Stuck in experimentation land

So I have this theory I'd like to run by you all.

It's about why so many marketing teams feel stuck in the pilot phase of using generative AI in their work, despite it being <<waves hands wildly>> everywhere.

I think there is an s-curve style path in adoption that companies take when they start to use generative AI seriously with three phases: individual acceleration, team acceleration and business acceleration. Each stage comes with its own learning curve and its own set of benefits but often teams stall out in the first stage because a) they think they've reached the pinnacle of what AI can do for their needs and b) moving to the next stage requires not only a vision for it but also a change in how you operate as a team or business. I made a rudimentary illustration of it below.

Individual Acceleration

The use of AI on a marketing team typically starts with a handful of marketers tapping it for individual acceleration. You could have one marketer on your team experimenting with it or twenty, but the trait remains that they're each mainly using it to speed up their own workflow.

• AI benefits at this stage: Faster content creation driving efficiency

• Benefits measured in: Time savings and cost savings

• Team changes at this point: Minimal, adopting the right tools and standards

Most companies stop here. Why? Because this is the furthest you get without changing anything about the way that you operate. Moving to the next stages of benefit requires a shift in how you approach your work.

Team Acceleration

Companies that enter into team acceleration start to use AI as the backbone of how they organize their ideas and align their messaging. They ground their AI copilot in their style guide, brand voice, company positioning and more and therefore using the AI copilot as a default and team standard becomes a way of ensuring message alignment and consistency. All work gets run through AI as an editorial check to make sure it adheres to these key brand guidelines and teams collaborate on campaigns connected through the AI copilot. This stage repositions much of the marketing focus and work at the front and end of the process rather than in the production itself. Marketers invest in building robust source documents to ground the AI tool, leaving more of the repackaging and orchestration to AI, and putting in stronger human-led editorial checks at the tail end.

• AI benefits at this stage: Better informed content driving quality

• Benefits measured in: Message, tone and style consistency

• Team changes at this point: Intentionally changing the workflow across a team

Business Acceleration

Speed is great but marketers don't just want to market faster, they want their marketing to perform better. In the third stage of AI copilot adoption, marketing teams expand from using AI for content generation to using it additionally to spot patterns in what is working and not working about that content in the market. This stage bridges generative AI and analytical AI so that you can not only see the data but use AI to surface insights and opportunities in that work and, after review, automate the optimization of that content based on what works.

• AI benefits at this stage: Higher performing marketing driving business outcomes

• Benefits measured in: engagement, conversions, revenue

• Team changes at this point: Connecting to data sources. Shifting to continuous optimization.

I've started to say a lot now that the learning curve of generative AI is not a technical one, it's cultural and strategic. You can get value out of AI at each of these stages but the latter ones require more of a shift in long-held approaches to how we work. In my mind, it's not only worth it, it's how we move AI from a generic copy machine to a catalyst for better work.

What do you think? Does this resonate with what you're seeing? Where in this adoption curve is your company right now?