Your teams use AI, but do they think with it?

Every year, Audience Audit publishes a study on what agency clients really want—and the 2025 edition revealed a stat that should stop any agency leader in their tracks: 77% of clients say they’re more likely to hire an agency that’s a recognized AI expert (not just self-proclaimed). But only 32% believe their current agency fits that description.

Here’s what’s more telling: When asked what they expect from their agency when it comes to AI, clients didn’t say “efficiency” or “cheaper deliverables.” They want new ideas, sharper analysis, and real guidance on how to use AI themselves. In other words, they’re not just looking for agencies that use AI. They want partners who know how to think with it.

At Quantious, being “AI-fluent” isn’t a role, it’s a team standard. Every producer, strategist, and designer is expected to not just keep up, but lead. And we don’t just talk about it in pitches, we practice it every day.

Want to build real AI fluency across your team? Here are five ways we’ve made it part of our everyday work.

1. Invest in professional development like it’s our job (because it is)

Professional development isn’t a once-a-year checkbox here, it’s a cultural value. We budget for AI courses, certification programs, and conferences because we believe time spent learning is time well spent. We’ve encouraged team members to tackle everything from AI marketing bootcamps to building apps with vibe-coding tools like Replit, Lovable, Replay.io, or Base44 (Seriously, one project lead with no coding background just built his own app!).

We believe in fostering a culture of experimentation, and to some, our approach looks a little risky. When we invest in our team members’ professional development, we know it’s not always going to instantly translate to value for our clients. But guess what? Innovation stems from learning and exploring, and that’s exactly how our teams end up ahead of the trends, every time.

2. Host team-led AI workshops

Our favorite AI tipsters are each other. When a team member cracks a new use case—like building out a personalized GPT, or using AI to develop complex Excel formulas—they host internal workshops to share what they’ve learned. We’ve had workshops on everything from AI product image generation to deepfake identification. We document our processes, record quick tutorials of what we’ve learned, and aim to keep knowledge moving fast.

3. Encourage experimentation on live work

We don’t treat AI like a lab project. We build with it every day. Designers test layout variations with image generation tools. Marketing producers use AI to pull research for brand sentiment audits or to map out user journeys. Copywriters turn notes into outlines, organizing their thoughts before drafting. We’ve learned how to craft meaningful prompts, how to develop our own agents, and how to build out some seriously complex spreadsheet formulas using AI. We automate time-consuming processes, using Bluedot, Slack, and Limitless to transcribe company meeting notes in real time. We use these tools with our brains, not instead of them.

In every aspect of our work, we remember that AI is a collaborator, not a replacement for hard work and creativity. Say it with me: You cannot just check out and have AI do it all for you. (Just ask Randy Marsh of South Park; it doesn’t end well!)

4. Treat AI safety and usage guidelines as a living document

AI is moving fast, and so are the conversations around safety, security, and ethical use. That’s exactly why we treat our AI guidelines as a work in progress, instead of a static rulebook. Leadership actively invites input from across our team to flag new risks, suggest safeguards, and share best practices.

AI responsibility is a shared approach we take, and we want to ensure everyone has a role to play in mitigating data privacy and bias. This has led us to embrace a smarter, safer, and more thoughtful AI practice that evolves along with the tech.

5. Help clients navigate the AI maze

AI tools are evolving daily—and most of our clients are trying to make sense of what’s worth their time, what’s secure, and what actually works. The real value lies in making AI feel less overwhelming, and more actionable.

That’s why it’s vital to not just use AI to drive internal efficiencies, but to help clients make it work for them in their own workflows. Whether it’s creating custom GPTs, mapping out automated content workflows, or guiding teams through prompt strategy, we treat AI as a collaborative layer in the client relationship.

And we’re transparent about it. When AI plays a role in our work, we explain how, why, and what it means for the outcomes. That clarity builds trust and helps future-proof our clients’ teams.

Our job isn’t just to use AI—it’s to help our clients understand it, apply it responsibly, and stay ahead of the curve. That’s where the real value is.

The future of creative work isn’t going to be driven by opening up a browser tab and launching ChatGPT. It’s going to be driven by humans who can automate a tedious quality assurance process, use AI to spot brand inconsistencies across campaigns, or extract insights from raw customer feedback, safely. Because knowing when not to use AI is just as important as knowing how.

Lisa Larson-Kelley is founder and CEO of Quantious.

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