Throughout the last year, we’ve spoken with hundreds of data leaders from governments and the Fortune 500. All of them are thinking about AI. Most are piloting new technologies. Many homegrown projects have already unraveled or never launched. As we explain here, most of these are going to fail.

With that said, a new wave of technology is coming and those organizations that harness it correctly will have a distinct market advantage. Those that remain stagnant will absolutely be playing catch up.

The devil, of course, is in the details. Here we discuss what such a world looks like, why you should care, and where where the challenges lie.

So what does Generative Analytics look like tangibly? In the beginning, chatbots and co-pilots for everything data related. Want to query your database? Just ask your SQL copilot to generate a query for you. Want to create a chart? Ask your BI tool’s chatbot to generate a new visualization for you.

The problem with these surface-level approaches is that they conform to the old paradigms of analytics and are only a marginal improvement on existing processes for existing users of such tools.
‍
The next phase of analytics is to teach the enterprise data assets across current platforms to AI, converting them into a conversational format so that all employees (with correct credentials, of course) can access fragmented insights and knowledge more naturally. This is where organizations will begin to draw value from the 97% of enterprise data that sits unused (cue platitude about only seeing the tip of the iceberg).

This is just the beginning, however. Once AI is natively able to understand enterprise data, nuances to particular tables, executive’s concerns, and the drivers of the business, the natural next step is to have AI proactively surface insights you never thought to ask. That is, AI-assisted insights generated in real time and at the speed of relevancy. This has long been the holy grail for most executive teams, but the technology is nearly there.
‍
Prudent organizations should be laying the groundwork to get their enterprise data AI-ready without getting locked into the old way.

Executives have been dreaming of the day where “have the report on my desk by Monday” becomes instantaneous. Relatedly, analytics teams are tired of fielding the impromptu asks for for every stakeholder’s “absolutely pressing” question instead of focusing on deeper projects. From our interviews, analysts spend anywhere from 25-40% of their time fielding ad hoc questions. For a team of 10, that costs up to half a million dollars a year at the current market rate.
‍
With the right security and governance in place (more on this below), the universe of self-service possibility has just grown tenfold. In fact, 67% of today’s managers still aren’t comfortable using existing tooling for analytics. The data revolution came for some but not for most. With the assistance of more intuitive, natural-language data interfaces, there is no excuse not to be data driven. And those that rise to the occasion will win.

What this means is that organizations can begin to address the unknown unknowns. These are the questions that executives and stakeholders choose not to ask because analytics resources are limited or because they may not line up with a given project or business priority. Today’s analytics serve to answer questions versus help you ask them. Most analytics projects are by definition reactionary, where the questions are to the effect of: “help me understand why X product category is lagging this quarter,” “what are the 'best' profiles of the customers I target,” “where can we find cost savings in our supply chain?”

When the barrier to inquiry is lowered, teams can begin to explore data rather than use it in service of what they are already partially aware of. This opens up an entire universe of insights currently left undiscovered. Current ad hoc investigation and self-service tooling attempts to achieve this, but fall woefully short. Executives instead choose to self-filter and most of the value remains below the proverbial surface.