The competition to turn artificial intelligence (AI) into business value is heating up, but business leaders need the right tools to make AI deliver meaningful results.
The potential upsides from using AI are clear: ranging from simplifying complex data through natural language prompts to automating and predicting processes. Yet, while businesses see its potential, many are not yet prepared to make the most of the technology.
Research by McKinsey estimates that generative AI could boost AI’s overall impact by up to 40%, adding $4.4 trillion to the global economy. However, 91% of business leaders still don’t feel ready to implement it responsibly.
One challenge comes from AI hallucinations, where generative AI ‘makes up’ answers that seem real but are incorrect. For businesses, the risks are far higher than for consumers. Mistakes—like incorrect information being shared with a customer—can lead to legal, financial, and ethical issues, along with reputational damage. Large organisations, especially, are understandably cautious.
With the right tools, however, AI can be trained to provide accurate, useful answers and uncover insights from company data in ways that weren’t possible before.
Process Intelligence and Reliable Data
The key question for businesses is how can they ensure AI is working with the most trusted and accurate data? The answer lies in Process Intelligence, the “connective tissue” of a business.
Process Intelligence allows leaders to train AI models using data that flows through their organisation every day—such as invoices, shipment records, or employee workflows. It builds on process mining, which analyses ‘event logs’ created by business systems (like those used for invoicing). By reconstructing these event logs, process intelligence delivers structured, up-to-date data that AI can use to understand how processes connect and impact each other.
This structured data ensures that AI systems deliver accurate results and reduces the risk of hallucinations. Process Intelligence also supports real-time data use, so businesses can act quickly on fresh, relevant information.
Some companies are also exploring smaller AI models, which are trained on more focused sets of enterprise data for specific business purposes. These smaller models often deliver results more cost-effectively, with higher accuracy, and are easier to deploy securely on-premises or within private cloud environments, which helps reduce data breach risks.
Another helpful approach is Retrieval-Augmented Generation (RAG), a method that combines the power of large AI models with a company’s specific knowledge base. RAG helps ensure AI outputs are relevant and accurate, as it retrieves data from trusted sources before generating answers.
Turning Data into Real Results
Advancements in how employees interact with AI are also continuing to come thick and fast. Generative AI is making it easier for people to interact with large amounts of data using natural language, giving everyone the ability to write code as simply as if they were asking questions to a colleague. Tools like ‘Copilots’ make it easy for business users to access new insights without relying on complex systems or dashboards. This helps organisations achieve faster returns on their AI investments.
Process Intelligence makes this even more powerful. It improves AI scalability by enabling efficient data retrieval and handling large, complex queries. AI can then extract insights from unstructured data (like free-text emails) and identify patterns that might otherwise be missed. These capabilities open doors to new ideas, products, and strategies.
For example in the Healthcare sector, secure access to patient data helps professionals spot patterns in health records that may predict diseases or other issues. AI models can process everything from incoming emails to notes in free text fields, making it possible to deliver better patient outcomes.
Over in the IT department, AI for IT (AIOps) can handle big data to streamline repetitive tasks, optimise systems, and improve processes. This means reduced costs, improved infrastructure, and fewer delays across the business.
Another key innovation is the use of AI agents, software programs that understand how businesses operate and improve workflows on their own. When combined with process intelligence, AI agents can automate tasks, increase productivity, cut costs, and deliver better customer experiences. These agents can even be instructed using simple natural language, making them easy to use across different parts of the business.
The Right Tools for AI Success
Process Intelligence is one of the most important tools business leaders can use to unlock the value of AI while avoiding its pitfalls. It helps bridge the gap between AI’s potential and what it can deliver by ensuring the technology is accurate, reliable, and trustworthy.
With Process Intelligence, businesses get data-backed insights that they can act on immediately. Combined with other techniques, like smaller AI models and retrieval-augmented generation, Process Intelligence provides a solid foundation for AI innovation.
By adopting these approaches, business leaders can ensure AI doesn’t just promise value—it delivers it, helping organisations innovate, grow, and operate with confidence in the years ahead.
About the Author
Rupal Karia is VP & Country Leader UK&I at Celonis. Celonis makes processes work for people, companies, and the planet. The Celonis Process Intelligence Platform takes the data from the systems you already use, and presents you with a living digital twin of your end-to-end processes. It’s system-agnostic, without bias, and provides everyone with a common language for understanding and improving processes. Because when processes work, everything works.
