These days, I spend a lot of time talking with customers about AI. They are facing imperatives to improve efficiency, and the potential is clear.
Our data on the value of applying AI to running revenue backs up the hype:
- 90% reduction in forecasting time for RevOps
- 67% increase in productivity across revenue-critical employees
- 10% increase in win rates
But driving AI adoption is a new muscle for nearly every organization. Many companies are paralyzed by the seemingly simple question: Where do I start?
MY RECOMMENDATION
Set aside new and unfamiliar technology and start with what you know - your process.Mapping your revenue process
A solid AI strategy starts with a good understanding of your revenue process and the business outcomes you are trying to drive.
At the risk of oversimplifying, you can break your revenue process down into three stages as a good starting point:
- Create
In this stage, your team generates leads and moves them into the pipeline. The create stage is all about capturing interest.
- Convert
Next, your team moves leads down the funnel. This stage involves identifying the customer’s top problems and addressing pain points directly.
- Close
Finally, your team closes the deal. Address objections, engage decision-makers, wrap up the logistics, and create a sense of urgency.
The next dimension to consider is how your key user personas play a role in each of these stages of the revenue process:
- Reps: People who are out there engaging with customers and prospects to drive revenue.
- Frontline managers: People who support and uplevel your reps to meet quota.
- Revenue leaders: People who orchestrate your revenue process to make it predictable and align with corporate goals.
Granted, none of this is revolutionary. But put these two dimensions together, and you have a framework for identifying the root problem that your AI strategy needs to address: revenue leak.
Spot your leaks
Revenue leak refers to revenue your team has earned, but not closed. Every year, over $1.7 trillion is lost to revenue leak. This translates to 14.9% of total revenue per company.
Revenue leaks from multiple points in most organizations.
With your process map in hand, it’s time to identify the source of your top revenue leaks. This is going to look different for every organization, but there are a few different kinds of revenue leak that apply to most businesses:
- Revenue leak that is caused by a lack of predictability– for example, falling short of the quarterly target due to unexpected deal slippage and insufficient time to mitigate risk or pull in other deals to backfill.
- Revenue leak that is caused by a lack of visibility– for example, missed cross-sell opportunities due to a slow and ineffective feedback loop on your cross-sell playbook.
- Revenue leak that is caused by a lack of efficiency– for example, deals that do not progress due to delayed follow-up from sales reps who are bogged down in administrative tasks.
Once you’ve mapped out the range of different sources of revenue leak in your organization, assess the magnitude of the impact. Are these minor annoyances or do they make the difference between beating and missing your revenue targets?
It’s almost time to answer that simple but powerful question: Where do I start when it comes to implementing an AI strategy?
Now it’s time for AI
Why did I focus on these three particular types of revenue leak? In the spirit of this exercise, each of these sources of revenue leak can be effectively addressed with AI.
But not all AI is created equal - different types of AI are effective at solving different types of problems. Or, in our world of revenue, different types of AI can be useful for addressing different sources of revenue leak.
Leaking revenue due to lack of predictability? Turn to Predictive AI. Paired with a strong revenue database, teams can forecast their quarterly revenue with 99%+ accuracy. Predictive AI can also be used to anticipate customer churn and market demand.
Or maybe your issue is visibility? Turn to Descriptive AI. With signals like sales conversations as inputs, Descriptive AI can synthesize large volumes of data into instantaneous insights.
If efficiency and manual work are your issue, Generative AI is a game changer for productivity needs. With automated follow-up and pre-drafted emails, Generative AI can streamline and simplify the sales process.
See below for example applications of each of these types of AI to plug revenue leaks:
Predictive AI
- Forecasting
- Customer churn
- Account engagement
- Pricing optimization
Descriptive AI
- Data visualization
- Relationship management
- Operational efficiency
- Communication summarization
Generative AI
- Auto-generated emails
- Follow-up routines
- Sales support
- Next best actions
Lastly, plan for implementation
If you’ve made it this far, you’ve got the foundations for a good AI roadmap.
But there’s one final consideration — and this is a big one. Trust. Rolling out AI solutions to your team involves trust. For high levels of adoption, AI has to directly address a need and empower the team to move deals through their pipeline.
As you plan for AI integration, include discussion of trust. Identify the AI solutions your team is most likely to adopt and consider prioritizing those. (More on this in next week’s blog.)
Remember: Even the best AI tool is worthless if nobody uses it.
Ready to integrate AI into your revenue process?
Focus on problem spaces where AI helps.
- Map your revenue process
- Spot the leaks
- Identify the best AI solution
- Factor in trust
This framework will help you take full advantage of the power of AI. And it will enable you to do this at key inflection points in your business. Ultimately, the goal of any AI adoption is to impact your bottom-line.
Will yours grow this year?
P.S. We’ll continue this series on AI adoption next week. Stay tuned for a deeper look at the intersection of AI tools and trust. We’ll discuss how to identify the trust bar you’ll need for high-impact AI integration. Talk soon.
Learn more about Clari’s AI suite, purpose-built for revenue, here.