Feature
Business process management (BPM) at its root is about finding ways to increase the efficiency of business processes. For years, AI and automation have been part of the BPM mix. But with the emergence of generative AI, BPM — like many other workplace technologies — stands to benefit well beyond the content-generating capabilities the software has become synonymous with.
Emerging BPM Possibilities
The possibilities created by integrating BPM and generative AI are only starting to emerge, said ISG partner Alex Manders.
As enterprises continue to accelerate cloud transformations, many BPM platforms offer cloud-oriented capabilities, he said. "If we use cloud data as one example, generative AI can analyze patterns and trends in cloud data to automatically generate new business processes or optimize existing ones." Here he cited two specific examples:
Predictive Process Modeling
Generative AI can use historical cloud data to predict future scenarios or trends, he said. BPM platforms can then simulate these scenarios to understand potential impacts on business processes, allowing for proactive adjustments.
Integration Plans
Generative AI can analyze cloud data to understand resource usage patterns and suggest optimal allocation strategies, ensuring maximum efficiency and minimal waste.
By integrating generative AI into a BPM software platform that utilizes cloud data, processes will be continuously refined based on real-time data, leading to better decision-making, improved resource allocation and enhanced overall business performance, said Manders.
However, he cautioned that while BPM can help organizations create better processes, any business planning to integrate generative AI into their BPM platform should keep a few things in mind:
1. Data Quality and Integrity
Generative AI is only as good as the data it is trained on. Ensure the data being fed into the AI system is high quality, accurate and has followed internal data governance processes.
2. Transparency and Explainability
Understanding how generative AI makes decisions or generates processes is essential, he said. Explore systems that offer transparency and explainability, which helps users to understand and trust the system outputs.
3. Scalability
As business usage grows, generative AI systems should be able to manage increased data and more complex processes, he added. Organizations need to ensure the integrated generative AI solutions and capabilities are scalable.
Related Article: Good Business Processes Are Key to Resilience During Disruption
Improving Internal Partnerships
Hexaware president and global head of consulting for generative AI Arun Ramchandran sees generative AI as a means to build stronger business partnerships between IT and business, primarily through accelerated design, low-code/no-code dashboards and faster proofs of concepts.
“Through the utilization of generative AI algorithms, companies can automate mundane and rule-based tasks, which is at the core of BPM,” he said. “For instance, intelligent process automation (IPA) powered by generative AI can analyze large volumes of data, identify patterns and generate automation scripts that smoothen workflows.”
Similarly, by accelerating the development cycle, especially aspects like code generation and UI generation, he said would boost the low-code and no-code aspects of BPM.
Related Article: Intelligent Process Automation Pushes the Boundaries of Business Process Automation
Ushering in Hyper-Automation and Dynamic, Personalized Processes
The integration of generative AI into BPM is a quantum leap forward that will optimize operations, reduce costs and improve organizational agility, said Ajay Manglani, CMO of BPM provider Kognitos. While BPM has helped organizations incrementally enhance process efficiency for decades, AI propels it to entirely new heights, he continued.
Citing figures from IDC, he said, global spending on artificial intelligence is forecast to reach $110 billion in 2024, up from $50.1 billion in 2020. As investment in AI explodes, these capabilities are rapidly being integrated into BPM software, services and solutions across industries.
He notes that BPM platforms such as Appian, Nintex, IBM Business Process Manager, and others already incorporate AI to analyze data, identify transformation opportunities and drive automation. But new generative AI-driven BPM software allows users to bypass the process discovery step altogether. While AI is revolutionizing every facet of BPM, he says two areas stand out in particular:
Hyper-Automation
The most transformative effect is hyper-automation powered by AI algorithms and solutions. Repetitive, mundane workflows and tasks that once required human effort can now be automated at an unprecedented scale, in theory freeing up people to focus on work requiring higher-value judgment, creativity and thinking.
Conversational Interfaces
A key way AI is transforming BPM is through conversational interfaces like chatbots, he said. Based on natural language processing, these tools guide employees through processes turn-by-turn based on situational context. The just-in-time guidance frees workers from manuals or static instructions.
This enables dynamic, personalized processes that can be tailored to each user and scenario. As employees interact with conversational interfaces during processes, the AI collects data to continuously refine its recommendations.
Industries like supply chain, CPG, transportation, high-tech, insurance and more have already seen major dividends from deploying AI-driven automation solutions powered by human language interpretation capabilities.
“The possibilities for AI in BPM are endless. AI algorithms can continually analyze data to optimize decisions and automate responses at each process step,” said Manglani. “BPM is evolving from a reactive tool for incremental gains into an engine for transformation powered by artificial intelligence. In the coming years, expect leaps forward in how AI enhances process automation, intelligence, analysis and discovery.”
Related Article: Why the Process Mining Market Is Heating Up
Organizational Efficiency
As a senior manager of product design at Smartsheet, Dilip Jagadeesh said the integration of AI into BPM software, while not an entirely novel concept, has taken remarkable strides propelled by innovations like AI-based translation for process descriptions and process mining performance indicators.
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Generative AI holds the power to revolutionize how we approach BPM, he continued. Among the advantages he identifies are:
Data-Driven Decision Support
Generative AI can supply BPM professionals with intelligent decision support systems. These systems, backed by machine learning algorithms, sift through vast data troves and provide actionable recommendations.
Predictive Analytics and Forecasting
Generative AI's predictive prowess heralds a seismic shift in analytics and forecasting within BPM. Jagadeesh said that using historical data, generative AI algorithms can anticipate outcomes, detect anomalies and forecast trends. This foresight, he added, can help businesses optimize inventory, slash costs and position themselves as proactive industry leaders.
Continuous Process Improvement
The marriage of generative AI with continuous process improvement is transformative. Generative AI mines data, extracts actionable insights, and serves as a catalyst for process evolution. From identifying bottlenecks to suggesting reengineering and automation opportunities, generative AI ensures BPM processes remain adaptable in the face of evolving dynamics. Real-time data monitoring empowers AI to detect deviations and trigger proactive corrective actions.
“The role of BPM in organizations evolves into a strategic orchestrator and enabler. BPM professionals shift from manual process draftsmen to curators of AI-generated excellence," he said.
“Generative AI becomes a formidable ally, crafting intricate processes while BPM experts provide guidance, context and oversight. This symbiotic partnership heralds a new era where BPM transcends the conventional to become an agile enabler of innovation and operational excellence.”