Business Intelligence and artificial intelligence are increasingly crucial yet often misunderstood tools in an enterprise context.
Put simply, artificial intelligence (AI) explores the use of computer systems to mimic various attributes of human intelligence, such as problem solving, learning, and judgment. Though in its technological infancy, businesses see huge potential in AI for speech recognition, decision-making, and everything in between. A 2017 survey conducted by PwC shows that over 72 percent of business leaders believe that the using AI can “enable humans to concentrate on meaningful work.”
Both AI and BI have key, and in some cases overlapping, enterprise applications.
Business intelligence (BI) refers to the use of various technologies and tools to collect and analyze business data. The main purpose of BI is to provide companies with useful information and analysis to aid decision-making. Using BI allows businesses to make decisions nearly five times faster than they otherwise could.
Both AI and BI have key, and in some cases overlapping, enterprise applications. There are, however, important differences between these technologies that businesses should grasp. This article provides an overview of some of AI’s and BI’s goals and use-cases. Understanding these differences can clarify how AI and BI complement one another and may help businesses save valuable resources down the road.
The Goals of AI and BI Are Very Different
The Main Goals of BI
BI aims to streamline the process of collecting, reporting and analyzing data. Using BI allows companies to improve the quality of the data they collect and the consistency with which they collect it.
As Michael F. Gorman, professor of operations management and decision science at the University of Dayton in Ohio, said in an article published by CIO Magazine, “[Business Intelligence] doesn’t tell you what to do; it tells you what was and what is.”
In other words, BI tools can turn reams of noisy data into a coherent picture, but they are not designed to provide clear prescriptions for how that data should be used in decision-making.
Companies like Microsoft, Oracle, and Tableau have developed BI tools for a range of business functions, including HR, sales, and marketing. By monitoring everything a business does on a daily basis – and utilizing data to create spreadsheets, performance metrics, dashboards, charts, graphs, and other useful visualizations – businesses can organize data and make traditionally difficult decisions much more easily. The adoption of BI solutions has grown nearly 50 percent in the past three years.
The Main Goals of AI
Modeling human intelligence is one of the primary goals of artificial intelligence. By modeling human behaviors and thought-processes, AI programs can learn and make rational decisions.
The technology professionals who build and operate AI programs are often trying to answer certain questions: Can machines learn and adapt? Can machines develop reliable intuition?
Exploring these questions can yield significant benefits for businesses willing to invest and experiment. As past Toptal Insights articles have explored, using AI-driven applications, like chatbots, can drive greater efficiency and profits.
Beyond simply clarifying a messy picture, AI can provide human operators with prescriptions, and can act on those prescriptions autonomously.
Unlike BI, which makes analyzing data much easier but leaves decision-making in the hands of humans, AI can enable computers to make business decisions themselves. For example, chatbots can, without human intervention, answer customer questions. Beyond simply clarifying a messy picture, AI can provide human operators with prescriptions and can act on those prescriptions autonomously.
BI vs. AI Use-Cases
BI Enterprise Use-Cases
BI has become so ubiquitous and fundamental to the way enterprises operate that many may not even realize they rely on it. Anyone who has used Microsoft Excel or another spreadsheet program in a business context has interacted with BI. Spreadsheets allow businesses to organize, analyze, and visualize data with far greater efficiency than would be otherwise possible.
Many companies also use BI to better understand their customers. Businesses interact with their customers through a range of interfaces, including emails, chatbots and social media. BI tools can gather customer data from these disparate sources and present it in a cohesive, unified format. By collecting and synthesizing data from these touchpoints, businesses can gain a deeper understanding of who their customers are and how to serve them best.
Companies also use business intelligence to improve operational efficiency. BI tools can track key performance indicators in real-time, allowing businesses to identify and solve problems much faster than they otherwise could.
General BI applications include spreadsheets, data visualization tools, data warehousing tools, and reporting software.
AI Enterprise Use-Cases
There is a wide range of AI enterprise use-cases, from improving medical diagnoses to designing more efficient energy grids and better understanding retail customers. As a recent Harvard Business Review article describes, AI-powered enterprise applications usually fall into one, or a combination, of three buckets: process automation, cognitive insight, and cognitive engagement.
Process automation stands as the least flashy, but most common and perhaps most valuable type of AI-powered enterprise application. Such applications can automatically update customer information and records, handle boilerplate customer communication, and provide basic guidance on standardized contracts and documentation. As Harvard Business Review notes, these applications, which can replace human back-office and administrative functions, often come with a high return on investment.
Cognitive insight applications, which Harvard Business Review describes as akin to “analytics on steroids,” are more advanced than process automation applications in that they can learn and improve over time, as they interact with users and data. Such applications can predict customer behavior, provide improved IT security solutions, and devise personalized ads.
Applications that employ cognitive engagement interface directly with employees and customers. These include chatbots, which can offer medical advice, answer internal company questions, provide general customer service, and more.
Does Business Intelligence Need Artificial Intelligence?
BI and AI are distinct but complementary. The “intelligence” in AI refers to computer intelligence, while in BI it refers to the more intelligent business decision-making that data analysis and visualization can yield. BI can help companies bring order to the massive amounts of data they collect. But neat visualizations and dashboards may not always be sufficient.
By embracing the confluence of AI and BI, businesses can synthesize vast quantities of data into coherent plans of action.
AI can enable BI tools to produce clear, useful insights from the data they analyze. An AI-powered system can clarify the importance of each datapoint on a granular level, and help human operators understand how that data can translate into real business decisions. By embracing the confluence of AI and BI, businesses can synthesize vast quantities of data into coherent plans of action.
A range of tech companies, from established giants to startups, are seeking to capitalize on this approach. IBM’s research division has sought to “rethink enterprise architecture and transform business processes by combining AI algorithms, distributed systems, human computer interaction, and software engineering.” A recent article in CIO Magazine profiled DataRobot, a company that develops BI solutions driven by predictive modeling and machine learning. DataRobot, CIO reports, helped a healthcare company infuse AI into its BI systems: “240 doctors and nurses get the predictions and recommendations right in their PowerBI dashboards, which they can access through tablets and smartphones.” With DataRobot’s help, the healthcare company was able to flag high risk patients and formulate proactive treatment plans.
AI can also lead to the development of smarter, more adaptive BI tools. As these tools take in more data, interact more with users, and internalize the outcomes that their recommendations yield, they can learn what kinds of recommendations and analyses are most useful, and self-adjust accordingly. AI, rather than human software engineers, may ultimately provide the incremental improvements that take BI tools to the next level.
It seems likely that the future of BI will, in some capacity, depend on AI. Though AI and BI have important differences, they make a powerful team. Going forward, businesses would do well to not regard AI and BI as completely separate technologies, but rather explore and invest in ways to fully realize the potential they have in working together, helping businesses solve their greatest challenges grow to new heights.