From buzzword to must-have: How AI is transforming revenue strategy

It’s easy to declare AI the buzzword of the month, year, and maybe even decade. But in its short time in the mainstream spotlight, it’s already become a must-have for business leaders across all industries.

There’s no question that AI has evolved from trend to long-term tactic. Now, leaders must find ways to leverage AI in their strategies without disrupting workflows and negatively impacting revenue.

Revenue Brew’s Kristine Wyatt caught up with Outreach’s Paul Rosen during Revenue Brew’s The Modern Revenue Engine: Embracing New Data and Tools to Accelerate Growth event. They chatted about everything AI, from what revenue leaders struggle with to how the right automations can drive change. Here’s what you missed.

Running in lockstep

In his role as director of enterprise sales at Outreach, Rosen’s work focuses primarily on connecting with revenue leaders strategizing their org’s next big move. From launching a new product to building a five-year plan for their business, Rosen has seen it all. He spoke about what it's like on the ground working with the leaders driving the market—and about what’s slowing them down.

For businesses growing faster than they can scale, more money = more problems for revenue leaders.

“There are so many moving parts [for revenue leaders],” said Rosen. “Whether it’s your tech stack, your team, or if you’re getting pressure for growth heading into the new year. Or if you’re launching new products and thinking, ‘How do I refine this pitch?’ It all comes down to a couple of main items: seller productivity, visibility, and collaboration across the entire go-to-market system.”

Why? Those areas allow revenue leaders to boost predictability when looking at future sales and allow decision-makers to make confident, data-driven choices.

“You want to make sure everyone’s running in lockstep,” said Rosen.

So what’s the answer? How can revenue leaders give their teams the support they need while managing every moving part in the GTM ecosystem?

“Is AI the answer?” asked Wyatt.

“It can be,” said Rosen. “AI can be amazing. Don’t get me wrong. Honestly, it doesn’t really mean anything if the data’s not accurate, right? [The question is] how do you make sure that data is in the right place every single time, making sure that your tech stack is unified from prospecting all the way on down to retention of customers.”

For example, let’s imagine that a business’s marketing and sales leaders are butting heads. If the heads of the marketing department are getting leads in the funnel that don’t convert, it creates conflict. If sales leaders are bringing in new deals that don’t renew, it creates conflict. When the leaders of each department aren’t communicating and looking at accurate data together, AI doesn’t matter. Frankly, it could make things worse.

Fixing the fractures

But repairing a fractured, high-performing team with messy data isn’t an impossible task.

“[You start by getting] leaders on the same page,” said Rosen. “Then, figuring out the data you need to have visibility into would be your next step. Ultimately, [you need to] understand what type of data you want to take action on. Charts and dashboards are amazing, but if you can’t take action on it, does it really matter?”

Once cross-department leaders are collaborating and data is organized, AI can start to open new doors for businesses. First on the docket? Finding new efficiencies in the funnel.

Rosen spoke about how businesses can focus on a small, select number of use cases where leaders can incorporate AI into existing workflows. Not cases where data is just shifting from place to place, but where AI can be used to actually boost productivity and efficiency.

“If it takes sellers or account managers 45 minutes to prep for a call, how can you half that?” said Rosen. “How can you bring that down to 10 minutes? Add those 30 minutes up consistently over the 20 calls made over a week—that’s a pretty significant [amount of time saved]. You need to boil AI down to those granular use cases in order to see that impact long term.”

All about the outcome

Once those AI use cases are established and go-to-market teams are a well-oiled machine, what comes next? For Rosen and Outreach, it’s all about outcome-based reporting and getting a deep understanding of what strategies are working.

“If somebody gets a 90% reply rate, it doesn’t matter if 100% replies are kick rocks,” said Rosen. “I want to know how many of those are positive replies. And if [the replies] are negative, how can we adjust how we’re communicating to shift that into positive? How can we understand our customer further to align to their needs?”

The Outreach team uses its own platform and outcome-based reporting to power its everyday work. Whether team members are looking to understand customer sentiment or evaluating how to better prepare for client calls, Outreach helps them to understand where to start—and where to course-correct.

And if you’re ready to build an AI-powered revenue strategy, get started with Outreach.