Artificial intelligence is a transformative technology that will transform every industry it touches, especially enterprise technology. The magnitude of AI’s impact on the world is reflected in the fact that global labor productivity is predicted to increase by 1% year-over-year.
Similarly, 79% of respondents to McKinsey's 2023 Global AI Survey said they have had at least some exposure to generative AI, and 40% said they will increase investment in AI across their organizations – the biggest game changer since the advent of the microprocessor and the birth of the internet.
However, not all AI is created equal.
When we hear the term artificial intelligence, many immediately think of GenAI superstars like ChatGPT. In fact, this is just one in a series of technologies that are growing and differentiating. As different use cases are conceived, new subjects are identified, and different datasets are utilized, new AI variants are developed to keep up with the ongoing changes.
For organizations looking to drive business transformation, enterprise AI offers a greater opportunity for change than consumer AI, especially when powered by the right tools.
Consumer and enterprise AI
There is a fundamental difference between Consumer AI and Enterprise AI models: Consumer AI tends to focus on leveraging vast (largely publicly available) datasets to create or analyze content and provide personalized solutions, such as ChatGPT, Perplexity AI, DALL-E, Midjourney, etc. In contrast, Enterprise AI involves the strategic deployment of artificial intelligence technologies trained and supported by proprietary, confidential business data to optimize business processes, decision-making, and productivity.
Enterprise AI addresses pressing business challenges, solving problems that were previously unsolvable or solving them much more cost-effectively and quickly than before, resulting in a higher return on investment for customers. For example, Enterprise AI can be tasked with analyzing supply chain data to optimize logistics processes and enable intelligent process automation.
While it may use many of the same techniques, such as machine learning, predictive analytics, and natural language processing (NLP), enterprise AI ties them directly to achieving specific business goals.
When governed by clear business rules and accurate, comprehensive process intelligence, enterprise AI protects data privacy, ensures process compliance, and avoids AI hallucinations.
Powering Enterprise AI with Process Intelligence
The impact enterprise AI has on your business processes is directly dependent on the data it is trained on and fed, and insights provided by specific tools, such as process intelligence, can help you maximize the value of your AI software.
Process Intelligence is the integration of detailed process data from process mining with standardized process knowledge. Process Intelligence enables AI to understand an organization's end-to-end processes. Data collected from Process Intelligence can be observed through a Process Intelligence Graph, a system-agnostic digital twin of the business, providing a single process intelligence layer that supports improvement, automation, and system transformation across all applications.
Combined with process best practices derived from thousands of Celonis customer deployments, the Process Intelligence Graph provides enterprise AI with real-time, data-driven insights into how your business operates, your governance rules, and how your processes interact.
So AI not only understands how your business operates and potential value opportunities, it can also accurately model the impact of process changes and respond quickly and effectively to anomalies with automated countermeasures. In short, process intelligence is the enabling layer for maximizing the ROI of your AI solution.
Process Copilot aims to make understanding how work flows within an organization and process analysis faster, more intuitive, and more collaborative. Using natural language processing (NLP) and a conversational interface, it empowers all team members, not just process specialists, to query process data in everyday language, meaning everyone can identify and realize hidden value opportunities in their business processes.
Finally, the Process Intelligence Graph also provides AI explainability and traceability, helping business users understand the reasoning behind automated reasoning and decisions. This is a major step forward towards transparent AI governance and helps allay concerns about so-called “black box” AI.
Preventing AI hallucinations
Process intelligence can also be used to prevent known challenges of generative AI solutions, such as hallucinations. Hallucinations occur when an AI produces misleading results, creates plausible but false information, or draws incorrect inferences from data patterns or inputs. For example, an AI may misinterpret legitimate online activity as potentially fraudulent and lock out customers' accounts.
The main causes of AI hallucinations include insufficient or biased training data, lack of accurate knowledge and data-based justification, or over-reliance on GenAI capabilities to fill the above gaps.
Process intelligence, and specifically process intelligence graphs, can help prevent these illusions. By incorporating detailed process data and intelligence into enterprise AI through search augmentation generation, systems will have the comprehensive, accurate data they need, as well as a structured framework for how to use that data.
Choose your AI path carefully
Commercial FOMO levels are at an all-time high when it comes to AI. In this technology gold rush, it may be tempting to quickly assemble a team and put together a GenAI solution in-house.
Before embarking on this path, it is important for business leaders to consider whether their future AI-driven plans reflect their company's technological capabilities and resources. Can their hardware withstand the added strain of AI technology? GenAI doesn't work well with messy data, so organizations will need to organize and sort through potentially gigabytes of legacy data. The final aspect that must be considered is whether the company is ready to keep up with an industry that is evolving at breakneck speed.
Enterprise AI is not something to take lightly. It's all too easy to get left behind by industry-shaking innovations and spend months developing an application that's outdated, irrelevant, or no longer works. Agility and adaptability are two aspects businesses need to thrive in an AI-driven world.
For many, the more logical path to AI success is to leverage outside expertise. Partnering with a specialist provider allows organizations to focus on how enterprise AI can unlock value and reinvent processes.
Specialized enterprise partners help you plan, execute, manage, and monitor your AI implementations, customizing solutions to your specific success metrics. They can ensure that AI is trained and enabled with the right data and knowledge, such as provided by a process intelligence graph. And of course, they also carry the burden of staying on top of AI innovations. In fact, their business model demands that they drive innovation for their customers.
But whether developed and deployed in-house or in collaboration with a strategic partner, companies can improve the chances of enterprise AI success by optimizing the information that powers the AI: process intelligence.
