AI vs BI is an increasingly important but frequently misunderstood workplace instrument. This article highlights the contrasts between each field of AI vs. BI and how they will collaborate in the future.
Proper understanding of AI vs BI in an enterprise context
Simply explained, Artificial Intelligence (AI) is the study of the use of computer systems to replicate various aspects of human intelligence, such as problem-solving, learning, and judgment.
Calling specific applications “Artificial Intelligence” is akin to referring to a car as a “Vehicle” – it’s theoretically right, but it doesn’t cover any of the details. We must delve further to determine which sort of AI is prevalent in business. Despite its technological infancy, businesses see huge potential in AI for speech recognition, decision-making, and everything in between. According to a PwC survey done in 2017, more than 72 percent of corporate leaders feel that employing AI can “enable humans to concentrate on meaningful work”.
Business Intelligence (BI) is the collection and analysis of corporate data using various technologies and techniques. The primary goal of BI is to give companies with meaningful information and analysis to help them make decisions.
The ultimate purpose of BI efforts is to drive better business decisions, allowing firms to grow revenue, improve operational efficiency, and gain a competitive advantage over competitors. To do this, BI combines analytics, data management, and reporting tools, as well as numerous data management and analysis approaches. Businesses that use BI may make choices roughly five times faster than they might otherwise.
Differences between AI vs. BI
The enterprise applications of AI vs BI are significant, and occasionally they overlap. However, there are important differences between these technologies that businesses should be aware of. Understanding these differences can help firms understand how AI Vs BI complement one another and may help businesses save valuable resources down the road.
The Purposes of AI vs BI Are Very Dissimilar
The Main Goals of AI
One of the fundamental goals of artificial intelligence is to model human intelligence. AI algorithms can learn and make rational conclusions by imitating human actions and mental processes.
The technology professionals who design and run AI programs are frequently attempting to address the following questions: Can robots learn and adapt? Can machines develop trustworthy intuition?
Investigating these questions can result in huge rewards for organizations willing to invest and experiment. As previously discussed in Innovature BPO articles, utilizing AI-driven apps such as chatbots can increase efficiency and profits.
Unlike BI, which makes data analysis considerably easier but leaves decision-making to people, AI can empower computers to make business decisions on their own. Chatbots, for example, may answer client questions without the need for human participation. AI may deliver prescriptions to human operators and act on those prescriptions autonomously, which goes beyond simply clearing a muddled image.
The Main Goals of BI
The goal of BI is to simplify the process of gathering, reporting, and analyzing data. Companies can use BI to increase the quality of their data and the consistency with which they acquire it.
According to Michael F. Gorman, professor of operations management and decision science at the University of Dayton in Ohio, “Business Intelligence doesn’t tell you what to do; it tells you what was and what is.”
In other words, while BI tools can transform massive amounts of noisy data into a coherent picture, they are not meant to provide specific guidelines for how that data should be used in decision-making.
Companies such as Microsoft, Oracle, and Tableau have created BI solutions for a variety of company tasks such as human resources, sales, and marketing. Businesses may organize data and make historically difficult decisions much more quickly by monitoring everything they do on a regular basis and using data to build spreadsheets, performance metrics, dashboards, charts, graphs, and other useful visualizations. In the last three years, the adoption of BI systems has increased by roughly 50%.
Use-Cases for BI vs AI
Use-Cases for AI Enterprise
AI industry applications span from enhancing medical diagnosis to developing more efficient energy systems and a better understanding of retail shoppers. According to a recent Harvard Business Review article, AI-powered corporate solutions often fall into one of three categories: process automation, cognitive insight, and cognitive engagement.
Process automation is the most prevalent and possibly most valuable sort of AI-powered enterprise application. These systems can update client information and records automatically, handle boilerplate customer interactions, and provide rudimentary help on standardized contracts and documents. According to the Harvard Business Review, these programs, which can replace human back-office and administrative duties, frequently provide a good return on investment.
Cognitive insight applications, dubbed “analytics on steroids” by Harvard Business Review, are more advanced than process automation apps in that they may learn and improve over time as they interact with users and data. Such programs can forecast client behavior, increase IT security, and create targeted advertisements.
Cognitive engagement applications communicate directly with employees and customers. Chatbots, for example, can provide medical advice, answer internal company issues, and provide general consumer support, among other things.
Use-Cases for BI Enterprise
BI has become so pervasive and integral to how businesses work that many may be unaware they rely on it. Anyone who has worked with Microsoft Excel or another spreadsheet tool in a business setting has come into contact with BI. Spreadsheets enable organizations to organize, analyze, and visualize data more efficiently than would otherwise be feasible.
Many businesses also use BI to have a better understanding of their clients. Businesses communicate with their clients using a variety of channels, including emails, chatbots, and social media. BI systems may collect client data from diverse sources and deliver it in a unified, cohesive style. Businesses can obtain a better picture of who their consumers are and how to serve them by gathering and synthesizing data from various touchpoints.
Business intelligence is also used by companies to increase operational efficiency. BI technologies may measure important performance indicators in real-time, helping firms to discover and solve problems much more quickly than they could otherwise.
Spreadsheets, data visualization tools, data warehousing tools, and reporting software are examples of general BI applications.
Is Artificial Intelligence Necessary in Business Intelligence?
AI Vs. BI are distinct but complementary. The term “intelligence” in AI refers to computer intelligence, whereas the term “intelligence” in BI refers to more intelligent business decision-making that data analysis and visualization can produce. BI can assist businesses in organizing the huge amounts of data they collect. However, attractive visualizations and dashboards may not always be enough.
AI can help BI systems generate clear, actionable insights from the data they study. An AI-powered system can clarify the significance of each data point at the granular level and assist human operators in understanding how that data may be translated into real-world business choices. Businesses may synthesize massive amounts of data into coherent plans of action by embracing the convergence of AI vs. BI.
This technique is being pursued by a wide spectrum of tech organizations, from established behemoths to start-ups. IBM Research has attempted to “rethink enterprise architecture and transform business processes by combining AI algorithms, distributed systems, human-computer interaction, and software engineering”.
AI can also lead to the development of smarter, more adaptive business intelligence solutions. As these tools collect more data, engage with users more frequently, and internalize the results of their recommendations, they can learn which types of recommendations and analyses are most valuable and self-adjust accordingly. AI, rather than human software engineers, may eventually offer the incremental advances that propel BI systems to new heights.
AI appears to be crucial to the future of business intelligence. Despite significant contrasts, AI vs BI form a formidable partnership. Organizations would be well to stop viewing AI vs. BI as separate technologies and instead explore and invest in ways to fully realize the potential they offer in working together to help organizations solve their greatest difficulties and develop to new heights.
Synergies AI vs BI – 6 examples of AI in today’s famous business intelligence applications
SAP – AI for Turning Databases into Useful Intel
HANA is SAP‘s cloud platform, which businesses utilize to manage information databases. In a nutshell, it replicates and ingests structured data from relational databases, apps, and other sources, such as sales transactions or customer information.
The platform can be installed on-premise to run on company servers or in the cloud. HANA collects data from various places within the organization, such as mobile and desktop computers, financial transactions, sensors, and manufacturing plant equipment. If your sales team utilizes business cellphones or tablets to record purchase orders in the field, HANA can analyze and understand the data to discover trends and abnormalities.
Walmart, for example, has used HANA to handle its large volume of transaction information (the corporation manages over 11,000 locations) in seconds. At an SAP-hosted conference in 2015, then-CIO Karenann Terrell explained why Walmart chose HANA to function faster and lower back office costs by combining the processes and resources required to do the work.
How Walmart uses HANA
Unexpected deviations can occur in almost any place in the course of doing business; it could be an excess product order that appears unusual for a specific customer or machinery at a plant that begins to run slower than it should. Machine learning can be used to detect such variations automatically. For instance, if a factory manager has an application installed on their computer to monitor the equipment on an assembly line, data from a production slowdown may be collected and processed using HANA. The collected data can be analyzed to determine whether a new course of action, such as a service inspection of the equipment, is required.
According to SAP, HANA outperforms comparable platforms by storing replicated data in RAM rather than on disk. This enables real-time data access for usage with apps and analytics built on top of the HANA platform for speedier decision-making.
The goal of HANA and other machine learning technologies is to make data-driven, potentially better-informed decisions. Small and medium businesses, not just corporations, can experiment with this type of technology in many sectors of their operations if the solution fits within their budget. “This can be found in any organization, whether it’s for HR, risk, or marketing,” says Ronen Meiri, CTO of DMWay, a machine learning for predictive analytics provider.
Machine learning solutions for business intelligence are expected to save money on infrastructure and improve operational efficiency. According to a survey sponsored by SAP, ten firms that utilize HANA predict a five-year return on investment of 575% on average. They also estimated an average yearly benefit of $19.27 million per firm from utilizing HANA, compared to a $2.41 million average annual expenditure over five years. International Data Corp., an IDG subsidiary, performed the poll and prepared the report on SAP’s behalf. The identities of the organizations polled were withheld.
DOMO – AI for Business Dashboards
Domo, a fast-growing business management software startup that has raised over $500 million in funding, has designed a dashboard that collects data to assist businesses in making decisions. The cloud-based dashboard scales with the company’s size, so it may be utilized by teams as small as 50 or much larger companies. Domo has over 400 native software connectors that allow it to collect data from third-party apps, which can then be leveraged to provide insights and context to business intelligence.
This allows Domo users to pull data from Salesforce, Square, Facebook, Shopify, and a variety of other systems to obtain insight into their customers, sales, and product inventory. Domo merchants, for example, can pull data from their Shopify point-of-sale and e-commerce software, which is used to operate online stores. The gathered data can be utilized to generate reports and identify trends in real-time, such as product performance, which can then be shared with any device used by the firm.
Domo announced Mr. Roboto in March, a combination of new platform capabilities based on AI, machine learning, and predictive analytics. The expectation is for Mr. Roboto to offer advice and insights to decision-makers at companies. Once these capabilities are implemented, which is planned in late spring 2017, the platform will deliver new warnings and notifications for important changes, such as the detection of abnormalities or new patterns in data. (similar to approaches used in cyber security already).
Detecting these shifts and patterns is expected to power Mr. Roboto’s predictive analytics side and assist firms in predicting the return on investment for real-time marketing, customer churn, and sales projections.
Domo claims that its platform is used by MasterCard, Univision, eBay, the Honest Company, and SAB Miller. Univision, a television broadcaster, provided a testimonial about how it uses Domo to increase the exposure of its own data, which is then utilized to unify and focus targeted marketing. To maximize the value of its programmatic advertising, Univision said it employs the Domo platform, which has connectors for programs such as Google Analytics, Facebook, and Adobe Analytics. “We were able to quickly optimize and achieve an 80 percent increase in yield during our first quarter by launching Domo,” said David Katz, Univision’s VP general manager for programmatic revenue and operations.
Apptus – AI in Sales Enablement
There are several ways for machine learning to improve applications, such as those offered by Apptus, which provide advice on activities that businesses may do to improve their sales channels. Apptus claims to specialize in the link between a customer’s intent to buy and a company’s revenue realization.
The Apptus eSales technology is intended to automate merchandising based on a predictive understanding of consumers, among other things. The software uses big data and machine learning to predict which things will appeal to potential customers as they browse online or receive recommendations.
For example, when a consumer visits an Apptus eSales-powered online store and begins typing in search terms to seek up products, the machine learning system may predict and automatically present relevant search phrases. It may also show products related to those search terms.
Apptus is used by businesses of all sizes, like the automated bookseller Bokus.com in Sweden, which employs roughly 30 employees according to its testimonial. Bokus, which needed to keep overhead low, claimed that using automated technologies to convert clients is one method to assist reach that goal. For example, the eCommerce company revealed that each open of its digital, tailored suggestions email raised its average customer turnover by 100%.
AI and machine learning platforms are becoming more adept at predictive jobs, such as predicting what customers could desire based on the data provided to them. In an interview conducted just for this topic, Nicholson indicated that deep learning, a subset of machine learning, achieves an accuracy of 96% in interpreting data in many circumstances. “That is the limit of what humans can do,” he says.
Avanade – AI for Business Insights
Avanade is a Microsoft and Accenture joint venture that uses the Cortana Intelligence Suite and other tools for predictive analytics and data-driven insights.
Pacific Specialty hired Avanade to create an analytics platform to provide its employees with a better perspective and insight into the business. The goal was to use consumer and policy data to assist the team in driving additional growth. The goal was to better assist the creation of new products by understanding policyholder behavior and trends using analytics.
According to Avanade, the world is heading toward a future inhabited by smart technology in which machines undertake more of the job that people normally do. According to an Avanade-commissioned report, a poll of 500 business and IT leaders from around the world indicated that they expect smart technology to enhance revenue by 33%. They also predict that this will result in new and redefined employment roles for employees, as well as increased customer advantages. However, the study did not specify which specific jobs would be affected by the introduction of smart technologies.
So far, these use examples indicate that machine learning is primarily employed in service industries such as insurance and retail to address customer, sales, and operations-related tasks; however, synergies AI vs. BI applications are already in the manufacturing and industrial sectors.
Siemens’ AI-based Reporting and Analytic Platform
Siemens is employing ML technology to monitor and certify the operation of its industrial machinery equipment. MindSphere, an open industrial cloud platform, was introduced in beta by the firm.
This cloud platform’s major goal is to monitor machine performance and discover flaws in service requirements using machine tools and drive train analytics.
Many industries use synergies AI vs. BI solutions to monitor machines and measure critical performance parameters. Such equipment prediction will assist businesses in making informed judgments about expected maintenance and will also be used to effectively manage their equipment over the long term.
When comparing Predix to MindSphere, the Siemens platform can operate efficiently on every equipment or plant, independent of the industrial industry. The platform’s primary goal is to assist plant operators in increasing the uptime of their equipment and making maintenance more competent by anticipating when mechanical breakdown is likely.
Industrial plants are saving money on maintenance by utilizing these platforms. When you choose MindSphere, Siemens will give you a box that you can attach to the machines, and it will gather information about the performance of the equipment so that the engineer may take action.
GE (General Electronics)
The most recent technology is playing an important role in the most recent advancements in many industries. Sensors are increasingly being used in physical equipment such as automobiles, equipment spaces, machinery, and manufacturing plants, and these can be automated and analyzed by artificial intelligence.
When it comes to the Internet of Things, it is not only about consumer electronics; oil rigs, commercial trucks, cargo ships, and trains may all be automated or digitalized, inspected, and predicted via networks.
Aviation and oil and gas industries are utilizing GE‘s Predix operating system to learn about the historical performance data of their equipment through the use of industrial apps, which can be used to identify various types of operational outcomes, such as when there is a possibility of machinery failure.
If you believe GE’s operating system is only for automating basic tasks, you’re mistaken. It can analyze a significant quantity of data and generate a forecast report in seconds.
Accenture’s intelligent pipeline technology is being used by the oil and gas industry to investigate pipelines that span millions of miles. It collects data from pipelines and external sources to ensure the safety and proper utilization of resources.
When it comes to the airline business, they use a Predix-based program called Aircraft Landing Gear. The program assists airline engineering personnel in determining how many days a flight will be in service before it is brought into service. Based on the facts, the app will create a timetable that will help to reduce unforeseen or unplanned equipment faults and flight delays.
For example, an AI-powered BI solution optimizes equipment performance. Pitney increased its machinery yield by 20% by developing an automated solution on top of Predix.
These are a few examples of artificial intelligence-enabled business intelligence applications. The BI application developers listed above are utilizing cutting-edge AI and ML technology and tools to create world-class BI solutions for businesses of all sizes.
This might be a turning point for business and industry, when machine learning might become more integrated into how tasks are managed, choices are made, and resources are allocated. It will rely on whether or not businesses as a whole see AI as having actual value; the investment in the technology needs to prove its worth.
The AI vs. BI debate has been going on for a long time. While both assist businesses in making crucial decisions, there are significant differences between the two. While artificial intelligence (AI) has gained prominence recently, it is no surprise that business leaders are working to find ways to incorporate AI into their technology framework. When business officials are asked to clarify what they expect to receive from AI, they regularly respond with solutions that will help them make better business decisions. AI refers to computer intelligence that is similar to that of humans, while intelligence in BI refers to intelligent decision-making. Synergies AI vs BI can help drive any company to success.
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