By Preetha Raghu, Project Director, Customer Success - Yseop, United States
A few years ago, I stepped into the AI workspace in a client-facing role when AI was just at the starting line. In the early stages, becoming part of the new and exciting AI world was all the motivation I needed to jump right in and learn everything I could. It was a proud moment and an amazing opportunity.
As I grew more comfortable explaining AI, there was a gap: many conversations stayed at a foundational level, while my goal was to link concepts to real business impact, such as increased efficiencies. The focus necessitated bridging the technical explanations with the outcomes clients were aiming for, learning how to connect AI capabilities to their strategic initiatives.
ChatGPT Changes the Game
Then in 2023, ChatGPT changed everything. The shift showed up immediately in the questions. Suddenly, the conversations with clients were elevated. Clients began asking more sophisticated questions on topics that demand careful framing, intentionality, and confidentiality. These included model approaches, evaluation methods, and risk considerations.
For example, a point of discussion in the AI landscape that continues to remain relevant is whether using LLMs risks producing hallucinations in results; an important governance and compliance consideration. The language for discussion became both diligent and extensive.
ChatGPT didn’t just introduce AI. It gave everyone a common language to talk about it. For client-facing roles, this meant proving value could now be grounded in shared terminology and industry standards, making conversations with clients more strategic than ever. Now we are able to evaluate between different approaches for particular use cases and collaborate on proving the value for business.
From Teacher to Advisor
As a result, the day-to-day of this role changed in the best way. I was no longer required to just explain the high-level aspects of technology and hope I was connecting. Instead, I could clarify, specify, and instruct in much more detail about what was required for the implementation approach. I could envision a true pathway to success because the language had changed and evolved. The playing field had become more level, and the conversation had matured. As I navigated this transformation, I began studying AI implementation patterns more systematically. The statistics are sobering.
As a keynote speaker, Dr. Cindy Gordon noted the following in one of our CAITL™ masterclasses:
A large share of AI projects do not realize intended value.
Several analyst reports describe a common trap where pilots stall before deployments can scale.
Dr. Gordon noted that many of these failures followed predictable patterns around governance gaps, data quality issues, and people adoption challenges.
This new level of understanding has transformed the strategic aspects of how I approach my role. My role is no longer about implementation, but also large-scale adoption, operating rhythm, and change enablement; the role is to actively help clients navigate the factors that determine success or failure. When proposing solutions now, I also must analyze and propose a framework that mobilizes organizations toward real success.
The Paradox: Technology Becomes Easy, Humans Become Essential
This transformation in vocabulary and understanding has had tangible effects on outcomes.
Technical knowledge is now commonplace; everyone speaks the language. What differentiates success now is whether organizations can turn that shared language into sustained change. The future for a customer-facing professional must be part technologist and part change consultant.
It will be our role to identify early warning signs of common failure points and proactively address them before they derail the project. When data quality issues arise, we must help clients think through the governance and process changes needed, not just the technical fixes. We guide clients through organizational transformation.
As AI becomes more accessible, success depends less on technical fluency and more on truly understanding clients; specifically the goals and challenges behind their questions.
Conclusion
Looking back at the beginning, when I struggled to find the right way to explain AI clearly, I never imagined how quickly the landscape would shift. The widespread AI literacy we see today, sparked by ChatGPT and quickly followed by others (Claude and Perplexity, who are competing in the same space) and fueled by curiosity across industries, has fundamentally changed what it means to be successful in customer-facing roles.
But here's the paradox; as AI becomes more accessible and technical conversations become easier, the human element becomes more crucial. The shared vocabulary that once seemed like the finish line turned out to be just the starting point. Now that everyone can speak the language of AI, the real differentiator is understanding the unspoken needs, the organizational dynamics, the change management challenges, and the human resistance that no algorithm can solve.
For anyone guiding organizations through AI adoption, the direction is clear: we must evolve beyond being technology teachers to becoming true partners. We must listen not just to what clients ask, but to what they're struggling to articulate. Those who understand people in an AI-impacted world will define the future.
