AI Unleashed: Exploring Trends, Strategies, and Best Practices in Marketing

Shownotes

In this episode of Sunny Side Up, Chris Moody interviews Jana Eggers on explainability, trends, and tools in AI marketing. Jana emphasises the need for transparency to identify and correct biases, urging the prioritisation of user needs over technological capabilities. She argues that explainability should not be an afterthought but a fundamental aspect of AI. Chris and Jana discuss trends in AI-driven personalisation in marketing, pointing out the need for more nuanced feedback mechanisms. The conversation then shifts to best practices for utilising AI in marketing strategies. Jana advises a balanced approach, combining trust in AI with human expertise and scepticism.

About the Guest

Jana Eggers is CEO of the neuroscience-inspired artificial intelligence platform company Nara Logics. She’s an experienced tech exec focused on inspiring teams to build great products. She’s started and grown companies and has also led large organizations at public companies. She is active in customer-inspired innovation, the artificial intelligence industry, as well as running and triathlons. She’s held technology and executive positions at Intuit, Los Alamos National Laboratory, Basis Technology, Lycos, American Airlines, Spreadshirt, and more.

Connect with Jana Eggers

Key Takeaways

• Understanding the reasoning behind AI’s decisions is crucial for practical sales, marketing, and healthcare applications.
• Transparency in AI helps identify and correct biases, ensuring fair and ethical use of technology.
• Prioritise understanding user needs over purely focusing on technological capabilities.
• Explainability in AI should be viewed not as an add-on but as a crucial element for effective and contextualised user interaction.
• Challenge the ‘better results’ notion to include practical usability and relevance to users’ contexts.
• Engaging with LLMs helps develop a broader understanding and literacy of AI among users and organisations.
• Popular items can sometimes skew AI recommendations, leading to less relevant suggestions.
• The goal is to evolve AI systems to a point where users feel that recommendations are genuinely tailored for them.
• Don’t over-trust AI; use it as a tool while maintaining critical thinking and scepticism.
• Employing AI when scaling beyond human capabilities, such as handling multiple data segments, is needed.

Quote

“Tuning these AI systems without having that explainability is really kind of like surgery with your eyes closed.”

– Jana Eggers

Please share insight into your background and give our listeners a deeper understanding of Nara Logics.

Jana’s career in AI, beginning at Los Alamos National Laboratory, has been marked by her pragmatic view of AI as a practical tool rather than a primary research focus. Her experience spans supercomputing, computational chemistry, and logistics and freight carrier industry implementations. Initially hesitant to join Nara Logics, she was eventually drawn in by their emphasis on explainability and transparency in AI. This approach resonated with her professional challenges, particularly enhancing AI tool effectiveness and understanding. Her decision to join Nara Logics, driven by the company’s commitment to explainable AI, has led to nearly a decade of advancements in this field, focusing on aiding high-end customer decision-making.

How would you advise individuals to recognise and emphasise the importance of explainability in AI and decision-making processes?

Understanding the reasoning behind AI recommendations drastically improves the effectiveness of actions. Transparency in AI is crucial for identifying and addressing biases and ensuring equitable and practical solutions. It’s essential to seek and integrate diverse data sources to gain a comprehensive view, enhancing AI’s accuracy and reliability. The key takeaway is the importance of explainability in AI: it’s not just about what AI does but understanding why it does it, which leads to more informed decisions and ethical practices.

How are you observing changes in the adoption of explainability in AI technology, and do you often find yourself still needing to explain its importance?

The tech industry often prioritises technology’s capabilities over user needs, creating a gap in understanding and application. This gap is especially pronounced in AI, where the complexity can alienate users. To bridge this, involving subject matter experts in the development process is crucial. Explainability in AI should be viewed not as an optional feature but as a fundamental component that contextualises and improves user experience. Redefining what ‘better results’ mean in a practical, user-centric context is essential.

Could you shed some light on the most innovative ways marketers have harnessed AI in recent months? What trends or examples have caught your attention?

Large language models (LLMs) like GPT are gaining significant attention, but it’s best not to view them as a one-size-fits-all solution. These models have made AI more accessible due to the consumerisation of PCs, yet they have limitations, particularly in areas beyond language processing. It’s essential to recognise that AI, including LLMs, is a tool with specific strengths and weaknesses. Engaging with AI in practical, playful ways within organisations can lead to a deeper understanding of its potential and pitfalls. This approach can demystify AI, revealing its capabilities and limitations and fostering a realistic perspective on its role in various applications.

Please share your observations on how AI is reshaping the daily marketing strategies of B2B tech companies.

Significant progress has been made regarding AI and personalisation, but there’s still a way to go. Companies like Starbucks, Netflix, and Amazon are leading the way in creating individualised experiences, moving away from traditional segmentation. However, challenges persist, such as accurately aligning recommendations with individual preferences. Despite not being relevant to a specific user, popular item-skewing recommendations highlight the need for more sophisticated personalisation strategies. As users, providing more nuanced feedback could help refine these AI-driven systems.

Have you seen any great examples of marketers using AI in extraordinary or innovative ways lately?

Starbucks stands out for its AI-driven optimisation strategies that have tangibly increased foot traffic. Beyond marketing, AI is making strides in artistic fields, offering new perspectives and possibilities for creativity. While there are valid concerns about copyright and AI’s impact on traditional roles, the technology is seen more as a complement to human ingenuity. In marketing, AI’s ability to analyse and segment at a much granular level offers exciting possibilities for personalisation and innovation, encouraging marketers to think beyond conventional boundaries and explore new creative approaches.

If you could distil it down to the essential best practices for utilising AI in marketing strategies, what would those essential guidelines be?

The key is to balance trusting AI and relying on human expertise. AI should be seen as a tool to augment human abilities, not replace them. It excels in processing large-scale data and identifying patterns, which can be combined with human strategic thinking to create effective marketing strategies. Remember that AI is limited by the context and data it’s fed and that human intuition and conceptualisation are irreplaceable assets in decision-making processes.

What advice would you offer those marketers standing at the threshold of deciding on AI-powered martech for their strategies and objectives?

Jana stresses the importance of understanding the context in which AI operates. She uses the example of Microsoft Tay to illustrate how AI can fail dramatically when applied in a different context than it was trained for. Jana advises scepticism towards claims of superiority based on ‘big data,’ highlighting that more data doesn’t always equate to better AI. She emphasises the need for marketers to assess whether the AI they are considering has been trained in a context relevant to their needs. Jana also points out the importance of feedback mechanisms in AI, questioning whether the AI can adapt to individual or company-specific needs and how it incorporates performance metrics.

Good reads: Is there a book, blog, newsletter, website, or video that you would recommend to our listeners?

Elements of AI: https://www.elementsofai.com