Operations
Strengthen operational performance by redesigning how information, decisions and workflows are managed with AI.
COOs are accountable for delivering the plan: meeting service expectations, managing capacity and resources, controlling cost, resolving exceptions and keeping execution aligned across functions.
AI can improve that management system. It can bring together operational information, detect deviations earlier, support planning and decision-making, coordinate standard activities and make it easier to focus management attention on the issues that genuinely require intervention.
The opportunity is not to apply AI to every process. It is to identify where it can improve the way Operations performs, then redesign the relevant workflows, controls and responsibilities around it.
I work with COOs and Operations leaders to identify those opportunities, define the future operating model and move priority workflows from concept into implementation.
The exact remit of a COO varies by company, but the underlying management questions are remarkably consistent.
AI is relevant when it improves the quality or speed of those answers, reduces the coordination required to act on them, or helps redesign the process itself.
Operations leaders need a current view of performance without relying on repeated manual consolidation.
AI can support:
The aim is not another dashboard. It is better management information, with the context behind the numbers available when a decision is required.
Planning decisions often depend on information held across different teams and systems.
AI can help combine demand, workload, resource availability, operational constraints and historical performance to support:
The value comes from improving the quality of trade-offs, not from removing management ownership of them.
Many operational delays occur between functions rather than within a single process.
AI and automation can help translate decisions into actions, maintain visibility over dependencies, follow commitments and escalate issues when execution begins to drift.
Typical applications include:
This is particularly relevant where execution depends on several functions, owners or systems moving in sequence.
A large share of management time is absorbed by exceptions: incomplete requests, service failures, unusual cases, missing information, policy questions or issues that do not follow the standard process.
AI can help classify the issue, retrieve the relevant context, check procedures and determine whether a standard response applies.
Routine, low-risk and reversible actions can be handled within defined controls. Material, unusual or low-confidence cases can be escalated with the information required for a decision.
This can reduce the time spent investigating routine issues while improving the quality of escalation.
Operational improvement requires a clear view of how work actually moves.
AI and process intelligence can help identify:
The objective is to redesign the process where necessary, not simply automate the existing sequence.
Operations also has to perform when demand changes, resources become constrained or an unexpected event disrupts the plan.
AI can support earlier identification of operational risk, scenario assessment, dependency analysis and response planning.
The relevant question is not whether AI can predict every disruption. It is whether management can see emerging constraints sooner, understand the available options and respond with better information.
The largest gains usually appear when AI is embedded into recurring management and operational workflows rather than used only for isolated tasks.
A weekly or monthly operating review typically draws from project updates, performance measures, financial information, open issues and management commentary.
AI can consolidate these inputs, highlight material movements, identify unresolved actions and prepare the first version of the management pack.
The review itself remains a management process. Leaders interpret the situation, challenge assumptions, make trade-offs and decide what needs to change.
Once decisions are made, automation can update actions, owners and deadlines in the relevant systems and monitor follow-through.
This creates a tighter link between performance review and execution.
Capacity issues are often visible before they become critical, but the information needed to act may sit across different teams.
AI can bring together workload, backlog, staffing or resource availability, forecast demand, service commitments and known constraints to prepare scenarios.
Management can then compare the operational and financial implications of different options before reallocating resources or changing priorities.
Transformation programmes, launches, integrations and major operational initiatives depend on coordinated execution across several functions.
AI can help maintain a current view of milestones, dependencies, decisions, risks and commitments, while automation keeps underlying systems and action registers up to date.
When a dependency slips or a decision is missing, the issue can be escalated with the relevant context rather than requiring another round of status collection.
For recurring requests, incidents or service exceptions, AI can structure the case, retrieve the relevant policy or operating history and determine whether an approved standard procedure applies.
Where it does, routine steps can be executed within defined controls.
Where judgment is required, the case is escalated with the facts, history and options needed for a decision.
The same information can then be used to identify recurring root causes and improve the underlying process.
Introducing AI into Operations changes more than the technology. It changes who — or what — performs parts of the workflow.
That division of work should be explicit.
The design principle is straightforward: delegate standard, controlled work where the economics and risk justify it, and preserve human decision rights where the consequence is material or the situation requires judgment.
A workflow that prepares an internal review should not be governed in the same way as one that can update a customer commitment, allocate material resources or change an operating parameter.
For each workflow, the control model should define:
A practical progression is to begin with AI assisting the workflow, move to recommendations where the evidence is reliable, and allow controlled execution only for standardised, low-risk and reversible actions.
Material commitments, policy exceptions and irreversible decisions should remain subject to explicit human approval.
Review the operating model, management cadence, priority processes, systems, decision points and performance measures.
The purpose is to understand where performance is constrained and where management effort is being consumed by coordination, rework or lack of visibility.
Assess potential AI and automation opportunities against operational impact, frequency, data readiness, implementation complexity, control requirements and scalability.
This creates a short list of workflows worth redesigning rather than a catalogue of possible use cases.
Define the future process, including:
The future workflow is designed around the operating requirement, not around a preferred technology.
Translate the workflow into the existing environment: ERP, CRM, project systems, service platforms, BI, documents, databases and APIs.
I work with internal teams and the appropriate specialist partners to define how the design should be implemented.
Test the redesigned workflow with real users, real data and real operating conditions.
Validate output quality, adoption, escalation logic, controls and impact before extending the scope or level of automation.
Measure the indicators relevant to the workflow, which may include service level, cycle time, backlog, throughput, capacity utilisation, rework, exception rate, decision latency or cost-to-serve.
Scale what improves performance and redesign what does not.
AI Opportunity Assessment
For most Operations teams, the challenge is not finding possible AI use cases. It is deciding which ones are worth changing the operating model for.
The AI Opportunity Assessment provides a structured review of the function and identifies where AI, agents and automation can create meaningful operational value.
The assessment examines:
The output is a prioritised roadmap showing which workflows should be addressed first, what should change and what capabilities are required to implement the new model.
Discuss an AI Opportunity AssessmentMy work combines executive leadership experience with a background in finance, operations and business transformation.
I approach AI from the same questions used to run a business: where performance is constrained, what is driving the issue, what needs to change in the operating model, and how the result will be measured.
Technology comes after that diagnosis.
Depending on the mandate, I work with management teams, internal technology functions and external specialists to move from assessment to workflow design, implementation and performance follow-up.
