It’s business as usual: One headline chases the next. More agents, more features. More, more, more. Yet, despite the marketing buzz, public success stories are rare at this point. So how is Agentic AI adoption actually happening in organizations, and what’s next as the technology and the organizations exploring it mature alongside it?
Jon Reed, Industry Analyst and Co-Founder of diginomica, joined me on “What’s the BUZZ?” for the third episode of our mini-series on Agentic AI to talk about what’s next as orgnaizations move from hype to production. If you’ve missed our previous conversations, you can learn more about the state of the Agentic AI market and the common AI myths we see.
Why Most AI Projects Fail
You may have seen the headlines: “95% of AI projects fail.” That sounds discouraging, right? But the 5% that succeed realize significant gains. That’s the lesson buried inside the (controversial) MIT study that’s being cited everywhere now.
When you dig deeper, you find that most of these projects never make it past the pilot stage. Not because the technology wasn’t capable, but because organizations didn’t start with a clearly defined problem or lacked the right strategy for scaling. The projects that did succeed share common traits: they target high-value problems, they measure impact carefully, and they integrate AI deeply into workflows instead of chasing generic “let’s try AI” initiatives.
Also, success rates jumped when organizations partnered with trusted vendors rather than trying to build everything from scratch. That’s not surprising as enterprise AI is messy, and reinventing architectures for a single project rarely pays off. But if you focus on solvable problems, validate impact, and leverage external expertise, AI becomes a serious differentiator.
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It’s tempting to think AI readiness means using the latest model or hiring a team of engineers. But in reality, AI readiness is less about technology and more about people and data. As it turns out, technology is rarely the limiting factor. The bigger challenges are: Do you understand the problem well enough? Have you brought your people along for the ride? Is your data usable?
AI amplifies what you’re already good at. If your processes are disciplined, your data is accessible, and your culture encourages experimentation, AI will accelerate your success. But if your organization is siloed, your data is outdated, or your workforce feels threatened by AI, then the technology won’t save you and, instead, will magnify those weaknesses.
That’s why successful companies empower middle managers and frontline teams to learn, experiment, and share best practices. Some create AI champions across departments who showcase real use cases and build collective learning. Others embed “phase zero” steps into projects, using AI budgets as an opportunity to fix critical data gaps before scaling. Readiness means starting where you are, empowering teams, and weaving AI into workflows gradually. AI succeeds when humans are equipped, data is trusted, and the culture is ready.
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Why Security and Trust Will Define the Future of Agentic AI
AI agents are actors that make decisions and take actions on your behalf. That creates a whole new set of security and trust challenges. When a human employee gets access to sensitive systems, they exercise judgment. They know when to ask before proceeding. But once an agent is authorized, it won’t stop to question whether sending confidential data to the wrong recipient is a bad idea. It just does it. That’s why security, trust, and authentication have to be front and center from the beginning, rather than being treated as afterthoughts.
What happens when agents start interacting with each other? Imagine your procurement agent negotiating with a supplier’s agent. How do you know that this supplier agent is who it claims to be? How do you verify trust between systems? These questions and scenarios are just around the corner and they demand new frameworks for authentication, identity, and risk management.
For small businesses, the risks are just as real. If you give an AI tool too much access to your email, CRM, or financial systems, it could easily misfire and expose sensitive data. For enterprises, the stakes scale exponentially. As adoption grows, trust and security will become the make-or-break factors for enterprise AI. The companies that think about this upfront will lead. Those that don’t may find themselves dealing with costly breaches or reputational damage.
Summary
So, what’s next with AI agents? Three things stand out. First, don’t obsess over the 95% of failed projects. Study the 5% that succeeded and learn from them. Second, recognize that AI readiness isn’t a tech problem; it’s a people and data challenge. Finally, understand that as agents become more autonomous, trust and security will define their success.
If you’re a business leader, the path forward is clear: start with meaningful, high-value use cases, empower your teams to experiment, invest in your data, and treat security as foundational, not optional. Do that, and you’ll not only avoid being in the 95%. You’ll position yourself to reap the outsized rewards of being in the 5%.
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