Adoption of email was gradual. Office workers had in and out trays, teams had secretaries, and internal postal services delivered memos. People walked to colleagues’ desks when they had a question. These behaviours did not disappear overnight.

Email did take away secretarial and postal roles. It enabled remote communication and facilitated outsourcing. It may also have contributed to a culture of unproductive meetings, as people seek confirmation that messages have been received. I have worked with teams that only treated something as important if they were called into a room.

Digital communication is the most significant shift in how we work over the last five decades. It enabled globalisation. Yet at the time, adoption appeared unplanned. Firms dealt with the consequences rather than working from a defined blueprint.

If AI follows the same pattern, why is there such a strong push to redesign operations in advance?

Agentic AI is gaining traction because the tools are now reliable enough for real workflows. Firms are also under sustained pressure to improve efficiency, reduce risk, and extract more value from data. The technical and commercial drivers have aligned.

Firms can now connect models to internal systems, data feeds, and operational platforms. This allows software to take action rather than just generate text. It can retrieve data, apply logic, and trigger workflows.

Larger firms have also accumulated years of process complexity. Many workflows rely on manual intervention, fragmented tools, and legacy infrastructure. Reconciliation, reporting, and internal tooling often require significant human effort. These processes create delays, introduce errors, and limit scale. 

Agentic AI fits into this environment because it does not require a complete rebuild of systems. It layers on top of existing infrastructure and automates specific workflows. This makes adoption practical. Firms can start with contained use cases and expand from there

In finance, reconciliation remains resource-intensive. Teams compare internal records with counterparties, identify discrepancies, and resolve exceptions. This often involves manual investigation and communication across systems.

An agentic workflow can automate much of this. It ingests data from multiple sources, compares records, and identifies mismatches. It can then analyse historical patterns and system logs to determine likely causes. Where possible, it initiates resolution workflows or flags issues with clear explanations.

Firms are trying to grow revenue, control costs, and improve client experience in areas where delays and inconsistencies still exist. Agentic AI targets these constraints through focused automation.

Despite this, many firms still resist adoption. The barriers are both cultural and operational. Senior stakeholders question reliability and governance. Teams worry about disruption to established workflows. Technology functions hesitate due to integration challenges with legacy systems. Regulators add another layer of uncertainty.

Research from YouGov shows that 32% of UK workers are using AI, with around a third of them using it daily. Government data suggests only a quarter of firms have formally adopted AI. This gap points to widespread informal use, which increases the need for a clear plan.

When email was introduced, firms had to provide access to employees. Early systems were limited to internal communication and were expensive. Free, browser-based services such as Hotmail and RocketMail came later.

AI is different. Free personal tools are available alongside paid corporate systems from the start.

In this environment, a common mistake management makes is to discuss AI in abstract, strategic terms. This can trigger concern over job losses. Without a clear plan for practical adoption that supports workers, employees assume the worst.

Firms need a structured approach to implementation to capture the potential value of AI. A clear pace helps maintain momentum and manage risk.

The first phase focuses on discovery. Firms assess workflows, identify inefficiencies, and prioritise use cases. This includes reviewing existing tools and understanding where agentic systems can integrate effectively. The goal is to create a clear map of opportunities with defined outcomes.

The second phase focuses on enablement. Firms build proofs of concept and minimum viable products within real environments. These systems should connect to live data and demonstrate measurable impact. Teams also establish governance frameworks, including security, monitoring, and access control. Training begins at this stage to ensure staff can engage with the new tools.

The final phase focuses on scaling. Firms move successful use cases into production and expand them across the organisation. They connect workflows, standardise approaches, and build internal capability. Over time, agentic AI becomes part of daily operations rather than a standalone initiative.

Adoption of agentic AI reflects a shift towards more automated and decision-capable systems. The technology is now capable of delivering practical value. Firms that focus on execution and integration will see gains in efficiency, revenue, and client outcomes. Those that delay will come under increasing pressure as the gap widens.