All industries are at a crossroads with the rise of AI. What will be key to success is executives who are leading any functional or business organization collaborating amongst themselves and with their technology partners to envision and create use cases that will grow their businesses. They will need to aspire to make their organizations stronger, faster, and better, rather than falling back to seeing AI simply as a way to replace their people and reduce costs. Only then will they unlock the innovation and power of AI technologies.
Kurt Kendall On Leveraging Disruptive Technologies To Drive Growth
Another central point is to not overpromise on the tech just to satisfy investors or get more customers. This is dangerous on multiple levels and AI Washing, as it is called, is already endemic across the business landscape. For all these reasons, as the dialogue on AI ensues, I wanted to speak to someone with a clear understanding of best practices for embracing the technology, along with specific use cases, such as how to best leverage it to drive marketing performance. I recently sat down with Kurt Kendall, a data and analytics industry veteran who has worked at leading companies such as Glaxosmithkline, Under Armour and McKinsey & Company. Kurt is currently the Co-Founder and Co-CEO of Kairos Growth Advisors. Following is a recap of our conversation:
Billee Howard: Can you talk about how companies should be approaching AI at the highest level?
Kurt Kendall: Over thirty years ago, Geoffrey Moore wrote a seminal book on technology adoption called Crossing the Chasm. Talk with anyone today about what it will take to realize the aspirations of AI and they will talk about the chasm ahead. Yes, there is a potential utopia at the end of the AI journey but the path to get there is unclear. And it’s not just unclear, it’s fraught with obstacles and risks. Already you are seeing organizations spend as much time on mitigating the risks of AI as aggressively pursuing the opportunities.
Companies, and specifically the CEOs leading them, need to have the courage to take risks. Give Sam Altman credit. OpenAI didn’t become one of the most talked about companies that is now being valued at $150 billion because they took a cautious approach. They took the risk to spend billions of dollars and launched an imperfect product precisely because they wanted to transform the world through AI. I’m not suggesting other companies take on that level of risk but the winners in AI are going to be those companies who best manage through the risk and reward tradeoffs.
Howard: What are the greatest challenges with companies adopting AI? Seems like finding the applications that can yield the best results would be the ideal path forward?
Kurt Kendall: The biggest challenge facing companies today who are beginning their AI transformation journey is the lack of clear business use cases that justify the investments that will be required. I recently attended a business gathering of over 100 senior executives who are tasked by their organizations to lead their respective AI journeys.
Two things were abundantly clear. The first is that the costs to develop and implement AI solutions are going to be material. Maybe not to the level needed to justify some of the current tech valuations, but certainly at a scale that will disrupt companies’ historical budget allocations. And these costs won’t just be for the technology. It will include all the money needed to reengineer business processes and restructure organizations. For many companies, these other costs likely will exceed the technology costs.
What’s missing from many of the discussions on AI is where the “returns” on the investments will come from. At best you hear vague references to cost savings from eventually reducing headcount, which is rather uninspiring. Talk with senior leaders in organizations, excepting the CFO, and see how many Business Unit Presidents, CMOs, and CSCOs are excited about reducing the size of their organizations on the promise of AI. What needs to change is that AI needs to move beyond a technology discussion led by the CIO and become specific use cases “owned” by business leaders for the betterment of the company’s overall mission, customer experience, and business performance. The use cases need to be clear of sufficient scale to move the needle, and explicitly incorporated into the annual business plan.
Howard: Marketing and CX (consumer experience) seem to be areas that could be wins with AI, particularly as the need for customer understanding grows alongside a need for improved performance. What are your thoughts on this in general and how competitive differentiation can be achieved?
Kurt Kendall: Absolutely! Marketing and CX start with a very significant advantage over many other functional areas in a company. They have been using variations of AI for decades and for consumer-facing companies in particular, the data science discipline first took hold in the marketing functions. Ironically, one of the critiques of modern marketing is that the “science” has pushed aside the “art”. The hard data found in customer databases has supplanted the softer insights of consumer emotion and attitudes. One of the opportunities for Generative AI (Gen AI) is that it has the potential to bring more of the art back to marketing. The unstructured nature of language, audio, and imagery were not as well suited for the prior AI tools. Paradoxically, these same data are exactly what Gen AI thrives upon. What Gen AI will enable is the incorporation of these unstructured data types into traditional applications like digital marketing optimization, attribution modeling, and without question the customer experience. It will also have more unique applications like creating original brand content in a more scalable and cost-effective way.
Howard: You mention harder data supplanting softer data like consumer emotion, which I believe is the skeleton key to creating brand loyalty, trust and spend. Can you speak to how AI can be used as an ingredient married with other disruptive technologies to improve customer understanding, particularly as visibility into the consumer mindset is shrinking daily due to myriad factors?
Kurt Kendall: A big advancement in marketing over the last 20 years is the dramatic improvement in using data and analytics to measure and optimize marketing. This is especially true as digital marketing has grown in importance. However, as someone who’s contributed to advancing marketing optimization, I also believe that something has been lost with the focus on financial returns. What’s lost is the “art” of marketing that I referenced previously and that’s why I am so excited about the newer applications of AI in marketing.
One innovative use case my company is working on with your company Brandthro is a wonderful example of both the art and science of AI. While everyone gets excited about using data to improve marketing, the reality is that the data sets typically used are incomplete and to some extent even flawed. The data marketers have for digital marketing is very good for understanding behaviors (e.g., what did you do) and reasonably good for understanding a person’s attributes, especially demographics (e.g., age, income, etc.). What it doesn’t do, and which, at best, marketers try to infer, is to understand attitudes and emotions. Current data and analytics toolsets don’t really
seek to understand the why behind the actions. This leads to the question: what’s a brand without a true emotional understanding of its customers? Today brands need to behave like humans and that can’t be done without actionable emotional insights.
What AI is now starting to enable is the ability to directly measure and understand attitudes and emotions. To build insights around emotions, you need to be able to work with data sets of language, imagery, and sound. The classical data science algorithms did a poor job of mining these data sets for emotions. But Gen AI’s purpose is built for understanding and communicating with these unstructured data types. Even more important, AI allows for then using these emotional insights in your digital marketing. Google has introduced a new approach called value-based bidding. Imagine being able to adjust how much you pay for an impression based on whether someone has the most optimal emotional profile for your brand. That’s not just an innovative dream, that’s now becoming a reality with AI.
Howard: Great points and very well said. What we just discussed ties nicely to a white paper you recently wrote on “Ensemble AI.” What is it exactly and why is it critical to how people should be thinking about AI moving forward?
Kurt Kendall: A poorly kept secret about Gen AI is that for many of the use cases being considered, it’s not yet ready for primetime. The good news is that with the billions and billions of dollars being spent on these technologies that progress is happening quickly. However, there is much work to be done and one of the conclusions that many now have is that there won’t be one singular AI solution. No one AI to rule them all.
My overarching message with my discussion of Ensemble AI is that rather than a singular technology solution, many use cases require a combination of technologies all working together, potentially uniquely to the use case. Anyone who’s worked in marketing knows that it takes a lot of different tech components (e.g. the marketing tech stack) to support marketing’s business needs. For AI, companies should expect similar dynamic and not assume that a single Large Language Model (LLM) from OpenAI, Meta, Google, etc. will be sufficient. What will be required will be based on the specific business needs and use cases.
