AI is revolutionizing business landscapes, driving revenue growth and operational efficiency.
Companies are using AI to gain competitive edges in many ways – predictive analytics that forecast market trends, AI-powered customer service enhancing user satisfaction, and more.
The above introduction was written by AI via this Claude 3.5 Sonnet prompt: write a 30-word introduction about how important AI is today in the business & revenue space. (A human wrote the rest of this article).
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In May 2024, we hosted the online edition of Charge: The Revenue Summit, our virtual event for revenue leaders who want to capture more revenue and stay ahead of the competition. This year’s focus: recharging your revenue engine with AI.
That’s because AI is on a lot of CROs and RevOps leaders’ minds.
35% of CROs Gartner surveyed plan to establish a generative AI operations team in their GTM organization by 2025. And there’s our own 2024 Revenue Leak Report data, which found that 32% of companies have already adopted AI in the revenue process. Another 67% plan to do so within the next 6-12 months - bringing the total who use AI or plan to soon to 99%.
So how do you go from planning to implement AI to actually using AI to make your revenue process more efficient?
We’ve got some answers from the “AI Council: Developing Your AI Strategy to Drive More Revenue” session.
During that, Brian Cody, Clari’s VP of Sales Engineering and Sunil Rao, CEO of Tribble, provided concrete guidance for how to:
- Apply descriptive, predictive, and generative AI across the revenue process
- Develop an AI strategy and measure yield
- Create an AI Council in your organization - and why you need one
Here are some of the highlights of their conversation, including video clips of key takeaways.
How can you use AI to help Go-to-Market (GTM) strategy?
There are so many ways that AI technology can make your revenue and go-to-market (GTM) processes more effective.
From top-of-funnel pipeline generation to engagement to deal close — every single revenue moment has inefficiencies that AI can help you optimize (or automate away).
Our data shows applying AI to running revenue leads to:
- 90% reduction in forecasting time for RevOps
- 67% increase in productivity across revenue-critical employees
- 10% increase in win rates
What does that look like in the real world? Here’s one example from Sunil most RevOps folks will relate to: kick-off season:
“All of a sudden there are terabytes of decks being created that people have to read over the course of maybe two weeks and magically become enabled. Is there a more intelligent way to distribute that over time and give that to people when they need it? I think so. I think this [AI] technology lends itself really well to helping intercept moments where people need to learn something.”
Here’s another example: What if you could easily identify the exact moment when a deal slips from this quarter to a future quarter?
And what if you could do that at scale? Not just for one deal, but for the thousands of deals in your organization?
That’s the scenario Brian outlines here:
Deal slippage is one form of Revenue Leak, revenue lost to breakdowns in your revenue process. With 26% of revenue lost to leaks, plugging even one source leak with AI could put serious numbers back into your bottom line.
How can creating an AI Council help unify your AI investments?
Optimize RFPs to win. Write incredible emails. Transcribe and summarize calls automatically.
These are a few of the ways your revenue team can move faster with AI.
But if you are not thinking holistically across your organization, AI investments can get unruly, scattered, and expensive.
Without an AI Council to establish your AI revenue strategy, you’re signing up to get left behind. That’s one of five key things we’ve learned from managing $4 trillion in revenue over the past ten years.
Clari launched our own AI Council to address this for ourselves. Here’s Brian on its origins:
The single purpose of Clari’s AI Council at creation? To be a unifying throughline across different roles and connecting various tests, pilots, proofs of concepts, and investments in AI.
Clari’s AI Council works to answer questions for the entire revenue, including:
- How does your Customer Support Team leverage calls you had in pre-sales?
- How do you provide a through-line across each GTM role so everybody gets better and you're not constantly solving one-off productivity issues?
- How do you start to get lift from each of these technologies that are already in your tech stack?
Or is there a consolidation option - one single tool like a Revenue Platform, or maybe a few shared technologies that every revenue-critical employee can use to do their jobs better?
Creating an AI Council helps you not only find solutions to these questions - but also identify which questions you should be asking in the first place.
How do you measure AI outcomes and drive ROI?
Without a doubt, AI can help with productivity. But translating those productivity gains into compelling value statements — win rates, reduction in churn, etc. — can be challenging.
Like Fight Club, the #1 rule is don’t talk about “AI.”
Instead, lean into the functional area you impact with the AI technology you're bringing to the table. How is AI impacting that team’s productivity?
That’s Sunil’s take from his experience building an AI-first company. Here’s his breakdown:
Decoupling AI from the discussion helps because it moves away from being an experimental project into one that’s an obvious business problem you're solving.
Find out how much time is spent on the activity that's the focal point of the product being sold. Ask: How can you improve that, and by what percentage of the time? What are the metrics you usually measure them by? How can you improve those metrics?
What is the #1 thing to consider to ensure your AI strategy works?
Expectations.
That’s Sunil’s answer when it comes to avoiding pitfalls while implementing an AI strategy:
“It’s very easy to get excited by some of the demos that we see out there. …[But] The moment you start rolling that into production and you look at the complexity of your business, it falls off pretty steeply in terms of what it can do consistently well.”
Here’s how to get it right.
Focus on having a very clear indication on what you’re hoping AI can help you solve.
Figure out the use cases - or set of use cases - for which you’re using AI first. Nailing the use cases for which you’re trying to use AI equates to proving its value as a tool in your revenue process.
Explore More Key AI Questions for Revenue Leaders: Watch Charge 2024 On-Demand
There’s more to the AI for revenue operation story. Watch the full event from Charge 2024 on demand and get ahead of the lightning-speed pace of AI.