Organizations that leverage data to drive decision-making significantly outperform their competitors. A recent study by Harvard Business Review on behalf of Google Cloud found that data and AI leaders outperform their peers in several key areas.
For instance, 81% of data and AI leaders have improved their operational efficiency, compared to just 61% for those that haven’t increased their reliance on data. Moreover, more data-driven firms reported revenue growth (81% vs 58%), greater employee satisfaction (68% vs 39%) and improved customer loyalty and retention (77% vs 45%).
These findings come at a time when businesses are amassing more data than ever before. The information companies collect can be a goldmine for business intelligence, yet many still struggle to make the most of this resource, with Immuta’s State of Data Engineering survey revealing that a whopping 89% of firms contend with data access bottlenecks.
Clearly, there’s an urgent need for information to be made more accessible, and the rise of generative artificial intelligence might just be the key enterprises need to unlock the value buried within.
Challenge of using traditional BI
When it comes to extracting value from data, most organizations rely on specialized business intelligence tools, but these can be challenging for the average worker to use. Digging up insights requires navigating through complex interfaces and an understanding of the SQL that’s needed to query most data. As a result, BI tools are often restricted to a small number of specialists.
Adding to these problems, the fragmented nature of common data storage approaches can impede the creation of a cohesive strategy around it.
Pyramid Analytics CEO Omri Kohl recently highlighted these challenges, saying they’re among the main reasons why most companies fail to make use of their data. BI tools can be powerful, he said, but they’re often also “too complicated, too intimidating or just not designed with different users in mind.” Such tools, he said, are “typically built for data scientists or technical teams who then have to service the entire company.”
It's a clear bottleneck that prevents many organizations from being able to embrace a data-driven culture, and it means that most employees make decisions based on their intuition or hunches, as opposed to data-powered insights.
How does generative BI help?
These bottlenecks mean that an employee wanting to analyze customer behavior or sales trends would have to request a report from the IT department. Very often, this can lead to days or even weeks of delay, as the teams in question likely have dozens of people all hounding them for reports.
Generative AI has the potential to ease the burden on these IT teams and put business intelligence at every worker’s fingertips. This combination, known as “generative BI” or “GenBI,” means that instead of going to an analyst, workers can simply ask an AI assistant to dig up what they need. In this way, generative AI can transform data into a highly accessible resource, allowing everyone instant access to proactive and predictive insights.
By integrating generative BI into existing workflows, organizations have the opportunity to rapidly develop a more data-driven culture. According to Kohl, the key enabler here is its user-friendly nature, which eliminates the need to know SQL and enter complex queries manually.
“Imagine if, instead of struggling with a BI tool, you could just talk to your data like you would to a colleague,” he wrote. Indeed, generative BI allows users to simply ask in their natural voice about the company’s sales performance in a specific region, or request insights on what’s driving customer churn in a certain product category.
“Instead of having to navigate through menus or run complicated queries, with GenBI you’d just get instant, clear answers based on live data, presented in any way that’s convenient to you,” Kohl said. It means no more guesswork, and it promises to have a tremendous impact on the adoption of business intelligence.
Combining AI with human intuition
Generative BI can transform business decision-making processes and help to boost the performance of every organization, large and small, but Kohl stressed the importance of humans remaining in the mix. He pointed out that while generative AI has some clear advantages over humans, it will never replace them entirely.
“AI is incredible at processing data, spotting patterns and making predictions based on these findings,” Kohl said. “But it doesn’t have intuition, nor does it understand the nuances of your market or the complexities of your company’s strategy.”
Kohl argues that the real magic of generative BI occurs when you combine it with human creativity, experience and judgment to make the most appropriate decisions. It’s an idea that’s well-founded, with researchers from Harvard Business Review warning against so-called “dataism,” or the belief that machines will always make better decisions.
That’s because decisions involve more than just aggregating data and analyzing it for insights. In most cases, decisions should be based on multiple, nuanced elements, such as knowing which data sources are trustworthy, as well as the ability to imagine possibilities that go beyond the data-based predictions. Humans possess an innate advantage over AI when it comes to these elements, as they involve reasoning that cannot just be trained.
Take the example of Netflix, which has been vocal about using viewer data to aid in its production choices. It’s likely that the decision to go ahead with the hit show Stranger Things, for example, was based partly on the underlying data, which reveals that shows based on supernatural themes go down well with viewers. The data also probably highlighted the enduring appeal of 1980s-based content.
But even with these insights, Netflix’s decision makers also had to weigh up their reliance on inexperienced child actors and the vision of two unproven producers. That meant watching them audition, and judging for themselves if they thought the actors were good fits, and if the storyline was entertaining or not. It’s not something an AI could easily repeat.
“AI could never replace people who are curious, excited and engaged,” Kohl stated. “These people will be asking the non-trivial questions and bringing their own perspectives in the mix, while AI will be there to provide the information they need and walk them through the journey.”
More effective decisions across the board
It can be said that Generative BI’s real strength lies in its ability to enhance everyone’s ability to make the correct decisions, and to encourage this, it will be necessary for organizations to promote a culture of utilizing generative AI at work.
Kohl believes that the responsibility for this falls on the shoulders of corporate leaders, and argues they can set the tone by making data a central part of every decision. They should lead by example, he says, and also make sure their employees have the tools and the training they need to feel confident in using data.
“The goal isn’t to turn everyone into a data scientist, it’s to make data feel approachable and relevant,” Kohl explained.
When data is more approachable and people are confident enough to use it, they’ll start making better-informed, accurate and timely decisions, helping their companies to adapt more quickly to market changes and seize on any new opportunities that arise.
“I see BI becoming as natural and essential to all business functions as emails are today,” Kohl stated. “Just like we don’t think twice about checking our inbox, we won’t hesitate to ask our data for insights. It’ll be seamless, intuitive and embedded in everything we do.”
