The promise and ultimate goal of artificial Intelligence is to make machine intelligent. With advancement in machine learning, statistical reasoning and pattern recognition, as well as the exponential growth in big data and computing power, AI has become the front and center of technological innovation and business transformation in the second decade of 21 century and beyond.
In this respect, AI is perfectly aligned to the goal of business Intelligence, which is to make business more intelligent by augmenting and, in some cases, automating human intelligence. As AI is getting smarter, it is not unreasonable to expect that BI will too.
Traditionally, BI, along with data warehousing and big data technologies, provides systems, tools and processes to help companies harness data from disparate sources and turn them into high quality and actionable information to drive competitive advantage. On the other hand, AI is the simulation of human intelligence processes by machines, including the ability to understand, learn, reason and self-correct.
AI in popular culture often captures the imagination of general public in the form of HAL 9000 as in “2001: A Space Odyssey” or intelligent humanoids such as “Terminator” and “Transformers.” However, it has very little to do with reality and the current state of AI.
Today, all real world AI adoptions and successful implementations are artificial narrow intelligence (ANI) and one area where AI has been quietly making remarkable progress is the expansion of intelligence into business applications and processes via smart algorithms, intelligent bots, cognitive virtual agents and deep learning programs, such as deep neural network (DNN).
In a recent study, IDC predicts “by 2025, at least 90% of new enterprise apps will embed AI; by 2024, over 50% of user interface interactions will use AI-enabled computer vision, speech, natural language processing, and AR/VR.”
Although AI powers many systems and applications ranging from improving business operational efficiency to analyzing patterns and trends or detecting intrusions and fraudulent transactions, the focus here is limited to exam AI’s current and potential future impact on BI and how it could empower BI and decision making in general.
The synergy between AI and BI starts with data. Both AI and BI requires lots of data. One argument that can be made is modern successful AI implementations, such as many deep learning approaches to real world problems, rely entirely and excessively on data.
How to generalize beyond training dataset and acquire intelligence becomes one of the key measures of general intelligence. Current AI is data-driven by nature while BI is data-driven by definition.
From this perspective, data management under traditional BI framework could provide AI with critical data inputs. Furthermore, AI system or intelligent device generated data or data parsed, classified and interpreted by AI systems could in turn be new information sources for BI to better correlate and understand events, e.g. audio, video, image and other multimedia data.
In “Unplugged – The Disconnect of Intelligence and Analytics” (2011), I argued that business intelligence and business analytics (BA) need to converge toward one integrated approach to data-driven decision making as well as proposed a reference architecture. The first major transformation in BI is the modern statistical and analytical methods, tools and algorithms that give rise to BA which reciprocally extends the traditional BI scope and capabilities. Now, AI could transform BI yet again and make it more robust and intelligent.
The most important thing to understand AI is it is not one dimensional but multifaceted, contrary to the common misconception of AI - a single Being. And perhaps more surprisingly to a layman, often it doesn’t have to take humanoid form or even intelligent device.
