The growing integration of artificial intelligence (AI) in business operations is underscoring the critical importance of high-quality data and process intelligence. As AI technologies evolve, businesses are tapping into the potential of advanced systems to process vast amounts of complex data, automate tasks, and predict future trends. However, while the potential of AI is increasingly recognized, many businesses remain at an observational stage, still grappling with how to fully integrate these solutions.
Research by McKinsey highlights that generative AI could boost AI’s impact by up to 40%, potentially contributing an additional USD $4.4 trillion to the global economy. Despite these promising numbers, a staggering 91% of business leaders feel ill-prepared to adopt these technologies responsibly.
According to the 2025 Process Optimisation Report, 89% of enterprise leaders emphasize that AI must have clear context about their business operations to generate significant results and enhance efficiency. Furthermore, 79% of executives stress the need to better understand their processes in order to unlock greater opportunities, with 92% acknowledging there is untapped value within these processes.
Yet, AI’s adoption is not without its challenges. One significant hurdle is the risk of AI “hallucinations,” where generative AI produces incorrect or fabricated information. A notable example involved Elon Musk’s xAI platform, Grok, which mistakenly accused NBA player Klay Thompson of misconduct, shedding light on the dangers of deploying AI without rigorous oversight.
For businesses, the risks are far greater than for consumers. Companies must navigate strict regulatory environments and face the legal, financial, and ethical consequences of AI errors. Misinformation from AI systems could damage customer trust and lead to substantial repercussions.
This is where process intelligence becomes essential. By providing accurate, contextual data, process intelligence ensures that AI systems are properly equipped to understand and improve business processes. Pascal Coubard, Vice President of Sales APAC at Celonis, stresses the importance of feeding AI models with reliable data to ensure optimal results. “The key question for businesses is how to ensure that the AI model is provided with the most accurate and trusted data to deliver the best outcomes,” Coubard explains.
Process intelligence, which draws upon process mining techniques, helps businesses train AI models with data from critical business functions like invoicing, inventory management, and shipments. This technology reconstructs event logs from business processes, giving AI a clearer understanding of how different operations affect each other. By providing AI with this context, businesses can avoid inaccuracies and ensure that the AI is operating with real-time, relevant data.
Smaller, specialized language models are also gaining traction, particularly in industries where specific data sets are required. These models offer more accurate results, lower costs, and reduced risks of data breaches. Techniques like retrieval-augmented generation (RAG) combine large language models (LLMs) with external knowledge retrieval, allowing AI to enhance the quality of its outputs by leveraging a broader knowledge base.
Generative AI, which enables business users to query large data sets using natural language, has the potential to significantly shorten time to value. By leveraging process intelligence, businesses can scale AI implementations and unlock new capabilities in data analysis, fostering innovation and refining strategic decision-making.
In sectors like healthcare, AI’s ability to access secure patient data allows for the identification of patterns that could indicate the onset of diseases, thus improving patient care. In IT operations, AI is helping process large data sets, optimizing infrastructure, and cutting down on operational inefficiencies, ultimately reducing costs.
AI-driven agents are also poised to transform business operations. These software programs, empowered by process intelligence, understand the inner workings of a business and can autonomously perform tasks, streamline workflows, and enhance productivity. This, in turn, can lead to significant cost reductions and more efficient business management.
Coubard concludes by highlighting the importance of process intelligence in making AI’s promises a reality. “Process intelligence closes the gap between AI’s potential and its actual performance, ensuring that AI remains credible, effective, and trustworthy,” he states.
As AI continues to shape the future of business, process intelligence will be pivotal in ensuring its full potential is realized, fostering smarter, more efficient, and innovative business practices.
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