- Pro
Exploring the challenges and opportunities of AI agent deployments
When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works.
(Image credit: Getty Images)
- Copy link
- X
- Threads
Deployment has become the wrong measure of progress.
Across every sector, the conversation about AI agents has moved on from whether to deploy them to how quickly more can be added.
Within that shift, a critical assumption has taken hold which now needs re-examining; running agents and getting value from them are not the same thing.
Latest Videos FromWatch full video here: Aaron PerrottSocial Links Navigation
Chief Technology Officer (CTO) at KTSL.
Recent research has found that 88% of UK enterprises are actively deploying AI agents, but only 20% have reached measurable business impact.
That is a sequencing problem rather than a technology one.
You may like-
AI agents are the new unmanaged endpoints
-
AI Agents at your service
-
Why Agentic AI demands business process re-engineering
The wrong business case
When AI agents first appeared on enterprise roadmaps, the business plan was almost always built around cost reduction: automating that, reducing headcount here, cut spend there. But this playbook was borrowed from every previous wave of enterprise technology, and for early-stage pilots it was a serviceable framing.
Since then, organizations that have moved beyond pilots into live operations have largely dropped it. The returns they care about now are faster resolution of operational problems and better experience for the people those systems serve. Cost reduction, where it appears, tends to be a byproduct rather than the objective.
Are you a pro? Subscribe to our newsletterContact me with news and offers from other Future brandsReceive email from us on behalf of our trusted partners or sponsorsBy submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over.A deployment designed to cut costs will be measured on costs. If the same deployment was actually improving resolution speed or reducing failure demand on support teams, that value would go unrecorded and unmade as a case for further investment. The lesson is one as old as time, but one we need to keep reminding ourselves of: get the objective wrong at the start and you can easily make a successful deployment look like a failed one.
Why deployments underperform
A meaningful proportion of AI agent implementations do not meet expectations, and a significant share of organizations have responded by pausing further investment. Before treating this as evidence that the technology doesn’t work, it’s worth looking at what is actually causing this underperformance.
The most common barriers we see are skills gaps, poor business case definition, data quality problems, and the absence of a capable technology partner. Again, none of this is to do with tech problems, but more to do with preparation and execution.
What to read next-
Why single-player AI is holding back the agentic enterprise
-
From demo to production: What agent-based AI must actually deliver
-
AI agents create new risks requiring continuous monitoring and oversight
In practice, I see a further problem in that agents need to be perceived as genuinely better than the process they replace by the people doing the work. If engineers and operators don’t feel the benefits, you’re never going to see effective adoption. After that, deployments will fade away before they have the chance to prove themselves. Buy-in, as ever, needs the same attention as the technical implementation.
Defining what success actually looks like
One consequence of deploying agents without agreed success metrics is the inability to demonstrate value even when it is being created. This is a particular problem in IT management, where AI agents are increasingly handling incident detection, triage and resolution.
Mean Time To Resolution (MTTR) is the metric that matters most in this context, and it repays closer examination. The stages of an incident lifecycle are:
Identification,
triage,
isolation,
diagnosis,
fix, and
Verification
Each of these carries a different weight depending on where the current process is slowest. An organization that takes ten minutes to identify an incident but two minutes to resolve it once identified has a different problem than one where diagnosis is more of a constraint. So agents need to be applied to the stage where they will provide a genuine efficiency gain.
Establish the baseline before selecting the intervention and know where time is actually being lost. Then you can set a specific target for reducing it, and measure against that. Without this, it is genuinely difficult to distinguish a successful deployment from a busy one.
The governance gap
Security and governance frameworks are still for the most part built for environments where humans make consequential decisions, even if software executed them. When you introduce autonomous agents into the mix, with the ability to access sensitive data and act on it in real time with limited human oversight, those frameworks become ineffective. This is not a criticism of how they were designed, more a description of a gap that has opened up as deployment has scaled.
When I look at where organizations are most exposed, it tends to be the enterprises whose existing frameworks are too deeply embedded to revisit easily. Legacy architecture is the constraint, and larger organizations carry more of it.
There’s a comparison to be made here with the eras SaaS sprawl and shadow IT. In both cases the technology moved faster than the controls around it, and the cost of establishing those controls retrospectively was higher than building them in would have been. With this in mind it’s easy to see that governance does not act as a brake on deployment of new tech, it’s a pre-requisite that ensures long-term effectiveness.
Integration decisions made late are expensive
Enterprise IT infrastructure is heterogeneous in ways that technology planning tends to underestimate. The mix of public cloud, private hosting and hybrid environments - layered over legacy systems running processes that are poorly documented and harder to change than anyone would prefer - creates conditions that require deliberate architectural thinking from the start. Agents designed without accounting for this environment will require significant rework once they encounter it.
There is also a less obvious use for AI in this process. Applied earlier in the planning cycle, it can identify where legacy systems are creating the most friction and where integration investment will produce the most return. Most organizations deploy AI to generate output; fewer use it to improve the quality of the decisions that shape deployments in the first place, making this application of agents a competitive differentiator.
The sequencing question
Fundamentally, the technology used in successful AI agent deployments and failed ones is the same. What separates them is sequencing: the conditions for success were established before the agents went live.
Those conditions require more discipline than sophistication, including tightly-scoped use cases, clean, well-governed data, integration as a priority and security frameworks that account for the presence of autonomous systems.
The question worth sitting with is whether your organization knows, specifically, what each AI agent is supposed to improve, whether it is improving it, and what will happen to that agent in eighteen months if it is not. Most enterprises cannot answer all three – if you can, you’ll already be one step ahead of the curve.
We list the best IT Automation software.
This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.
The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit
TOPICS AI Aaron PerrottSocial Links NavigationChief Technology Officer (CTO) at KTSL.
View MoreYou must confirm your public display name before commenting
Please logout and then login again, you will then be prompted to enter your display name.
Logout Read more
Pro
AI agents are the new unmanaged endpoints
Pro
AI Agents at your service
Pro
Why Agentic AI demands business process re-engineering
Pro
Why single-player AI is holding back the agentic enterprise
Pro
From demo to production: What agent-based AI must actually deliver
Pro
AI agents create new risks requiring continuous monitoring and oversight
Latest in Pro
Pro
Best World Cup 2026 eSIM deals — Stay connected from the opening game to the final whistle
Pro
The working class are rallying to oppose data centers at 5 times the rate of wealthy neighborhoods – the great unifier is helping workers punch up, and it's super effective
Pro
World Cup kicks off in 48 hours — Save 10% on ByteSim's single eSIM for all games across the US, Mexico, and Canada
Pro
Brother MFC-L8970CDW color laser printer review
Website Hosting
'The biggest risk of AI is over-reliance': How AI is changing the way web agencies deliver value
Pro
Why Anthropic’s closed approach may be safer than OpenAI’s
Latest in Opinion
Memory
The RAM crisis will last 'quite a few years' says Nvidia CEO Jensen Huang
Pro
Why Anthropic’s closed approach may be safer than OpenAI’s
Pro
Why hackers hit Canvas during finals week
Pro
How AI is outpacing cybersecurity and what firms must do next
Pro
The next identity frontier: Verified workforce and the rise of agentic trust
Pro
The impact of the data center obsession on construction
LATEST ARTICLES- 1Best World Cup 2026 eSIM deals — Stay connected from the opening game to the final whistle
- 2Amazon wants to end dodgy knockoffs with its own AI-generated custom merch printing
- 3Hands on: I've spent over 35 hours sitting in Secretlab's new Atlas task chair and there's a lot I like about it — but I'm still not sure it will dethrone my Herman Miller
- 4Bowers & Wilkins just blew me away with its new 801 D5 speakers — here’s what I made of the model after attending a demo at at High End Vienna
- 5Why I wouldn't buy a super-cheap huge-screen TV for the World Cup