How to use process intelligence and AI to rewire business for sustainable transformation

Processes are our greatest lever for value and our fastest lever for change. By optimizing processes, organizations can eliminate waste, create more value, delight their customers and better adapt to the accelerating pace of change and the current moment of polycrisis.

Artificial intelligence (AI) has a powerful role to play in optimizing our processes, in making them work for people, companies and the planet. But to do that successfully and safely, AI needs process intelligence.

In the past year, AI has gone from a visionary idea to an everyday tool. Generative AI (GenAI) and large language models (LLM) offer unprecedented opportunities to transform business operations and workforce dynamics. Businesses and individuals across industries, around the globe and at all levels are using GenAI tools to create content, summarize information, improve customer experiences and interact with their technologies in more intuitive ways.

The 2023 McKinsey Global Survey on AI found that one-third of respondents said their organizations are using GenAI regularly and 40% said their organizations will increase their AI investment because of advances in GenAI. McKinsey & Company also estimates that GenAI could add between $2.6 trillion and $4.4 trillion in annual value to the global economy.

A survey by the IBM Institute for Business Value found that 69% of CEOs believe GenAI has broad value for their organizations, 50% are integrating it into products and services and 43% are using it to inform their strategic decisions. Research from Celonis found that among more than 1,200 global business leaders, 89% said their organizations are already using or actively implementing AI.

The GenAI uptake is real and shows no signs of slowing, but hurdles remain. Over half (57%) of CEOs who responded to the IBM survey said they were concerned about data security and 48% were worried about data accuracy or bias. Nearly three quarters (72%) of respondents to the Celonis survey said they had concerns that process shortcomings may hold back further AI implementation.

For AI to reach its full potential, it needs to speak the language of business; it needs to understand how work flows; and, it needs process intelligence.

Processes are the fabric of business – every invoice paid, every purchase order approved and every shipment delivered. Process mining technology takes data from the systems where work happens (ERP, CRM, SCM, etc.) and creates a living, breathing digital twin of those processes, end-to-end. It’s fact-based and system-agnostic. Combine and enhance that digital twin with decades of process improvement knowledge and you create process intelligence.

Process intelligence is the connective tissue for the enterprise, a common language for how business runs. It reveals where value is hiding and enables teams to capture it. Process intelligence enables the generation of automated, actionable insights, faster identification and removal of duplicate processes and better customer experiences, backed by reduced emissions and more sustainable supply chains.

Business leaders are taking notice. According to a 2023 study from HFS Research, commissioned by Celonis and IBM Consulting, 88% of enterprise leaders expect to increase process intelligence investments over the next 12-18 months.

In addition to connecting people to their processes and teams to each other, process intelligence connects emerging technologies, such as GenAI, to the business. As Chris Monkman, VP of Product Intelligence at Celonis, wrote, process intelligence makes LLMs smarter and can deliver:

• Tighter compliance

If an SLA is broken, like a late invoice payment, process-intelligent AI (using a piece of LLM-fed information about company rules) can resolve the issue and suggest corrective action.

• Fewer process breakdowns

AI solutions built on an LLM infused with process intelligence can understand the common causes of process breakdowns, predict when they may occur and even provide advice on how to prevent them.

• Work with counterfactuals

Businesses can combine process intelligence and object-centric process mining to create models for counterfactual situations, such as, 'How would my SLA improve if I increase the capacity of my shared services centre?'

Beyond the above examples, leading companies are already using process-intelligent AI to drive change and deliver value.

At a recent industry event, a large multinational retailer shared how it uses process intelligence and GenAI to better identify price mismatches in its procure-to-pay process. Instead of doing a three-way match, between its master data, the purchase order (PO) and its invoices, the new solution allows it to do a four-way match, pulling unstructured data from thousands of contracts. Doing this by hand would be prohibitively time-consuming and isn’t feasible at scale. Additionally, GenAI allows the retailer's employees to more easily work with the data and spot invoices that have out-of-sync payment terms or pricing.

Business leaders everywhere are working to transform their organizations in smarter, more sustainable ways. To succeed, it’s crucial for them to invest in the right technologies, such as process intelligence and AI, but also to develop the right strategy, talent and culture.

They must set clear objectives for AI implementation, making sure they align with business goals. Companies that are just beginning their journey, should look for specific opportunities, like the ones above. They should hire AI specialists or upskill current employees, work with trusted AI vendors and process experts and foster a data-driven culture throughout the organization. They should also prioritize ethical AI practices and, as with all transformation efforts, regularly assess AI performance against set metrics to ensure it’s meeting business needs and adjust when necessary.

The pace of change in the world is accelerating and organizations are using technologies like AI to adapt. Because processes are the lifeblood of an organization, that adaptation requires taking a 'process-first' approach enabled by process intelligence.