How To Become AI System Leaders

How To Become AI System Leaders

Setting up an AI strategy for our CS or GTM functions is not a walk in the park, and often, I see leaders jumping headfirst into becoming an AI coder and trying out all of the latest AI tools themselves and getting even more confused.
Excitement for the latest tools or being driven by the pressure to "get AI up and running asap" can get in the way of seeing the bigger picture. Sometimes we tend to overestimate what AI can actually do for us right now and underestimate the risks.

In this article, I look at five areas I think are essential to master now to build a CS function that can tell its own success story and, with that, gain back a seat at the table.

1. The Power of the Human-AI Partnership

For all their power, LLMs still have a relatively short-term "memory" and cannot operate effectively without continuous context. At the moment, only humans can provide the deep, nuanced context and long-term memory required to tackle complex issues like churn, identify expansion opportunities, and truly understand the multifaceted reasons behind customer behaviour.

Instead of seeing AI as a replacement, we should view it as a powerful co-pilot. By strategically feeding AI with data, we can leverage its processing and predictive capabilities to help us in very specific, targeted ways. Where AI excels at scale and prediction, humans excel in relationship-building, empathy, and strategic foresight.


2. Data Quality is Non-Negotiable

We've all heard the phrase "garbage in, garbage out," and it couldn't be more relevant for AI. I frequently see organisations struggling with data silos and overwhelming data masses. AI works exceptionally well when the underlying data is impeccable.

If we haven't done the foundational work of establishing robust data quality, our brilliant AI strategies and systems are destined to fail. This means prioritising data governance, cleaning, and structuring before expecting AI to deliver transformative results. Your data is the fuel for any successful AI initiative; without clean fuel, the engine won't run.


3. Knowing the AI Fundamentals

As Customer Success leaders, our core strength is our deep understanding of our customers and our ability to drive value. To translate this strength into the AI era, we need to be really fluent in the language of AI. This means understanding concepts like prompt engineering, tokens, and vector storage. We don't need to build the engines ourselves, but we must understand the principles of how they work.

Our role then is to build and leverage a network of AI experts who can build and implement sophisticated agents far more efficiently than we ever could. This allows us to remain focused on our core terrain: CS leadership.


4. AI Demands System Design

Many organisations still treat AI as a standalone tool. The real power, however, lies in understanding the fundamental components of 1. humans, 2. automation, and 3. AI, and when and where to deploy them within our existing infrastructure.

Customer Success is an excellent playground for deploying automation and AI agents, but the challenge lies in stepping back to examine the entire system. What are the most significant levers of success and drivers of ROI? A true AI strategy leader can look at their GTM motion and design new systems—a combination of humans, automation, and AI—that reduce costs and time while accelerating value.


5. To Buy, Build, or Both?

When implementing AI, the question isn't just "should we buy or build?" but "what is the right mix for our specific needs?"

  • Buying pre-built solutions offers a faster time-to-market and ongoing vendor support. This is often the best path for foundational tasks like chatbots for FAQs or content creation.
  • Building an in-house solution provides complete control and a unique competitive advantage, but it requires a significant investment in talent, time, and infrastructure.

In many cases, the most effective approach is a hybrid model. You can buy a foundational AI platform and then build custom applications or agents on top of it, creating a unique solution tailored to your specific GTM needs without reinventing the wheel. Step by step.



We can strategically feed AI with data and leverage it to help us in very specific, targeted ways, but we must also understand its limitations. Where AI excels in processing and prediction, humans excel in nuance, relationship-building, and strategic foresight.

    By doing this, we can showcase how Customer Success directly moves financial needles—impacting churn and expansion.

    If we approach this smartly as an AI system leader, with a clear understanding of both AI’s power and our strategic guardrails, we will get to the place where we belong: at the forefront of business value creation.

    If you like this newsletter article, please follow me on LinkedIn for more or discover my A.C.E Framework - the way I see Customer Success really making a dent in our GTM systems.