AI inconsistency, where generative models produce varied brand recommendations even with identical prompts, poses a significant challenge for ecommerce. This "coin flip" nature of AI means every customer interaction can yield different results, impacting brand visibility and customer experience. Ecommerce operators must move beyond simply measuring brand presence to understanding and strategizing around this inherent variability in their AI deployments.
Key takeaways
Conduct internal audits of your deployed AI chatbots (customer service, sales assistants, etc.) to understand the extent of output inconsistency and its potential impact on customer journeys.
Shift focus from expecting specific brand recommendations to ensuring your core value proposition is consistently communicated, regardless of the AI's varied outputs.
Develop robust, ongoing measurement frameworks that track a range of AI outputs, sentiment, and customer feedback to adapt to the probabilistic nature of generative AI.
Educate marketing, sales, and customer service teams on AI's inherent variability to better manage expectations and refine AI interaction strategies.
Diversify AI deployment strategies by identifying areas where inconsistency is less critical versus those requiring more predictable outputs, and choose tools accordingly.
Themes
ai strategybrand managementcustomer experiencemarketing technology
Rand Fishkin's team ran 2,961 prompts across ChatGPT, Claude, and Google AI. 600 volunteers, 12 different prompts, two months of runs. They wanted to answer one question: how often do you see the same list of brand recommendations twice, even with the exact same prompt?
The answer? Less than 1% of the time. The odds of seeing the same list in the same order are closer to one in a thousand.
Most conversations about AI inconsistency treat it as a measurement problem: how do I know if my brand is showing up? That's a legitimate question. But it's not the only question. And it might not even be the most important one.
If AI systems give different recommendations essentially every time, the same inconsistency is already baked into every AI chatbot you've deployed — your hotel chat widget, your B2B sales assistant, your customer service tool. Most teams have never measured it. And some of those inconsistent answers are already drivi
Conduct internal audits of your deployed AI chatbots (customer service, sales assistants, etc.) to understand the extent of output inconsistency and its potential impact on customer journeys.
What does this episode say about brand management?
Shift focus from expecting specific brand recommendations to ensuring your core value proposition is consistently communicated, regardless of the AI's varied outputs.
What does this episode say about customer experience?
Develop robust, ongoing measurement frameworks that track a range of AI outputs, sentiment, and customer feedback to adapt to the probabilistic nature of generative AI.
What does this episode say about marketing technology?
Educate marketing, sales, and customer service teams on AI's inherent variability to better manage expectations and refine AI interaction strategies.
What does this episode say about ai strategy?
Diversify AI deployment strategies by identifying areas where inconsistency is less critical versus those requiring more predictable outputs, and choose tools accordingly.
