Simplifying complexity: AI's role in unifying marketing and sales tech

TL;DR: The lines between MarTech and RevTech are blurring, with AI emerging as a key player at the growing intersection between the two. AI-powered tools have the potential to revolutionize GTM systems by offering contextual understanding, proactive suggestions, and seamless integration of marketing and sales data. However, it’s still early days.

In a recent episode of the GTMnow podcast with Scott Barker, I got to dive deep into the evolution and convergence marketing technology (MarTech) and revenue technology (RevTech). As someone who's been in the trenches of both fields for years, I've witnessed firsthand as these two domains hurdle toward each other faster and faster each year.

Scott and I spent a good amount of time talking specifically about the role and potential of AI at the intersection of MarTech and RevTech, and the gap between where the tech currently is and where it could be in the next 10 years. So much so that I wanted to revisit the topic in a dedicated post here.

Let’s dive in.

The evolution of complexity

If we rewind to ten years ago, the MarTech and RevTech stacks were relatively simple. We had basic tools like HubSpot and Salesforce, with a handful of other tools connected to them. At the time, this setup was innovative and exceptional. We were just beginning to explore the possibilities of automated sequences, outbound marketing, and data enrichment.

As the years progressed, we experienced what we can call "the great expansion of tools." Companies began adding more and more specialized tools to their stacks. While this expansion brought new capabilities, it also introduced greater complexity and higher management and overhead costs.

As Scott pointed out during our conversation, the ecosystem of tooling options out there has grown quickly from startups to bigger incumbent players that have gone horizontal and started to play in each other’s space. On top of that, there are a ton of new AI entrants–whether actually AI or just marketing it–adding noise to everything.

What's interesting is that as this expansion was occurring, we were also seeing a shift in how we dealt with this complexity. We moved from solutions that required developer expertise to low-code options to foundational automation, and now we're on the cusp of an automated, AI-driven approach that will likely continue over the next decade as AI grows up.

The push toward convergence

Today, we're entering a new phase: "the great contraction of tools." The lines between MarTech and RevTech are blurring, especially for B2B and B2B2C companies with smaller teams. It's increasingly common to find a single decision-maker overseeing both areas.

This convergence isn't just about organizational structure; it's about the tools and technologies themselves. At Ramp, one of our biggest challenges was unifying our MarTech and RevTech stacks, both in terms of tools and operations. This struggle isn't unique - it's a common pain point for many growing companies.

As Scott said during the interview, "Buyers are thinking more and more about consolidation. There's more scrutiny on every dollar that's spent." This scrutiny is a major driver toward consolidation as teams look to cut fat in their stacks and squeeze out better and better margins over time.

However, the root of the challenge of streamlining our GTM stacks lie in the fundamental differences between traditional marketing and sales systems. MarTech systems are typically built around user objects and events, while CRMs, the backbone of many RevTech stacks, often struggle to comprehend events. This disconnect leads to workarounds that, while functional, are far from ideal and lead to process, time, and cost waste.

The role of AI at the convergence

AI is everywhere, both as a feature and as a buzzword. Beyond the hype, AI truly has the potential to bridge the gap between MarTech and RevTech as the two grow closer and closer–but its full potential isn’t here yet.

Despite some of the wild claims for AI you’ve likely come across online or at happy hours, we’re still very much at the infancy of what’s possible with the tech. What the ecosystem of players can deliver today with the models available and the tooling available is still pretty limited. If someone is selling you on the ability of AI to change how you work today, they’re likely overselling you.

So what is the role of AI in helping sales and marketing teams during this “great contraction”? Simply put: It’s to augment and support a better product experience overall.

As Scott said during our interview, “A lot of RevTech, what we were driving toward, was can we serve up the next best action for our sellers? That’s what we’re all aspiring to.”

If we look at that goal within the context of the CRM–which is where I spend most of my time thinking about AI applications these days–it means that we as vendors are primarily focused on just building a better product experience for operators. And then, as AI continues to evolve over the next five years, you add in AI to increasingly improve how the CRM is taking work off the plate of the people using it. You can think of it as designing a CRM like the folks at Linear have built a project management software, where the best application of AI isn’t one that’s in your face all the time but just works better alongside you.

Source

At Clarify, we're incorporating AI and natural language processing into the core of our product only as it makes sense, allowing users to interact with their data in more intuitive ways. For example, users can ask a question about creating views and we’ll actually run the SQL in the background and reconstruct that view.

Here are some of the larger ways I’m excited to see AI applied to the CRM space in the coming years as marketing and sales continue to converge:

1. AI can help handle unstructured data: AI has great potential in its ability to take unstructured data from a variety of channels from video recordings, calls, emails, transcripts, voice memos, text messages, LinkedIn messages.
2. AI can provide actionable insights: Beyond just summarizing data, AI is capable of providing the recommended next step in a way that doesn’t stress the user out, and can handle taking the next recommended action or flagging it for human intervention.
3. AI can act as an extension of the user's brain: We can build a CRM that acts as an extension of the user’s brain and operates as if it knows everything that they know.
4. AI can support questions directly from users with natural language processing: AI can help users interact in real time with their data, as we were talking about with view creation and reconstruction features within Clarify.
5. AI can improve user experience: AI offers a way to create "a better product experience" in CRM systems by reimagining the playbook of how sales is done so operators and sellers can get support in the workflows they actually use today.
6. AI can bridge MarTech and RevTech: AI has the potential to unify marketing and sales data in an event-centered world, providing a holistic view of customer interactions across all of GTM.

While the potential is exciting, we need to be realistic about the challenges. Maintaining the human touch in customer interactions is crucial - nobody wants to receive an email from an AI agent.

The goal isn't to replace human creativity and relationship-building skills, but to enhance them. By automating everyday tasks and providing intelligent insights, we can create AI-powered tooling that gives you more than you put into it.

Other challenges include data privacy and security concerns, integration with existing systems, and user adoption. Balancing automation with human oversight is critical, and data quality remains an ongoing challenge that companies will need to design for–we all know the garbage in, garbage out issue. 🗑️

Case study: How AI-native tooling supports everyday sales workflows

To illustrate the potential of AI in bridging MarTech and RevTech, let's consider a hypothetical case study. Our fictional company, Deep Learning & Sons, wanted to explore a more AI-powered CRM to help them with lead scoring and cut down on the amount of time their sales folks were sinking into updating contact records and following up.

Here are some of the early ways their new tool contributed to the team:

1. The system processed and analyzed unstructured data from sales calls, emails, and meeting transcripts and automatically extracted key information like product interest, budget, and timeline to keep lead profiles updated without manual input from sales reps.
2. Based on the analyzed unstructured data, the tool calculated lead scores and provided actionable insight into each account, including when to follow up, likelihood of conversion, and deal timeline.
3. The CRM remembered details from every interaction and served up pertinent information about the account and its contacts sales reps quickly before meetings or at key moments in the customer lifecycle.
4. The AI automatically created, prioritized, and executed on follow-up tasks based on the content of conversations, reducing the need for manual task creation and ensuring no leads fell through the cracks.
5. Sales reps could ask proactive questions about their data like, "Show me all leads who mentioned budget concerns in the last month," and the AI would analyze conversation transcripts to provide accurate results.
6. The platform connected marketing campaign data with sales conversion outcomes, allowing for more accurate lead scoring based on both marketing engagement and sales interactions.

All of the above helped our fictional sales team spend way less time on manual data entry and built trust in the data they were collecting about accounts across marketing and sales. This helped drive an increase in MQLs and SQLs, reduced sales cycle length, and improved overall conversion rates.

Looking ahead to the AI-powered convergence of MarTech and RevTech

As we look to the future, I believe we'll see a new generation of RevTech stack that mirrors recent MarTech developments.

For startups and growing companies, this evolution presents both challenges and opportunities. The key is to work backwards from the problem you're trying to solve. Consider what issues need addressing at your company's current stage, and be open to using different tools for traditional functions. You might find that a tool you never considered for a use case could serve that function temporarily, or even permanently.

The tried and true playbooks of how to build successful sales motions aren’t built for the near future of modern business. Staying flexible in our tool stacks and approaches will help us make the most out of new tech–including AI–as it solidifies in coming years. By embracing this change and focusing on creating value for our teams and customers, we can navigate this transition successfully and build more effective, efficient, and human-centric businesses.

As you navigate this convergence, keep your focus on the tangible, human benefit AI-native tooling offers: better customer experiences, more efficient operations, and ultimately, stronger relationships with your customers. Because at the end of the day, that's what drives business success.

These very-human AI outcomes are something I–and the whole Clarify team–spend a lot of time thinking about as we build the next generation of CRM. If it’s something you’re curious about, I’d love to connect. You can also request access below to see what we’re building first hand. 👀