Current C-suite and board views of AI can be summed up in a single phrase with the famous line from the American movie classic Jerry Maguire: "Show me the money!"
For many enterprises, AI's honeymoon period has ended. Poll after poll makes clear that today's top bosses want AI to turbocharge business KPIs and digital transformation to provide clear value -- and fast.
The opportunities to quickly create cost-saving and revenue-enhancing AI sought by organizational leaders are huge, says Divya Krishnan, VP of product marketing at Celonis. "Right now, there's a big disconnect between AI's potential in organizations and its actual performance," she explains. "Large language models (LLMs) are impressive, but many enterprises are struggling to translate their use into meaningful business outcomes."
Similarly, while AI agents can automate tasks and workloads, she explains, they lack understanding of important business context and nuance, and often fall short.
"Without process intelligence, there is no class of data that captures how work gets done that is being given to enterprise AI models," she notes. "And that means there's always going to be a ceiling on what they can realistically automate for you until they have that input at hand."
Fast, impactful AI that drives the right actions and outcomes must be trained with specific performance data from a company's own process intelligence, not generic industry modes, she says.
The key: Powering AI with PI
At Celosphere, its annual user conference in Munich, Celonis announced multiple product innovations and extended partnerships that make it easier for customers to power AI with process intelligence.
The company unveiled AgentC, a suite of tools, integrations and partnerships that enable enterprises to develop AI agents and CoPilots powered by Celonis Process Intelligence or use AI agents pre-built by partners like Rollio and Hypatos. Organizations can choose to build agents with leading platforms such as Microsoft Copilot Studio, IBM watsonx Orchestrate, Amazon Bedrock Agents and open-source developer environments like CrewAI. Enterprises creating their own agents can benefit from support of expert consulting partners Accenture, EY and IBM.
"Those integrations are crucial," said Krishnan, "because that's what's going to enable people to build these agents with the right data at hand, data that can make sure the agent you build is tailored to your unique business, data that you won't get anywhere else."
Celonis Process Intelligence powers AI agents with process data and business context -- key to improving processes across systems, departments and organizations. Users of LLM AI fed by process intelligence can now ask conversational questions like those enjoyed by consumers:
"Why is my on-time delivery rate low and how much is it costing us?"
"Give me three recommendations for improving working capital."
"Which regions are likely to have late deliveries and what can we do about it?"
Early adopters report real value
According to Gartner, the global market for process mining software grew 40% in 2023. Worldwide sales for process automation are expected to reach $26 billion by 2027. Nearly 90% of corporate leaders surveyed by HFS Research plan to increase investments in process intelligence. A big part of the appeal, Gartner concludes: "Generative AI helps organizations use process mining to uncover hidden patterns, optimize operations and make informed decisions."
Maureen Fleming, VP for Intelligent Process Automation at IDC, concurred. "Understanding the intricacies of processes and their interdependencies is crucial to achieving effective AI-driven digital transformation."
Companies deploying AI fed with process intelligence are reporting clear benefits in understanding how their businesses run and how to make them run better.
A sampling from across industries:
Cosentino, a leading manufacturer of design and architectural surfaces, implemented a Celonis-powered AI assistant for credit block management. The assistant helps the team analyze blocked orders within seconds, enabling credit managers to process up to 5x more orders per day without additional risk.
A European packaging company has implemented an agent that allows plant technicians to view spare part inventory levels in nearby plants, enabling them to utilize stock transfers instead of placing orders with suppliers. A multinational construction material provider employs a similar agent to link inquiries and requests to their corresponding invoices and purchase orders, automating the resolution process with features like auto-responses, ERP updates and internal forwarding.
A global consumer goods company uses an agent to extract payment terms from PDF contracts, compare them against terms in their master data, purchase orders and invoices, and recommend actions to accounts payable clerks to resolve any inconsistencies.
A global car manufacturer has adopted an agent that automatically generates email replies to supplier inquiries, such as questions regarding the status of invoices. Lastly, a major technology leader plans to implement an agent that enhances the customer funding request process by predicting the likelihood of request rejections and notifying the applicants accordingly.
Building AI agents in-house or on partner platforms
Developing agents, fed with process intelligence, in-house allows enterprises to tailor the agents to their specific processes, workflows and industry nuances. Taking this path can provide tight intellectual property protection by keeping proprietary algorithms and insights within the company. Companies can quickly adjust and improve agents based on immediate feedback and changing needs. And because internal teams have intimate knowledge of the company's operations, they can potentially develop more effective AI agents to competitive advantage.
At the same time, bringing in multiple parties to develop AI agents fed by process intelligence also brings numerous advantages: Diverse expertise, faster innovation enabled by an ecosystem of developers, greater industry customization, wider scalability and faster continuous improvement from a larger ecosystem.
Celonis provides a foundation for both in-house development and integration of external AI agents, says Krishnan. This allows companies to remain adaptable, choosing the best approach for each specific use case.
Platform innovations on the horizon
Celonis also announced multiple innovations that are being rolled out to enhance scalability, ease of use and overall value realization:
Celonis Data Core, Celocore for short, is a platform enhancement designed to help customers get data into Celonis more quickly and once it's there, dramatically reduce "extraction, transformation, load (ETL) and query times. This allows businesses to harness insights more rapidly and on a larger scale. The introduction of a GenAI-powered user experience will streamline how users engage with data, simplifying dashboard creation and enhancing the analytical experience.
Celonis Networks facilitates connections across company boundaries, enabling optimization across processes that span multiple organizations. This collaborative approach can drive unprecedented efficiency and effectiveness.
Use-case-specific applications are being launched across multiple sectors, including logistics, finance and manufacturing, to accelerate the realization of value from AI initiatives.
"We're not trying to take over the whole field, "says Krishnan." We're working to bring everybody into it."