Developers and content creators may work within very different business functions, but they ultimately belong to the same team, and enterprise AI adoption should be designed to reflect that.
This isn’t how it’s tended to work amid other waves of innovation. The first computers filled entire rooms and were primarily intended for government agencies and scientists before consumers were offered desktop PCs. Command-line interfaces like Unix forced users to memorize obscure commands to run a program or open a file. Email was reserved for engineers until services like Hotmail and Yahoo Mail came along.
So far, enterprise AI adoption has followed a similar trajectory. Both generative and now agentic AI solutions have been accompanied by a slew of APIs, custom integrations, and tooling that were clearly launched with developers as the primary audience.
The needs of those who will actually use AI to transform everyday business processes, such as marketing teams producing and managing content on their brand’s behalf, can sometimes be a secondary consideration.
In a virtual event hosted by Human Made, WordPress VIP CEO Steph Yiu raised this concern for the wider WordPress ecosystem and offered a call to action worth heeding.
“We’re building all of these amazing foundations of how AI, LLMs, and agents interact with WordPress. And right now, much of it is very developer-forward… I hope to see that as we evolve the AI components of WordPress, it is also as inclusive to the content creators as it is to developers.”
– Steph Yiu, CEO, WordPress VIP
What ‘developer-forward’ means in practice
It’s understandable that developer capabilities often become the first priority as new technologies emerge. These are the people who need to connect innovations to their legacy tech stack.
They need to ensure new platforms and applications don’t increase cybersecurity risk. And developers are there to serve end users, like content creators, by helping deploy and customize technologies like AI.
You only have to look back to the generation of applications that preceded AI to know where this can go awry. Before predictive analytics became a staple within large organizations, they turned to a product category called business intelligence (BI) to make strategic use of data.
BI platforms and applications were great at creating reports and visualizations to help teams understand how their organization was performing. It wasn’t nearly as easy as posing a question to Claude or ChatGPT, however.
Line-of-business users leaned on developers to write SQL queries, produce new dashboards, and assist with ad hoc report requests. This was on top of the work developers were already doing to manage data warehouses and cleanse data.
Business users, meanwhile, could be waiting in a queue to have their request handled, which might take anywhere from days to weeks. There was also little flexibility to make changes without custom coding, all of which took developers away from other tasks.
Eventually, BI tools became more self-service, but there was still a good chance that developers would be called upon to address misinterpreted data or duplicate reporting efforts. It’s a textbook case of the AI adoption challenges enterprises should be designing out of their AI rollouts rather than repeating.
Why enterprise AI adoption stakes are different
AI should benefit developers who want to automate tasks to yield productivity and efficiency gains, but within most enterprises, the real value comes on the front lines. Content teams create marketing assets that influence every phase of the customer journey, from awareness and consideration to conversion and support.
AI is helping by summarizing and organizing information like customer call transcripts and assisting with generating first drafts. It can also play a part in reviewing content and determining the best way to reach its intended audience.
This is why AI for content creators has to be built in tandem with a richer toolset for developers. Yet according to McKinsey’s 2026 Global Marketer Survey, more than three-quarters of individual contributors report AI-related anxiety. That may point to a bigger usability gap slowing AI adoption than their leaders realize.
The same research found that while 60% of marketers say they’re using AI multiple times a week, fewer than 10% are capturing value across end-to-end workflows. A lack of usability could stall AI adoption in siloed deployments, limiting how well it can benefit organizations as a whole.
What the WordPress ecosystem is actually doing about it
There’s a lot of WordPress AI experimentation underway, from AI services and technology solution providers to early adopters, and some of it is putting the needs of content creators in the spotlight.
The Human Made virtual event also included Gabriel Koen, senior vice-president of technology at Penske Media Corp. PMC is a longtime WordPress VIP client, which means it has seen firsthand the benefits of using a CMS that builds AI into an established managed platform rather than a series of developer-focused add-ons.
Best known as the publisher of Rolling Stone, The Hollywood Reporter, and the South by Southwest (SXSW) Festival, PMC is like many organizations in that it’s been evaluating AI use cases for some time.
“The AI projects we’re working on are all to power things we’re putting forward for users. The one that’s probably been in production the longest is to translate some of our content in Spanish for Billboard Español.”
– Gabriel Koen, SVP of Technology, Penske Media Corp
PMC is also using AI to digitize the print archives of its publications, some of which go back 100 years. The technology can identify where articles continue from one print page to the next and distinguish editorial copy from ads.
Koen started at PMC as a developer some 16 years ago, so he’s used to seeing significant changes in technology, but he notes that the organization has been on WordPress VIP for most of his tenure.
“Part of our strategy has been to centralize all of our publishing on the platform, and that was WordPress, because everybody knows it, and all the writers are used to it.”
– Gabriel Koen, SVP of Technology, Penske Media Corp
The platform layer is where enterprise AI adoption gets solved
PMC’s experience shows why enterprise AI adoption depends on the platform. The right CMS makes AI available to everyone who needs it: developers and content teams, not developers and then content teams.
Taking an inclusive approach to enterprise AI is not just a nice thing to do. It belongs at the center of any enterprise AI strategy because it avoids delays in time to value, when organizations most want to see it realized.
