Business Process Management (BPM) has traditionally focused on defining, executing, and monitoring structured workflows. You model a process, implement it in a BPM engine, and track its performance through dashboards and KPIs. This approach works well for predictable, rule-based processes.
But what about processes that involve judgment calls, unstructured data, or patterns too complex for human analysts to spot? This is where artificial intelligence enters the picture.
From Rules to Intelligence
Traditional BPM relies on explicit rules: if a claim exceeds a certain amount, route it to a senior reviewer; if a customer's credit score falls below a threshold, trigger additional verification. These rules are defined by humans based on their understanding of the process.
AI-enhanced BPM takes a different approach. Instead of encoding rules explicitly, machine learning models learn patterns from historical process data. They can identify which cases are likely to require escalation, predict processing times, and detect anomalies that human-defined rules would miss.
Key Applications
Process Mining
Process mining uses event log data from your systems to discover how processes actually execute — as opposed to how they were designed. Machine learning algorithms can automatically identify process variants, bottlenecks, and deviations from the ideal path.
Intelligent Task Routing
Rather than routing tasks based on simple rules (round-robin, skill-based), AI models can consider workload, expertise, historical performance, and task complexity to optimise assignments. The result is faster resolution times and more balanced workloads.
Predictive SLA Management
ML models trained on historical case data can predict which ongoing cases are at risk of breaching their SLA targets. This allows process managers to intervene proactively rather than reactively.
Document Classification and Extraction
Many business processes involve unstructured documents: invoices, contracts, correspondence. Natural language processing (NLP) models can classify documents, extract relevant data fields, and feed structured data into the BPM workflow automatically.
The Integration Dimension
For AI-enhanced BPM to work, you need robust data pipelines feeding process data to ML models and returning predictions to the BPM engine. This is where enterprise integration expertise becomes essential.
At KONDEVS, we combine our deep BPM experience with our AI consulting practice to build solutions that bridge both worlds. Our integration engineers ensure that data flows reliably between BPM engines, ML model serving infrastructure, and the enterprise systems that generate and consume process data.
Practical Considerations
AI-enhanced BPM is not about replacing human judgment — it's about augmenting it. Start with processes where you have good historical data, clear performance metrics, and measurable business impact. Pilot with a single use case, measure results rigorously, and scale what works.