FAQ

What makes All In on Data different from other AI and data consulting firms?

We combine deep enterprise experience with a modern, agile approach. Our team doesn’t just theorize—we implement. We’re known as the “grown-ups in the room” when it comes to AI: seasoned experts who partner with executive teams to demystify AI, align it with business objectives, and deliver production-ready solutions at scale.

We've built AI prototypes before—but why is it so hard to get them into production?

Many AI initiatives stall after proof of concept due to a lack of strategy, architecture, and readiness. We bridge that gap through our structured methodology: readiness assessments, solution design reviews, and roadmap creation to move from POC to scalable deployment.

How do you ensure AI is implemented responsibly in our organization?

Responsible AI is embedded in everything we do—strategy, architecture, training, and governance. We help organizations assess risk, create transparent systems, and align with regulatory and ethical frameworks to avoid the kind of backlash we've seen in other high-profile AI failures.

Where do we start if we're behind on AI and data strategy?

Start with a two-day readiness and use case workshop. We align executive teams, evaluate AI maturity, identify high-value opportunities, and craft a roadmap that balances impact and feasibility.

How can All In on Data help private equity firms or portfolio companies?

We provide full lifecycle AI consulting—from tech due diligence to portfolio-wide AI strategy execution. We help identify tech-driven EBITDA opportunities, reduce risk, and scale AI across operations—especially in sectors primed for GenAI disruption like customer service, software development, and finance.

What kinds of companies do you work with—and what roles typically engage you?

Our clients range from fast-scaling startups to mid-market enterprises (Fortune 501–2000). We frequently engage with Chief Data Officers, COOs, Legal and Compliance Officers, and CTOs—often when they're facing challenges with AI integration, data management, or digital transformation initiatives.