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Elevating Brand AI Models with Your Biggest Asset: Your People

AI AI, AI & Emerging Technology Consulting, Digital transformation 5 min read
Profile picture for user Rafael Fittipaldi

Written by
Rafael Fittipaldi
EVP, Innovation, Products & Solutions Development

Two hands reaching towards each other with fingers slightly extended, set against a soft, gradient background of purple, blue, and orange hues. One hand has a black wristband.

In today’s rapidly evolving digital landscape, integrating artificial intelligence (AI) is essential to stay competitive, and one transformative way to use AI in your business is to build AI-powered brand models. These custom, bespoke generative AI models are tailored to a brand’s unique needs, ensuring that outputs generated by AI reflect the brand's voice and values.

As EVP of Innovation, Products & Solutions Development, I’ve had firsthand experience helping brands innovate through our brand model practice. This service, strengthened by our recent partnership with Adobe, delivers three key components: fine-tuned models, updated brand prompt guidelines for the AI era, and a prompt bank to jumpstart AI usage. Together, this enhances our ability to help brand marketing teams quickly plan, create, manage, activate and measure on-brand content.

As you can imagine from that description, building brand models relies on more than just technology. Change management and the people element are just as crucial. With that in mind, here is a recipe to bring data, people, processes and technology together to create powerful, bespoke brand models—and some of the successes we’ve seen so far with their implementation.

Data is essential to building brand models—but is only part of the equation.

First, it’s no secret that artificial intelligence relies on vast amounts of data for training, including customer insights, media data and historic brand IP. Therefore, it may seem as though the more data you have, the better—but it’s not that simple.

While a data lake is nice to have, its benefits will diminish over time. Brands seeking to build AI models tailored to their specific business needs require a constant stream of fresh data to adapt and improve over time. This begins with the adept orchestration of data pipelines: the process of designing, managing, and optimizing the flow of data from various sources to ensure it’s current, relevant and seamlessly integrated into your AI systems.

But equally critical is fostering a collaborative environment where your team can effectively leverage the technology and ultimately keep these models up to date. A Forrester report by Forrester VP, Principal Analyst Jay Pattisall—titled “Brand AI Models Will Reinvent How Marketing Creates Business Value”— discusses brand models in detail and hits on the role that people play in filling in those gaps left by historic data. There, he quotes me saying, “[Foundation] models are a picture from the past with data from the past. The future will be continuing to iterate with the model, and that’s the people.” Let’s dig deeper into how aligning your team around these data-driven initiatives can drive transformational change.

Organize people and pipelines into efficient, automated workflows.

We help brands re-engineer their workforce for AI readiness with a consultative phase focused on change management and breaking down silos. This involves pragmatic advisory to understand where you are in your AI journey and implementing a workflow infrastructure for your tech stack and teams, because ultimately the job of the orchestration layer—or the part of the tech stack that manages, coordinates and optimizes interactions between services, application and processes—is to amplify the best of the technology you have and the best of the people you have.

By leveraging technologies like Monks.Flow, for example, we can streamline how people and AI work together. Monks.Flow is an AI-powered professional managed service that connects employees, vendors, AI and enterprise software into efficient, automated workflows. This platform-agnostic environment is designed to work seamlessly across an organization’s existing tech stack, helping businesses achieve benefits like the integration of tools, people and processes; building business intelligence; and maximizing the impact of their marketing efforts to drive growth. Those who marry skillful use of the technology with deep brand expertise can provide strategic oversight throughout this workflow, thereby achieving stronger outcomes than if you relied on the technology alone.

A perfect example of this is an incredibly promising brand lift study we did with Forever 21. The fashion retailer leveraged Monks.Flow and Meta’s AI Text Generation to elevate the creative strategy of their shopping campaigns. We created 50 variations of Forever 21’s business-as-usual (BAU) creative and used Meta’s AI to generate copy variations powered by Llama 3. This conversion lift study aimed to measure the impact of these AI strategies on campaign performance. By incorporating AI to generate diverse copy and imagery, we were able to test and optimize for the best-performing creative. The results were impressive, showing significant improvements in click-through rates and conversion rates compared to BAU assets alone.

Talent is key to domesticating brand models and AI-generated output.

Just as it's crucial to prepare and collaborate with your internal workforce, it's equally important to engage with partners within your vertical for AI consultation and implementation. These experts are deeply immersed in your market of choice, understanding its nuances and dynamics, and can leverage this knowledge to fuel your strategic AI efforts. Moreover, a partner who knows your brand inside and out can provide tailored insights that align perfectly with your business objectives, ensuring a cohesive and effective AI strategy.

For instance, I spoke with April Huff, who has successfully incorporated AI into her role as VP of Research & Insights at Media.Monks to build audience personas and aggregate data from focus groups. As a researcher and strategist, she seeks intriguing differences in focus group data, because those unique viewpoints often trigger the best creative ideas.

Monk Thoughts We’re looking for what’s unexpected or feels new—a different perspective. Often, it’s that nuance that provides the hook or insight.
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Headshot of April Huff
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She emphasized that because AI models excel at predicting the next most likely word when generating text, there’s a tendency toward homogenization of experiences—which can be valuable in market research, but not always. “What makes humans interesting is that they will  say something that is unpredictable,” she says, underscoring the role of talent who can parse these nuances and add depth to AI-generated insights.

One example of how humans teased a surprising creative insight from AI data is our work with Sephora on its “mAI colpevoli” campaign, which launched on International Day for the Elimination of Violence Against Women. We used AI to analyze online data and create three scripted monologues that retold typical episodes of gender-based violence.

The team realized the AI-generated scripts had something in common: written as monologues, they each featured a speaker who blamed herself for what happened to her. The output highlighted the pervasiveness of victim blaming, mimicking societal biases, and we realized we could use this insight to encourage audiences to reflect on their own propensity for blaming victims (or themselves) of injustices. By marrying AI insights with strategic oversight, we were able to create a powerful, purpose-driven campaign that supported women's freedom and self-expression, demonstrating how deep brand expertise and AI can work together to drive transformative change.

The secret to brand model success: people and tech working together.

The integration of artificial intelligence within the enterprise is a multifaceted endeavor that goes beyond mere data accumulation or technological foundations. To truly unlock the potential of AI, brands must focus on creating self-evolving models that adapt and improve over time, driven by continuous data generation and strategic human oversight. This approach ensures that AI systems remain relevant and effective in an ever-changing market landscape.

Ultimately, the successful implementation of AI hinges on the symbiotic relationship between advanced technology and skilled human operators. It requires breaking down organizational silos, fostering collaboration, and engaging with partners who understand both the market dynamics and the brand’s intricacies. As brands navigate the complexities of AI adoption, focusing on these strategic elements will pave the way for sustained success and innovation.

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The website has been translated to English with the help of Humans and AI

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