investment more than any specific business purpose – a clue as to why there is such a lack of clarity around where AI should be applied. CIOs would do well to remember that operational impact always beats hype. Doing regular work with superhuman efficiency In the rush to chase flashy AI breakthroughs, it’s easy to overlook the everyday wins that truly move the needle. We don’t need more experiments – we need AI that gets to work, solving real problems and freeing up people to focus on what matters most. Take Deloitte, which is rolling out a foundation model across its global workforce. Rather than chasing novelty, it is pointing AI at some of the most routine but unavoidable work in a professional services firm: research, document review, drafting and internal communication. Consultants, auditors and technologists spend a huge amount of time reading long reports, summarising findings, preparing briefs and turning notes into client-ready material. Now, a generative AI assistant helps with that heavy lifting. It sits inside the tools people already use, analysing long documents, extracting key points, suggesting first drafts and standardising formats. At Deloitte’s scale, even small efficiency gains across repetitive tasks compound into significant productivity improvements. Why boring builds trust No news is good news when it comes to AI governance. If a customer is denied a loan without explanation, or a regulator finds discriminatory patterns, it’s front-page news and a reputational crisis. If the system is governed properly – auditable models, human-in-the-loop, consistent data use – no one hears about it. To return to the Deloitte example, precision and compliance are non-negotiable in regulated industries. Client documents must be accurate. Audit trails must be maintained. Advice must be grounded in verifiable information. AI doesn’t remove these responsibilities — it supports them by reducing the manual burden while keeping humans firmly in control of the decisions that matter. There are no headlines here—and that’s the point. The absence of drama means AI systems are working safely, risk is managed, and customers (and regulators) are confident. Low-code platforms give organisations the structure to make AI governance as boring as it should be. With built-in transparency, process orchestration, human review, audit trails, and data security, you’re not scrambling to retrofit controls—you’re embedding them from day one. The boring AI revolution AI isn’t here to replace work. It’s here to liberate us from the most frustrating parts of it: the repetitive tasks, the coordination chaos, the gaps between systems. And that’s the only way the numbers will add up. Companies are making huge investments to make the AI revolution a reality. The only way to justify that investment is by embedding AI where it delivers repeatable, measurable value. That means thinking beyond the surface, starting with process, and yes — it means celebrating boring AI. Because another word for boring AI is serious AI: the kind that is backed by real data and proper value. The future didn’t arrive with jetpacks. It arrived with processes and workflows that work smarter, better, and faster than ever before. Mark Talbot is the director of architecture and AI at Appian Read more: Look to the human brain for a glimpse of AI’s future Sign up for our regular news round-up! Give your business an edge with our leading Tech Monitor Sign up