Published by Wanda Rich
Posted on June 26, 2025
Greg Genung leans back in his office in Austin, Texas, a map of interconnected data from Snowfire AI’s proprietary intelligence streams glowing on his screens, pulling together everything from the data layer that runs some of the most complex enterprise companies in the age of AI—all into one centralized, large metric model in combination with military-style signals intelligence. This is the new business brain.
On his left, a live dashboard tracks business signals from the outside markets about all of the relevant news, suppliers, and customers like eyes that are always watching what's happening in the outside world.To his right, a real-time internal insights data feed that compares data from across the internal part of the business—akin to SAP and Palantir—where these AI generated data streams produce real-time insights and correlated intelligence from data systems used to take months to correlate, now only seconds. Genung calls this new experience of internal and external intelligence synthesis the new, modern “operating system of the business” for the busiest execs.
This is Snowfire AI’s war room, where dirty data becomes a $4.1 trillion opportunity for AI data and business decision intelligence in the enterprise, transforming the way that executives lead in the age of AI. Genung’s office is no different than the modern CEO’s office, which is the place where company leadership takes command over data with internal insights and external intelligence as a decision intelligence data mesh. This is the new world of AI-transformed executive intelligent decisioning and Snowfire is leading the way to make it affordable to turn data into decisions in 24 hours.
Morgan Stanley, McKinsey, and Marketwatch forecast the AI market will hit this staggering $4.1 impact figure by 2030. Snowfire, however, isn’t waiting. The company’s proprietary “signals and decision intelligence” framework—inspired by military-grade analysis—is already changing how executives harness chaotic data. Running alongside billion-dollar behemoths like SAP, Palantir, and Glean AI—Snowfire is the new challenger to take on the space with a subscription model and 24-hour turnaround. This enables companies to forgo the expensive data warehousing investments, data engineering costs, and the business intelligence custom implementations that burden the business.
Dirty Data Is the New Gold
Most enterprises drown in fragmented information. Sales teams juggle Salesforce entries while finance battles NetSuite spreadsheets. Marketing drowns in HubSpot metrics. For decades, executives treated this mess as a problem to fix. Genung sees it as fuel to the fire; turning cold data fragments and flakes of snow into decision fire.
“You can’t afford to wait for clean data—it never happens,” Genung says, echoing conversations with 7 out of 10 enterprise clients paralyzed by siloed systems that are waiting for correlated value across the business decision layer. Snowfire’s solution cross-correlates these disjointed sources, using AI to provide the desired executive ROI on enterprise business data.
Analyzing patterns across these silos or “fragmented” data pools—from CRM entries to internal reports, from hyperscaler cloud environments to spreadsheets—the platform identifies hidden signals, metrics, and decision impactful intelligence in real-time. A recent PwC study found businesses using such tools cut decision-making time by 50% while boosting accuracy by 30% and Snowfire AI’s platform is no different. In early trials, Snowfire customers saw a 30% improvement of time to decision.
The result? Executives no longer waste months reconciling systems. Snowfire’s AI maps financial forecasts against operational bottlenecks, customer sentiment issues, and growth mechanics in one place. One manufacturing client reduced inventory costs by 18% within weeks by letting the platform link procurement data to real-time supplier risk alerts.
The SAP and Palantir Challenger: How “Upstarts” Outpace Legacy Systems
Genung laughs when clients call Snowfire AI “the next big platform”. Yet the analogy sticks. Traditional enterprise software often requires six-month deployments and million-dollar contracts. Snowfire delivers tailored executive dashboards in 24 hours—a fraction of competitors' timeline—by treating messy data as a feature, not a bug.
“We’re the AI that every executive wants working on their behalf when they are not in front of a screen, one that outpaces legacy systems and gives them the highest return on investment for their time,” Genung explains. The platform’s 800+ connectors ingest everything from PDF invoices to IoT sensor feeds.
Proprietary algorithms then build what Snowfire calls a “large metric model”—a dynamic web of KPIs that adapts to each executive’s role. CFOs see cash flow risks tied to geopolitical events. CROs are warned ahead of time on any potential income gluts that competitor movements may trigger. CEOs are provided with AI-generated boardroom strategic risk and financial adjustments based on live data analysis from internal and external business data - tailored to make the business run on insights, not hindsight.
This agile, real-time internal business systems and external business risk data fuels explosive growth for customers, harnessing margin intelligence for financially focused AI contextualization of all data in the business. Early adopters include Fortune 500’s in financial, manufacturing, retail, healthcare, and technology. These giants are using the platform to predict, contextualize, and ready their businesses through the transformative AI Economy, leveraging Snowfire in monday morning executive meetings all the way to the boardroom quarterly reviews.
Accountability in the Age of AI
Critics warn AI could dilute executive responsibility. Genung argues the opposite. “When a decision fails, you can’t blame an algorithm,” he says. Snowfire’s monthly boardroom narratives—automated reports tracking strategic decisions—create the link from decisions and the choices that become business outcomes.
The system also combats AI’s tendency to “hallucinate” by grounding analyses in cross-verified data, based in mathematics and metrics, while contextualization happens from external signals that are personalized to the user, the business, and the industry. The result is significantly higher levels of response accuracy. If Salesforce claims a 20% sales spike but financials show flat revenue, Snowfire flags the discrepancy and can help to share a signal to help interrogate the intelligence. This rigor aligns with Gartner’s prediction that 75% of Fortune 500 firms will adopt decision intelligence by 2026.
Genung’s vision extends beyond profit for Snowfire AI. The company’s patent-pending proprietary framework underpins Snowfire’s AI platform of agents, ai-automated data engineering and architecture, keeping client data isolated yet actionable, layering in the business nuances that inform the best decisions.
“We’re not here to replace executives,” he insists. “We’re here to make them superheroes, we want to be the business operating system and business brain for executives to be able to make data-driven decisions in real-time.”
As industries brace for AI’s workforce disruptions, Snowfire bets on transparency. The same boardroom reports that track margins also monitor AI-driven job shifts—a nod to Genung’s warning that “33% of human roles will change, not vanish.” For leaders facing this storm, inaction will become irrelevance. For the market movers, accepting dirty and fragmented data with the might of a Snowfire AI to synthesize it, might just be their lifeline to harnessing the $4.1T economy and taking this opportunity to transform data to decisions in 24 hours.
