Want AI ROI? Follow the Money and Start with Sales

August 18, 2026

By Damien Chan, North American CEO and Global Chief Revenue Officer, Adastra.

Every enterprise leader I talk to faces the same challenge: turning AI investment into real business results. Their teams pour resources into data platforms and AI tools, yet most initiatives stall at the pilot stage.

The problem isn’t the technology; it’s proving value fast enough to build momentum.

I’ve seen this happen with client after client. If you want to prove AI works, start with sales.

Why Most AI Initiatives Struggle

AI projects typically begin with big ambitions: modern data platforms, enterprise-wide programs, and advanced analytics. The expectation is that the business will naturally adopt these capabilities.

It rarely works that way. The common obstacles:

• No clear, measurable ROI
• Frontline teams don’t adopt
• Value takes too long to materialize
• Business leaders remain skeptical

When leadership positions AI as a “technology initiative,” operational teams measured on quarterly performance, not long-term innovation, tune out.

To break this cycle, you need a high-impact entry point: sales.

Why Sales Works

Sales is where performance ties directly to revenue, in ways leadership already tracks. A better win rate shows up in the pipeline report. A shorter sales cycle shows up in the forecast. Nobody has to build a case for why these numbers matter, they’re already on someone’s dashboard.

Even modest gains translate into significant revenue growth, which is why AI initiatives in sales earn quick support from leadership and frontline teams alike.

What AI Sales Tools Actually Do

The best AI tools work inside the flow sellers are already in. They guide decisions in the moment, the kind of thing that used to mean a report someone had to go read later, after the moment had already passed.

I’ve never believed in technology for technology’s sake, and I try to apply that same skepticism to AI. The question I always ask is whether a given tool actually helps the people who’d be using it do their jobs better. And in sales, doing the job better means serving customers better. In sales, that means a seller responds faster and shows up to a call already knowing what the customer needs. The sales AI tools that work do a small number of things well, rather than trying to cover every use case a vendor can dream up.

Two categories stand out as consistently delivering value: AI-powered coaching and recommendation engines.

AI-Powered Coaching

Virtual coaches that combine product knowledge with real-time guidance, personalized training by role, call analysis with actionable feedback, scenario practice for objection handling, and faster onboarding for new hires.

Recommendation Engines

Data-driven guidance on where a seller should focus next, which accounts are most likely to convert, what to propose, when to reach out, and where cross-sell opportunities exist.

Sellers get clear direction instead of guesswork, and that’s usually what decides whether a tool gets used every day or quietly forgotten.

A Real-World Example: Mark Anthony Group

We saw this approach work at Mark Anthony Group, one of North America’s leading beverage companies. Rather than beginning with a broad enterprise rollout, they chose to start with sales.

Working with Adastra, MAG deployed a GenAI sales assistant that allowed commercial teams to ask natural-language questions, such as depletion trends by channel or brand, and get clear answers they could actually act on.

The result was 100% adoption across the commercial team within the first month, which is not a number you see often in enterprise software rollouts. That early success built the confidence and momentum to extend GenAI into demand planning, marketing, and finance on the same foundation.

MAG customers benefit from better-aligned sales, supply, and marketing decisions that help ensure the right products are available in the right stores at the right time.

Adoption Happens When Value Is Obvious

The biggest challenge with enterprise AI is getting people to use it. Sales teams operate differently. When AI helps sellers close more deals and increase commissions, adoption takes care of itself.

Top performers try it first, and everyone else notices when their numbers move. At that point nobody’s talking about AI as a technology rollout anymore, they’re talking about whoever’s closing more deals.

Sales Success Opens the Door to the Enterprise

Once AI proves itself in sales, leaders across the business start asking:

• Can AI improve our marketing campaigns?
• Can it optimize supply chain forecasting?
• Can it enhance customer service?

Sales becomes the proof point that unlocks broader adoption and AI stops being an experiment and becomes how the organization operates.

Start Where the Proof Is Fastest

Most AI initiatives fail because they start with technology instead of business impact. Sales offers immediate ROI, natural adoption incentives, and direct alignment with revenue growth.

Start with sales, since that’s where the case for AI proves itself fastest. Once it does, you’ll have real numbers to bring into the next conversation, instead of a pitch.

For organizations serious about operationalizing AI, starting with sales isn’t just practical. It’s strategic, and it’s exactly where we start with our own clients.