Advancements in artificial intelligence have really excited progressive business leaders. From a technological perspective, we’ve never had a bigger opportunity to turn ideation into action. That’s why many organisations are embarking on major AI journeys, inspired by the ‘art of the possible’.
For someone who has spent their whole career in the world of digital transformation, this appetite is electrifying – especially because, to some extent, AI has levelled the playing field for smaller companies. Transformation on the scale we’re now seeing was once reserved only for vast enterprises, or those divesting their budgets to lower cost – and often lower quality – offshore engineering.
But the ever-evolving obsession with increasingly intuitive technology risks creating more problems than it solves, if C suites aren’t careful.
We already know that 95% of AI projects reportedly fail to generate an ROI, and sadly, the technology itself often takes the ‘blame’. But there are many reasons projects ‘fail’, including a lack of success measurement clarity; weak transformative leadership; cultural adoption oversight; data flaws and poor governance – not to mention a common eagerness to kickstart a colossal AI program before the business is ready.
However, if you know the pitfalls, you’re better able to avoid them, enabling you to keep moving towards your ‘North Star’ without becoming one of the statistics.
Are AI agents really autonomous digital beings?
That North Star may involve AI agents.
Unlike a prompt-driven or conversational Large Language Model (LLM), agentic AI is goal-orientated. With access to knowledge, a set of defined instructions outlining what it’s trying to achieve, and the ability to connect to other tools and systems to collect the data required, this AI ‘brain’ can operate, reason and learn, without human intervention.
Let’s apply that to a real world use case such as accounts payable. Whereas traditional automation would struggle with inconsistent data across different invoice layouts, supplier-specific quirks or missing fields, a fairly straightforward finance agent can handle invoicing, expenses and reconciliations with ease, alleviating admin by 47%. I’ve also seen an incident outage agent reduce IT downtime by 30%, in what was again a simple use case. But of course the possibilities really are endless, with multi-agent orchestrations capable of handling complex end-to-end e-commerce orders, inclusive of customer fulfilment, for example.
Whatever the brief, AI agents can certainly do much more than follow a script. They’re intelligent enough to follow intricate, multi-step activities, think independently, and handle variation.
But this doesn’t mean they’re fully autonomous digital beings – if they were, you wouldn’t want them anywhere near your business.
The importance of guardrails
AI agents work within the boundaries you define. They never decide what’s allowed.
So, instead of viewing them as ‘magicians’, think of them as digital workers with a job description. Like any employee, they should have a clear scope of responsibility, follow established processes, use approved systems and tools, and adhere to policies. Importantly, they need to know to stop when something doesn’t look right, and escalate instead of guessing. Because they may be able to handle ambiguity but you don’t want them to bypass governance.
If your AI can’t explain its guardrails, it isn’t production ready. Guardrails make AI safe, useful, and deployable in real business environments. Without these ‘rules’, agents will ‘hallucinate’ or attempt to fill the gaps of what they don’t know. You therefore need to clearly define outcomes, policies, and accuracy thresholds, early on. Otherwise, the risk of non-compliance and data leakage escalates exponentially.
Human feedback loops remain important too, to validate outputs and train the AI on how to behave, especially when confidence is low. This also provides traceability and auditability, so that if anything goes wrong, you can quickly track why, to enable improvement.
How to deploy AI agents safely
To ensure safe progress, that will not break a business, start by identifying processes that are well-defined, documented and deeply understood. This might be a mundane, repetitious ‘back office’ activity such as timesheets and billing – important but high volume ‘admin’ that stifles colleague productivity. Agents can augment this human workload, giving colleagues time back to complete the value-oriented parts of the role requiring deeper thought.
Elsewhere, in JML, I’ve seen HR agents help handle up to 60% more job applications while maintaining a fair and compliant process. The result is and accelerated – and still effective – recruitment strategy, which also ensures wider people management initiatives are maintained during busy periods.
Truthfully, agentic AI can thrive anywhere a level-4 process or Service Operational Procedure (SOP) can be defined – even highly-regulated or compliance-driven environments. But AI agents aren’t magic, and that’s the point.
The intelligence helps with the messy parts. The rules decide what’s allowed.
