While every go-to-market leader is talking about AI, few have a practical plan to use it for real revenue impact. The gap between the hype and the results is widening. How do you move from surface-level experiments to building a durable competitive advantage?
To answer this, we turn to Sreedhar Peddineni, the Co-Founder and CEO at GTM Buddy. Sreedhar is not just a tech leader; he is a proven builder who has been at the forefront of major tech transitions. Throughout his 25-year career, he co-founded Host Analytics, helped create the entire Customer Success category with Gainsight and is now pioneering the next wave of sales technology.
In an in-depth conversation with Amy Osmond Cook, Ph.D., the Co-Founder and Chief Marketing Officer at Fullcast, Sreedhar shared a founder’s perspective on the evolution of go-to-market technology.
Read on to discover his practical framework for leveraging AI revenue enablement to solve persistent business challenges.
Lessons From The Last Tech Wave Why Past Is Prologue For AI
Sreedhar Peddineni’s career provides a clear framework for identifying and solving market-defining problems before they are widely understood. His track record offers a blueprint for how today’s leaders should approach implementing AI.
The biggest opportunities come from solving fundamental business problems that everyone experiences but no one has adequately addressed.
Solving The “Leaky Bucket” How Identifying A Core Business Pain Created The Customer Success Category
The genesis of Gainsight came from a simple but profound observation. While scaling Host Analytics, Sreedhar noticed a troubling pattern. “We started doing well and we are adding all these customers, and then you start seeing that you’re also losing customers,” he recalls. “There’s a leaky bucket here.”
At the time, the industry’s response to customer retention was remarkably primitive. “The state of the union back then was Net Promoter Score surveys and CSAT surveys. We thought, ‘Okay, we are listening to our customers.’ That was the voice of the customer.”
In 2011, when Sreedhar and his partner Jim Eberlin launched what would become Gainsight, the customer success function simply did not exist. “You could count the number of companies on your fingers that had a function called customer success,” Sreedhar explains.
Why The Smartest Move Today Isn’t Creating A New Category, But Shifting An Existing One
When Sreedhar began thinking about his next venture, he made a deliberate strategic choice. “One, I did not want to create a new category,” he states firmly.
The reasoning is pragmatic. “When you’re talking about a true new category, it really implies that there’s no budget line item. There’s nobody in the market looking for it.” Creating a category from scratch requires enormous investment in market education, and even well-funded attempts often fail when the total addressable market proves too small.
Instead, Sreedhar identified an existing billion-dollar category with a clear opportunity for improvement. “There’s one category that really caught my attention. That category back in the day was called digital content management for sales, which is now part of revenue enablement,” he explains. “There were really significant companies, with Highspot and Seismic as the market leaders… If I were to add it all up, it was already a billion dollars on technology spend.”
Pinpointing The Breaking Point In Traditional Sales Enablement
What made this established market vulnerable? Despite massive investment, the core problem remained unsolved. “Look at G2 reviews. What’s the number one problem statement? ‘I love these platforms, but my biggest problem is I can’t find content,'” Sreedhar observed.
The traditional approach was fundamentally flawed. Companies would get content and create a tag taxonomy, and then manually map tags against those documents, all in the hopes of making search work well.
But this manual system was brittle. “The tag taxonomy deteriorates the moment the person who tagged the first time moves on and the second person comes in,” Sreedhar notes. “The content deteriorates and there was not enough intelligence in the whole thing.”
This inefficiency creates real business consequences, particularly for new hire ramp time. The 2026 Benchmarks Report shows AI-enabled teams ramp 32.7% faster by removing these exact bottlenecks.
A Pragmatic Guide To AI Revenue Enablement, Moving From Search To Answers
Understanding the problem is only the first step. Sreedhar’s insights translate into actionable guidance for GTM leaders looking to implement AI effectively.
To use AI effectively, GTM leaders must evolve beyond basic prompting and deliver answers directly in their team’s workflow.
Stop Tagging, Start Understanding and The Power Of Ontological AI
The technological leap that makes true AI revenue enablement possible is the shift from manual keyword tagging to semantic and ontological understanding. Sreedhar explains the approach: “Let’s build an ontology layer. A fancy way of saying, ‘This is the language of my business.’ Here are my competitors, here are my products, here is my domain terminology. Take all of that and use that to read every single word in every single document or video, and AI will automatically tag them.”
The difference is profound. “You have a semantic and ontological understanding of what this really means. You know that if you’re talking about Einstein, you’re talking about Salesforce, not Albert Einstein.”
This transition from manual processes to intelligent, automated understanding is the core of what defines an AI-native GTM system.
How To Become An AI Power User, Not Just A Prompter
Sreedhar observes a troubling gap in how most professionals engage with AI. “People on LinkedIn still talk about, ‘Hey, I came up with this cool prompt, and you comment on this to get this prompt.’ That’s so behind us. Today, the world is all about skills.”
He advocates for a more sophisticated approach that includes leveraging skills and workflows to execute complex tasks. “The skills layer that Anthropic launched with Claude has become integral to many software companies. You can’t think of a world where if you’re not utilizing skills, then you’re not really taking advantage of the LLM.”
Sreedhar also recommends using cross-LLM fact-checking to ensure accuracy. “In my workflow, I use a cross-LLM to fact-check. Anything important I’m doing, I have a fact-checker job separate from the primary job used for research or creating content.”
The bottom line: “If I’m not a power user of AI as my wingman, then I’m dramatically left behind.” To elevate your entire team’s capabilities, create an AI action plan that moves beyond basic prompting.
The New Mandate For GTM Teams. Deliver Answers, Not Documents
ChatGPT fundamentally shifted user expectations. “You were going to Google and searching for content and getting a bunch of links. You go to that website and hopefully find what you want,” Sreedhar explains. “Now, you can ask a question and get an answer.”
This expectation now applies to sales enablement. Reps do not need another slide deck buried in a folder structure. They need an immediate, accurate answer to a customer’s question delivered in their workflow. This is the ultimate goal of AI revenue enablement: delivering just-in-time knowledge that closes deals. As Louis Poulin noted on The Go-to-Market Podcast, the goal is “AI augmented decision making… to proactively give me insights and analytics.”
Building Your Moat in the Age of AI through Revenue Activation
As AI capabilities become more accessible, how do software companies and GTM organizations build durable competitive advantages?
Your competitive moat is not the AI model itself; it is the deep, workflow-integrated application of AI to solve persistent, high-value business problems.
Why Core Business Problems Are Your Best Defense Against Commoditization
Sreedhar pushes back against the fear that LLMs will make all software obsolete. “The business problems that we deal with… every company is going through a massive transformation. Change now happens at a constant, accelerated pace.”
The fundamental challenges remain: new hire ramp time is still critical, constant product and market evolution requires continuous learning, and the pressure to drive growth with greater capital efficiency is more intense than ever. “Just because I have a project set up with all of my company knowledge base, I can’t throw a new person into it and say, ‘Go query this, ask it to teach you, and you become good at it.’ Not going to happen.”
The moat is not the AI model. It is the deep application of AI to solve these persistent problems. Just look at how Copy.ai managed 650% growth by building a repeatable GTM foundation.
The Category Shift on How AI Elevates Enablement to “Revenue Activation”
Sreedhar introduces a forward-looking concept that reframes the entire enablement function. “Do I want to be known as a company that’s a content management system or a sales learning system? No. That’s something that I do, yes, it’s a basic capability. But don’t come to GTM Buddy because we have a fancy CMS and LMS.”
Instead, he is positioning around outcomes. “We are putting ourselves out there in terms of how we help you deliver revenue outcomes. How is your enablement initiative contributing to your revenue? We are talking about revenue activation.”
This shift from revenue enablement (providing tools and content) to revenue activation (directly influencing revenue outcomes in the flow of work) is only possible with AI. This is the future of RevOps.
The Future of Software Is Delivering Revenue Outcomes, Not Just Features
Modern software is measured by the business results it delivers. AI is the key that unlocks this potential, allowing companies to finally answer the question: “How is our enablement initiative contributing to revenue?”
As Sreedhar puts it: “In the world of revenue activation, how do I get enablement to support sellers and GTM teams in their flow of work with things that were not possible earlier?”
Software that can prove its impact on quota attainment, forecast accuracy, and pipeline conversion will win. Platforms like Fullcast Revenue Intelligence are built to deliver these exact outcomes.
From Hype To Revenue. Your AI Action Plan.
Success in this new era will not come from chasing hype. It will come from applying AI pragmatically to solve the core business problems that have persisted for decades.
Sreedhar Peddineni’s framework offers three essential takeaways for GTM leaders:
- Focus on disrupting existing categories where pain is high and budgets already exist.
- Cultivate a power user mindset that goes beyond basic prompting to leverage skills, workflows, and cross-LLM verification.
- Shift from providing tools (enablement) to driving outcomes (activation).
Sreedhar’s journey across three company-building experiences reveals a consistent pattern: the leaders of each technological wave see beyond the technology itself to the fundamental business problems it can solve. He identified the leaky bucket problem before Customer Success was a category. He recognized that sales teams still could not find content despite billions in enablement spending. Now, he is positioning for the shift from enablement to activation.
The AI era will reward those who follow this same pattern. The question for every GTM leader is no longer whether to adopt AI. It is whether you are building a GTM engine that simply uses AI as a feature or one that is truly activated by it to deliver measurable revenue outcomes.
