AI in Business Process Management: Use Cases, Benefits, and Implementation

AI is already part of daily business operations. Companies use AI tools to answer customer questions, sort support tickets, forecast inventory needs, and analyze performance data.

Business process management (BPM) connects AI tools to the workflows where they can be useful. That connection helps teams use AI in repeatable ways instead of in isolated experiments.

Adoption is growing, but maturity is still low. According to Stanford HAI's "2025 AI Index", more than three-quarters of respondents say their organizations use AI in at least one business function. McKinsey reports 92% of companies plan to increase their AI investments between 2026 and 2028. Yet in the same McKinsey report, just 1% of companies consider themselves “mature” in deployment.

Many organizations still face challenges translating AI adoption into measurable business value. This article covers how AI in business process management works, where it can help, and how to implement AI tools with clear workflows, clean data, governance, and human supervision.

What is AI in business process management?

AI in BPM is the practice of designing, executing, and improving business processes with artificial intelligence. AI-assisted workflows and decision-making models can help with several business functions:

• Identifying patterns in customer sentiment and audience segments
• Analyzing data, such as inventory or customer support data, to suggest improvements
• Using historical data to support supply chain management and other business processes

BPM provides the structure for applying AI across workflows.AI in BPM still requires human oversight. Effective machine-learning (ML) implementations connect AI to clear, structured business processes.

Why AI matters in BPM now

Using AI in business processes isn’t a new development. According to Microsoft’s 2025 "Work Trend Index", 82% of business leaders said 2025 was a key year for implementing digital labor to expand the capacity of their workforces over the next 12 to 18 months. McKinsey also reports that many organizations are redesigning workflows and improving governance to capture more business value.

Thinking about changing business processes with AI is different from achieving specific efficiency gains. Before machine learning can improve business processes, companies need to establish several foundations:

• Structuring the rules of AI and automation for predictable results 
• Establishing governance to ensure workflows function with human review
• Measuring the results of AI in business processes to show a return on investment

Once these are in place, AI upgrades can go well beyond IT processes. Workflows might touch customer support, inventory, and other core operations. 

This can create opportunities for machine-learning algorithms to take on routine tasks. But getting the most from AI requires understanding which processes it can support, and how to manage it as it scales into core business processes.

How AI is used in BPM and business automation today

Today’s digital tools may contain AI systems that can support specific workflow tasks, but implementing them requires planning and workflow design. Teams need to assign AI to specific tasks within clearly defined workflows, especially when those tasks don’t require human intervention. 

1. Intake and classification

One common use case for AI is data processing. AI tools can read large volumes of information and log it in a usable format. Examples of manual tasks include:

• Reading emails
• Managing customer support tickets
• Handling product returns
• Reviewing feedback forms

AI can help convert unstructured data into structured, ready-to-use data. Leadership can use human review to verify and interpret the data. But now teams can work with well-formatted data, often presented in near-real time. 

2. Routing and prioritization

AI can assist with decision-making: deciding where the work goes and which higher-priority issues need to be handled first. For example, a business can define clear rules for handling customer support tickets, such as sorting by urgency and customers’ buying history. 

AI can also sort email inboxes for customer support. It can handle queue management in customer relationship management (CRM) tools, assigning specific tasks to specific owners based on prioritization and workloads. This can reduce the need for manual triage. 

The work can then move through internal systems consistently, highlighting task assignments for team members without additional input from their end. Team members can then work on assigned tasks, resolving customer issues.

3. Decision support

Decision-making requires interpreting incomplete or changing information. An AI tool trained or configured with relevant data can be a decision-support tool because of its ability to process and analyze large amounts of data. It can then recommend actions for human review, and flag any specific risks or opportunities business leaders might not have been aware of.

Data analysis is another common use case for AI. Even when speed isn’t the main benefit, recommended next-best actions can help a business make informed decisions. AI may improve forecasting, for example, or highlight risks that may not have been previously identified. 

Take the example of fashion brand Jaded London. Jamie Evans, their head of ecommerce, was fielding at least 10 analytics requests each week, each of which took hours. A new process meant AI could step in; business leaders could ask the AI to analyze the data with “conversational queries.” That saved 10–15 hours each week and helped a small team manage seven international markets.

4. Monitoring and optimization

AI systems can monitor workflows continuously. This is useful for monitoring and workflow optimization. AI can watch workflows, identify specific logistical bottlenecks, and even look through data for inefficiencies that may not have been identified manually.

This may not produce immediate results, but it can improve the process over time. AI monitoring workflows can surface rework loops or improve other areas of the business, such as forecasting potential inventory delays or spikes in customer demand. 

Implementing AI into specific business processes doesn’t always require a specific output. It can mean installing AI and asking it to observe. Later, the AI tool can flag inefficiencies that, when fixed, improve how the business runs.

5. Customer-facing workflow improvements

Jaded London didn’t just use AI for decision support. They also used AI to improve a basic problem in their search functions. Customers were using style descriptors like “baggy streetwear joggers” without a specific product name in mind. The brand wanted to capture more of that interest and turn the search bar into a customer acquisition or conversion channel.

They were an early UK adopter of AI-powered customer searches, which gave the team time to test and refine the process. By the time competitors were adopting AI-powered searches, Jaded London had already resolved implementation issues.

And uses aren’t limited to product searches. One approach is to identify patterns in customer behavior and start with AI-powered process automation to offer solutions. Maybe that means handling multilingual searches. Or product recommendations that provide more contextually relevant recommendations.

6. Process building and modification

AI can support continuous improvement in business operations. AI isn’t just there for handing off time-consuming tasks; it can also assist nontechnical teams as they generate new workflow logic. AI can then handle more complex tasks like running detailed reports or handling complex queries.

AI makes it easier for nontechnical teams to adjust workflows over time. They don’t have to rely on technical teams for every iteration of a workflow. Instead, AI can refine how the work gets done. Process owners can still be assigned for specific workflows, but AI makes it easier for nontechnical people to iterate on new ideas or workflow tweaks without creating constant friction for internal teams.

How to implement AI in business process management

Improving business processes is a broad goal. AI implementation needs more specific steps. Use this step-by-step structure to identify where AI can improve business processes:

1. Pick a process with pain points.
2. Map the current workflow.
3. Identify decision points where AI can help.
4. Audit data quality and access.
5. Define success metrics before launch.
6. Start with a narrow pilot.
7. Keep humans in the loop for exceptions.
8. Add governance, documentation, and QA.
9. Monitor outcomes and refine governance.

1. Pick a process with pain points

The first step is to look for the business processes creating the most operational friction. Process optimization may be a long-term goal. But some bottlenecks need near-term improvement, especially when fixing them can measurably improve the business.

Take Chris Cote’s Golf Shop, for example. Having customers book time with instructors was handled ad hoc, with no predictable way to notify the instructors. Shopify Flow’s features automated the notification process. Now, instructors receive automatic emails, saving the team at least 10 hours each week.

2. Map the current workflow

The next step is to assess the current workflow. Draw an accurate map of the triggers, alerts, process owners, and gaps. Once the workflow is clear, process optimization with AI becomes easier to evaluate. 

A common risk is assuming AI will handle everything. Generative AI may be useful, but it still requires clear process design and human oversight. Specific process-automation ideas start with a map of how work already happens. From there, it’s easier to identify where AI can support the workflow.

3. Identify decision points where AI can help

Now that the map’s in place, identify the top friction points. Some common challenges with business processes include:

• Unclear ownership or unclear roles for specific team members in the workflow
• Key points where decisions are either inconsistent or slow

These issues can indicate areas where AI may help, like recommending, prioritizing, flagging risks, or setting alerts. Some teams may even ask an AI tool to identify bottlenecks in the workflow if they’re not obvious.

4. Audit data quality and access

If the workflow includes data extraction, confirm the data is reliable before AI enters the process. If the data isn’t reliable, audit the data each business process depends on. AI automation won’t be consistent if it’s working with inconsistent data.

Access management is another priority. Teams need to know where to find the data and how to access it so systems can share it across the full span of a business process. AI outputs are less reliable when the underlying data is incomplete, duplicated, or siloed.

5. Define success metrics before launch

At the strategic planning stage, it’s tempting to get excited about the prospects of AI automation and skip straight to launch. But first, the mission needs a goal, with a specific number to aim for. Some common success metrics include:

• Time saved
• Error-rate reductions
• Output volume
• Conversion impact
• Revenue impact

Without these variables, AI’s benefits can seem unclear. But if AI has a measurable impact on operational costs or time saved on repetitive tasks, this can create a case study for future BPM initiatives.

6. Start with a narrow pilot

It’s tempting to apply AI to every aspect of operations management. A narrower rollout is often easier to test and govern. Don’t roll out AI everywhere; instead, start with one simple workflow or use case. This makes testing easier, which helps every team learn the benefits and limits of AI. From there, it’s possible to expand and scale without breaking down processes that were already working.

Jaded London, for example, knew that the 10–15 analytics requests each week were eating up a lot of time. Pulling up reports was important work, but doing it all manually wasn’t helping. That simple use case led to more independence for the merchandising team, allowing their “lean” model to continue functioning—and even growing.

7. Keep humans in the loop for exceptions

AI might be helpful, but it’s not replacing the core building blocks of human judgment. Humans still need to be in the loop to supervise, verify AI-driven decisions, and review analytics dashboards for performance issues. 

Consult with the team and identify a key process owner who can check on process performance as AI steps in. In some cases, a human will always be needed to review exceptions. For example:

• Flagged fraudulent returns that require humans to review customer order history and intent before approving a refund
• AI-recommended inventory reorders that need a manager to confirm based on the current budget
• A high-priority customer support case where sentiment analysis flags urgency, while still requiring a human to judge the appropriate response

8. Add governance, documentation, and QA

AI failures don’t always happen because of problems with the AI itself. Sometimes the process needs proper support from management. According to Deloitte, 41% of respondents cited management support as a major adoption barrier to AI.

Management can offer that support by making it clear how AI will work in the organization moving forward. That might mean:

• Clearly defined rules for what AI is allowed to do and not do
• Assigning ownership for AI-assisted workflows and supervision
• Building documentation hubs for reporting AI issues or learnings
• Enforcing quality assurance (QA) reviews to make sure the AI is behaving as expected

9. Monitor outcomes and refine governance

Step 5 established the metrics to review. Now it’s time to review a completed business process—now assisted with AI—and look for:

• Potential improvements
• Errors that require review
• Unexpected results

Not every deviation from the plan means AI can’t perform tasks at a basic level. However, the governance and documentation added in Step 8 should be informed by what the team flags now. 

The future of AI in BPM

AI use in BPM is expected to increase. Automating routine tasks is a common application. But using AI in BPM also includes other applications, including:

• More conversational analytics and process querying in tools like AI-assisted search
• Greater integration between data intelligence, automation, and AI
• More pressure for governance as AI handles more workflows and decision-making
• Growth in human-plus-AI-agent operating models

According to Deloitte, 25% of respondents are already using AI in process mining, while 74% plan to include AI in future initiatives. AI adoption in process mining and BPM remains early, but these trends suggest several likely next steps. 

AI is becoming part of business process management. Organizations will need to determine where AI can create measurable value and how to govern its use.