AI agents trained on ‘process intelligence’ will be key to unlocking enterprise value

The AI performance gap: Enterprises are seeking to leverage AI for tangible business outcomes, but many are struggling to translate advanced technologies like large language models (LLMs) into meaningful results.

• C-suite executives and board members are demanding that AI investments demonstrate clear value and rapid improvements in key performance indicators (KPIs).
• There is a significant disconnect between AI’s potential in organizations and its actual performance, according to Divya Krishnan, VP of product marketing at Celonis.
• AI agents, while capable of automating tasks, often lack the necessary business context and nuance to deliver optimal results.

Process intelligence: The missing link: The integration of process intelligence data into AI models is crucial for overcoming current limitations and achieving meaningful automation and insights.

• Without process intelligence, AI models lack the critical data that captures how work is actually performed within an organization.
• Training AI with company-specific performance data from process intelligence, rather than generic industry models, is key to driving the right actions and outcomes.
• Celonis has introduced AgentC, a suite of tools and integrations that enable enterprises to develop AI agents and CoPilots powered by Celonis Process Intelligence.

Partnerships and integrations: Celonis is fostering an ecosystem of partnerships to enhance the capabilities and accessibility of process intelligence-powered AI.

• The company has announced integrations with leading platforms such as Microsoft Copilot Studio, IBM watsonx Orchestrate, and Amazon Bedrock Agents.
• Enterprises can also leverage open-source developer environments like CrewAI to build custom agents.
• Consulting partners including Accenture, EY, and IBM are providing expert support for organizations creating their own AI agents.

Real-world applications: Early adopters of AI powered by process intelligence are reporting significant benefits across various industries.

• Cosentino, a design and architectural surfaces manufacturer, implemented an AI assistant for credit block management, enabling credit managers to process up to 5 times more orders per day.
• A European packaging company deployed an agent allowing plant technicians to view spare part inventory levels across nearby plants, optimizing stock transfers.
• A global consumer goods company uses an agent to extract and compare payment terms from contracts, purchase orders, and invoices, streamlining accounts payable processes.

Market growth and expert insights: The process mining software market is experiencing rapid growth, with increasing investments in process intelligence.

• Gartner reports that the global market for process mining software grew 40% in 2023.
• Worldwide sales for process automation are projected to reach $26 billion by 2027.
• Nearly 90% of corporate leaders surveyed by HFS Research plan to increase investments in process intelligence.
• Experts from Gartner and IDC emphasize the importance of understanding process intricacies and interdependencies for effective AI-driven digital transformation.

Platform innovations: Celonis has announced several enhancements to its platform to improve scalability, usability, and value realization.

• Celonis Data Core (Celocore) aims to accelerate data ingestion and reduce extraction, transformation, and load (ETL) times.
• A new GenAI-powered user experience will simplify dashboard creation and enhance the analytical experience.
• Celonis Networks enables optimization across processes spanning multiple organizations.
• Use-case-specific applications are being launched for various sectors, including logistics, finance, and manufacturing.

Broader implications: The integration of process intelligence with AI represents a significant step forward in enterprise AI performance, but challenges remain.

• While the potential for AI-driven process optimization is immense, organizations must carefully consider the balance between in-house development and leveraging external partnerships.
• The success of these initiatives will likely depend on the ability of enterprises to effectively combine domain expertise with advanced AI capabilities.
• As the field evolves, companies that can successfully harness process intelligence to power their AI systems may gain a substantial competitive advantage in their respective industries.