{"id":22521,"date":"2025-02-05T15:50:48","date_gmt":"2025-02-05T15:50:48","guid":{"rendered":"https:\/\/interface.media\/?p=22521"},"modified":"2025-02-05T15:50:54","modified_gmt":"2025-02-05T15:50:54","slug":"grounding-ai-in-reality-why-high-quality-data-and-process-intelligence-matter","status":"publish","type":"post","link":"https:\/\/interface.media\/blog\/2025\/02\/05\/grounding-ai-in-reality-why-high-quality-data-and-process-intelligence-matter\/","title":{"rendered":"Grounding AI in reality: Why high-quality data and process intelligence matter"},"content":{"rendered":"\n

The race to turn artificial intelligence (AI) into business value is not slowing down, but business leaders need to ensure they are armed with the right tools to make the most of it. The power of AI is clear, from making complex data sets accessible through natural language prompts to not only automating but predicting processes. <\/p>\n\n\n\n

Businesses can see that implementing AI successfully holds huge potential, however, the fact that many can only \u201csee\u201d it right now is a problem<\/a>. Research by McKinsey<\/a> suggests that generative AI will enhance the impact of AI by up to 40%, potentially adding $4.4 trillion to the world economy, however 91% of business leaders still don\u2019t feel very prepared to use the technology responsibly.<\/p>\n\n\n\n

Instances of AI hallucinations, where Generative AI \u2018makes up\u2019 answers, have understandably made large organisations in particular cautious to trust the technology enough to implement it. The risks of \u2018false\u2019 output in generative AI are far greater for businesses than those faced by consumers. Businesses not only need to work within regulations, there are also a multitude of ethical, legal and financial implications if a Large Language Model (LLM) makes mistakes, for instance by \u2018hallucinating\u2019 and offering a customer an incorrect answer. <\/p>\n\n\n\n

But with the right technology, AI can be guided to deliver useful answers, and used to delve into company data in a way that was simply not possible before. Done correctly, this can deliver results in everything from improving internal efficiencies to revolutionising customer service. Chief amongst these technologies is process intelligence, which offers a unique class of data and business context, key to improving processes across systems, departments, and organisations.<\/p>\n\n\n\n

Finding the right data<\/h3>\n\n\n\n

The key question for businesses is how to ensure the AI model is fed with the most accurate and trusted data to deliver the best results. One important approach is to harness process intelligence, the connective tissue of any business. It enables leaders to train models directly on the data flowing through their businesses, from invoices to shipment details. Process intelligence is built on process mining and augments it with business context. It can reconstruct data from \u2018event logs\u2019 that business processes such as invoicing leave in systems, offering high-quality, timely data which allows AI models to \u2018understand\u2019 how processes impact each other across different departments and systems.<\/p>\n\n\n\n

Process intelligence is a key enabler for AI, allowing business leaders to ensure the Large Language Model (LLM) really works for the enterprise. It allows AI to be integrated into the business rapidly and effectively, and also helps to deal with common AI problems. By \u2018grounding\u2019 AI with a source of high-quality, structured data and business context, it helps to enhance accuracy and cut the chances of the AI \u2018hallucinating\u2019 and making up facts. Paired with AI systems, process intelligence can also enable fresher data for real time operational use, meaning that the data accessible through generative systems is always relevant.<\/p>\n\n\n\n

Some leaders are also turning to smaller language models, trained on more compact sets of enterprise data and built for specific purposes. These can deliver results less expensively than large models such as ChatGPT, often with higher accuracy and greater ease of on-premise or private cloud deployment, which can also reduce data breach risks. Other technologies such as retrieval augmented generation (RAG) combine the power of LLMs with external knowledge retrieval, and can boost the accuracy and relevance of AI-generated content, grounding answers in an enterprise\u2019s knowledge base.<\/p>\n\n\n\n

Delivering results for humans <\/h3>\n\n\n\n

One reason generative AI can be such a paradigm shift for businesses is that it allows business users to interrogate large data sets in natural language. Using ‘Copilot’ style tools, business users can uncover new insights and ways to engage consumers without relying on cumbersome systems and dashboards. This in turn drives faster return on investment (ROI). Process intelligence enhances AI scalability, enabling efficient large-scale data retrieval through Natural Language Processing (NLP). NLP handles complex queries, extracts insights from unstructured data, and uses algorithms to identify patterns humans might miss. These capabilities pave the way for innovation, new products, and improved business strategies.<\/p>\n\n\n\n

In healthcare, for example, secure and private access to patient data enables experts<\/a> to spot the telltale patterns that can lead to diseases and other problems. With AI models able to digest everything from inbound emails to free text fields in health records, the opportunities to deliver improved service for patients are near limitless. For IT teams, AI for IT operations (AIOps) helps to process big data, streamline repetitive tasks, optimise data infrastructure and improve IT processes. This means reduced costs and lower wasted time across the whole business.\u00a0<\/p>\n\n\n\n

Furthermore, AI agents have a central role to play in the world of enterprise AI. An AI agent is a software program that can understand how the business runs and how to make it run better, interacting with its environment and using data to perform self-determined tasks to meet goals. When powered by Process Intelligence they can enable businesses to automate processes, increasing productivity, reducing costs, and improving the customer experience. AI models can also instruct agents in natural language and autonomously run workflows, creating simplicity across the board.<\/p>\n\n\n\n

The right tool for the job<\/h3>\n\n\n\n

Process intelligence is one of the key enablers in any business leader\u2019s arsenal when it comes to delivering value from AI responsibly, while avoiding the pitfalls and mistakes AI can make. This technology closes the gap between AI\u2019s promise and what it actually delivers, allowing AI to be credible, effective and trustworthy. <\/p>\n\n\n\n

Adopting process intelligence offers business leaders data-backed, contextually accurate recommendations that you can act on immediately, unlocking the potential of AI. Alongside other techniques to limit the risks of \u2018bad\u2019 data, process intelligence will be a crucial foundation stone for AI innovation in the coming years. <\/p>\n","protected":false},"excerpt":{"rendered":"

Rupal Karia, VP & Country Leader UK&I at Celonis, looks at the critical data management steps to making AI a valuable business technology asset. <\/p>\n","protected":false},"author":480,"featured_media":22522,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"apple_news_api_created_at":"2025-02-05T15:50:53Z","apple_news_api_id":"b552fa37-fd51-4f25-9d83-ffe97f517808","apple_news_api_modified_at":"2025-02-05T15:50:53Z","apple_news_api_revision":"AAAAAAAAAAD\/\/\/\/\/\/\/\/\/\/w==","apple_news_api_share_url":"https:\/\/apple.news\/AtVL6N_1RTyWdg__pf1F4CA","apple_news_cover_media_provider":"image","apple_news_coverimage":0,"apple_news_coverimage_caption":"","apple_news_cover_video_id":0,"apple_news_cover_video_url":"","apple_news_cover_embedwebvideo_url":"","apple_news_is_hidden":"","apple_news_is_paid":"","apple_news_is_preview":"","apple_news_is_sponsored":"","apple_news_maturity_rating":"","apple_news_metadata":"\"\"","apple_news_pullquote":"","apple_news_pullquote_position":"","apple_news_slug":"","apple_news_sections":[],"apple_news_suppress_video_url":false,"apple_news_use_image_component":false,"footnotes":""},"categories":[3],"tags":[],"topic":[614],"class_list":["post-22521","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-the-interface","topic-data-ai"],"acf":[],"apple_news_notices":[],"yoast_head":"\nGrounding AI in reality: Why high-quality data and process intelligence matter - 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