Advancements in artificial intelligence have really excited progressive business leaders. And it’s not surprising. 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’.
But the ever-evolving obsession with intelligent automation risks creating more problems than it solves, if C-suites aren’t careful.
We already know from MIT data, that 95% of AI projects reportedly fail to generate an ROI, and sadly, the technology itself often takes the ‘blame’. But one of the primary reasons for this staggering statistic is that businesses jump straight to tackling the biggest projects they can think of, knowing AI could drive change. But if the organisation doesn’t have the necessary AI maturity, things quickly become unstuck. There’s often a lack of success measurement clarity; weak transformative leadership; cultural adoption oversight; partner experience limitations; or ineffective data governance, to name just a few.
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.
That means a legal agent could receive an input query, retrieve information and apply deep learning to summarise meaningful findings in a case report, for instance. Or, in a more complex scenario, a multi-agent orchestrator could manage an e-commerce order end-to-end, including fulfilment.
Whatever the use case, 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 an accelerated – and still effective – recruitment strategy, which 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.
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