I spend a lot of time thinking about how to ensure we’re future-ready, without losing sight of the work that needs to get done today. One of the biggest challenges on that front right now is integrating AI tools into our design and development workflows in a way that enhances productivity, without becoming a distraction or a crutch.
AI is not a fad. It’s a shift. And like any shift in our industry, there’s an initial wave of excitement, experimentation, and—if we’re not careful—over-adoption. Teams can quickly become fragmented by chasing the latest AI plugin or experimenting with every new beta that promises to “revolutionize” UX or code. On the other end, ignoring the wave entirely is just as risky.
So, how do we strike the balance?
Start With Use Cases, Not Tools
Rather than starting with “Which AI tools should we use?” we begin by asking, “Where are we losing the most time?” or “What tasks are high-effort and low creativity?” AI thrives in those zones: early ideation, documentation, testing, repetitive coding, and component generation.
For example, in our UX team, using AI to synthesize user interview transcripts has cut down the time to identify patterns by over 60%. For devs, AI-assisted code generation in boilerplate-heavy environments (think: form validation, CRUD operations, test scaffolding) has accelerated timelines without diminishing quality.
By rooting AI adoption in our specific bottlenecks, we avoid the trap of adopting tools that are exciting but ultimately irrelevant to how we work.
Build Guardrails, Not Bureaucracy
The next challenge is ensuring consistency once we’ve identified practical use cases. AI can be a powerful enabler or a source of chaos. Without clear guidelines, you can end up with inconsistent code patterns, unverified outputs, or design concepts that deviate from your brand system.
We’ve implemented lightweight guardrails: internal best practices for prompting, mandatory human review for all AI-generated outputs, and a shared library of approved tools that integrate with our existing workflows. We don’t block innovation, but we ensure it doesn’t come at the cost of quality or cohesion.
Designate AI Champions, Not AI Departments
It’s tempting to centralize AI into its own team or initiative. In our experience, that leads to siloed efforts that don’t scale. Instead, we identify AI champions within existing teams—people who are naturally curious and pragmatic about tech. These champions test tools in real-world scenarios and share their learnings, templates, and cautions with the broader team.
This grassroots approach fosters adoption from within and ensures that new methods are relevant to the context in which they’re applied.
Prepare for the Future of Work, Now
While we’re cautious about overusing AI in the present, we’re also deeply aware that its role will expand in the near future. The way we scope projects, staff teams, and define deliverables will evolve. Tasks that once took days may soon take minutes. The real value will shift from execution to decision-making, context, and integration.
That’s why we’re actively training our teams not just in how to use AI, but how to work alongside it. We’re hosting internal workshops on AI literacy, rethinking job descriptions to emphasize strategic thinking, and revisiting how we define velocity in sprints when AI accelerates the tactical output.
We’re also reimagining client value. If we can deliver faster, the conversation shifts from billable hours to outcomes. Clients don’t just want speed, they want confidence that they’re building the right thing. That’s where human insight, not automation, still holds the crown.
Final Thoughts
AI is an accelerant, not a strategy. It can make good teams faster, and great teams exceptional, but only if applied thoughtfully. At our agency, we’re focused on enabling teams to do more of the high-value work that makes us trusted partners to our clients, while removing friction from the rest.
The future isn’t about man versus machine. It’s about building the best teams by combining human creativity and judgment with the efficiency and scale of AI.
And that future starts now—quietly, strategically, and with purpose.
