With the rise of AI, every industry is at a crossroads. The key to success is for executives leading functional or business organizations to work with themselves and their technology partners to envision and create use cases that will grow the business. Rather than viewing AI simply as a means to replace human resources or reduce costs, organizations should aim to make their organizations stronger, faster, and better. Only then will the innovation and power of AI technology be unleashed.
Kurt Kendall on leveraging disruptive technology to drive growth
billy howard
Another important point is not to over-promise with technology just to satisfy investors or get more customers. This is dangerous on many levels, and so-called AI washing is already pervasive throughout the business landscape. For all these reasons, as the conversation around AI progresses, I want to articulate best practices for adopting the technology and specific use cases such as how to best leverage AI to improve marketing performance. I wanted to talk to someone who understood. I recently spoke with Kurt Kendall, a data and analytics industry veteran who has worked at major companies such as GlaxoSmithKline, Under Armor, and McKinsey & Company. Kurt currently serves as Co-Founder and Co-CEO of Kairos Growth Advisors. Below is a summary of our conversation.
Billy Howard: Can you talk about how companies should approach AI at the highest level?
Kurt Kendall: More than 30 years ago, Jeffrey Moore wrote a seminal book on technology adoption called “Crossing the Chasm.” Talk to anyone today about what it takes to make AI aspirations a reality, and they’ll talk about the chasm ahead. Yes, there is a potential utopia at the end of the AI journey, but the path to getting there is unclear. And it is not only opaque, but fraught with obstacles and risks. We’re already seeing that organizations are spending as much time mitigating AI risks as they are actively pursuing opportunities.
Companies, especially CEOs who lead them, need to have the courage to take risks. Give credit to Sam Altman. OpenAI’s cautious approach has kept it from becoming one of the hottest companies, currently valued at $150 billion. They risked spending billions of dollars and launching an imperfect product because they wanted to transform the world through AI. I’m not suggesting that other companies take on that level of risk, but the winners in the AI space will be those that best manage the risk-reward tradeoff.
Howard: What are the biggest challenges for companies implementing AI? Do you think finding the applications that can produce the best results is the ideal path forward?
Kurt Kendall: The biggest challenge facing companies embarking on AI transformation journeys today is the lack of a clear business use case to justify the required investment. I recently attended a business gathering of over 100 senior executives tasked with leading their respective AI efforts from their organizations.
Two things were abundantly clear. First, AI solutions are expensive to develop and implement. Perhaps not at the level needed to justify some of the current technology evaluations, but certainly on a scale that disrupts companies’ historical budget allocations. And these costs aren’t just for technology. This includes all necessary funding for business process redesign and organizational restructuring. For many companies, these other costs can exceed the cost of the technology.
What is missing from much of the discussion around AI is where the “return” on investment will come from. At best, we only hear vague references to cost reductions through layoffs, which is not very impressive. Talk to senior leaders in organizations beyond CFOs and find out how many line-of-business presidents, CMOs, and CSCOs are excited about downsizing their organizations based on the promise of AI. What needs to change is that AI will move beyond being a CIO-driven technology conversation and become a specific use case “owned” by business leaders to improve the overall mission, customer experience, and business performance of the enterprise. It means there is a need. Use cases need to be clear enough to make a difference and be explicitly incorporated into the annual business plan.
Howard: Marketing and CX (consumer experience) seem to be areas where AI can win, especially as the need for customer understanding increases along with the need for improved performance. What are your thoughts on this and how competitive differentiation can be achieved?
Kurt Kendall: Of course! Marketing and CX start with a huge advantage over many other functional areas within a company. They have been using variations of AI for decades, especially in consumer-facing companies, where the field of data science first took root in marketing functions. Ironically, one of the criticisms of modern marketing is that “science” has displaced “art.” Hard data in customer databases has replaced soft insights about consumer sentiment and attitudes. One of the opportunities for Generative AI (Gen AI) is the potential to bring more art back into marketing. The unstructured nature of language, speech, and images made them poorly suited to previous AI tools. Paradoxically, these same data are exactly where Gen AI will thrive. What Gen AI enables is the incorporation of these unstructured data types into traditional applications such as digital marketing optimization, attribution modeling, and certainly customer experience. It also enables more unique applications, such as creating original branded content in a more scalable and cost-effective way.
Howard: You’re talking about harder data replacing softer data like consumer sentiment, which I believe is the backbone key to creating brand loyalty, trust and spend. . In particular, how AI can be used as a combined element with other disruptive technologies to improve customer understanding, as visibility into consumer mindsets shrinks every day due to a myriad of factors. Can you talk to me?
Kurt Kendall: The big advancement in marketing over the past 20 years is that the way we use data and analytics to measure and optimize marketing has improved dramatically. This is especially true as digital marketing becomes more important. However, as someone who has contributed to the advancement of marketing optimization, I also believe that something is being lost by focusing on economic returns. What has been lost is the “art” of marketing that I mentioned earlier, and that is why I am so excited about new applications of AI in marketing.
One of the innovative use cases my company is collaborating with your company on, Brandthro, is a great example of both the art and science of AI. While everyone is excited about using data to improve marketing, the reality is that the datasets typically used are incomplete and even flawed to some extent. The data that marketers have for digital marketing is very good at understanding behavior (what they do, etc.) and personal attributes, especially demographics (age, income, etc.) Pretty good. At best, marketers are trying to infer attitudes and emotions, and they can’t do that. Current data and analysis toolsets don’t really
Try to understand the reasons behind the behavior. This leads to the question, what is a brand that doesn’t truly understand the emotions of its customers? Today, brands need to act like humans, but that’s not possible without actionable emotional insights.
What AI is now beginning to enable is the ability to directly measure and understand attitudes and emotions. Building emotional insights requires the ability to manipulate language, image, and audio datasets. Traditional data science algorithms have not performed well at extracting sentiment from these datasets. But Gen AI is purpose-built to understand and communicate with these unstructured data types. More importantly, AI allows you to leverage these emotional insights into your digital marketing. Google has introduced a new approach called value-based bidding. Imagine being able to adjust the amount you pay for an impression based on whether someone has the best emotional profile for your brand. It’s not just a revolutionary dream, it’s becoming a reality thanks to AI.
Howard: Great point and well said. What we just talked about ties in nicely with the white paper you recently wrote about Ensemble AI. What exactly is it, and why is it important to how people should think about AI going forward?
Kurt Kendall: The little-kept secret about Gen AI is that many of the use cases being considered are not yet ready for prime time. The good news is that billions of dollars are being spent on these technologies, and progress is coming quickly. But there is a lot of work to be done, and one of the conclusions many are drawing at this point is that there is no single AI solution. No AI can control them all.
The overall message of my discussion of Ensemble AI is that many use cases require a combination of technologies that all work together, rather than a single technology solution, and are sometimes unique to that use case. technology is required. Anyone who has worked in marketing knows that supporting marketing’s business needs requires a variety of technical components (such as a marketing technology stack). For AI, companies should expect similar dynamics and should not assume that a single large-scale language model (LLM) such as OpenAI, Meta, or Google is sufficient. What you need will depend on your specific business needs and use case.
