There is a new rhythm to corporate conversations.
A boardroom. A slide deck. A market update. Somewhere between cost pressures and growth targets, a familiar sentence appears:
“We’re investing heavily in AI.”
What often follows, whether explicitly stated or quietly implied, is the real motive behind the question that inspired this session:
Can I fire my ops team?
The question rarely sounds that blunt in public. But behind closed doors, it is being asked in one form or another. AI has entered the executive vocabulary as both promise and pressure. Markets react sharply to AI announcements. Investors reward perceived efficiency. And layoffs, increasingly, are accompanied by references to automation.
Over the past year, organisations including Salesforce, Amazon, Citigroup and Meta have cited AI alongside restructuring efforts. Sometimes the connection is direct. Sometimes it sits adjacent to wider economic or strategic shifts. The narrative, however, is clear enough to the outside world: AI is coming for jobs.
Spend time inside organisations, though, and the reality feels far more complicated. There are Copilot licences, pilot projects, half-built workflows and a scattering of enthusiastic individuals experimenting with tools. There is curiosity and confusion in equal measure. What you do not see are fully autonomous systems calmly replacing entire departments overnight. The lived experience feels transitional, exploratory, even messy.
The deeper story is economic. Automation has existed for years. Tools such as Zapier and Microsoft Power Automate have long allowed teams to stitch systems together. What has shifted is the cost and speed of experimentation. Large language models now introduce interpretation and reasoning into workflows. The API economy has matured to the point where nearly every SaaS platform can be connected to another. AI-assisted tooling can scaffold complex automations in hours rather than weeks.
When the cost of trying something collapses, behaviour changes. Problems that once required lengthy scoping exercises can now be prototyped in a day. The barrier to entry has fallen so far that experimentation often precedes formal strategy. The constraint moves away from technical feasibility and towards organisational maturity. Leaders find themselves less limited by tooling and more limited by clarity and governance.
Against that backdrop, the operations function sits directly in the spotlight. Traditionally, operations teams have been responsible for monitoring signals, triaging requests, escalating issues and ensuring the machinery of the business continues to run. Much of that work involves structured decisions based on defined inputs. Automation platforms can now handle significant portions of that flow. A support ticket can be summarised, categorised by urgency, analysed for sentiment and routed automatically. In some cases, it can even draft a response or propose an action before a human intervenes.
The nature of the work begins to shift. Processing gives way to oversight. Execution gives way to orchestration. Modern platforms such as n8n increasingly incorporate human-in-the-loop patterns, where automation pauses for approval or guidance before continuing. The human becomes the governor of the system, shaping its rules and validating its outputs, rather than manually performing each step.
This transition requires more than new tooling. It demands a change in how leaders define value. When execution becomes inexpensive and fast, the differentiator moves upwards. Judgment, prioritisation and system design grow in importance. Operations teams are no longer measured solely by how efficiently they process tasks, but by how intelligently they design and refine the workflows that process those tasks. Their role stretches towards governance, optimisation and continuous improvement.
There is, however, a shadow forming alongside this opportunity. The ease with which automations can now be created introduces a new wave of decentralised experimentation. Individuals spin up workflows. Teams create their own agents. Departments experiment in isolation. The pattern echoes earlier cycles of spreadsheet macros and low-code apps, where ingenuity flourished but oversight lagged behind. The difference today lies in speed and reach. An automation built in an afternoon can quickly underpin a mission-critical process.
Without clear ownership, such systems accumulate risk. Security vulnerabilities emerge. APIs are deprecated. Token costs rise unpredictably. The platform evolves while the workflow remains frozen in time. Governance must mature alongside experimentation. Platform ownership, cost monitoring and security patching are not bureaucratic obstacles; they are the scaffolding that allows innovation to scale safely.
Financial concerns add another layer of complexity. AI-enabled workflows often rely on token consumption from external model providers. In the early days of cloud computing, similar anxieties surfaced around elastic infrastructure costs. Over time, forecasting models, monitoring tools and financial operations disciplines developed to manage that volatility. AI will follow a comparable trajectory. Token pricing may fluctuate. Providers may adjust commercial models. Organisations that approach the space with discipline rather than exuberance will adapt more smoothly.
All of this brings us back to the original question. When leaders ask whether they can fire their ops team, they are reacting to a visible shift in what machines can now do. The temptation is to view automation as a direct substitute for human effort. Yet in practice, the picture proves more layered. Tasks may disappear. Entire categories of repetitive work may shrink dramatically. At the same time, the demand for people who can design, govern and evolve automated systems grows.
The organisations that thrive in this transition will be those that consciously migrate their people up the stack. They will invest in training operations professionals to become system designers and workflow architects. They will define clear guardrails around experimentation. They will treat AI as a multiplier of human capability rather than a convenient justification for reduction.
The conversation, then, becomes less about subtraction and more about redeployment. As execution accelerates and becomes cheaper, human contribution concentrates around areas where context, accountability and ethical judgment remain essential. The operations function does not vanish; it transforms. Whether that transformation strengthens or weakens the organisation depends largely on the choices leaders make now.
AI can serve as a scapegoat for cost cutting. It can also act as a catalyst for structural improvement. The headline question grabs attention because it hints at disruption. The deeper question demands more courage:
How do we redesign our organisation when execution is no longer the bottleneck?
Answering that well will determine who merely reacts to AI, and who uses it to build something more resilient.