Blog by | George Brown, Jet Global
The Future of AI in the Enterprise
The business world is at an inflection point when it comes to the application of Artificial Intelligence (AI). While the technology behind enabling computers to simulate human thought has been developing, at times slowly, over the past half-century, the cost of implementation, readily available access to cloud computing, an practical business use cases are primed to help AI make a dramatic impact on the enterprise over the next few years.
With the potential use cases on the horizon for AI in business, as well as the investment dollars and rate of change currently propelling AI, one thing is clear: you’ll need to get your foundation in place sooner, rather than later, to take advantage of the benefits coming to the business world.
Enter business intelligence (BI) software. By building the foundation now with this readily available, accessible, and affordable software, businesses can prepare themselves for the future while also reaping the benefits of today. After a couple of years with inflated expectations for AI that have yet to materialize, businesses are beginning to ask themselves whether it makes sense to push through a costly implementation that won’t yield tangible results for 2-3 years – when really, they should be focused on implementing BI today, yield results immediately, and layer AI on top of your established BI data to derive new insights and drive greater benefit once the technology matures/.
So how will BI software help set the stage for AI in your enterprise, and what possible use cases can be gleaned from the intersection of AI and BI?
How Can BI Software Help?
Regardless of where you’re landing in regards to Artifical Intelligence and Business Intelligence, one thing is true: you’ll need to have data to feed to both. Without data to act upon, there’s no ‘intelligence’ in AI or BI. There’s nothing to analyze, or apply a learning algorithm to – when it comes to any intelligence solution, data is the foundation upon which it must be built.
Thankfully, with the widespread of cloud computing and the Internet of Things (IoT), data has never been more readily available in today’s business world. But the vast reams of data generated on a daily basis are presenting a new problem for businesses – what does it mean and which data matters? How should data be tagged, sorted, grouped and analyzed? Which problems do disparate data points speak to? And how can the data be collected across multiple touchpoints, from all retail locations to the supply chain to the factory be easily integrated?
Enter data warehousing. Data warehouses are a means of taking data points from disparate touchpoints (such as point of sale, CRM, inventory, and warehouse management systems), standardizing the data collected, structuring it to extract necessary insights, and running analysis. Enterprise businesses cannot survive without robust data warehousing – data silos can rapidly devour money and resources, and any business still trying to make sense and cobble together ‘business intelligence’ from multiple reports and inconsistent data is rapidly going to lose ground to those businesses with integrated data and reporting.
The optimized data warehouse isn’t simply a number of relational databases cobbled together, however – it’s built on modern data storage structures as the Online Analytical Processing (OLAP) cubes. Cubes are multi-dimensional datasets that are optimized for analytical processing applications such as AI or BI solutions. Cubes are superior to tables in that they can link and sort data by multiple dimensions, allowing for non-technical users to choose from any number of role-specific and highly contextual data points to uncover new insights and adjust tactics and decisions on the fly. Chances are good that your average non-technical sales agent or purchasing representative will have difficulty joining multiple tables together with a standard report, but with Business Intelligence cubes, all that is required is to drag and drop the metrics and dimensions that matter to them into their own personalized dashboards.
So how is the data extracted? By using Structured Query Language (SQL), the language used to manipulate and extract data stored in cubes. SQL was developed as a standard language to communicate databases, regardless of exactly which type of database was being used, and is ultimately the means by which data in a table is extracted, deleted, updated and managed.
Beyond data warehousing and OLAP cubes, which provide the technical foundation, there are a number of additional components that can help enterprise businesses address their data requirements”
Data modeling: Data modeling is a method of mapping out individual data sources across an enterprise and determining how they need to interact with one another to extract the most valuable business insights. Data modeling can be performed at the conceptual (high-level, related to business objectives), logical (mapping to each business function), and physical (how the actual dimensions, measures, and hierarchies are related within a data cube).
Analytics and reporting: Capturing, structuring, and storing data is good – but being able to analyze and report on it is the ultimate end goal. Business intelligence solutions are capable of providing simple, accessible analytics and reporting functions for end-users, empowering them to find actionable insights they need with little technical expertise (or formal data science training). This also helps business functions avoid unnecessary data logjams and giving them instant access they need to the data they so desperately require.
Data visualization and dashboards: Analytics and reports are a crucial component of business intelligence, but if you’ve ever spent hours poring over a table of values trying to decipher exactly what the data is saying, you’re not alone. With data visualization tools, critical insights are displayed in rich graphical representations that are vastly easier for the human brain to interpret. According to a study by Aberdeen Group, organizations using visualization tools are 28 percent more likely to find timely information than those who rely solely on managed reporting; the same study also found that 48 percent of business intelligence users at companies with visual data discovery are able to find the information they need without the help of IT staff. Dashboards can easily assemble visualizations and reports into customizable displays by end-user or business units, giving individuals instant insight into KPIs that help drive better business performance from the bottom up.
Security, simplicity, speed – these are the three major benefits business intelligence solutions help to drive, and three critical measures of success in enterprise business. While artificial intelligence remains focused on helping computers glean insight entirely on their own, business intelligence is enabling entire organizations to gain access to the data they need to make rapid, informed decisions, and the importance of that in today’s quickly shifting business landscape simply can’t be overstated. In a survey of 2,600 business intelligence end-users, 91% responded that BI gave them faster reporting, analysis or planning, 84% said it enabled them to make better business decisions, and 79% said it improved employee satisfaction.
The Future is (Almost) Here
In the near future, AI algorithms will be able to be seamlessly applied to your existing data stores, unlocking further insight for your enterprise. As highlighted in this 2018 Harvard Business Review article, AI applications in response to business needs fall into one of three categories:
- Process automation: The most common current application for AI in business is by automating systems and business processes. While previous incarnations of automation focused on exchanging information between systems, AI can level up this ability by actually interacting with the data like a human – either inputting or consuming as necessary. Today, AI ‘robots’ are able to analyze legal contracts and extract relevant provisions, update customer records across a number of disparate systems, and automate customer outreach in response to situational conditions. As these algorithms grow ‘smarter’, businesses will be able to automate even broader swaths of processes.
- Cognitive insight: Cognitive insight is the ability to apply AI algorithms to vast existing stores of data to extract meaning and identify patterns. While BI software and data stores will undoubtedly provide the ‘diet’ for cognitive insight algorithms, as the algorithms learn, they’ll be able to apply those learnings to broader data sets, react to new data in real-time, identify potential data matches across multiple databases, or manage programmatic ad buying.
- Cognitive engagement: Cognitive engagement refers to the human-interfacing element of AI – think automated chatbots, knowledge bases, product recommendation engines, and more. Cognitive engagement applications can be used to automate interactions between people and systems, either externally (for customers), or internally (for employers). Most current applications focus on internal engagement as businesses are still apprehensive about the relatively new applications – but, again, as AI development and implementations continue to mature, expect objections to fall by the wayside as businesses will find new ways of using existing data to drive meaningful automated interactions with human beings around the world.
Over the next few years, you’ll see artificial intelligence finally begin to live up to the hype we’ve been hearing about in the business world, and computers will help usher in a new era of productivity and profitability for enterprises on the cutting edge – but only if you have the foundation in place today, and that starts with business intelligence.