These aren't the chatbots that politely answer a prompt and disappear into the ether. They're AI agents that can set objectives, plan tasks, adapt to feedback, and execute actions across multiple domains with a high degree of accuracy and consistency. They are, in effect, autonomous business agents – and they're arriving faster than most companies, regulators, or employees are prepared for, due to the recent breakthroughs in AI models and huge investments by the technology industry to meet the high expectations of strategic transformation.
Navigating agentic AI in the workplace
In retail, for instance, AI shopping assistants are deployed to autonomously handle customer queries, track inventory, suggest products and process returns – all without human intervention. Manufacturers use autonomous AI agents in their factories to optimise production lines. These agents continuously monitor machine sensor data and adjust parameters like temperature, pressure or line speed, to maintain efficiency and reduce downtime.
AI-powered agents are employed by finance functions to autonomously reconcile financial transactions, detect anomalies, and initiate follow- up actions like flagging or resolving mismatched entries.
These aren't proof of concepts. They're live, functioning systems embedded in the day-to-day operations of digital-enabled firms.
Powerful and unpredictable
But here's the challenge: autonomy cuts both ways. The same qualities that make agentic systems useful could also make them unpredictable without the proper controls or monitoring:
- A pricing agent, left unchecked, could collude algorithmically with competitors
- A quality-control agent might reject batches for spurious reasons, triggering costly supply-chain ripples
- A finance agent could make reconciliation decisions so fast and so confidently that humans fail to notice a systemic error until it's too late.
Navigating agentic AI in the workplace
This poses a knotty problem for business leaders. Ignoring agentic AI isn't an option: competitors who embrace it will enjoy lower costs, faster cycles, and sharper customer insight. Embracing it uncritically is risky. Agentic AI systems can act autonomously, make complex decisions, and evolve in ways that aren’t always transparent. Without a workforce with the skills to understands how these systems operate – and where their limitations lie – organisations risk financial exposure, compliance breaches, and reputational damage. Building AI literacy isn’t just a tech issue; it’s a financial imperative.
Professional training is an option, but it must evolve as quickly as the technology itself. Courses such as our AI & Digital Skills for Business Impact programme – particularly its module on Applied Generative AI – can provide a targeted way for employees to improve AI literacy and digital awareness across the workforce. Learners will explore how AI can improve business productivity and decision making, and automate complex processes. Training will include how to craft effective prompts to guide AI tools, enabling more accurate and useful outputs. The module will also address responsible AI adoption, including data privacy, bias mitigation and regulatory compliance. For early-career professionals, this is an opportunity to leapfrog into roles that demand digital fluency. For mid-career managers, it's a chance to remain relevant in a world where routine analysis is increasingly automated. And for change leaders, it provides the frameworks needed to harness autonomy without surrendering control.
The prudent path
Yet even with education and foresight, the risks remain. Gartner predicts that more than 40% of agentic AI projects will be abandoned within two years – victims of over-promising, under-delivering, or simply being too difficult to govern. This 'agent washing' – branding simple software as autonomy – threatens to discredit the field before it matures. With the right planning and governance, agentic AI can deliver real value. But without proper oversight, scaling it too fast could invite financial, regulatory, and reputational risks.
With the UK Government adopting a pro-innovation approach, sector-specific regulators will be monitoring AI developments and promoting responsible AI use in their respective areas. Organisations should adopt modular architectures that allow agents to act, but within effective guardrails. This should be supported by investment in AI literacy skills, shifting from repetitive execution to higher-order oversight.
Agentic AI isn't a passing fad. It's the logical extension of automation, analytics, and machine learning – capable of transforming how work is organised and how value is created. But whether it becomes a partner in prosperity will depend less on the brilliance of its algorithms than on how organisations choose to apply it.
For now, the technology’s trajectory is clear: businesses are exploring the use of these new agents in their operations. The question is whether they will also consider the foresight and governance required to realise AI’s full value. If they don't, they may discover that their most ambitious hire is also their most unpredictable.
For more insight and guidance, get in touch with Julia Rockcliffe.
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