Impact of AI in business hinges on process intelligence

The increasing adoption of artificial intelligence (AI) solutions within businesses is highlighting the crucial role of high-quality data and process intelligence.

The potential of AI is increasingly being harnessed to access complex data sets through natural language prompts, with the capability to automate and predict various processes. However, while the benefits are apparent, many businesses are still primarily at the stage of observing AI's potential without fully implementing these technologies.

Research from McKinsey indicates that generative AI will enhance AI's impact by up to 40%, possibly contributing an additional USD $4.4 trillion to the global economy. Despite this potential, 91% of business leaders do not feel adequately prepared to responsibly utilise such technologies.

The 2025 Process Optimisation Report revealed that 89% of enterprise leaders insist AI needs context regarding business operations to deliver significant results and enhance operational efficiency. Additionally, 79% stressed the urgency to understand their processes better to maximise opportunities, with 92% recognising untapped value within these processes.

AI hallucinations, where generative AI incorrectly fabricates information, have caused hesitancy in deploying AI. An instance involved Elon Musk's xAI, Grok, mistakenly accusing NBA star Klay Thompson of misconduct, exemplifying these risks.

The stakes are considerably higher for businesses than consumers, as companies must comply with regulations and face potential ethical, legal, and financial repercussions if a large language model (LLM) errors. Such errors could mislead customers by offering incorrect information.

Process intelligence stands out as a pivotal technology that can facilitate effective AI implementation. It offers unique data and business context crucial for improving processes across various systems and organisations.

Pascal Coubard, the Vice President of Sales APAC for Celonis, explains the importance of ensuring AI models are equipped with accurate data. "The key question for businesses is how to ensure the AI model is provided with the most accurate and trusted data to deliver the best results," he said.

Coubard highlights process intelligence as it enables leaders to directly train models on data within their businesses, drawn from various business functions such as invoicing and shipment details. This technology, based on process mining, reconstructs data from event logs left by business processes, aiding AI in understanding interdepartmental process impacts.

Process intelligence also enables rapid and effective AI integration within businesses, addressing common AI problems by grounding AI with high-quality data and context. This reduces the chance of AI inaccuracies, known as "hallucinations," and promotes real-time data relevance.

Business leaders are also considering smaller language models, purpose-built for specific data sets, offering results with reduced costs, increased accuracy, and decreased data breach risks. Additionally, techniques like retrieval augmented generation combine LLMs with external knowledge retrieval to enhance AI-generated content.

Generative AI is revolutionary as it allows business users to explore large data sets in natural language, potentially leading to faster returns on investment. Process intelligence supports AI scalability, enabling sophisticated data analysis through Natural Language Processing (NLP), promoting innovation and strategy development.

In healthcare, secure AI access to patient data can identify patterns indicating potential diseases, contributing significantly to patient services. For IT operations, AI helps process large data sets, optimize infrastructure, and reduce costs and inefficiencies.

AI agents, software programs understanding business operations, promise automation, enhanced productivity, and cost reduction when empowered by process intelligence. These agents can autonomously perform tasks and streamline workflows, thus simplifying business management.

Coubard concludes by affirming process intelligence's role in bridging the gap between AI's promise and its actual deliverables. "This technology closes the gap between AI's promise and what it actually delivers, allowing AI to be credible, effective and trustworthy," he stated.