AI Automation Workflows Won’t Work if You Don’t Understand This

AI workflow automation starts with operational clarity, not hype.

Proper AI automation starts with understanding workflows. Leaf Home, America's largest direct-to-consumer home services provider, discovered this when they deployed task mining across 13 business areas and over 120 users. Combined with AI automation, the task mining process delivered $120,000 in cost savings. 

In a separate case, a McKinsey study of an industrial distributor found that 65 percent of "routine" orders required manual intervention. Downstream, manual interventions were required on up to one-third of invoices. Via task and process mining, hidden complexities were discovered that enabled $30 million in efficiency savings.

The pursuit of operational efficiency has evolved from outsourcing to robotic process automation in the 2000s, to today's agentic AI workflow automation. Each development promised to transform how businesses operate, yet the fundamental challenge remains the same: organizations cannot automate what they do not truly understand.

AI Implementation Meets Reality

Leaders envision chatbots handling customer inquiries, AI agents processing invoices, and intelligent systems streamlining everything from expense approvals to contract reviews. Yet organizations attempting AI workflow automation without understanding and optimizing those workflows are hamstringing their efforts. Gartner's findings echo this challenge—only 48% of Gen AI projects ever reach production, with most taking eight months or longer to deliver measurable value.

‍ProcessMaker, a business process automation platform, identifies a typical example: organizations deploy AI chatbots for customer service, expecting them to handle routine inquiries. However, without mapping how human agents actually resolve complex issues—including escalation triggers, context switching between systems, and unwritten decision trees—these AI systems miss critical context and create more problems than they solve.

Making Base Camp: Task and Process Mining

Task mining is the foundation stage of AI workflow automation. Unlike traditional process documentation that captures what should happen, task mining reveals what actually happens by recording and analyzing real user interactions with business systems. 

This is different from process mining, which looks at workflows on a larger scale, such as accounts payable and order-to-cash; task mining targets the specific steps someone takes to carry out a particular task, such as creating a purchase order.

Task mining provides the foundation for AI workflow automation. Unlike traditional process documentation that captures what should happen, task mining reveals what actually happens by recording and analyzing real user interactions with business systems. The McKinsey study demonstrates this approach: the company first applied task mining to analyze activities of 100 sales employees, then used process mining to analyze 1.5 million transactions across their major systems. Task mining revealed how employees spent their time, while process mining showed the broader workflow patterns across the entire quote-to-cash process.

One method involves capturing user interactions—keystrokes, mouse clicks, application switching, and screen recordings—to provide a granular view of tasks' performance. 

Consider a hypothetical finance department implementing AI workflow automation for invoice processing. Traditional process mapping might document a clean five-step workflow: receive invoice, validate vendor, check purchase order, approve payment, update systems. Task mining reveals the actual reality: finance staff spend 65% of their time validating data across four different systems, manually cross-referencing vendor information, handling exception cases that don't fit standard rules, and working around system limitations through informal workarounds.

Rather than automating the documented “clean” (and unrealistic) process, organizations can design AI systems that understand and handle the complexity, extracting data from varied invoice formats, cross-referencing multiple systems intelligently, and routing exceptions to human reviewers with proper context.

‍Beam AI, an agentic workflow automation company, uses a specific four-step methodology:

1. Monitor and Collect: Capture user interactions through desktop monitoring, tracking mouse clicks, application usage, and time spent on tasks
2. Discover and Group: Identify individual tasks and group similar activities using advanced algorithms
3. Find Inefficiencies: Analyze data to identify recurring tasks and recognize unique paths employees take to complete work
4. Auto Agent Setup: Create Standard Operating Procedures (SOPs) from observed workflows, then configure AI agents to follow these documented processes

Market timing adds urgency to this strategy. MarketsandMarkets projects the process mining market reaching $12.1 billion by 2028. A host of companies are establishing themselves as go-to solutions for task mining, including:

• ProcessMaker offers business process automation capabilities, combining workflow design with AI-powered task automation
• Beam AI  specializes in agentic AI workflow automation, turning discovered processes into executable AI agent workflows
• ABBYY Timeline provides task and process mining with AI-powered pattern detection
• Celonis delivers enterprise-scale process mining for large organizations with complex, distributed workflows

Aim for the Bottlenecks (and Other Complications)

The most common trap is targeting simple, visible tasks like email sorting or calendar scheduling while ignoring high-impact bottlenecks that create real friction for customers and employees. Under pressure to demonstrate quick Gen AI wins, teams might be tempted to choose easy automation targets that deliver minimal value while consuming resources.

Privacy and monitoring concerns present another significant complication. Employees are understandably concerned about task mining data collection, citing surveillance or job displacement worries. Some platforms address this through anonymized data collection that captures workflow patterns without identifying specific individuals or monitoring personal activities.

Organizational size is an added complexity. Large enterprises often have distributed teams performing similar tasks in completely different ways across departments, regions, or business units. What appears to be a single "customer onboarding" process might represent seven distinct workflows with different systems, approval chains, and exception handling procedures. The solution lies in starting with high-friction, customer-facing processes, beginning with one specific process, and using anonymized task mining to understand how work gets done. Start on a manageable scale before expanding to more complex, distributed processes.

AI Workflow Automation as Competitive Stakes

Organizations that master AI workflow automation build sustainable competitive advantages, improvements that arise from intelligently augmenting complex workflows.

But successful AI workflow automation demands systematic implementation. Organizations need practical guidance on selecting the right processes, choosing appropriate tools, and managing the change management challenges that inevitably arise when introducing AI into established workflows.

Success often comes down to execution details: how to structure task mining initiatives, what metrics matter for measuring AI workflow automation ROI, and getting workforce buy-in.

Our next article will provide a detailed implementation guide for AI workflow automation, including methodologies for moving from process understanding to successful automation deployment. The foundation is understanding (and optimizing) your workflows—the next step is choosing and implementing AI systems that deliver on their promise.