Experts Reveal How To Rollout An AI Strategy

By Joyce Gordon, Head of Generative AI, Amperity

We’re experiencing a rapid evolution in the Gen AI landscape. Moving beyond experimentation, companies are now forging strategic pathways, identifying areas where it can genuinely make a transformative difference.

Joyce Gordon, Head of Generative AI at Amperity, recently joined forces with industry leaders, Rio Longacre, Managing Director at Slalom, and Jon Williams, Global Head of Agency Business Development at AWS. They discussed top tips to build an effective AI strategy to increase revenue, drive cost efficiencies and achieve brand success.

The Rapid Shift to Gen AI

“When ChatGPT launched, it quickly became the thing every client we worked with was suddenly dedicating significant resources to. They were building out Gen AI demos, investigating it. People were asking us to help them create a Gen AI strategy. And there was a lot of experimentation. There was a lot of wheel spinning too. It was like this Gen AI-mania – everyone just went all in,” Longacre says.

“Within the last few months, there’s been a big shift, which is very positive. Instead of ‘let’s just try different things’, it’s now, ‘let’s have a Gen AI strategy’. They are looking to identify areas where Generative AI could make a big difference and move the needle. They want to invest in those, whether it’s eCommerce, operations or creative; they want to come up with ideas that could work and test them. If they work, great. They’ll look to start to commercialise them. If they don’t, that’s OK too, then they can pivot and try something else.”

3 Ways Organisations are Leveraging Gen AI for Greater Efficiencies and Cost-Savings, According to Longacre:


  1. eCommerce company: This company has written descriptions for 10,000 product SKUs using Gen AI in a couple of weeks, saving them months of time and about a million dollars.
  2. Paid media: As it relates to paid media tools, such as those designed for Amazon Marketing Cloud, there’s a background image generator specifically tailored for crafting lifestyle images. Findings indicate a remarkable 20 to 25% increase in conversion rates for products showcased with lifestyle images compared to those with a plain white background. Swiftly deploying these features, testing their efficacy, obtaining results and subsequently, optimising based on these insights is a game-changer.
  3. Banks and finance: The bank’s creative briefs are now being generated by artificial intelligence, reducing the time spent on back-and-forth communication with agencies by approximately one week.

Even with segmented strategies, brands often face resource challenges. Accelerating the creation of creative briefs, creative imagery and product descriptions allows for a faster customisation of on-site experiences. This progression toward personalisation doesn’t require them to go in a ‘hands-off’ mode where Gen AI is really running the show. Instead, it’s truly like a genuine one-to-one chatbot interaction or conversational AI.

Rolling Out an AI Strategy

As executives consider integrating AI strategies into their organisations, Williams points out that it involves a multifaceted approach. Specifically, there’s a need for a comprehensive change process encompassing people, processes and policies. Establishing a cloud centre of excellence is also crucial.

“You have to focus on value-driven outcomes, making sure you have a responsible AI program and you’re mitigating bias,” Williams says. “What’s the human insight you’re incorporating into the processes, and what are the use cases that you’re testing on a regular basis?

“Also be careful with your addiction to prediction,” Williams warns. “Listen to the data and consider new paths. As you’re bringing your data into your environment, make sure you’re accessing a variety of LLMs for the tasks and checking between the LLMs that you’re using for their effectiveness in a specific use case. Then figure out what KPIs you want to be tracking.”

While those are some starting points for implementation, Williams does add that having the right team in place is paramount. “Organisations need a skilled and diverse team when rolling out AI and ML initiatives.”

This includes:

  • Data scientists, identifying relevant data
  • Machine learning engineers, designing infrastructure
  • Software engineers, maintaining systems
  • Cloud computing specialists and cloud security engineers, which are essential for secure cloud infrastructure management
  • Project managers, overseeing AI and ML project implementations and ensuring alignment with business objectives

Longacre then offers some practical advice, offering the reminder that, while AI is a tremendous innovation, it’s still just a tool. “Gen AI is not a problem, and it’s not a solution – it’s a tool that can solve many different problems. It’s not the ‘end all be all,’” he says.

“Also, it must be cross-disciplinary. My advice would be to refrain from throwing a lot of Gen AI experts together and think they’re going to solve everything. They need to be paired with someone who knows how to do prompt engineering or someone who has domain level expertise in marketing, personalisation and content or whatever the area is you want to improve or apply generative AI to.”

Start Small With AI for Big Results

My advice to brands and organisations when rolling out AI: start small. I would start with a small use case that’s highly measurable and one that doesn’t require major change. One place where clients we work with have seen a lot of success is just with subject line optimisation or optimising the body of emails or paid media ads. Since you can have a human in the loop here, it’s a great opportunity to experiment with creating different segmentation strategies and different messages. And it’s also really easy to measure and determine if those approaches are working or not.

At Amperity, we recently announced two new generative AI capabilities, Explore and Assist, that join our existing AI-powered capabilities, Stitch and Predict, to create a comprehensive suite collectively known as AmpAi. We are committed to fixing the data quality and access challenges many brands face with traditional CDPs. With AmpAI, brands can be confident that they are making decisions based on a trusted data foundation to determine the best way to engage with customers and power downstream AI technologies.

As the cookie crumbles and the marketing landscape continues to change, we want to ensure our technical and business users can put every last crumb of their customer data to use, unlocking more value and creating incredible user experiences. While this is a big leap forward, I can tell, it’s only the beginning.

Watch the full webinar here.