Integrating AI and GTM systems into your revenue strategy

Takeaways:

• AI integration is essential: AI is no longer a curiosity; it’s a critical part of modern GTM and RevOps strategies, offering opportunities to optimize processes and drive revenue.
• ROI is uncertain, but action is crucial: While AI’s ROI is still unclear, not adopting it is short-sighted. RevOps leaders must prioritize AI initiatives despite resource challenges to stay competitive.
• Focus on practical AI applications: AI excels at summarizing information and supporting written communication. Critical oversight is essential when using tools like ChatGPT.

I had the privilege of speaking on a panel at the RevOps Alliance, titled “Integrating AI and GTM Systems into Your Revenue Strategy,” alongside fellow panelists Jannik Nelson, VP of Revenue at Arm; Aneet Narang, Head of Global Revenue Enablement at PayPal; and Tana Jackson, Senior Director of Revenue Operations at Chronicle Heritage.

We are past the point where AI is a curiosity. Integrating AI into your go-to-market (GTM) and revenue operations (RevOps) strategies is now essential. As AI technologies, including large language models (LLMs), rapidly advance, companies must explore how these tools can optimize GTM processes and drive revenue growth. AI has the potential to significantly enhance efficiency and decision-making in RevOps by automating routine tasks, providing deep insights through data analysis and many other applications - most of which are still to be discovered. However, fully leveraging AI’s potential requires a clear understanding of its applications and limitations.

The real challenge lies in cutting through the flood of AI solutions from companies of all sizes to identify what can drive immediate impact, what might be a distraction, and what holds long-term potential. RevOps leaders are crucial in not only evaluating these tools but also in ensuring their responsible implementation, balancing innovation with strategic focus.

The ROI Challenge

A major challenge in adopting AI technology today is the unclear ROI of many solutions. Given the rapid evolution of AI, finding clear-cut, ROI-positive case studies is challenging. However, not embracing AI due to uncertain ROI is shortsighted. AI is not a fad; it’s here to stay. The more experience your organization gains today, the greater the future payoff.

There are, of course, trade-offs. Some projects may need to be postponed to create capacity to integrate AI technologies into your tech stack. For many already under-resourced RevOps teams, taking on projects with uncertain ROI may seem daunting. However, as a RevOps leader, if you want to keep your team engaged and prevent your tech stack from falling behind, it’s essential to champion these initiatives as strategic priorities throughout your organization. 

Even if there is still skepticism around individual technologies, finding ways to utilize the basics of generative AI, like the free version of ChatGPT, in order to become comfortable with how LLMs “think” and how you can most effectively work with them will give your organization a leg up to those that do nothing. 

‍Effective Uses of AI in GTM/RevOps and What to Avoid

AI in GTM systems isn’t new; companies like Gong have been leveraging AI in their call recording solutions for years. Generative AI now allows users to write, generate images, and answer questions about company data. As LLMs evolve, it opens up opportunities for revenue organizations, including personalized customer interactions and real-time enablement. For TigerEye, the focus is on turning LLM advancements into practical tools that deliver instant, data-driven answers for businesses.

In my experience, AI is particularly effective for summarizing information (written or spoken) and creating a foundation for written communication. However, it is less reliable for doing the writing itself or interacting with data in a foolproof way. It is crucial to establish guardrails to ensure your team doesn’t blindly copy and paste content or email pitches from something like ChatGPT without applying a critical eye.

‍Conclusion

Unlike many new tools, AI is not suited for a “wait and see” approach. There is a learning curve in adopting and tailoring these technologies to fit your needs. The longer you delay, the more you risk falling behind.

(And yes, I used AI to help me write this post.)

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