Agentic AI is one of the most exciting frontiers in technology today, but it’s also one of the most misunderstood...
In our recent episode of Atomic Conversations, I spoke to Hanah-Marie Darley, Co-founder and Chief AI Officer at Geordi AI, whose career spans government intelligence, cybersecurity, and product leadership.
Hanah brought clarity to a fast-moving topic, reminding us that the success of AI adoption isn’t just about models or benchmarks. It’s about governance, trust, and above all – people!
Here are some of my most resonant takeaways from our discussion.
Hanah started with how AI is not traditional software and so trying to wrap frameworks that are designed for traditional software around it is a recipe for disaster.
This distinction is easy to overlook, but it changes everything. Traditional applications are deterministic, you know what they will do, every time.
AI agents are different.
They reason, act, and even improvise. That makes them more like human contractors than like lines of code.
The implication is clear: frameworks, controls, and governance approaches built for software don’t fully apply. Enterprises must design with dynamism in mind, treating AI agents as active decision-makers rather than passive tools.
It’s tempting to celebrate early prototypes and proof-of-concepts, but shiny demonstrations mean little if they cannot be trusted in the long run. Reliability and explainability aren’t afterthoughts; they are the foundation of adoption.
The short hard truth is that performance without reliability doesn’t scale.
For enterprises, what matters most is not always model level explainability but behavioral explainability, knowing what an AI agent did, why it acted that way, and what the impact was. That’s the kind of clarity that builds confidence and prevents promising projects from stalling.
Innovation and speed are always under pressure in enterprise environments. But adopting first and governing later is a recipe for disappointment. Hanah’s advice was simple: build observability, auditability, and accountability into the rollout from the start.
Without this discipline, organizations risk short-term excitement that collapses into long-term friction, and my favorite term from the conversation - the cliff of disillusionment. True scale doesn’t come from shortcuts. It comes from laying the groundwork for good governance and trust early.
Technology doesn’t succeed in a vacuum, it succeeds in the hands of real people using it day in, day out in the way in which they actually work.
This might be the most human insight of the conversation. Enterprises often design for ideal users, the ones who always follow policy, never push boundaries, and never improvise. But in reality, people experiment, adapt, and sometimes behave unpredictably.
Ignoring that fact only leads to shadow AI and insecure workarounds. The sustainable path forward is to accept human behavior as it is and build systems that account for it. Designing with empathy (for messy, overloaded, curious users is what separates projects that thrive from those that fail.
Hanah brought out how in the next 3 to 5 years, AI agents will be a major technology, breaking process orchestration and bringing in agent orchestration. This forward-looking perspective reframes enterprise service management. The future isn’t about rigid workflows and tickets, it’s about ecosystems of agents collaborating across systems, resolving requests, and learning in real time.
The role of IT and service leaders will shift too. They won’t disappear; they’ll move from doing tasks to orchestrating these intelligent systems. But the foundation remains the same: control, oversight, and trust are non-negotiable.
Agentic AI represents both promise and peril. It has the potential to transform enterprises, but only if adopted with governance, reliability, and human realities in mind.
As Hanah put it:
AI adoption happens at the speed of trust.
And ultimately, that trust will determine whether agentic AI delivers lasting value or whether organizations tumble off the cliff of disillusionment.
Tune in to the full conversation here.
