Nick Cawthon got his first job at AltaVista in the late 1990s. When he told his dad how much he was making, his father was shocked. "You're making more than I am now, at the end of my career, in your first job out of college."
The job lasted 18 months. AltaVista became Google. But Cawthon felt something then that he feels again now: change is coming whether we're ready or not.
Today's AI wave feels similar. The question isn't whether AI will reshape work—it's whether leaders can guide their teams through the shift without leaving people behind.
Drive from San Francisco airport into downtown and you'll see billboards everywhere. Cawthon travels this route often and watches the buzzwords change every six months: cloud, Bitcoin, NFTs, edge computing. Companies chase investment dollars with whatever tech term is hot.
"I and others were jaded to what this hype cycle was going to produce," Cawthon says. "Bitcoin hasn't really changed my life very much."
But AI feels different. The potential impact spans different organizations and workflows in ways previous tech waves didn't. We've seen this progression before: businesses moved from pen and paper to spreadsheets, then came mobile-first design around 2010.
Now something more basic is shifting. "What if there is no interface? What if it is a language model instead of a UI?" This isn't just new tools—it's rethinking how work gets done.
Many leaders think AI transformation means starting from scratch. Tear everything down and rebuild with AI-first processes. This creates a false choice between radical change and staying stuck.
The County of San Mateo has a different approach. Despite being home to many AI companies, the county mandates: you cannot eliminate jobs through AI. Organizations must repurpose existing roles. People who used to transcribe town hall meetings now edit transcripts and publish documents online.
"I love that thinking because it acknowledges what we're good at as individuals and teams, then figures out how new tools can amplify that," Cawthon says.
The key word is "augment." Smart leaders ask how to level up existing staff rather than replace them.
For people who work with words, the change is already here. The old way meant spending hours on Google, downloading files, printing them out, highlighting key parts.
Now teams can build their own knowledge bases using RAG (retrieval augmented generation). They create custom language models that can cite sources and maintain transparency about outputs.
This splits into different skill levels. Junior people find sources, tag files, and build references. Senior people handle prompt engineering to get campaign concepts or conduct voice share audits across different brand models.
"If you believe prompting is going to replace searching in the next five to ten years, then figuring out strategy and copy and positioning becomes something to train on," Cawthon says.
Design has changed before. The early 2000s web was wild—everyone had their own visual style with leather textures and unique expressions. It was messy but creative.
Then mobile forced standardization. Google's Project Kennedy said all Google products had to look the same. Microsoft and IBM followed. Now we have standard patterns for tabs, buttons, dropdowns.
AI changes this again. Wireframes that took days or weeks now take minutes or hours. A technologist who knows how to go from concept to production-ready code becomes essential.
Cawthon tells the story of a creative director in his 50s who brought image generation tools like Midjourney into storyboarding for TV campaigns. He learned to keep characters consistent between shots—something that was hard to do even nine months ago.
Jesse James Garrett calls it "AI amnesty"—giving people permission to experiment without feeling like they're cheating. This matters for adoption.
Even when leaders endorse AI use and provide resources, teams still resist. Why?
First, using these tools well is hard. "As anybody who's tried to cheat on a test, it's hard to cheat," Cawthon says. "Using generative tools to make the fidelity of the idea you want to communicate is extremely difficult."
There's a learning curve. People need to stay curious, patient, and confident their process will work. The promise of speed is mostly false right now. It may be true in five years, but not yet.
Cawthon worked with someone who didn't trust the cloud. When SaaS products and cloud platforms emerged, she met them with distrust. Everything had to be email attachments and local files. She needed to see an icon on her desktop to know data was safe.
This limited her tools. She used spreadsheets for time tracking and budgets instead of CRM platforms and management software that could do the job faster.
"I think we're feeling that same thing again today," Cawthon says. Some people need that trust and transparency before they'll suspend disbelief and try new approaches.
When the internet emerged, some people asked how it could help what they were already doing. Seth Godin was working on a catalog—a book index. Larry Page and Sergey Brin saw the internet and said "let's index it" and built Google. Godin saw the internet and said "let me write a book." We know who won.
The lesson: you can't look at new technology through old eyes. Just adding AI to existing processes might not be enough.
Cawthon's young sons started with voice assistants, not point-and-click interfaces. Their first tech interaction is speaking to an algorithm. Within months, their home assistant will have Gemini built in for more complex tasks.
"Are we designing for agents or are we designing for humans anymore?" This generation might reject the awkward pointing, clicking, and typing that defined previous decades.
AI tools create tighter collaboration between departments. Traditional agencies and development shops end up "swimming in the same pool using the same tools."
This means less handoff, more side-by-side work. Instead of separate phases, teams mentor each other: "this is how I see this working" and "this is how it's actually working."
Cawthon found this crucial over the past year—having mentorship around application development and UX that he didn't traditionally worry about.
Cawthon built a survey tool to assess AI adoption maturity after seeing a design team of eight to ten people who might be left behind. He used generative tools to create production-level prototypes, skipping the entire Figma process. But the team was stuck in old workflows of creating interface abstractions to hand off to developers.
The assessment looks at several areas:
Current usage: Are teams using AI for ideation or in actual production workflows?
Process barriers: Is it approval issues, integration problems, or licensing constraints? Some companies only allow Copilot because of their Microsoft relationship, cutting off access to innovative startups.
Culture: Do people feel they have permission to fail, try, and innovate? Are there ethical concerns about AI use?
Data maturity: Can UX design tools integrate with other parts of the agency?
The tool provides scores by section and overall, comparing results to similar industries, organization sizes, and team sizes.
Leaders who want to guide teams through AI transformation should:
Grant amnesty. Make it clear people can experiment without feeling like they're cheating. Be transparent about the learning process.
Focus on skills, not replacement. Ask how to level up existing staff rather than eliminate positions.
Address trust directly. Some people need to see how data stays safe before they'll try new tools.
Create space to fail. The learning curve is real. People need time to get good at this.
Break down silos. When everyone uses similar tools, collaboration gets tighter.
Measure progress. Track where teams stand and what barriers exist.
This transformation is about people, not just technology. Every generation faces the choice between adapting to change or getting left behind.
The leaders who succeed won't be the ones who move fastest or adopt every new tool. They'll be the ones who bring their teams along instead of leaving them behind.
As Cawthon learned from his AltaVista days: change happens whether we're ready or not. The question is whether we help our people get ready for what's coming.
"This notion of amnesty, of AI amnesty in whatever field or process that you're in is to allow it not to feel like you're cheating because you do these things. To be transparent, be a mentor, and be questioning of the process and say, we're trying to figure out this transformation together." - Nick Cawthon
02:38 - Early tech career lessons from AltaVista to Google transition
06:55 - Internet paradigm shifts and building for new vs. old thinking
12:30 - AI workflow changes with prompting replacing search strategies
16:28 - AI adoption barriers and the need for "amnesty" in teams
28:05 - AI readiness assessment for measuring team transformation
Connect with Nick on LinkedIn: https://www.linkedin.com/in/nickcawthon-ux-digital-agency-product-design-leadership/
Fill out Nick’s AI Maturity Assessment to receive a report with a readiness score benchmarking your team against similar organizations: https://retrain.gauge.io/
Connect with Paxton on LinkedIn: https://www.linkedin.com/in/paxtongray/
Looking for an agency that'll be worth the investment? 97th Floor creates custom, audience-first campaigns that drive pipeline and conversions. Get started here: https://97thfloor.com/lets-talk/.
Nick helps design teams stay ahead of the curve with their AI transformation. He has been curating self-assessments for UX & Design Teams at retrain.gauge.io, helping analyze industry trends and removing barriers to adoption. Nick founded Gauge in 2001 in the San Francisco Bay Area to help organizations with evidence-based strategy and product decisions. Clients have grown to include Electronic Arts, Genentech, Airbnb, Adobe and many others. Nick is a professor in Data Literacy and Visualization in the Design Strategy MBA program at his alma mater, California College of the Arts.
