All industries are facing a pivotal moment due to the rise of AI. Success hinges on executives collaborating with their teams and technology partners to develop innovative use cases that enhance business growth. Rather than viewing AI merely as a means to reduce costs and replace personnel, leaders must aim to strengthen and improve their organizations to fully harness AI's potential. Additionally, companies should avoid exaggerating AI capabilities to please investors or attract customers, a practice known as "AI washing, " which poses various risks. To explore effective AI adoption strategies, I interviewed Kurt Kendall, a seasoned expert who has held key roles at firms like GlaxoSmithKline and McKinsey & Company. He is now the Co-Founder and Co-CEO of Kairos Growth Advisors. Here’s a summary of our discussion: Billee Howard inquired about high-level AI strategies. Kurt Kendall referenced Geoffrey Moore's influential book, *Crossing the Chasm*, noting that while there is great promise in AI, significant challenges and risks exist on the journey toward realizing its full potential.

Companies that succeed will be those that effectively navigate these risks, as exemplified by OpenAI's choice to pursue ambitious goals despite uncertainties. Howard probed the primary hurdles in AI adoption. Kendall highlighted that many organizations struggle to identify compelling business use cases to justify the necessary investments. He noted that considerable costs are associated with developing AI solutions, including the need for process reengineering, which often exceeds technology expenses. Many conversations about AI overemphasize cost savings through potential job reductions, which fails to inspire enthusiasm among business leaders. Instead, AI discussions should be driven by business leaders with clear use cases that align with overall company missions and performance objectives. Regarding marketing and customer experience (CX), both areas are positioned uniquely to leverage AI, given their long-standing use of data science. Kendall emphasized that Generative AI (Gen AI) could reintroduce the “art” of marketing, enhancing traditional applications by incorporating unstructured data like language and imagery, thus improving customer engagement and brand content creation. Kendall continued, pointing out the need to understand consumer emotions and attitudes rather than just behaviors, which traditional data often lacks. AI can facilitate deeper insights into emotional benchmarks, enabling brands to connect more authentically with consumers and tailor marketing efforts accordingly. He also discussed his concept of "Ensemble AI, " which posits that diverse technologies will be required for various use cases rather than relying on a singular AI solution. As he argued, effective marketing necessitates a comprehensive technology stack, suggesting a similar approach should be taken with AI where a combination of tools meets specific business needs. In conclusion, embracing AI effectively involves a collaborative, innovative, and integrated approach, moving beyond mere technological implementation to improve overall business strategy and customer connection.