Blog
By Jeffrey Dirrenberger, CEO
Over the past 15 years, I’ve watched technology ride wave after wave of innovation – mobile-first, the Internet of Things, W3, big data, blockchain, and now AI-first. Each cycle comes with the same challenge: how do you turn hype into real results? AI may be the most promising of them all, but it’s also the most misunderstood. One thing experience has taught me: technology rarely fails because it doesn’t work – it fails because organizations don’t know how to work with it.
A recent MIT report revealed a staggering statistic: 95% of generative AI pilots fail to reach production. As TechNexus Director of Strategy Matt Meyers aptly noted in a recent post, "Not because models don't work, but because organizations underestimate the hard part: integrating AI into messy, real-world workflows."
This resonates deeply with what we see at VAULT. The problem isn't the technology – it's the implementation discipline.
According to Informatica's CDO Insights 2025 survey, the top obstacles to AI success are:
These aren’t all technology problems. They’re organizational challenges that require a fundamental shift in how companies approach innovation.
After helping businesses build hundreds of custom applications and digital products, I've identified three critical missteps:
Too many organizations approach AI like they’re checking a box. They launch a pilot, celebrate a demo, and then wonder why adoption stalls.
The companies that succeed – the 5% that actually reach production – treat AI like any other product development cycle. They budget for iteration, bake learning into the timeline, and set expectations that the tech will need refinement along the way. With AI evolving weekly, expecting a one-and-done rollout is unrealistic.
Start small, deliver measurable business outcomes (e.g., “reduce customer support resolution time by 40%”), and iterate. Examples of high-impact but manageable starting workflows include:
Put simply: start small, win where you can, and grow from there.
At VAULT, we’ve seen brilliant AI models fail because they were built on fragmented, inconsistent, or inaccessible data. The winning programs we’ve observed dedicate 50–70% of their timeline and budget to data readiness – not model training.
This work isn’t glamorous. It won’t make your pitch decks sizzle or your stakeholder updates sing. But without it, your shiny model is just a demo, not a productive tool that moves the needle for your business.
Equally important: LLMs need a solid foundation of standard operating procedures (SOPs) or clean data sets. If you don’t define boundaries and expectations for how employees should use the tool, adoption collapses. The best prompt engineering isn’t clever phrasing – it’s detailed, structured knowledge of your business processes.
If your team doesn’t have documented SOPs for a workflow, that’s step one. Without it, you won’t achieve consistent, repeatable value from AI.
The most sophisticated AI in the world is useless if employees don't trust it, understand it, or integrate it into their daily workflows. According to a McKinsey analysis, companies that fully integrate AI into their organizational structure see a 54% increase in productivity – but only when the integration is intentional and human-centered.
Change management isn't an afterthought. It's the entire game. That’s why you must design with employees, not around them. Adoption is everything.
At VAULT, our framework prioritizes integration from the start:
Large Organizations
Success isn’t about being everywhere – it’s about being effective somewhere. Pick one platform, one workflow, one measurable outcome. Expand after you’ve proven the model.
Investors & Advisors
Ask about customer feedback, adoption, active users, paying customers – the fundamentals. Don’t get distracted by derivative AI features that won’t last. Push portfolio companies to show traction with real users, not just glossy demos.
Business Owners & Operators
Focus on usability, not hype. Build AI into your existing stack and current processes. If your product requires ingesting customer data, offer services to help clients prepare it. If your customers frequently rely on a specific tool, integrate with it early. AI is supposed to simplify life – but only if it’s easy to implement and adopt.
AI fatigue is real because organizations are tired of chasing shiny objects without seeing returns. The answer isn’t abandoning AI – it’s approaching it with the same rigor as any other strategic investment.
At VAULT, we’ve built our reputation on disciplined execution. We don’t just build software; we build solutions that fit into the messy reality of how businesses actually operate.
The 95% failure rate isn’t inevitable. It’s a choice. Companies that prioritize integration discipline over novelty will be the ones that unlock AI’s real transformative potential.
About the Author
Jeffrey Dirrenberger is the CEO and Founder of VAULT Innovation Group, a Chicago-based custom software and digital product development company. With over 15 years in the tech industry, he has helped launch over 100 companies and built products across consumer applications, B2B software, and tech-enabled businesses. Learn more about VAULT's work at vaultinnovation.com/work.
Sources & Further Reading:
