In an era where agility and efficiency are critical to competitive advantage, organizations can no longer afford to operate blind to their internal processes. Traditional process discovery methods — reliant on interviews, workshops, and documentation reviews — are too slow, too subjective, and too siloed to keep pace with modern business. 

Process mining, particularly through platforms like Celonis, has emerged as a breakthrough approach, illuminating how work actually gets done by analyzing the digital footprints left in enterprise systems. 

But today, a new frontier is emerging. By integrating AI into the process mining lifecycle, leading organizations are moving beyond pure data analysis toward a richer, more actionable understanding of how and why their operations flow — or falter. This fusion of machine precision with executive level AI insights opens powerful new paths for transformation. 

The State of Process Mining Today 

Process mining technology evaluates log data from systems like ERP, CRM, and supply chain platforms to map end-to-end workflows. It reveals bottlenecks, inefficiencies, and deviations from standard operating procedures — without relying on subjective accounts or manual documentation. 

At its best, process mining helps organizations:

• Visualize real process flows, not assumed ones
• Quantify the impact of process inefficiencies
• Simulate improvement scenarios before execution
• Monitor process adherence in real time 

Yet even with these strengths, traditional process mining has limitations. It draws only from structured system data and often overlooks critical operational realities such as:  

• Off-system decision-making. Employees routinely make judgment calls or use unofficial channels (e.g., spreadsheets) that never touch core systems. 
• Customer intent and sentiment. Digital traces show what customers did, but not why—leaving gaps in understanding the motivations behind churn, escalation, or abandonment. 
• Shadow workflows and frontline improvisation. In high-pressure environments like call centers or fulfillment ops, team members adapt in real time, often deviating from formal processes in ways invisible to log data. 
• Cultural and behavioral friction. Resistance to SOPs, passive delays, or role ambiguity aren’t captured in event logs but directly impact process outcomes. 

To overcome these blind spots, leading organizations are beginning to complement system-level data with AI-driven qualitative insights — surfacing not just what happened, but why it happened, and what can be done differently. 

The Innovation: Merging Data & AI for Process Intelligence 

Recent advances in generative AI and natural language understanding are enabling a new paradigm: augmented process mining. This approach enhances traditional event-log-based mining with AI-powered capabilities such as: 

• Conversational process discovery. Using large language models to conduct structured, digital interviews with frontline employees at scale, identifying context, motivations, and root causes behind behaviors.
• Semantic clustering of user behavior. Grouping variations in process execution not just by frequency but by intent or outcome, to uncover “process personas” that influence performance.
• Dynamic narrative generation. Creating executive-level summaries of complex process maps and KPIs that highlight issues and recommended actions in plain language. 

These techniques transform process mining from a diagnostic tool into a decision-making engine. AI acts as a translator between the raw reality captured in data and the nuanced understanding needed by leadership teams. 

Implementation Considerations 

To realize the full value of AI-augmented process mining, leaders must look beyond just tools and focus on embedding new capabilities across the enterprise. That starts with aligning three key pillars: 

Technology. Your process mining platform should support integration with LLMs or expose APIs that allow qualitative data augmentation. But integration alone isn’t enough — leaders must also ensure that unstructured inputs like chat logs, transcripts, or shift notes are captured and available for analysis. Safeguarding privacy and data provenance across this expanded footprint is essential to maintaining trust and compliance. 

People. The value of AI insights is only unlocked when people can understand and act on them. Equipping teams with smarter tools isn’t enough. Process owners and decision-makers must also learn to interpret AI-generated insights — which often surface patterns, probabilities, and recommendations rather than definitive conclusions. This is not about replacing humans with AI — it’s about amplifying their context, confidence, and speed of response. 

Governance. With new types of data come new responsibilities. AI models surfacing human behavior must operate with clear ethical guardrails: how is qualitative data sourced, what biases might be present, and who is accountable for acting on insights? Build a governance model that balances agility with accountability — and be explicit about how decisions are made in an augmented environment. 

Bottom line: AI-augmented process mining is not a plug-and-play solution. It’s a capability — one that requires deliberate enablement across your systems, your people, and your principles. 

Industry Spotlights

Apparel & Retail. Challenge: A global apparel brand operating both direct-to-consumer and wholesale channels was struggling to meet demand for its most popular SKUs, despite accurate forecasting and high inventory levels. Process mining revealed that the issue wasn’t stock—it was sequencing. Orders were being prioritized based on static rules that favored high-volume accounts rather than margin, market potential, or customer lifetime value. AI Innovation: Generative AI layered onto the Celonis process map was used to simulate fulfillment scenarios based on projected margin impact rather than rigid ERP logic. This surfaced “silent bottlenecks” in allocation rules that had gone unquestioned for years. Key Insight: Leaders in retail often invest in demand forecasting, but miss the process-level misalignments that prevent that intelligence from converting into revenue. AI-augmented process mining connects operational flows directly to business outcomes—unlocking profit, not just efficiency. 

Food & Perishable Supply Chains. Challenge: A high-growth produce distributor was scaling into new regions but suffering an increase in product spoilage. Traditional process mining pinpointed handoff delays across transport and cold storage providers—but leadership needed to understand why execution was breaking down despite well-documented SOPs. AI Innovation: Natural language processing was used to ingest field notes, driver logs, and SMS-based communications across the supply chain. AI then clustered these into recurring decision patterns — such as route deviations made at the driver’s discretion, improvised load consolidations, and informal swaps between regional depots. This surfaced an unseen “shadow network” of decisions happening outside formal systems. By incorporating these behavioral patterns into planning logic, the company eliminated extra spoilage in new regions and increased on-time delivery without adding headcount or infrastructure. Key Insight: In high-velocity supply chains, process mining needs to account for decentralized human judgment. AI bridges the gap between digital execution and field-level improvisation—making informal decisions visible and actionable at scale.

Healthcare Call Centers. Challenge: A regional healthcare provider saw rising patient churn in its urgent care network, coinciding with increased wait times and a spike in call escalations. Process mining traced these issues to a scheduling process that was technically compliant with SOPs but misaligned with patient expectations. AI Innovation: Instead of rewriting the script or launching more training, the provider used LLMs to simulate “persona-based call journeys.” AI-generated personas representing different demographics (elderly, low-income, working parents) revealed friction points that were invisible in the aggregate data—such as language gaps, appointment availability mismatches, and misunderstood triage logic. Key Insight: Operational data shows you how a process runs—but not how it feels. For patient-facing processes, leaders must design with empathy at scale. AI-generated personas layered onto process flows provide a new lens for equitable and efficient experience design. 

Closing Thought 

In a changing business environment where operations, customer experience, and strategic insight are converging, visibility is no longer a reporting function — it’s a leadership imperative. The organizations that lead won’t just deploy better tools; they’ll cultivate sharper awareness of how work actually gets done, and why it breaks down. 

AI-augmented process mining marks a shift from passive observation to intelligent orchestration. It reveals not just the structure of a process, but the behaviors, decisions, and exceptions that drive its outcomes. When paired with the right governance and talent, it becomes a compass — helping leaders navigate complexity, prioritize action, and adapt in real time. 

This is more than a data strategy. It’s a new operating model for change. 

Active Digital partners with organizations ready to lead in this new era — combining deep process expertise with cutting-edge AI to unlock clarity, alignment, and continuous performance.