×
AI agents trained on ‘process intelligence’ will be key to unlocking enterprise value
Written by
Published on
Join our daily newsletter for breaking news, product launches and deals, research breakdowns, and other industry-leading AI coverage
Join Now

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.
AI agents fed by process intelligence power the next gen of enterprise AI performance

Recent News

OpenScholar: The open-source AI tool that outperforms GPT-4 in scientific research

Academic researchers and institutions gain access to a new open-source AI system that processes millions of papers and provides verifiable citations at a fraction of GPT-4's operating costs.

In Trump’s shadow: Nations convene in SF to tackle global AI safety

Ten nations agree on first steps toward coordinated AI testing standards while committing $11 million in initial funding.

Aggie AI helps small businesses tackle social media management

AI-enabled social media tools are helping small businesses automate content creation and scheduling while reducing operational costs by up to 70 percent.