The first wave of enterprise AI adoption was characterised by chatbots and copilots: AI systems that assist humans in individual tasks, answer questions, summarise documents, draft content, and accelerate work that a human ultimately completes and approves. Valuable, but fundamentally a productivity tool for individual users. The second wave - Agentic AI - is something categorically different.
What Makes AI Agentic?
Agentic AI systems are characterised by three properties that distinguish them from conventional AI assistants: autonomy, multi-step planning, and tool use. An agentic system does not just answer a question - it takes a goal, breaks it into steps, executes those steps autonomously using tools (APIs, databases, web access, code execution), monitors the results, and adapts its approach when things do not go as planned.
In practice, this means an agentic system can be given a task like "process all incoming supplier invoices that match purchase orders, flag exceptions for human review, and update the ERP system" and execute that task end-to-end with minimal human involvement in the routine cases. Or "monitor news and market data for information relevant to our top ten customer accounts and prepare a daily briefing for the account management team." Or "identify procurement contracts due for renewal in the next 90 days, pull the relevant supplier performance data, and draft renewal recommendation memos for each."
These are not future capabilities. They are being deployed today in organisations that have the AI strategy and governance infrastructure to do so safely.
The Business Case: Where Agentic AI Creates the Most Value
The highest-value agentic AI use cases share common characteristics: they involve repetitive multi-step processes, they require the synthesis of information from multiple systems, the individual steps are well-defined even if numerous, and the cost of human execution is high relative to the value added at each step.
In Finance, accounts payable processing, financial close support, variance reporting, and compliance monitoring are all strong candidates. In Procurement, supplier research, contract review for standard clauses, spend categorisation, and vendor qualification are seeing early agentic adoption. In HR, employee onboarding workflows, policy question handling, and learning recommendation are natural fits. In Government and public sector, case management workflows, document processing, and citizen query routing represent significant opportunity.
The common thread is that agentic AI removes the human from the execution of routine steps while keeping humans in the loop for judgment, exception handling, and approval of consequential decisions. Well-designed agentic systems make human oversight easier, not harder, by surfacing exactly the cases that require human attention rather than routing everything through human review.
The Governance Imperative
Agentic AI raises governance challenges that are qualitatively different from those of AI assistants. When an AI system can take actions in the world - send emails, update records, process transactions, interact with external APIs - the failure modes are more consequential than when it can only generate text that a human must review and act on.
Organisations deploying agentic AI need to address several governance questions that are specific to the agentic context. What actions can the agent take autonomously, and what actions require human approval? How are the agent's actions logged and auditable? What happens when the agent encounters an ambiguous situation - does it proceed, halt, or escalate? How are errors detected and corrected, and what is the remediation process when an agentic error causes downstream consequences? Who is accountable when an agentic system causes harm?
These are not reasons to avoid agentic AI - they are the design parameters that responsible deployment requires. Organisations that develop answers to these questions before deployment, rather than after the first incident, are the ones that capture the value of agentic AI without the governance failures that make the technology unnecessarily risky.
Building the Foundation: What Needs to Be in Place First
Agentic AI performs best on a foundation of data quality, system integration, and process clarity. An agent that is trying to process invoices but cannot reliably access the ERP, or that is working with supplier data that is inconsistently classified, will produce more errors than it prevents.
Before deploying agentic systems in high-stakes processes, organisations should assess whether the data and integration foundations are adequate, whether the processes to be automated are sufficiently defined and stable, and whether the monitoring and audit infrastructure is in place to detect when the agent is making mistakes. Agentic AI deployed on a fragile data foundation amplifies the fragility rather than compensating for it.
The organisations that are capturing the most value from agentic AI today are not those with the most advanced AI technology - they are those with the best data foundations, the clearest process definitions, and the most thoughtful governance frameworks. The technology is ready. The organisational readiness is what varies.