I recently talked to a marketer whose company added Claude to their org chart. Not as a joke, but as a real role, with defined responsibilities and a clear place in the workflow.
I laughed at first, but the more I sat with it, the more it felt like a reflection of what’s already happening inside a lot of organizations, whether they’ve formally acknowledged it or not.
According to McKinsey, 88% of organizations regularly use AI in at least one business function. It’s clear that AI isn’t sitting off to the side anymore. It’s being embedded in the work we do.
THURSD-AI
At Quantious, we see this every week in our internal “Thursd-AI” sessions. What started as a casual forum for sharing prompts has turned into something more practical: people showing how they’re actually using AI inside real workflows.
One marketing producer set up an AI agent to automate competitive research that used to take hours. A project lead uses it to build out project timelines. By just providing start and end dates and describing the work to be done, it builds out all milestones and shares the timeline with the team for approval. Individually, the gains are small, but collectively they’ve started to provide the team with more breathing room for creativity.
And that’s why the org chart idea stuck with me. It’s less about the title and more about the formal recognition that AI is becoming part of the operating model.
3 REASONS TO DEFINE AI ROLES
More teams are defining distinct roles AI performs across their business. These are three reasons why it actually makes sense.
1. AI IS ALREADY IN THE WORKFLOW
AI isn’t experimental anymore; for many teams it’s just how work gets done. It’s embedded in everyday tasks like summarizing meetings, drafting content, and analyzing data.
AI Adoption has been relatively fast. Roughly 55% of U.S. adults are already using generative AI, a faster adoption curve than both the internet and personal computers at the same stage.
Inside organizations, it’s even more embedded. Ninety-one percent of companies reported using at least one AI technology in 2024, and employees using it regularly report saving as much as 7.5 hours a week on routine tasks.
Those hours are saved in faster research, cleaner drafts, and fewer hours wrestling with spreadsheets. None of that eliminates the work, but it does compress the very mechanical parts of it.
2. AI FLUENCY IS QUICKLY BECOMING TABLE STAKES FOR THE WORKPLACE
What’s been interesting to watch is that the people getting the most value from AI are rarely the most technical. They’re the most curious.
Someone experiments with a prompt that saves them an hour a week. Someone else builds on it and finds a faster way to analyze a dataset. Before long, the whole team is using a smarter, more efficient workflow.
The advantage compounds when teams share what they’re learning.
That matters, because there’s a growing gap between organizations that treat AI as an occasional tool and those that treat it as a capability. Research shows that leaders and managers already use generative AI several times a week at far higher rates than frontline employees, which is why many companies are now focusing on building broader AI fluency across their teams.
The companies moving fastest aren’t just buying AI tools. They’re building a culture where employees are encouraged to be curious, experiment, share their workflows, and figure out what actually improved the work.
3. AI CLEARS SPACE FOR THE WORK HUMANS SHOULD ACTUALLY BE DOING
The anxiety continues over whether AI will replace certain skills. But what we’re seeing in practice is a little different.
When AI handles the tedious parts of the work we do—the formatting, the summarizing, the first drafts—it frees people up to spend more time where human judgement really matters. Strategy, creativity, and storytelling are the work that really pushes companies forward.
The more AI takes over the mechanical pieces of knowledge work, the more important those human capabilities become. So, the real leadership question is whether the organization is learning how to use it in a way that actually improves the work.
WHAT DOES THIS LOOK LIKE IN PRACTICE?
Inside of our own workflows at Quantious, AI is showing up in a few places.
Research. Tools like Perplexity and Waldo help us synthesize industry news, identify emerging competitors, and cut through noise faster than traditional search.
Data work. ChatGPT helps our team members to write complex Excel formulas, a task that only the spreadsheet experts on our team would be able to do. What used to be tedious spreadsheet wrangling can now be achieved with a simple natural language prompt.
Brand voice. After our brand refresh, we trained a custom GPT on our voice and messaging guidelines. It helps us maintain consistency across everything from blog posts to press materials.
None of this replaces the thinking and judgement our (human) team brings to the work. We’ll all just have to get used to AI taking a few spots on the org chart. They just don’t get invited to the team offsite.
Lisa Larson-Kelley is founder and CEO of Quantious.
