Explaining the Generative AI Value Gap

Why does GenAI thrill individuals yet stalls in the boardroom? Discover 7 game-changing tactics to bridge the gap. What’s the GenAI value gap? Generative AI (GenAI) seems to promise unprecedented innovation and automation opportunities… yet so many leaders struggle to articulate the actual, tangible value that GenAI delivers at the organizational level. Acknowledgements: Thank you to Amazon Web Services (AWS) for sponsoring this video and to Tom Godden (https://bit.ly/4eQ7iQQ) whose Harvard Business Review article (https://bit.ly/3VxRniC) inspired my video and the longer blog post it’s based on (https://bit.ly/quaesita_endless). #Sponsored For an individual user, it may be enough that GenAI *feels* useful, but that’s not enough for your organization. Justifying continued investment requires you to prove impact, which can be the achilles heel of organizational GenAI until you face the problem head on. Here's what's special about #GenAI (which is also what makes measuring its performance and impact harder than ever): ♾️ Endless right answers... ...at machine scale. ♾️ Just as the world is starting to adapt to the new way of thinking needed for traditional #AI, #GenAI has brought us another mindset revolution to grapple with. Here’s how mindset has evolved: 👉 Traditional programming is for automating tasks where there’s one right answer, using human-written instructions. 👉 Traditional AI is for automating tasks where there’s one right answer, using patterns in data. 👉 Generative AI is for automating tasks where there are endless right answers… and, to pilfer shamelessly from Tolstoy, each right answer is right in its own way. When the right answers are endless, you need to change the paradigm to prove #ROI. I’m convinced that the opportunities in #GenAI are real, but it takes a special kind of #leadership mindset to tap into them, so this video is a quick guide for leaders on how to approach metric design in a GenAI world. The 7 principles in this video will help you overcome the challenge of endless right answers and succeed with GenAI in the enterprise: Principle #1 - Get clarity on the who Principle #2 - Get clarity on the what Principle #3 - Be the author of meaning Principle #4 - Think in terms of good enough Principle #5 - Use human ratings as a proxy Principle #6 - Try an experiment Principle #7 - Tie it back to the business Dive deeper into GenAI metrics with my detailed Medium article: https://bit.ly/quaesita_endless #AIInnovation, #ArtificialIntelligence, #BusinessStrategy #Enterprise ***Additional resources*** Technical Considerations for Business Leaders Operationalizing Gen AI https://bit.ly/3VxRniC The art and science of making metrics https://bit.ly/quaesita_opera What is a randomized, controlled experiment? https://bit.ly/quaesita_experiment How do A/B tests work? https://bit.ly/quaesita_ab If you enjoyed these tips, don’t forget to like and subscribe for more insights on AI and leadership, and also give Tom a follow on LinkedIn here: https://bit.ly/4eQ7iQQ