It’s long been a dream of leaders to empower not only senior executives, but all employees, to make data-driven decisions. After all, salespeople working with high-value leads and customer service agents handling customer issues all make daily decisions that affect the health of the business. If these frontline employees had easy access to relevant, reliable information in real time, they could base their decisions not on intuition, but on hard data.
The benefits of data-driven decision making are clear. For example, McKinsey found that companies using data-driven B2B sales growth engines saw 15 to 25 percent higher profit growth than the market. Imagine if these gains could be extended to every department in the company. Unfortunately, extending access to every employee has proven to be a nearly impossible task.
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The first barrier is data fragmentation. Critical information is siloed across different applications and locations within the enterprise, making it very difficult to get a big picture view, and CIOs are well aware of this problem. For example, when Foundry surveyed CIOs for its 2024 State of the CIO report, they were asked what their priorities were for becoming more data-driven. The third answer was making their data more accessible. But data transformation projects that unify data and break down silos can take years and cost millions of dollars. Organizations that can figure out how to 1) deliver actionable data to frontline workers on demand and 2) enable pervasive data-driven decision making will gain a significant competitive advantage over their competitors.
Challenges in enabling data-driven decisions across the enterprise
One might wonder why business intelligence platforms aren’t a good fit for this situation. After all, these platforms should already be tied to most of an organization’s data sources, so the lack of a successful data transformation and unification effort shouldn’t be a problem. Unfortunately, access to traditional BI isn’t easy to scale. The typical approach to enterprise BI is dashboard-centric, and building dashboards can be a time-consuming task. Designing one for every front-line role would likely result in a long backlog for business analysts, and the dynamic nature of the timely contextual data needed by the front lines would quickly become a maintenance nightmare.
Additionally, while it’s possible to drill down into the dashboard to get additional information, it requires some training. Frontline workers make a lot of quick decisions every day. If an insurance agent realizes on a call that they need a specific piece of customer information to sell an insurance policy, they often don’t have time to scroll through multiple dashboards to find it. They need that data right away.
Generative AI (GenAI) seems to offer a solution, since users can interact with it in natural language. Certainly, deploying generative AI is a top priority for businesses. Just over 60% of companies surveyed this year by Bain & Company said generative AI was a top three business priority, and nearly 90% said they were already working on deployment, development, or a pilot. But GenAI alone can’t solve the problem of making ubiquitous data-driven decisions. GenAI is, at its core, a pattern recognition and prediction engine, not a Data analysis platform.
So although it is very good for analysis natural language While GenAI provides answers in plain text that appear correct, GenAI’s answers aren’t always 100% accurate, and that goes for numbers and calculations as well. GenAI is a language model, so it wasn’t designed to perform mathematical analysis. Sure, AI researchers are improving models to reduce instances of GenAI “hallucinations,” but as it stands, GenAI alone isn’t a reliable source of business insights on which to base important decisions.
Better together: BI + AI
There is a solution that doesn’t depend on a long-term data unification project. When GenAI is integrated with a BI engine that provides reliable insights from data, GenAI can do what it excels at: parsing human language and communicating in a way that is more relevant to most users. Organizations can ensure that GenAI isn’t producing answers with fabricated numbers, and users can get the information they need without having to navigate multiple dashboards. Instead, users can simply ask for information using ordinary language.
There are many applications of combining AI and BI within the enterprise, but one model for rolling out access to information to all users is to create an overlay that works with the organization’s standard web browser, which is the interface through which users access most, if not all, of the applications they use. In this way, organizations can provide relevant data to employees as part of their daily workflow. For example, one could imagine a no-code interface that highlights keywords (customer, references, employee names, etc.) and produces a card with key data points on that topic when the cursor hovers over it. With GenAI, employees can ask for more details or entirely different information, and because it’s based on BI data, the answers will be reliable.
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No matter how the solutions are designed, the combination of BI and AI allows the business to move beyond the destination dashboards created by data analysts to empower frontline workers to ask for the information they need in their own words. We are on the verge of realizing the vision of a truly data-driven enterprise. Together, AI and BI will make it a reality.
