Getting Business Leaders To Buy Into AI Has Nothing To Do With AI

Do we really need more marketing about Generative AI? SAP and Coca-Cola both released “AI-infused” ad campaigns with completely different target audiences. The success of both campaigns can teach us something about AI adoption. Getting the business and the average consumer bought in has nothing to do with the technology or data.

The slogan behind SAP’s recent AI-inspired ad campaign hits many people in the data field wrong. It caused a strong dissonance in me, and I couldn’t really pin it down until a couple of days ago. SAP is telling its customers to “Be Ready.” For business leaders, it’s an inspiring call to action.

For data scientists, it’s something different. Be ready? I’ve been ready for 11 years. Where has the rest of the business been? Data teams are running out front with miles of untapped potential. We aren’t ramping up to meet the need or just coming online. We’re at the restaurant, ready to eat, but the rest of the business is still getting dressed.

SAP’s ad campaign is a simplistic example of how Generative AI will make businesses more productive, but most companies cannot execute something this basic. Most teenagers work with generative models like Stable Diffusion to create amazing art between classes and after school. Marketing teams at some Fortune 500 companies can’t replicate that.

SAP teamed up an illustrator with a text-to-image model. The marketing team took the day’s top headlines and used them as a prompt for the generative model. The illustrator edited and improved the image so it was ready to be used in the campaign.

Allie K Miller pointed out that this process used to take a week or more. SAP’s marketing team pushed out a new image every day for 2 weeks. Check out the image gallery here. They are really good compared with the raw materials that generative image models pump out before editing.

The campaign is proof of the productivity gains that Generative AI makes possible. Yet even after seeing the example, most marketing teams will continue to debate over how they can use Generative AI. SAP literally put the use case right under marketing teams’ noses and showed them exactly how to do it. And most marketing teams will respond by having another brainstorming session.

Data And AI Are Proven. So Why Are Businesses Still Debating?

I’ve been here for 11 years, and some data scientists have for even longer. SAP’s ad campaign is just the latest in a long line of attempts to bring businesses into the data and AI-driven marketplace. What’s even crazier is the simplicity of what SAP tells firms to be ready for.

SAP demonstrated Datasphere, and there wasn’t any Generative AI or advanced model at the front. SAP focused on getting the business’s data into shape in demo after demo. The ad slogan wasn’t telling companies to be ready for a futuristic vision of what’s next. SAP told its customers to be ready to make data accessible and actionable.

SAP’s marketing team developed the concept, pitched it to leadership, got it approved, and executed the campaign in less than 3 months. When businesses are ready, that’s how fast they can go from idea to implementation and ROI. SAP’s campaign has been seen millions of times. It helped drive a massive attendance at the Sapphire conference in Orlando.

I don’t have an exact count, but the conference was so packed that I almost couldn’t get a flight home on Thursday. The Orlando airport has over 2000 flights and handles 58,000 passengers per day. So many people were leaving Sapphire simultaneously that it overwhelmed one of the busiest tourist destinations in the US.

While SAP went with a simple campaign, other companies are already diving into more complex marketing applications. SAP’s campaign got a significant response. Coca-Cola recently showed businesses that if they go bigger, they’ll make an even bigger splash with customers. These campaigns scale, and so does the ROI.

Coca-Cola implemented 2 different marketing campaigns with Generative AI tools in less than 3 months. They partnered with Bane and OpenAI to deliver a tool to help their creative partners leverage Coke’s brand assets in their art and a 2-minute commercial built with Generative AI. With this campaign, there were also millions of views and massive engagement.

In this article, you can see how the creative animation teams leveraged Stable Diffusion to speed up the development process. People weren’t removed or replaced. As in the SAP campaign, the Generative AI tool was used to accelerate the process and deliver the result faster. According to the article, this was the first time an AI tool was used on a campaign of this size.

Most businesses spend years asking the same questions repeatedly. What use cases should the business target? What talent is necessary? How does the business make data more accessible? What’s the best way to make business users data and model literate?

The answers are everywhere. Use cases are increasingly common. Case studies span over a decade, detailing how big and small companies have achieved success. Businesses that are still debating and stuck in proof of concept purgatory have failed at step 1.

It’s The Data Stupid, But Also The Culture.

SAP’s “Be Ready” message is targeted at getting businesses to take their data seriously. Why not just say that? It won’t capture business leaders’ attention or imagination. Data teams are missing a crucial component of moving data and AI initiatives forward, people.

I spent some time with SAP’s Chief Strategy Officer, Sebastian Steinhaeuser. He was at Sapphire to explain the company’s approach and commitment to sustainability. Those initiatives cannot be supported or evaluated without data and models. I asked Steinhaeuser about how AI has impacted his role.

He acknowledged the need for data and emphasized transparency. In his view, businesses must operate more transparently. He called out data as a critical tool and enabler of that goal. But…

I told him that I teach business strategy to data scientists and asked if he had any advice for people working to make the transition into strategy and C-level roles. He said we should focus more on articulating vision and purpose than data and models. Business leaders would have never bought into sustainability if all Steinhaeuser brought was data.

Data professionals are great at selling the technology and delivering roadmaps. We need to improve how we connect the technology to purpose and deliver a vision for how data and AI will transform the business. It’s more flowery than our engineering and science minds typically function, but people won’t buy in without purpose and vision.

Being Ready

Data professionals come with a vision and purpose already built in. We all get into this field to build and because we see what’s possible. Something sparked our passion for data and AI, or we wouldn’t be here. Data science is one of the most demanding professions to learn, and the learning never ends. We are all here because we are passionate about the field, which fulfills our purpose.

Outside of the field, the passion isn’t there yet. Interest is growing, but business leaders are passionate about other parts of the business. We must articulate a vision for how AI can support senior leaders’ passions and align with their purpose. That’s what SAP’s campaign is trying to communicate. They aren’t telling business leaders to be ready for the technology. SAP is getting them excited about what can be achieved with the technology.

The deeper message is, “If you’re passionate about creating great content and customer experiences, look how Generative AI supports your passion.” We must adopt similar messages to get business leaders and customers to buy in and adopt. We connect with data, but the business connects with something else.

Delivering data and AI products must include pieces that align with purpose and articulate a vision. I teach that in my courses and dedicate 3 chapters to it in my book. Data teams must be ready to work with people who don’t love data or care if products do or don’t use AI. While businesses work on getting ready for data and AI, data teams must get ready to work with people.