Digital Economy Dispatch #262 -- Are We Ready for AI Agents in the Workforce?

If you believe the headlines, 2025 has been "the year of the AI agent". Tech vendors are falling over themselves to announce agentic capabilities, analysts are publishing breathless predictions about autonomous digital workers, and your inbox is probably full of invitations to webinars promising to revolutionise your operations with AI agents that think, plan, and act on your behalf.

But strip away the marketing gloss and a more interesting picture emerges. Yes, organisations are investing heavily. For example, PwC's latest survey shows 88% of executives plan to increase AI budgets specifically because of agentic AI. Yet when you look at actual implementation maturity, the scores are sobering. Research from theCUBE puts execution readiness at just 1.8 out of 5, even while aspirations score 4.1. That gap between ambition and reality should sound familiar to anyone who's lived through previous waves of business automation. Some of us have long memories and painful scars.

What Actually Is an AI Agent?

It’s time to cut through the hype. An AI agent, in its current practical form, is software that can observe its environment, make decisions based on what it finds, and take actions to achieve a defined goal. This can involve tying together multiple steps, often without requiring human approval at each stage.

The key difference from traditional automation is the level of autonomy provided by AI agents. A conventional automated workflow follows predetermined rules: if X happens, do Y. An AI agent is defined to interpret ambiguous situations, decide on an approach, and adapt when things don't go as expected. For example, rather than previous workflow automation to handle emails (“if you receive a message from X, flag it as high priority”), an AI agent might be able to analyse your calendar, draft an email, check for conflicts, revise the draft based on the recipient's previous responses, and send it -- all from a single instruction.

In practice today, most AI agents sit somewhere on this automation spectrum. At one end, you have agents embedded in enterprise applications. The most common include Microsoft's Copilot surfacing common desktop insights and Salesforce's Einstein automating routine customer interactions. These are useful but incremental. At the other end, you have experimental autonomous systems that can conduct research, write code (such as Cognition’s Devin), or manage complex multi-step processes with minimal human oversight (such as CrewAI). Most organisations are firmly at the first end, whatever their vendor pitches might suggest.

Haven't We Been Here Before?

If you're experiencing a sense of déjà vu, you're not wrong. The promise of intelligent automation transforming how we work has a long and somewhat chequered history.

In the 1990s, Business Process Automation (BPA) and Business Process Reengineering (BPR) promised radical transformation through fundamentally rethinking and automating how work gets done. The results were mixed at best. Many organisations found that "reengineering" became a euphemism for cost-cutting, and the promised productivity gains from automating the steps often failed to materialise because the resistance of the broader organisation to change was dramatically underestimated.

More recently, Robotic Process Automation (RPA) arrived with similar fanfare. Software robots would handle repetitive tasks, freeing humans for higher-value work. And RPA did deliver real benefits, but within strict limits. It excelled at structured, rule-based processes with clean data. Throw in exceptions, ambiguity, or the need for judgment, and the robots struggled. Many RPA implementations hit a ceiling, automating the easy 20% while the complex 80% remained stubbornly manual.

So, is the current wave of AI agents any different?

I think the honest answer is potentially yes, but not in the ways the hype suggests.

The genuine breakthrough with AI agents isn't that they can follow more complex rules, but rather that they can handle greater ambiguity. They can interpret intent from natural language, make reasonable judgments when information is incomplete, and learn from feedback. That's a meaningful step change from RPA's brittleness.

But that advance is in danger of being swamped by persistent challenges that surround any business process change. What hasn't changed is that the organisational challenges remain remarkably similar. BPA/BPR failed not because the technology was wrong but because redesigning processes means redistributing power, changing job roles, and challenging entrenched ways of working. RPA stalled not because the bots couldn't cope but because organisations couldn't integrate them into workflows that crossed departmental boundaries.

AI agents will face exactly the same issues. The technology may be more capable, but capability was never really the limiting constraint.

The Questions You Should Be Asking

McKinsey's latest research reinforces this point. It concludes that organisations achieving real value from AI aren't just deploying better tools. Instead, they're fundamentally rewiring how work gets done. That means asking uncomfortable questions that go well beyond technology selection.

Who is accountable when an AI agent makes a decision that goes wrong? How do you measure productivity when a team consists of three humans and a dozen digital agents? What happens to middle management when much of their coordination role can be automated? How do you maintain institutional knowledge when AI agents handle processes that humans used to learn from?

Some forward-thinking organisations are already experimenting with answers and creating new roles, rethinking performance metrics, and establishing governance frameworks for human-AI collaboration. But they're the minority. Most are still treating AI agents as a technical implementation rather than a workforce transformation.

The lesson from BPA/BPR and RPA is clear: the organisations that succeeded were those that recognised automation as an organisational change programme first and a technology project second. There's no reason to think AI agents will be any different.

The technology may well be much more capable this time. But if we’re honest, technology has never been the hard part in driving organisational change, has it?