Rebecca Sykes Leads AI Implementation At The Brandtech Group
Rebecca Sykes, a partner at The Brandtech Group, says generative AI can work with imperfect data, and that the biggest risk for brands is convergence rather than divergence. She emphasizes the importance of transforming company operating models to fully utilize AI.
Original reporting · adexchanger.com
Original: “What Frogs And Socks Can Teach Us About AI Implementation”
Read “What Frogs And Socks Can Teach Us About AI Implementation” at AdExchanger →
How this was written. Awesome B2B wrote this article with a language model from AdExchanger’s reporting (model: @cf/meta/llama-3.3-70b-instruct-fp8-fast); it describes that reporting rather than adding to it. The original, linked above, is the full story.
Key points from AdExchanger’s report
- Generative AI can work with imperfect data, allowing brands to maintain their unique identity.
- The biggest risk for brands is convergence rather than divergence, which can result in generic outputs.
- Companies must transform their operating models to fully utilize AI and achieve end-to-end efficiency.
- The majority of effort in AI transformation should be dedicated to people and processes, rather than just technology.
- Transformation is an ongoing capability, rather than a one-time initiative, requiring continuous effort and investment.
Rebecca Sykes, a partner at The Brandtech Group, is rethinking the conventional wisdom that clean data is necessary for AI implementation. According to Sykes, generative AI can work with fuzzy, siloed, and sometimes badly labeled data. This approach is critical for brands, as it allows them to maintain their unique identity and avoid generic outputs.
Sykes explains that the biggest risk for brands is that AI takes them toward convergence rather than divergence. Divergence is essential for brands, as it allows them to stand out and maintain their individuality. To achieve this, brands must focus on transforming their operating models to fully utilize AI.
To illustrate the challenges of AI implementation, Sykes uses a fable about a frog and a sock. The frog represents AI, which is born to leap, but is trapped in a sock, representing a company's existing operating model. This analogy highlights the need for companies to transform their operating models to unlock the full potential of AI.
Sykes notes that many AI pilots stall at scale due to the limitations of existing operating models. For example, a company may use AI to reduce the time it takes to complete one part of a task, but the overall process remains inefficient due to multiple systems and manual processes. To address this, companies must look at the entire end-to-end efficiency of a work process, rather than just automating individual steps.
To fully utilize AI, companies must shed their existing operating models and build new ways of working around what technology can do. This requires a significant effort, with the majority of resources dedicated to people and processes, rather than just technology. Sykes emphasizes the importance of creating new roles, including jobs related to AI governance, systems ownership, and workflow maintenance.
As the cost of making content falls, the value shifts to owning the system. This requires treating transformation as an ongoing capability, rather than a one-time initiative. Sykes stresses that transformation is not a project, but a muscle that must be exercised continuously.
The Brandtech Group has launched a new global data and AI practice, which aims to help companies navigate the complexities of AI implementation. Sykes also discusses the role of video in AI brand recommendations and the importance of tracking what AI models say about a brand. This article is based on reporting by AdExchanger.
Questions and answers
What is the biggest risk for brands when implementing AI?
The biggest risk for brands is that AI takes them toward convergence rather than divergence, resulting in generic outputs.
Why do many AI pilots stall at scale?
Many AI pilots stall at scale due to the limitations of existing operating models, which can prevent companies from achieving end-to-end efficiency.
What is required for companies to fully utilize AI?
Companies must shed their existing operating models and build new ways of working around what technology can do, with a focus on people and processes, rather than just technology.
