This article was based on the interview with Braze Chief Product Officer Kevin Wang on how AI has forever changed product development by Greg Kihlström, AI and MarTech keynote speaker for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
We find ourselves in a peculiar moment. The pressure on marketing leaders to adopt, integrate, and demonstrate Return on Investment (ROI) from AI is immense and unyielding. The landscape is a cacophony of vendors promising transformation, each with a shinier “magic wand” than the last. In this environment, speed is often mistaken for progress, and action is confused with strategy. The temptation to simply bolt an AI feature onto every available text box—a trend we all witnessed with a collective, knowing sigh—is a testament to the market’s demand for an “AI story.” Yet, as leaders, we know that a story is not a strategy, and a feature is not a solution.
The real work lies beyond the hype cycle. It requires a fundamental shift in how we, as product and marketing leaders, approach innovation. The central challenge is no longer merely about moving faster, but about cultivating the wisdom to move in the right direction. It’s about discerning the genuine, long-term customer impact from the fleeting technological novelties. This requires a delicate balance: embracing the incredible potential of AI to accelerate our work while fiercely protecting the space for human judgment, creativity, and strategic foresight. The most critical role for Chief Marketing Officers and other marketing leaders today may not be the accelerator, but the conductor—the one who understands the entire score and knows precisely when each section, human or machine, should play its part.
Deeper Than the Demo: The Mandate for Real-World Experimentation
In the early days of generative AI’s public explosion, experimentation often meant dipping a toe in the water. We prompted, we tweaked, we marveled at the output. But as the novelty wears off, the limitations of this surface-level engagement become clear. True understanding doesn’t come from a “hello world” test; it comes from applying these powerful new tools to the complex, messy, and often frustrating realities of our daily workflows. Kevin Wang of Braze argues that to separate the impactful from the empty, you have to move past the sandbox and into the factory.
“A lot of people I think are bringing the same level of dip your toe in experimentation with AI… but they don’t end up actually trying to apply AI to real problems or try to apply AI inside of like a real workload, like, I’ve got a whole bunch of teams that need to do a bunch of work. And that to me is the way to actually figure out where’s the impact going to be.”
This is a critical directive for marketing leaders. It’s the difference between asking an LLM to generate ten subject lines and integrating it into your entire campaign development process to see where it genuinely saves time, improves performance, or surfaces insights you would have missed. It means empowering your teams to test AI not in isolation, but within the context of their most pressing business problems. Can it analyze our attribution data more effectively? Can it help us build more sophisticated customer journey maps? Can it automate the personalization of content across a multi-touchpoint campaign without sacrificing brand voice? Answering these questions requires more than casual experimentation; it demands a structured, intentional effort to embed AI into real operational processes and measure the outcome. The goal is to move from “Look what this can do” to “Look what we can now do.”
The New Bottleneck: From Technical Challenge to Educational Imperative
For a brief period, the primary challenge of AI seemed to be a technical one: building the models, integrating the APIs, and making it all work. That phase is rapidly concluding. Now, a more nuanced and, frankly, more human problem has emerged. The bottleneck is no longer just technology; it’s comprehension. With a tool as novel and powerful as modern AI, the greatest barrier to adoption is often that users simply don’t know what’s possible or how to begin. As Wang points out, product development must now solve for this educational gap, sometimes by using AI to teach AI.
“This isn’t just a technical problem anymore. There’s also this whole element of it actually being a sort of product-led education problem at the same time… it’s so much simpler if the AI can sort of teach you how to be better at AI.”
This insight should reshape how marketing leaders evaluate and select their technology partners. A platform that merely offers a suite of AI features is delivering only half a solution. The true value lies in a platform that guides, educates, and empowers your team to become more sophisticated users. When vetting a new MarTech solution, the questions should evolve. It’s no longer just, “What are your AI capabilities?” It is, “How does your platform make my team smarter? How do you reduce the cognitive load of adoption and help us understand the art of the possible?” This transforms the vendor relationship from a simple transaction to a partnership in innovation. It also underscores the leader’s role in championing not just a new tool, but the new mindset and workflows required to harness its full potential.
The North Star Remains Unchanged
Amidst the whirlwind of technological disruption, it’s easy to lose our bearings. The constant chatter about AI can create the illusion that the fundamental principles of business have been upended. They haven’t. AI is a profoundly powerful tool, perhaps the most significant of our generation, but it remains just that: a tool. It is a means to an end, not the end itself. The metrics that have always defined a healthy customer relationship and a successful business—retention, engagement, and lifetime value—remain our unwavering North Star.
“But it didn’t upend like capitalism. And so retention, engagement, LTV, those things all still matter. This is just another tool which is opening up the methods and the strategies that you can use to drive those metrics that really matter.”
This is a grounding, essential reminder for every leader feeling the pressure to pursue “AI for AI’s sake.” Strategy does not begin with the technology; it begins with the customer and the value you aim to create for them. AI should be applied in service of that strategy, whether it’s to deliver hyper-personalization at scale, predict customer churn with greater accuracy, or optimize marketing spend for maximum LTV. The leader’s most crucial function is to maintain this strategic discipline, constantly asking, “How does this application of AI get us closer to our core business objectives?” Without this focus, even the most advanced technology becomes a distraction—an expensive and resource-intensive solution in search of a problem.
The Emergence of the Strategic Conductor
As AI automates more tactical and analytical tasks, the skills that define effective leadership are evolving. The value a leader provides is shifting away from deep, narrow expertise and toward a broader, more integrated understanding of the entire business ecosystem. In a world where every team member has access to a stable of powerful AI agents, the leader’s role becomes less about being the most skilled player and more about being the most insightful conductor, capable of orchestrating a complex symphony of human talent and machine intelligence.
“…being a strategic conductor of kind of an army of agents. And to do that, you have to have an understanding of the full symphony and what the what the whole thing is going to sound like and what should all sound like together.”
This metaphor is a powerful blueprint for the future marketing leader. It requires cultivating a versatile skill set and a holistic view of the organization. A marketer must understand the language of sales, a product manager must grasp the fundamentals of marketing, and everyone must be focused on the ultimate business outcome. This reality will change how we build teams, how we foster development, and what we look for in the next generation of leaders. The future belongs to those who can see the whole board, who can connect disparate functions, and who can guide their “army of agents” toward a single, harmonious objective. It is a shift from managing tasks to orchestrating value.
In the final analysis, navigating the AI era is not a technological challenge, but a leadership one. The true competitive advantage will not come from having the most advanced AI, but from the wisdom with which it is applied. It will come from leaders who insist on moving beyond superficial demos to solve real-world problems, who champion education alongside implementation, and who never lose sight of the foundational metrics that drive sustainable growth. The goal is not to replace human judgment, but to augment and amplify it.
The most effective leaders will be those who embrace their role as strategic conductors. They will understand that their primary function is to provide the vision, foster a culture of intelligent experimentation, and ensure that every technological advancement is tethered to the creation of genuine, lasting customer value. It’s a less sensational path than chasing every new trend, but it is the only one that leads to meaningful, defensible success. The symphony is just beginning, and it is up to us to ensure it’s a masterpiece.
