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Companies should consider automating repeatable, high-volume workflows where delays or errors have a measurable cost—not assume every business needs the same ten projects. Finance, HR, customer service, sales, marketing, procurement, supply chain, IT, compliance, and reporting are common candidates, but the right starting point depends on the process, its risks, available data, and the effort required to connect systems.
1. Finance and accounting
Finance workflows often combine routine data handling with approval decisions, making them useful candidates for targeted automation. Consider accounts-payable approvals, reconciliations, cost analytics, fraud checks, cash-flow optimization, and forecasting. Define the workflow from receipt of an invoice or transaction through validation, approval, posting, and exception handling; automating only one step may simply move the bottleneck.
Keep people responsible for unusual transactions, disputed invoices, and decisions that require financial judgment. In McKinsey’s 2024 Corporate Functions CXO Survey, CFO respondents reported cost analytics (47%), accounts-payable approval optimization (44%), and fraud-prevention checks (44%) among the generative-AI use cases they had piloted or deployed. These are survey results about reported use cases, not proof that every finance team will benefit equally.
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2. HR and employee administration
Employee administration includes recurring exchanges and record updates from hiring through ongoing employment. Candidate workflows include onboarding correspondence and documents, routine employee inquiries, benefits questions, and updates to personnel records. An automated workflow can route a request, retrieve relevant information, prepare a response, and send uncertain or sensitive cases to an HR professional.
Use human review for exceptions and employment decisions with significant consequences. The 2024 corporate-functions survey describes HR service inquiries and document work among potential applications; it does not establish that an automated system should make hiring, performance, or benefits-eligibility decisions on its own.
3. Customer service
Automate the intake and triage of routine customer questions when the answer can be grounded in approved information. A workflow might classify a request, suggest a response, retrieve account context, and route unresolved cases to the right service team. Customer-facing chatbots are among the use cases identified in McKinsey’s 2024 corporate-functions survey.
Set clear escalation paths for complaints, unusual account situations, and cases where a mistaken answer could harm a customer. Track whether automation resolves requests accurately and improves service outcomes—not just how many conversations it handles.
4. Sales and lead handling
Sales automation is most appropriate for predictable early-stage work: prioritizing incoming leads, preparing follow-ups, and coordinating meeting schedules. A workflow can gather available lead information and support timely next steps while leaving relationship-building, qualification judgment, and negotiation with sales specialists.
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McKinsey’s workflow analysis describes agents supporting early sales stages while specialists retain relationship and negotiation work. That division is a practical boundary, not a rule that fits every sales model.
5. Marketing operations
Marketing teams can examine repetitive campaign and content workflows, such as organizing inputs, preparing draft materials, and coordinating review steps. The opportunity is not to automate brand strategy or publish unverified claims; it is to reduce routine handling around work that still needs human direction.
McKinsey’s 2025 workplace report identifies sales and marketing as functions with notable potential for AI-supported work. It does not prescribe a single marketing task for every organization. Choose a bounded workflow and retain human approval for positioning, accuracy, and claims.
6. Procurement and vendor management
Procurement workflows can include supplier intake, sourcing coordination, approval routing, and routine vendor-process updates. Map the path from a business request through review and authorization to supplier action, including the points where policy or exceptions require a person.
Procurement is identified as an operational workflow category in McKinsey’s workflow analysis. Its overlap with finance and supply chain makes process ownership important: define which team owns each decision and handoff before automating the flow.
7. Supply chain and inventory
Planning, logistics, and inventory workflows may be candidates when teams repeatedly gather data, coordinate updates, or respond to predictable conditions. The relevant workflow differs substantially by industry and operating model, so an approach built for one company’s supply chain should not be assumed to transfer directly to another.
Begin with a specific operational outcome—such as more timely planning information or fewer manual handoffs—and identify which actions can be automated safely. McKinsey’s workflow report includes supply-chain and sector-specific operations, but it does not establish one tool or workflow as suitable across industries.
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8. IT service management
IT teams can consider automating routine ticket handling, basic service tasks, and parts of coding support. For service requests, automation might categorize a ticket, gather required details, or route it to the appropriate queue. Coding support can assist technical work, but generated changes still need suitable review and testing.
McKinsey’s 2024 corporate-functions survey identifies coding support as a use case, while its workflow analysis includes IT operations and service management. Limit system access to what a task requires, define escalation for exceptions, and keep accountable staff in control of sensitive changes.
9. Compliance and risk
Compliance workflows can use automation to review documents, flag potential fraud signals, and check records against defined rules. The useful role is often to surface items for review and make routine checks more consistent, rather than to treat an automated flag as a final finding.
Use accountable human review for consequential or ambiguous decisions. McKinsey’s 2024 survey includes fraud-prevention checks among finance use cases, and its corporate-functions research describes document review in HR and legal contexts. These examples support considering such workflows; they do not establish that automated systems can replace professional oversight.
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Recurring reporting can involve preparing data, refreshing dashboards, and assembling management updates. Automating repeatable collection and preparation can give teams a more consistent starting point for analysis, provided the underlying data and definitions are reliable.
Map the reporting workflow from source data through validation and dashboard or executive report. McKinsey’s 2021 business-process case source describes business intelligence feeding executive reporting dashboards; that example illustrates a workflow, not a guaranteed outcome for other organizations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose what to automate first
Do not select projects by department name or by the novelty of a tool. Define an end-to-end workflow and the outcome it serves, then compare candidates on operational fit and the work required to deploy them.
- Volume and repeatability: How often does the workflow run, and how much of it follows a predictable pattern?
- Exceptions: How often does a case depart from the standard path, and can the system recognize when to stop and escalate?
- Cost of delay or error: What do slow handling and mistakes cost in time, money, service, or risk?
- Customer and employee impact: Would faster or more consistent handling materially improve someone’s experience?
- Data readiness: Is the required information accessible, sufficiently accurate, and usable for the task?
- Integration effort: Which systems, handoffs, and approvals must connect for the workflow to work end to end?
- Security and compliance exposure: What access is needed, what information is involved, and what controls or review are required?
- Measurable outcome: Which service or financial measure will show whether the process improved?
These are comparison dimensions, not a universal scoring formula. McKinsey’s workflow research notes that workflows vary with business model, company size, and technological maturity, so a candidate that fits one organization may not be the best first project for another.
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- Specify the workflow and its owner. Document the starting event, steps, systems, handoffs, exceptions, and intended outcome. Make one team accountable for the end-to-end process, even if several departments contribute.
- Bound the first use case. Start with a repeatable segment where the expected benefit is meaningful and exceptions can be identified. Avoid automating a process whose rules or ownership are still unclear.
- Check data, access, and integration. Confirm that the system can reach the information it needs, that data quality is adequate, and that permissions are limited to the task. Map dependencies on existing systems before rollout.
- Set review and escalation rules. Decide which actions can proceed automatically, what requires approval, and what should stop for human handling. Preserve clear accountability for consequential decisions.
- Measure the baseline and the result. Choose a small set of outcome measures tied to the workflow, such as time, error rates, service outcomes, or cost. Compare results with the prior process rather than relying on activity counts alone.
- Involve the people who do the work. Ask employees to validate how the process behaves in ordinary and exceptional cases, explain role changes, and incorporate feedback. In AI-supported workflows, people may act as validators and orchestrators rather than simply handing work to a system.
- Expand only when the process works reliably. Use observed results and exception patterns to decide whether to adjust, extend, or stop the automation. A case study from another organization is context, not a forecast for yours.
What published results can—and cannot—tell you
McKinsey’s 2024 Corporate Functions CXO Survey included 276 senior leaders across finance, HR, IT, customer care, and legal in 18 industries in North America and Europe. In that survey, 22% of surveyed corporate-function CXOs reported an active generative-AI use case in 2024, compared with 4% in 2023. More than 75% of surveyed organizations that had deployed generative-AI tools at scale said those systems met or exceeded expectations. That last figure is respondents’ assessment, not an independently measured return.
McKinsey’s 2021 case studies describe a 60% reduction in operational costs at a telecom company after a particular outsourcing and automation arrangement, and an industrial case with productivity rising about 40% and customer satisfaction increasing more than 35% after a particular process redesign. Those outcomes belong to those organizations and arrangements; they are not typical-return estimates or promises for a new project.
The 2024 survey and 2021 cases show that organizations have pursued automation and reported results, but they do not establish a definitive list of ten operations that every company must automate. Treat the ten areas above as a starting set of workflow candidates and judge each one against your own process, systems, risks, and goals.
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