The Future of Business Intelligence in an AI-First World

Historically, Business Intelligence has revolved around one promise: to enable organisations to make smarter decisions with their data. For many years this was achieved through dashboards, reports, KPIs, and scheduled performance reviews. Leaders would look back at what happened in the previous quarter, see how things have been trending, and then decide what actions to take next based on those analyses. This model works well when there are slower-moving markets and data was relatively easy to handle. However, the exponential growth of Artificial Intelligence has forced Business Intelligence into a new era.

The current state of Business Intelligence represents a significant transition for companies due to the shift from solely asking what has occurred previously to additionally asking what will occur next (predicament question), as well as determining what further actions need to occur now (action question). This transformation is evolving Business Intelligence from a report-oriented to an intelligent decision-making function.

Business Intelligence will see greater levels of speed, predictive analysis, automation, and accessibility for non-technical users. Companies that are prepared for this Business Intelligence transformation will have an advantage over their competition, specifically in markets where speed and accuracy of analysis are critical.

From Dashboards to Decision Intelligence

Traditional BI has tended to be largely dashboard-focused in nature. While dashboards are helpful, they often require users to possess knowledge of the information being sought. For example, a sales leader may view a dashboard and observe a decline in revenue; then subsequently contact their analytics team to investigate. By the time a response is produced, the opportunity may have been lost.

With the introduction of an AI-enabled BI platform, users will not have to wait for their requests for insights to be fulfilled as intelligent systems now automatically surface abnormal occurrences and forecasts; Therefore, rather than ask users for information, AI-enabled BI platforms provide users with the ability to take immediate action. For example, should an AI-enabled BI system identify a spike in customer churn in a given geographical area, it would automatically detect the root causes of this anomaly, as well as recommend actionable steps (e.g. implement targeted retention campaigns; revise pricing strategies).

BI is just beginning to expand from reporting to include additionally providing decision support intelligence. The value of BI no longer resides solely in being able to view your data; The value of BI lies in the ability to understand what the data means, and to know how to act on it.

The Rise of Natural Language Analytics

One of the major obstacles to the adoption of BI has been the disconnect between business users and the systems utilized to house data. Although there are many professionals aware of the information they need (and therefore would benefit from using BI), they may lack the skill necessary to build queries, configure dashboards and/or interpret complex models.

Natural language analytics are helping to bridge this gap with regard to AI. Users are now able to ask questions in everyday language (e.g. which type of product category experienced the highest level of margin improvement last month? Why did Q2 customer acquisition cost increase?) Instead of relying solely on analysts, users can more readily access data themselves.

This does not eliminate the need for data professionals. On the contrary, data professionals will become increasingly invaluable as their level of expertise will increase due to the reduced amount of time that will be spent preparing repetitive reports and instead be utilized for validation of model integrity, data quality improvement, and solving higher-level organisational issues than were previously addressed. While increasing access to BI for users through AI-driven solutions is beneficial, it is also critical that important BI measures be maintained through proper management of the BI process and strong governance.

Augmented Analytics Will Become the New Standard

Augmented analytics will establish the future state of Business Intelligence through the implementation of AI and machine learning technologies to automate portions of the BI process (i.e., data preparation, discovering insights, detecting trends, creating forecasts, generating narratives, etc.).

Companies will continue to develop BI solutions using augmented analytics solutions that rely less heavily on entry of manual data by relying on the output of automatic explanations and recommendations from BI applications. Rather than a user spending tens of hours on data preparation and various ways of interpreting data, they will now have the ability to receive automated information and suggested course(s) of action regarding analysed data.

Through artificial intelligence applications and other methods, businesses are finding it increasingly difficult to manage the vast amount of data generated and collected by today’s enterprises (the pace at which enterprise data grows is significantly larger than the capabilities of most BI teams can manage). Generally speaking, by observing current trends in data, organizations that would otherwise have been unable to identify important signals within large volumes of unstructured data can now leverage intelligent automation to locate these signals much more quickly. Additionally, with the implementation of augmented analytics, companies can eliminate human bias by identifying patterns in data that otherwise may not be apparent during manual review.

Predictive and Prescriptive BI Will Gain Ground

Historically, the majority of Business Intelligence systems have focused primarily on Descriptive Analytics: What occurred, when it occurred, and where it occurred. However, in an AI First World, the majority of Business Intelligence will transition to economic predictive and prescriptive intelligence.

Predictive BI uses both historical and real time data to make predictions about probable future states. Retailers use predictive BI to forecast demand; banks implement predictive BI for the assessment of credit risk; healthcare uses predictive BI to anticipate patient demand; and manufacturers leverage predictive BI to predict equipment failure.

Prescriptive BI takes the forecasting one step further and provides recommendations on how to respond to the forecast. For example, if predictive BI indicates that demand in a specific market will rise, prescriptive BI will suggest specific recommendations for inventory management, pricing strategy, and campaign management.

Companies will experience a radical transformation with Business Intelligence. Companies will evolve from reactive decision-making to proactive strategic planning. Businesses will identify potential risks before they escalate and create an action plan to capitalize on opportunities prior to competitors gaining an advantage.

Real-Time Intelligence Will Matter More

As a variety of markets continue to evolve, reliance on delayed analytics is decreasing. Weekly or monthly reports continue to hold importance to rate decisions; however, they lack the ability to enhance the speed and accuracy of on-demand decision-making.

AI First Business Intelligence will continue to rely upon real-time and near real-time data as the predominant requirement for Businesses. Real-Time Business Intelligence is critical in; Financial Services, Retail, Logistics, Cyber Security and Digital Commerce. The ability to recognize sudden changes in consumer buying behavior, disruptions in supply chain, or suspicious transactions must be identified quickly in order to trigger a timely response.

With a Real-Time BI capability, Organizations will have the capability to identify change as it occurs. When used in conjunction with Artificial Intelligence tools, Real-Time BI systems allow Organizations to generate alerts, automate processes and respond at accelerated speeds. The intent is to provide Organizations with the means to not only observe, but also effectively influence the Organization's performance.

Data Quality and Governance Will Become Even More Critical

The introduction of Artificial Intelligence into Business Intelligence has elevated the importance of Quality of Data as it relates to the utilization of AI technologies. AI tools are only as reliable as the data they consume. Poor quality data can contribute to inaccurate forecasts, ineffective recommendations and/or expensive business decisions.

Therefore, organizations must invest in establishing a strong Data foundation. Organizations must establish a comprehensive Data foundation to enable success with AI-based technologies. Such a foundation requires Clean, Connected, Well Governed, Chains of Custody Compliant Data. Additionally, Organizations must have well-defined rules and responsibilities around Data ownership, privacy, access, lineage and transparency of the data.

Companies will benefit from the support of Trusted Business Intelligence and Analytics services to develop a solid Foundation for Business Intelligence Implementation, and a comprehensive BI Strategy built around the Data Management process, BI Analytics Operations and the Business'. Without the proper foundation, AI tools will be limited in their ability to provide consistent value to Organizations.

How Will AI-Driven BI Affect the Role of Humans

There is the prevalent fear that AI will take the place of Human Decision Makers. In fact, the future of Business Intelligence is more likely to result in a collaborative environment between Humans and Intelligent Systems.

While AI can evaluate enormous amounts of data, recognize patterns within those data and generate recommendations rapidly, Humans contribute context, judgement, ethics, creativity, and a Strategic understanding of the decision-making process. For example, while an AI-generated model may recommend Lowering Costs in a specific department, Human leaders should validate the recommendation against all factors: Customer Experience, Employee Impact, Company Brand and Long Term Growth.

The optimal model of Business Intelligence will not remove Human input from the Decision-making process. Business Intelligence will enable Humans to make Better Decisions with More Evidence, More Options and Faster Insights. Therefore, the Organizations that will Win will be the Organizations that integrate Machine Intelligence with Human Wisdom throughout their Business Operations.

Preparing for the AI-First BI Future

To successfully prepare for the future state of AI First Business Intelligence, Organizations need to assess their current maturity of Business Intelligence capabilities; Is the Organization still reliant on Manual Spreadsheets? Are the Organization's Dashboards connected to Reliable Sources of Data? Do the Business User community have the ability to generate Insight without Waiting Days or Weeks for Reports? Are the AI-generated Models Explainable and Governed?

The Transition to AI First Business Intelligence does not have to occur in a single instance. Organizations may identify High-Impact Use Case Scenarios to implement AI First BI Technology, such as; Sales Forecasting, Customer Churn Analysis, Marketing Performance Optimization, Supply Chain Visibility, and Financial Planning. The critical factor will be to tie the use of AI First BI Technology to the desired Business Outcomes rather than a Technology-for-Technology's-Sake approach by Organizations.

Organizations should also invest in Data Literacy. As organizations deploy AI-first Business Intelligence technology, Data Literacy will be critical for Non-Data Experts across all departments. Data Literacy will allow employees to Understand Insight and Question their Assumptions and ultimately use Data in a Fashion that Honors the Integrity of the Data.

Conclusion

The next Phase of Business Intelligence will not only result in Better Dashboards and reports but will create Intelligent Systems that allow Organizations to Understand Change, Predict Outcomes and Act with Certainty.

As AI continues to Impact Business Intelligence, Business Intelligence will Transition from a Reactive Analyst, to Proactive Decision-Making Automations that are; Conversationally Integrated, Proactively Real-Time Responsive, Fully Automated and Deeply Embedded into the day-to-day Business Processes and Decisions of Organizations. However, the Organizations that will continue to compete in their Industry, will be those that; create a Trusted Data-Foundation, Create an Environment for Teams to Create a Data-Driven Culture, and Leverage AI Technology to Enhance Human Judgement and Decision-Making.

In the most rapidly evolving phase of Business Intelligence, Data will not only serve as an Analytical Tool to Effect Decisions, Data will Create Decisions for Companies.

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