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Key Takeaways
- With nearly half of AI initiatives failing to launch, business leaders need a better way to measure success than just ROI.
- Speed is the ultimate multiplier for value, and ROAI (Return on AI) helps you capture it faster.
- Traditional ROI falls short for AI because AI solutions rarely follow a predictable build-and-launch cycle. ROAI reframes value not just as what you earn, but when you start earning it.
In recent years, we’ve all watched companies pour staggering amounts of energy and money into “AI initiatives.” Some of these efforts were transformative, but many others took far longer than expected or quietly stalled out — stuck in planning cycles, technical sprawl, or endless experimentation. The average deployment time is eight months, and nearly half of the initiatives never make it to production at all.
After stepping into my role at Vida, where we build AI phone agents used across various industries, I noticed something consistent across customers, prospects and partners. The companies earning the most meaningful wins weren’t the ones with the largest AI budgets. They were the ones who moved the fastest. That observation eventually led me to a concept we now call ROAI: Return on AI. It’s a simple but powerful way to evaluate not just what AI returns, but how quickly those returns begin.
ROAI didn’t begin as a buzzword. It emerged from numerous conversations — customers sharing their breakthroughs, internal teams comparing timelines, and business leaders trying to make sense of AI costs versus value. In every conversation, one variable kept rising to the top — time:
The companies that treat time as a multiplier are the ones pulling ahead.
Why traditional ROI falls short for AI
ROI is familiar territory in business. We calculate the cost-to-return ratio, wrap it in a clean percentage and call it a day. But AI exposes the limitations of that model.
AI solutions rarely follow a predictable build-and-launch lifecycle. They require workflow design, iteration, data work, oversight, compliance reviews and often a second or third rebuild once real users begin interacting with them. AI is powerful, but it’s not magic.
If an AI tool “saves” $64,000 a year but takes 12 months to deploy, the organization earns nothing during that entire year. Meanwhile, customers and competitors continue moving. The opportunity cost quietly compounds.
This is where ROAI came into focus for me. It reframes value not just as what you earn, but when you start earning it.
A very simple formula
I wanted ROAI to feel intuitive enough that any operator, founder or finance leader could calculate it in their head. The simplest way I’ve found to explain it is this:
That’s it.
If you build an AI workflow that delivers a 100% ROI, fairly typical for automation-styled deployments, and you deploy it in one month instead of twelve, your ROAI isn’t just 100%, it’s 100% × 12.
That difference isn’t theoretical. It’s the difference between earning value all year versus earning nothing until next year. While the math is simple, very few teams actually think about their AI initiatives this way. The businesses that do tend to see measurable differences across revenue, customer experience, operational load and competitive positioning.
Related: 5 Reasons Why Your AI Deployment Isn’t Delivering
Why ROAI matters for the next era of AI
AI has crossed the line from novelty to necessity. What felt experimental 18 months ago now runs inside customer support centers, CRMs, logistics platforms, healthcare systems and even your local handyman’s service app.
But most companies aren’t struggling with “Does AI work?” Instead, they’re struggling with:
How long until we can actually use it?
How quickly can we make it safe and reliable enough for customers?
How do we justify the investment without waiting a full budget cycle?
ROAI gives teams a more honest framework for answering those questions.
I’ve seen customers move from months-long timelines to weeks when they prioritize speed-to-value. That shift doesn’t just unlock financial returns — it accelerates learning loops across the entire product and organization. Once a team sees a working AI process in production, scaling to the next use case becomes dramatically easier.
Where companies see the biggest impact
Across hundreds of conversations with operators, revenue leaders and implementation teams, the same four patterns emerge again and again:
1. Removing internal bottlenecks
Many organizations try to build AI entirely in-house, underestimating the engineering, oversight, regulatory and iteration cycles involved. When they shift to more modular or prebuilt approaches, deployment drops from 12-18 months to 1-3 months. Their ROAI spikes because value starts flowing far sooner.
2. Revenue teams see gains fastest
AI is often framed as a cost-saver, but the fastest ROAI tends to appear in revenue-facing workflows — lead engagement, qualification, follow-ups, renewals. When AI shortens response times or captures missed opportunities, the financial impact is immediate. Deploying these workflows quickly multiplies that impact.
3. AI becomes a product line
Some Vida customers repackage and resell AI capabilities as part of their platform. For them, ROAI isn’t about internal efficiency — it’s about transforming AI from a cost-saver to a money-maker. Quick market deployment determines whether they capture that revenue or whether a competitor beats them to it.
4. Momentum dissolves resistance
Teams adopt AI more enthusiastically when they can see real results quickly. Long deployments drain momentum. Fast wins build confidence, reduce fear and create buy-in from customers and internal stakeholders. ROAI is as much about psychology as it is about financial outcomes.
Related: Is AI Worth the Investment? Calculate Your Real ROI
ROAI comes from watching real teams, stretched thin, overloaded with expectations, struggle to reconcile the promise of AI with the practical reality of deploying it. It was also born from watching organizations achieve measurable revenue gains because their deployments happened in weeks, not quarters.
When time becomes part of the equation, the picture gets clearer. Leaders stop asking, “What will AI do for us?” and start asking, “How fast can we prove it?” And in my experience, a shift like that changes everything.
Key Takeaways
- With nearly half of AI initiatives failing to launch, business leaders need a better way to measure success than just ROI.
- Speed is the ultimate multiplier for value, and ROAI (Return on AI) helps you capture it faster.
- Traditional ROI falls short for AI because AI solutions rarely follow a predictable build-and-launch cycle. ROAI reframes value not just as what you earn, but when you start earning it.
In recent years, we’ve all watched companies pour staggering amounts of energy and money into “AI initiatives.” Some of these efforts were transformative, but many others took far longer than expected or quietly stalled out — stuck in planning cycles, technical sprawl, or endless experimentation. The average deployment time is eight months, and nearly half of the initiatives never make it to production at all.
After stepping into my role at Vida, where we build AI phone agents used across various industries, I noticed something consistent across customers, prospects and partners. The companies earning the most meaningful wins weren’t the ones with the largest AI budgets. They were the ones who moved the fastest. That observation eventually led me to a concept we now call ROAI: Return on AI. It’s a simple but powerful way to evaluate not just what AI returns, but how quickly those returns begin.
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