AI Readiness: Is Your Organization Ready for AI? It Depends

I recently sat down with SuperOps to talk through AI readiness in IT: what it actually means, what’s holding organizations back, and where agentic AI fits in. You can watch the full conversation on YouTube. This article covers some of the key points.

AI Readiness: Is Your Organization Ready for AI?

When someone asks me whether their organization is ready to adopt AI, my honest answer is: it depends. I know this sounds like a consultant’s cop-out, but I mean it seriously. I like to compare it to asking yourself whether you’re ready to lose weight. Are you doing it because it’s fashionable, because everyone else is? Or do you actually want to lose weight? Do you have a goal, a plan, and metrics to track against? And be honest with yourself: have you got a cupboard full of chocolate that’s going to undermine the whole effort?

AI readiness is no different. Organizations that adopt it because they feel they should, without a clear reason, the right foundations, or a realistic plan for making it work, are probably going to struggle. It’s easy enough to write a check and invest in new technology. Making that technology successful is a completely different challenge. Not every organization is ready, and not every organization has the mindset needed to get the most from what AI can offer.

The Challenges Facing IT Right Now

Some of the challenges IT organizations face are old ones that have never gone away. Cost pressure. Security. These have been with us for years, and they aren’t going anywhere. But others are becoming significantly more important.

Ticket volumes, for one. We’re probably seeing more IT service desk tickets than ever, and, because technology is now so central to business operations, the tolerance for slow resolution is lower. It’s not just volume that’s the challenge; it’s the velocity required to deal with it.

Employee experience is another. I remember talking about this 12 years ago. Hence, it’s not a new concept, but it’s become far more prominent as a priority. The question has shifted from “Is the technology working?” to “Is the technology making people more productive?” That’s a harder question to answer and a harder outcome to deliver.

Then there’s value. For the last 15 years, I’ve been asking how we quantify the value that IT delivers, whether to internal employees or external customers. We still haven’t fully cracked it. And now, layered on top of everything else, there are the challenges specific to AI adoption and AI readiness.

Two Myths About AI Worth Addressing

The first is that AI is coming. It isn’t coming. It’s already here. The level of AI inclusion in IT service management (ITSM) tools and platforms is actually quite remarkable when you look closely. Whether people are actively using those capabilities is a different question, but the technology isn’t sitting somewhere on the horizon waiting to arrive.

The second is the assumption that AI offers a clean slate: that it will fix the issues we’ve accumulated over the years in IT service delivery and support. It won’t. We need the ITSM basics in place to fully succeed with AI. We need proper organizational change management (OCM) because this isn’t just a technology change; it’s a process and people change too. Adopting technology without addressing these things (now as part of AI readiness) hasn’t worked for us in the past, and AI isn’t going to be different.

What Makes Agentic AI Different

When people talk about agentic AI, they tend to focus on autonomy: the idea of “no human in the loop.” This is part of it, but for me, it’s not the most important differentiator right now. The bigger issue is trust.

If we want to move IT organizations from reactive operations to genuinely proactive operations, we need to trust agentic AI to operate in that mode. And trust doesn’t appear from nowhere. I see it as a three-part chain: AI investment leads to AI success, and AI success breeds AI trust. That AI trust then drives more investment, which creates more success. It’s really more of a cycle than a chain. The point is that you can’t expect trust to arrive without first making the investment, and you can’t realistically expect success without that investment either. Organizations that are succeeding with AI tend to show exactly this pattern.

Where to Start with Agentic AI and AI Readiness

If you’re looking for lower-risk entry points, service request management is a good place to begin. For most service requests, a failure isn’t a life-or-death situation, which gives you room to test and learn. It also allows you to demonstrate what agentic AI can actually do: not just understanding the request, but pulling in everything required to fulfil it.

Software deployment is a straightforward example. Agentic AI should know who you are, which device you’re using, which software you need, whether you’re entitled to it based on your role, and whether it requires additional authorization, and then execute the deployment. No human involvement is needed, and the risk is relatively contained if something goes wrong.

AI Governance is Bigger than Most Organizations Realize

Most AI tools come with built-in guardrails. These guardrails are useful, but they’re only a small part of what’s needed. AI governance sits within IT governance, which sits within enterprise governance, and the introduction of AI changes governance needs significantly more than most organizations initially appreciate. The risks, when you actually sit down and map them out, tend to be more numerous than expected.

There’s also a speed issue. Traditional IT governance is too slow to keep pace with the rapid development of AI. We don’t just need to adapt governance to address AI risks; we need governance that can keep up with the rate of AI change itself.

Accountability is another element that’s easy to overlook. We don’t yet have good data on whether people hold AI to the same standard as they hold other humans when things go wrong. My instinct is that they don’t, which creates its own set of governance considerations.

Organizations have broadly understood that AI governance matters. I’m less convinced that understanding has translated into sufficient action. Too many organizations treat AI governance as an add-on to existing practices when it requires more deliberate investment than that, including in terms of AI readiness.

What AI Means for IT Roles

A lot of the conversation about AI and jobs focuses on which tasks will be automated. That’s worth thinking about, but there’s a more important question: what new roles does AI create?

Someone needs to monitor whether AI is doing what it’s supposed to do, and to step in when it isn’t. AI drift is real: you configure an AI system that works well initially, but over time, it may stop delivering the outcomes you set it up for. People need to be watching for that.

There’s also a relationship management dimension. If AI is serving people and not serving them well, someone with genuine empathy needs to smooth that over. In the near term, at least, that’s going to be a human job. Think of it as a stewardship role: ensuring the technology continues to deliver what the organization actually needs.

This is part of AI readiness.

The Shift from Reactive to Preventative

The most important thing I want IT leaders to take from all of this is that agentic AI gives us a real opportunity to move beyond fixing things faster. In the past, automation has often just accelerated existing processes without fundamentally changing outcomes.

Agentic AI should allow us to move into a more preventative mode: not just resolving issues more quickly, but preventing them from occurring in the first place. That’s the dial worth turning.

But there’s a big but – teh need for AI readiness.

My (AI Readiness) Advice to IT leaders

Agentic AI is not optional. From a competitive standpoint, organizations that don’t engage with it will fall behind. And there will be pressure from above: boards and leadership teams are already asking questions about AI, and they’ll soon be asking about agentic AI, just as they asked about ChatGPT in 2023.

But excitement isn’t a substitute for governance. Your IT organization may have built up a solid level of trust with the business. The last thing you want is for poorly governed AI to erode that. Make sure the AI governance foundations are in place before your AI ambition runs too far ahead of reality.

Watch the full conversation with SuperOps here: AI Readiness in IT with Stephen Mann.