Generative AI (GenAI) is revolutionizing how we generate, distribute, and act on data, fundamentally transforming the metaphorical “economy of insights.” In this new paradigm, data remains the currency of decision-making, but the rules of the game have shifted. From a Decision Scientist’s perspective, this evolution creates both challenges and opportunities, reshaping how we deliver value.

As I wrote in a blog post more than a decade ago:

Data as currency. I have to credit Noah Elkin (@NoahElkin) at eMarketer for implanting this metaphor deep into my psyche. I love it because it implies an “economy” for data. Data as a “tradable commodity.” Data as something that circulates between agents, teams. It has value. It is invested for growth. It isn’t warehoused somewhere. It’s right there in front of us, in our hands. Of course, it also has a more literal meaning, as the title of Brian Eisenberg’s (@TheGrok) book suggests, “The Money is in the Data.” But the point I wish to emphasize is that data must circulate within marketing teams. Data without action is overhead. So, channel your inner Rod Tidwell and shout out at your next meeting, “Show me the DATA!”

This metaphor still resonates. However, what changes with GenAI is how this currency is generated, the speed at which it circulates, and its quality—increasing action and reducing overhead. In essence, GenAI has redefined the economy of insights, and the implications for Decision Scientists are profound.

The Economy of Insights in a GenAI-Driven World

The introduction of GenAI into the data science ecosystem is setting the stage for a transformation akin to moving from a barter system to a high-frequency trading market. In the near future, insights will no longer be scarce resources—they will become increasingly abundant, rapidly produced, and widely distributed. However, this abundance will bring new complexities that Decision Scientists must anticipate and navigate to ensure the economy of insights thrives.

The Evolution of Insights as Currency

If we are being honest, the relationship between data, insights, and action has always been tenuous. Even before GenAI, the challenge wasn’t a lack of data—it was an overabundance of data points, summary tables, and metrics that often sat idle in “doorstop reports,” waiting for someone to extract genuine insights. Valuable, actionable insights have always been rare because they required effort to synthesize meaning from this ocean of information. In this way, genuine insights were (and still are, but quickly changing) labor-intensive to extract, giving them inherent value through scarcity (e.g. the idea of “Out- of-Sights” by Avinash Kaushik is a call to fundamentally reimagine how we approach analytics and decision-making because of these shortcomings. As he boldly states, “…chances are 90% of what you do today needs to die.”)

With GenAI, however, the landscape is evolving. Over the next few years, this shift will accelerate. GenAI is poised to increase not just the volume of insights, but the volume of valuable, actionable, and prescriptive insights—those directly aligned with the decision-making. This is a game-changer. Instead of trying to extract insights through labor-intensive means or a mass of pre-existing reports — aka the “insight industrial complex” — Decision Scientists will collaborate with GenAI tools to generate insights dynamically and in formats tailored to the specific needs of the stakeholder (see “Bespoke Economy” below).

While Decision Scientists will still play a key role in directly generating these insights for some time to come, GenAI will handle much of the heavy lifting that used to slow us down. Tasks like synthesizing data, identifying patterns, and running scenario-based analyses will become increasingly automated. As a result, the role of the Decision Scientist will evolve. Beyond being analysts or model builders, they will take on additional roles as:

• Curators: selecting the most relevant methods and insights for the problem at hand.
• Validators: ensuring AI-generated outputs are accurate, unbiased, and aligned with organizational goals.
• Strategists: connecting the dots between insights and action, ensuring that insights support long-term objectives.

This transition enabled by GenAI will not happen overnight, but it represents a paradigm shift in how organizations will approach data-driven decision-making in the years ahead.

In this environment, the value of an insight isn’t just in its generation but in how it’s contextualized and applied.

The “Bespoke Economy”: Hyper-Personalization of Insights

The term “bespoke economy” refers to the growing ability to tailor insights to the unique needs of individual stakeholders within an organization. In a traditional data environment, insights are often created as one-size-fits-all reports, designed to address broad audiences with generalized metrics. These often lack the specificity required to drive action for specific roles or decisions.

With Generative AI, however, we will be entering a new phase where insights can be dynamically customized—or bespoke—to match the exact preferences, goals, and decision-making contexts of different users. For example:

• Executives might receive high-level strategic insights framed around company-wide objectives and KPIs, but with simulations, scenario-based forecasts and prescriptive recommendations. For example, instead of simply reporting quarterly sales performance, Decision Science-powered insights could provide a probabilistic analysis of how different investment strategies (e.g., increasing spend in a specific channel or region) would impact future growth, helping executives make informed trade-offs in resource allocation.
• Marketing managers could be provided with channel-specific forecasts and ROI estimates to guide budget allocation, enhanced by incrementality models and saturation analyses. Rather than showing raw metrics or simple historical trends, Decision Science tools could deliver insights into which campaigns are driving true incremental conversions, how close certain channels are to diminishing returns, and where budget reallocation could maximize future impact.
• Frontline teams may get hyper-detailed operational insights, such as real-time performance alerts or customer segmentation tailored to their workflows, but with dynamic prioritization guidance. For instance, instead of just flagging leads with low engagement, Decision Science-enhanced insights could identify the highest-probability opportunities and suggest the next best action (e.g., an email, a discount, or a call) based on historical patterns, customer preferences, and the current stage of the sales pipeline.
  

This personalization creates an economy where value is maximized through relevance. Insights are no longer static outputs but adaptable products that meet the precise needs of their recipients.

Conclusion: Circulating Insights in this New Economy

As I wrote years ago, “data without action is overhead.” In many ways, the arrival of Generative AI makes this point even more pressing. By redefining how insights are generated, distributed, and applied, GenAI offers the potential to shift from the static, generalized “doorstop reports” or “water is wet” analysis of the past to a future where insights are dynamic, actionable, and tailored to decision-making needs.

But just as currency requires trust to hold value, insights require expertise to ensure they remain actionable and aligned with strategic goals. This is where the Decision Scientist’s role becomes essential—not just as a generator of insights, but as the curator, validator, and strategist who ensures that the right insights circulate to the right people at the right time.

As this transition unfolds, the focus of data-driven organizations must evolve from “Show me the DATA!” to “Show me the IMPACT!” The success of the new economy of insights will be measured not by the abundance of data but by the ability to consistently support decision-making, drive action, and deliver results.