Revolutionizing Revenue: Challenging 5 Common AI Myths

  • 2 April 2024
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Originally written by Julien Sauvage and published in Sales Tech Series


Like many, I have a love/hate relationship with AI. And I love Generative AI (GenAI) for different reasons than many.

The main reason? GenAI has been driving high demand for RevTech, in some cases for the first time. More non-tech verticals like financial services and healthcare will adopt the technology as GenAI sparks more curiosity about RevTech, its use cases, potential, and obstacles. As the risk of tech commoditization becomes more real, GenAI has also been putting more pressure on vendors to deliver high-quality products.

What I hate about AI? It has created so much confusion and just a whole lot of noise. Vendors making silly claims, buyers having unrealistic expectations.

This is why I wanted to take a shot at busting a few myths when it comes to “AI for Revenue” while expressing my POV in a more pragmatic way. Let’s go.

Myth 1: Generative AI Is Mostly About the Top of the Funnel

Our perception of GenAI comes, of course, from us using ChatGPT as consumers. So, the immediate use case that comes to mind is often an “AI writer” to craft prospecting or follow-up emails. While this use case is true and has become table stakes, the industry should start thinking more about the value AI brings mid-funnel. Value for the Account Executive, the Account Manager, the Customer Success Manager, addressing mid- and bottom-funnel use cases for revenue practitioners.

What happens after prospecting? Opportunity management. There, time becomes even more critical, right? So, the rep needs every piece of technology at their disposal to be as efficient as possible. After all, AI is about saving time. What’s more important in an active sales cycle than a senior rep’s time?

Mid- and bottom-funnel AI use cases will include: a “cockpit view” of what needs to be done, an AI summary of top customer conversations, a list of suggested actions, real-time suggested talk tracks, prioritized lists of accounts, opportunities, or contacts to spend time on, etc. The list is endless.

Takeaway: Start with two lists: your “jobs to be done”, and your main KPIs. From that, create a list of use cases where AI can help. Not the other way around.


Myth 2: Generative AI Is the Best AI for Revenue Teams

Generative AI is indeed amazing, but there are many forms of AI that still hold untapped potential for revenue teams: Descriptive AI (what has happened), and Predictive AI (what will happen).

With that said, perhaps I’ll come across as old-school, but let’s not overlook a significant unsolved problem: Next Best Action. It represents a promising intersection of descriptive, predictive, and generative AI.

Takeaway: It’s crucial to think beyond merely generating and summarizing content. AI encompasses predictive and descriptive functions as well, offering valuable insights for revenue teams.

Myth 3: Smaller Companies Will Adopt AI First

In a classic ‘crossing the chasm’ type theory, you’d think that small and medium tech businesses are adopting AI first. Maybe. The reality is that bigger enterprises might have a more natural fit for AI.

Seems counterintuitive? Think again. Enterprises have more data, which potentially means better, more accurate AI. They also have a larger sales force, meaning an even greater need to automate mundane revenue tasks than smaller companies. Bigger companies probably have more revenue workflows, all of which can be enhanced with AI.

Takeaway: AI is imperative. Pave your own way and define your AI roadmap today. Focus on developing an enterprise-wide governance strategy around AI, as well as a realistic timeline aligned with new expectations. Don’t wait.

Myth 4: It’s a Race to Consolidation — At Any Cost

The revenue industry has continued to see the benefits of complete platforms that will rip and replace multiple redundant point solutions. Consolidation has been the most used term in RevTech since last year.

While I believe it will continue to prevail this year, consolidation will not happen at the expense of the quality of the outcomes that the stack generates. I call that ‘Consolidate Without Compromise’.

TakeawayScientifically assess the value of your existing revenue apps and platforms. Think about the cost of switching, cost of ownership, and cost of doing nothing.

Myth 5: AI Is Creating New Revenue Workflows

While this might be true, I believe that it’s still on a relatively small scale.

Tech is always chasing the ‘next new thing’ – in this case, instead of inventing new revenue workflows, think about the ones you already have (deal inspection, churn forecasting, etc.) that have the potential to be improved with AI – with GenAI in particular.

In 2023, the majority of the AI use cases were around sales rep productivity, but I am increasingly seeing CROs and Sales Managers adopt AI to automate their governance workflows.

In other terms, AI and automation will move up the revenue ranks, driving forward complete revenue platforms that address all internal and external revenue workflows over limited point solutions. Think beyond just workflows. Create a set of workflows that you will address on a recurring basis. That’s what I call a “Revenue Cadence”.

Takeaway: Build a two-dimensional table listing all your existing workflows (internal and external) for each of your user personas (reps, sales manager, RevOps, CRO, etc.). See where AI can help in each ‘cell’ of that table.

The hype is real. The value is real. Cut through the noise. Think about your top use cases across the funnel for all types of AI: generative, predictive, descriptive. Don’t wait for others to adopt it before you. Enhance your existing revenue cadences with AI while thinking about new ones to tackle next.

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