What Is Decision Intelligence? A Guide for Founders
Every tool you use gives you more information. Decision intelligence is the discipline of turning that information into better decisions. For founders, this distinction is the difference between drowning in data and actually knowing what to do next.
A Working Definition
Decision intelligence is the application of data science, social science, and managerial science to improve decision-making. The term was popularized by Cassie Kozyrkov, who served as Google's Chief Decision Scientist, and has gained significant traction since Gartner named it a top strategic technology trend.
But let us skip the academic definition and talk about what it means practically. Decision intelligence is a system, or a set of practices, that starts where data and analytics end. Data tells you what happened. Analytics tells you why it happened. Decision intelligence tells you what to do about it.
The simplest framing
Decision intelligence is the last mile between "here is information" and "here is what you should do." It bridges the gap that every other tool leaves open.
This is not just semantics. The gap between information and action is where most founders spend a disproportionate amount of their cognitive energy. Every report, dashboard, alert, and newsletter creates information. Almost nothing synthesizes that information into clear, prioritized, context-aware recommendations for action.
Decision Intelligence vs. Business Intelligence
Business intelligence (BI) is primarily backward-looking. It answers the question: "What happened?" Revenue went up 15%. Churn increased. Pipeline conversion dropped in Q3. These are valuable data points. But they are descriptions of the past, and they leave the "so what?" entirely to you.
BI tools are excellent at their job. Tableau, Looker, Power BI, and their peers do a good job of visualizing historical data and making it accessible. The problem is not that BI is bad. The problem is that BI is incomplete for decision-making.
Knowing that pipeline conversion dropped in Q3 is a fact. Understanding whether you should change your pricing, adjust your sales process, retrain your team, or do nothing because it is a seasonal pattern is a decision. BI gives you the fact. Decision intelligence gives you the framework for the decision.
BUSINESS INTELLIGENCE
DECISION INTELLIGENCE
Decision Intelligence vs. Data Analytics
Data analytics goes a step further than BI. While BI tells you what happened, analytics tries to tell you why it happened and sometimes what might happen next (predictive analytics). This is closer to decision intelligence, but still stops short.
The gap between analytics and decision intelligence is the gap between "here is a prediction" and "here is what you should do about it." A predictive model might tell you that churn is likely to increase next quarter based on current trends. Decision intelligence tells you: which specific customers are at risk, what actions would most likely retain them, and whether those actions are worth the investment given your current priorities.
Analytics answers "what is happening and why." Decision intelligence answers "given what is happening, what should I do, and what should I specifically not waste time on?" That last part, telling you what to ignore, is perhaps the most valuable and most underappreciated aspect of decision intelligence.
Decision Intelligence vs. Competitive Intelligence
Competitive intelligence (CI) focuses on gathering information about your market, competitors, and industry. Traditional CI tools like Crayon, Klue, and Kompyte do this well. They track competitor websites, pricing changes, job postings, press releases, and social media activity. This is valuable intelligence gathering.
The distinction: CI gathers and organizes competitive information. Decision intelligence acts on it. CI tells you "your competitor changed their pricing page." Decision intelligence tells you "your competitor changed their pricing page, here is what that means for your three open enterprise deals, and here is whether you should adjust your pricing in response or hold steady."
CI is a critical input to decision intelligence, but it is not the same thing. Many founders treat CI as the end product. They gather competitive intelligence and then still have to figure out what it means and what to do about it. Decision intelligence closes that loop.
Why Decision Intelligence Matters More for Founders
A VP at a large company makes decisions within a defined domain. The VP of Sales makes sales decisions. The VP of Engineering makes engineering decisions. They have teams, processes, and institutional knowledge to support them.
A startup founder makes 10x more types of decisions. Product, engineering, sales, marketing, hiring, fundraising, legal, operations, sometimes all in the same day. There is no institutional knowledge because you are building the institution. There are limited teams to delegate to. And the stakes on any individual decision can be existential in a way they rarely are at a large company.
This is why decision fatigue hits founders harder than almost anyone else. The volume of decisions, the diversity of domains, the stakes, and the lack of support infrastructure create a perfect storm of cognitive load. Decision intelligence is not a nice-to-have for founders. It is a survival tool.
The founder decision tax
Every decision a founder makes has an opportunity cost: the cognitive energy spent on that decision is not available for other decisions. Decision intelligence reduces this tax by handling the triage, synthesis, and prioritization that currently consumes a huge portion of a founder's mental bandwidth.
The Decision Intelligence Stack
If we think of decision intelligence as a stack, it has four layers. Most tools address one or two layers. A complete decision intelligence system addresses all four:
Layer 1: Data Collection
Gathering signals from diverse sources: news, social media, industry reports, regulatory filings, patent databases, job postings, financial data. This is the commodity layer - many tools do this well.
Typical solutions: Google Alerts, RSS feeds, Crayon, Klue, social listening tools
Layer 2: Synthesis
Combining signals into coherent narratives. Not just 'here are 50 data points' but 'here are 3 themes emerging from these 50 data points.' This is where AI adds significant value over manual aggregation.
Typical solutions: AI-powered research tools, analyst reports
Layer 3: Contextualization
Interpreting synthesized intelligence through your specific lens. The same market trend means different things to different founders depending on their stage, market position, current priorities, and decision mode.
Typical solutions: Human Chief of Staff, board advisors, DESTA
Layer 4: Action Recommendation
Translating contextualized intelligence into specific, prioritized actions: DO this, DELEGATE that, WATCH this, IGNORE that. The most valuable layer and the one most tools skip entirely.
Typical solutions: Human Chief of Staff, DESTA
Most founders cobble together tools for Layers 1 and 2, and do Layers 3 and 4 in their heads. The whole point of decision intelligence as a discipline is that Layers 3 and 4 can be systematized, especially with AI that learns from your decision outcomes.
The Gartner Prediction and What It Means
Gartner has predicted that by 2026, more than 75% of organizations will adopt some form of decision intelligence. This is a significant claim from a research firm that is usually conservative in its predictions. What does it actually mean?
It means the market is recognizing what founders have felt intuitively for years: we have a data abundance problem and a decision support deficit. Organizations have invested massively in data infrastructure (data warehouses, analytics platforms, BI tools) and are now discovering that more data does not automatically produce better decisions. In many cases, more data produces worse decisions because it creates more noise and more cognitive load for the humans who still have to figure out what to do.
Decision intelligence is the natural next phase. Not more data. Not better dashboards. Systems that actually help humans make decisions, and that get better at it over time as they learn from outcomes.
Cassie Kozyrkov's Framework
Cassie Kozyrkov, who led decision intelligence at Google, frames the discipline around a critical observation: most organizations invest heavily in data and analytics but almost nothing in the decision-making process itself. It is like investing in the best ingredients and kitchen equipment but never learning to cook.
Her framework emphasizes that decision intelligence is not purely a technology problem. It involves understanding human cognitive biases, organizational dynamics, and the specific context in which decisions are made. Technology is an enabler, but the discipline itself is about the decision process.
For founders, this framework is particularly relevant because it acknowledges that better decisions are not just about better data. They are about better processes for turning data into action, which includes knowing when to ignore data entirely. As Kozyrkov has said: "The purpose of data is to help you make better decisions. If data is not connected to a decision, it is just noise."
What Decision Intelligence Looks Like in Practice
If you are a founder reading this, you might be thinking: this sounds nice in theory, but what does it actually look like in my day?
Here is the practical version: instead of starting your morning by opening email, Slack, Twitter, news sites, and your analytics dashboard (reactive mode), you start with a single operating brief that has already done the gathering, synthesizing, contextualizing, and prioritizing. The brief tells you what happened that matters, what it means for your specific situation, and what you should do about it, ranked from most to least urgent.
Some items require your action. Some should be delegated. Some should be watched. Some should be actively ignored. Each recommendation comes with sources you can verify and reasoning you can interrogate. And the system learns from your feedback, getting sharper every day.
That is decision intelligence. Not another dashboard. Not another alert. A system that respects the scarcity of your attention and ensures that when you do make a decision, you are working with the right information, in the right context, at the right level of detail for your current situation.
Getting Started with Decision Intelligence
You do not need to overhaul your entire information diet overnight. Decision intelligence is a practice that compounds over time. Here is a reasonable starting point:
Audit your morning: count how many information sources you check before your first real decision of the day. Most founders are shocked by the number.
Identify the 'so what?' gap: for each piece of information you consume, ask whether it came with a recommendation or whether you had to generate one yourself.
Start triaging explicitly: before reacting to a signal, ask: should I DO this, DELEGATE it, WATCH it, or IGNORE it? Just making this categorization conscious is a form of decision intelligence.
Measure outcome quality: start noting which decisions you made well and which you would have made differently. Over time, patterns emerge about when you make good decisions (usually early, with clear information) and when you make poor ones (usually late, under cognitive load).
Consider systematic support: whether it is a human Chief of Staff, a tool like DESTA, or your own structured practice, find a way to move the triage and synthesis off your plate so your cognitive resources go to the decisions that actually need your strategic judgment.
Decision intelligence is not another buzzword. It is the recognition that the bottleneck in modern business is not data, not analytics, and not information. The bottleneck is turning all of that into good decisions, consistently, under load. For founders facing 10x the decision volume of anyone else in the organization, this is not a theoretical concern. It is a daily reality.
Learn how DESTA applies decision intelligence with action recommendations, adaptive decision modes, and quality gates that make AI intelligence trustworthy.