.png)
Why Revenue Teams Need A New Approach
The Rise of Accessible AI
Back in 2023, tools like ChatGPT and Claude went from experimental to indispensable practically overnight. Suddenly, anyone with curiosity and an internet connection had the power of AI at their fingertips for everyday tasks like email writing, meeting notes, idea generation, and more. This shift didn’t just help individual knowledge workers. RevOps teams began to build AI into their existing tech stacks, sometimes using very creative methods to do so! Vendors, meanwhile, scrambled to add AI-driven features to their platforms. We did the same at Rattle, launching AI-based meeting summaries, deal intelligence.
Since then, we've seen the use of AI explode. In fact, based on Rattle's 2025 State of RevOps report, we now see that 67% of revenue teams use at least one AI capability in their tech stack. This is a massive jump from just a year earlier. And while there’s been constant debate around AI’s accuracy, revenue teams themselves are far less concerned than they used to be; only 5% now list “trust in AI” as a major barrier, down from 32% the prior year. In other words, AI is no longer just an experimental toy. It’s now baked into core processes: generating call summaries, managing chatbots, and personalizing content. Tasks that once swallowed up hours or required specialized expertise are now more accessible and far more efficient.
The Expectation Shift: Strategic AI
Fast-forward to 2025, and we’re seeing a new wave taking shape. The first wave of AI was all about efficiency—helping teams accomplish the same tasks much faster. But now, we’re moving into Strategic AI, which isn’t just about speed. It’s about generating insights that can shift behavior and yield better business outcomes.
This leap makes sense, given that major AI hurdles—like trust and technical know-how—are no longer top of mind. Instead, the lingering concerns revolve around practicalities like bandwidth (20%) and budget (13%). In other words, with trust issues largely behind us, it’s time to ask bigger questions: “How can AI help us discover what we didn’t even know we needed to know?” That’s where things get interesting.
What Strategic AI Looks Like in Practice
Understanding the goal of Strategic AI is not difficult. The goal is to surface insights that were invisible before and use them to gain a strategic advantage. Two examples from early adopters of our Wizard platform show how powerful this can be.
Use case 1: Closed Lost Reasons
Most sales organizations rely on reps to fill in a quick dropdown or note when they lose a deal. The result is often vague, unhelpful reasons like “no budget” or “went with a competitor.” There’s rarely enough information to change your strategy or even make useful decisions.
One of our Wizard users, a VP at a fast-growing SaaS company, wanted to do more with closed-lost data. Instead of depending on manual inputs, he used Wizard to analyze unstructured call and email data. Here’s what changed:
- Real Product Gaps
One deal was labeled “lost to competitor,” but Wizard flagged overlooked product functionality issues that never reached the product team. - Follow-Up Opportunities
Another deal said “budget issue,” but Wizard spotted a timing mismatch that could be revisited in a quarter or two.
These insights didn’t just help the sales team. They also guided Marketing on more precise campaigns, Product on urgent feature gaps, and Sales Leaders on nipping potential threats in the bud. A once-boring “closed-lost” step morphed into a goldmine of strategic intel.
Use case 2: Validating the Sales Process
Another early Wizard adopter was a sales leader who swore by one core principle: you had to have an active executive buyer identified by stage 4. He had plenty of anecdotes to back this up, but he wanted real data, especially with the new pressures his team faced.
Wizard dug through their pipeline and found two main insights:
- Confirmed Hunch
Successful deals almost always had a genuine executive involved by stage 4. - Coachable Moments
Deals labeled “executive buyer identified” often turned out to be outdated or incorrect. Some reps had simply checked the box or confused a mid-level manager for an exec. With Wizard’s data, the leader could coach reps more precisely, showing them exactly what happens when they fail to confirm the real decision-maker.
As he put it: “It was like putting on glasses for the first time.” Instead of guessing, he could prove that correctly identifying executive buyers really did separate wins from losses and not just tell his team about it but coach them deal-by-deal.
The Competitive Advantage of Strategic AI
Examples like these hint towards what’s on the horizon. Strategic AI empowers revenue teams with insights once hidden behind mountains of unstructured data. The only way to access these insights without AI is to use huge analytics budgets or entire squads of data scientists which is not feasible for most teams. Those who embrace this new wave early will gain a competitive advantage, while those who delay risk being left behind.
At Rattle, our first step into this realm is Wizard. Our beta users’ reactions speak volumes:
- “Oh, s***! It can do that?”
- “I cannot wait to play around with this all weekend!”
They’re not just talking about incremental tweaks. They’re discovering entirely new ways to see their pipeline, coach their teams, and close gaps faster.
Looking Ahead
We’re still in the early days of what’s possible. As we continue refining Wizard and exploring new Strategic AI use cases, we’re aiming to help revenue teams operate in ways they’ve only dreamed about. This isn’t just another feature update. It’s a new mindset about how data is gathered, analyzed, and turned into action.
If you’re ready to take the next step in this evolution, we’d love for you to explore Wizard in our public beta and help shape the future of Strategic AI for revenue teams. Check it out at gorattle.com/wizard.
