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Companies can often benefit from automating repeatable work in finance, HR, customer service, sales, marketing, procurement, supply chain, IT, compliance, and reporting—but no fixed list applies to every business. Choose an end-to-end workflow where volume, delays, errors, or service problems matter, then weigh its data readiness, integration effort, risks, and need for human judgment before automating it.
Which business operations are common automation candidates?
The ten areas below are starting points, not a universal ranking or a prediction that every company must automate all of them. Departments also overlap: finance may own reporting, procurement may sit within supply chain, and compliance checks may be embedded across several workflows. Define the work by what enters the process, what outcome it must produce, and who is accountable for that outcome.
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| Operation | Potential first workflow | Keep people responsible for |
|---|---|---|
| Finance and accounting | Invoice approval routing, reconciliations, recurring cost analysis, fraud-signal checks, or forecast preparation | Approving exceptions, investigating suspicious activity, and making consequential financial decisions |
| HR and employee administration | Onboarding document collection, routine record updates, benefits inquiries, or standard employee correspondence | Sensitive employee matters, policy interpretation, and decisions affecting an individual’s employment |
| Customer service | Answering common questions, categorizing incoming requests, and routing cases to the right team | Unusual, high-impact, or unresolved cases and communications where empathy or judgment is needed |
| Sales and lead handling | Prioritizing incoming leads, preparing follow-up reminders, and scheduling meetings | Relationship-building, negotiation, and commitments that require commercial judgment |
| Marketing operations | Moving campaign assets through review, preparing routine content variations, or coordinating repetitive campaign steps | Positioning, audience insight, brand decisions, and verification of factual or regulated claims |
| Procurement and vendor management | Routing purchase requests, collecting supplier information, and tracking routine approvals | Supplier selection, negotiation, exception approvals, and decisions with material risk or cost |
| Supply chain and inventory | Preparing inventory or logistics updates, flagging exceptions, and coordinating routine planning inputs | Responding to disruptions and making trade-offs that depend on local operations or industry context |
| IT service management | Classifying tickets, handling basic service tasks, and assisting with coding or documentation | Access approvals, security-sensitive changes, and incidents that need expert investigation |
| Compliance and risk | Checking documents against defined rules, assembling review materials, and flagging possible anomalies | Interpreting ambiguous requirements and signing off on consequential or regulated decisions |
| Reporting and business intelligence | Preparing recurring data extracts, refreshing dashboards, and assembling management reports | Checking whether figures are complete and correctly interpreted, and explaining what they mean |
These workflows can use conventional rules-based automation, AI assistance, or a combination. A predictable approval route may be handled by fixed rules; summarizing an inquiry or document may benefit from AI, but still needs controls appropriate to its impact. The technology should follow the workflow and its risks—not the other way around.
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Start with a specific business outcome, not a software demo. McKinsey’s 2024 operations article records leaders asking, “How can we leverage intelligent technology to capture more value faster, increase productivity, and outperform our competitors?” That question is useful only when translated into an observable result, such as shorter request-handling time, fewer invoice exceptions, or more reliable reporting.
#1 Best Overall
- Map the workflow end to end. Record how work arrives, who handles each stage, which systems are involved, where handoffs occur, what exceptions arise, and how the process ends. Include work that crosses department boundaries.
- Set a baseline and target. Choose measures tied to the outcome: processing time, backlog, error or rework rate, cost per completed case, service level, or customer and employee experience. Record the current value and how it is measured before changing the process.
- Assess fit and exposure. Consider transaction volume and repeatability, exception frequency, the cost of delay or error, customer or employee impact, data quality and access, integration complexity, security and compliance exposure, and the amount of human review required.
- Choose a bounded first step. Automate a repeatable segment with a clear input, output, owner, and escalation route. Avoid making the first project depend on perfect data, broad system access, or an untested chain of decisions.
- Test, monitor, and adjust. Compare results with the baseline, inspect failures and exceptions, and collect feedback from the people who use or oversee the workflow. Expand only when the process is reliable and its controls work in practice.
There is no evidence-based universal score that determines the best first project for every company. A high-volume workflow may still be a poor starting point if its data is inaccessible or its errors have severe consequences. Conversely, a modest automation may be valuable if it removes a recurring bottleneck without weakening accountability.
What does adoption data say—and what does it not say?
McKinsey’s 2024 Corporate Functions CXO Survey found that 22 percent of surveyed corporate-function CXOs reported an active generative AI use case in 2024, compared with 4 percent in 2023. The survey covered 276 senior leaders across finance, HR, IT, customer care, and legal in 18 industries in North America and Europe. It indicates increased reported adoption among that group; it does not establish that every company needs the same technology or workflow.
Among CFO respondents, cost analytics was identified by 47 percent, accounts-payable approval optimization by 44 percent, and fraud-prevention checks by 44 percent as generative AI use cases they had piloted or deployed. These are survey responses about piloted or deployed use cases, not independently verified savings or proof that those applications work equally well in other organizations. The same survey reported that more than 75 percent of surveyed organizations that had deployed generative AI tools at scale said the systems met or exceeded expectations. That is a respondent assessment, not an independently measured return.
The reported examples are consistent with choosing concrete workflows over vague department-wide goals. They do not remove the need to check the process, data, controls, and implementation conditions at the company adopting the system.
Where should human review remain?
Automation should not mean that responsibility disappears. Keep an identifiable owner for the workflow, especially where errors can affect money, employment, customer outcomes, security, or regulated decisions. Human review should be designed into the process at the points where judgment is needed, rather than added as a vague final check after an automated system has already acted.
- Set boundaries. Specify which inputs and actions the system may handle, and which conditions require a person to review or approve.
- Route exceptions. Define where incomplete records, conflicting information, low-confidence outputs, unusual requests, or failed integrations go—and who resolves them.
- Control access. Limit systems and people to the data and permissions needed for their part of the work. Treat sensitive records and consequential actions with particular care.
- Keep the process observable. Preserve enough records to understand what happened, identify errors, and correct the workflow when its assumptions fail.
- Involve employees. Ask the people who perform and supervise the work to identify exceptions, test changes, and validate whether the redesigned process is usable.
McKinsey’s 2025 workplace report quotes Anthropic cofounder and CEO Dario Amodei: “[It] is critical to have a genuinely inspiring vision of the future [with AI] and not just a plan to fight fires.” For an operations project, that means explaining how the change should improve the work and involving affected employees—not presenting automation solely as a response to a crisis.
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How should a company judge results?
Measure the outcome the project was meant to improve and check for costs or problems shifted elsewhere. A faster step is not a successful automation if it creates more downstream rework, worsens service, or leaves staff unable to resolve exceptions. Track quality and risk alongside speed or cost, and assign an owner to review both routine performance and failures.
Published cases illustrate possibility, not a forecast. A 2021 McKinsey case described a 60 percent reduction in operational costs after a particular telecom outsourcing and automation arrangement. Another 2021 McKinsey case described productivity rising about 40 percent and customer satisfaction increasing by more than 35 percent after a particular industrial process redesign. Those organization-specific outcomes do not establish typical returns; a company’s process, scale, systems, and implementation determine what it may achieve.
Automation works best as process redesign supported by appropriate technology, rather than as a tool layered onto unclear work. McKinsey’s workflow report emphasizes that workflows differ by business model, company size, and technological maturity. Treat the ten areas as a way to find a candidate, then make the decision at the level of a well-defined workflow and its measurable outcome.
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