Our Perspective

Why teams are slow to adopt AI and how to fix it

Engineering teams are not adopting agentic coding fast enough. Despite the transformative potential, most teams remain stuck in traditional workflows, unable to capture the productivity gains that early adopters are seeing.

It's not just about being slow to try new tools. It's about the compounding cost of delay: while your team deliberates, competitors are shipping faster, learning faster, and building better products.

The Challenges

Bad first experiences Developers try AI tools, get poor results, become lasting skeptics

"Won't work for our codebase" No concrete proof that agents deliver value on your specific code

Setup mystery Successful setups are hidden in personal dotfiles and undocumented workflows

→

Our Solution

Battle-tested skills Start with proven patterns that work, move past the rough start

Quick wins on your code Get immediate proof of value, share with teammates

Portable config Make your setup transparent and shareable to replicate success

One developer proves it works → Team replicates the setup → Adoption accelerates

Why Teams Are Slow to Adopt

Three core issues block adoption across engineering teams:

Bad first experiences create lasting skeptics

A developer tries an AI coding tool, gets broken code or unhelpful suggestions, and concludes "these tools aren't ready." That developer becomes an influential voice against adoption before the rest of the team has even tried it. Without proven patterns to follow, these negative first impressions spread faster than positive ones.

Skepticism that agents will work for your codebase

Even when developers hear success stories, they assume "that won't work here—our codebase is too complex, too legacy, too unique." Without concrete proof that agents deliver value on your specific code, adoption stalls while the team waits for someone else to prove it first.

The mystery of what makes successful users successful

Some developers get massive productivity gains from AI coding agents. Others struggle. The difference isn't the tools—it's the setup. But successful configurations are hidden in personal dotfiles, tool-specific settings, and undocumented workflows. Teams can't replicate what they can't see.

The Catch-22: You need proof to drive adoption, but you can't get proof without adoption. Teams get stuck with a few enthusiasts seeing huge gains while the majority waits for evidence that never feels conclusive enough.

How Nori Helps

The biggest blockers to adoption are bad first experiences, skepticism that agents will work for your codebase, and the mystery of what makes successful users successful. Nori solves these by getting your team to proven results quickly, then making it easy to spread those results.

Skip bad first experiences

Start with production-tested skills that work today, not blank configurations that require each developer to figure out from scratch. Your team gets good results immediately.

Prove it works on your codebase

Once one developer has a working setup, the entire configuration is transparent and portable. Share it with skeptical teammates—they can see exactly how it's configured and try the same setup themselves.

Accelerate team adoption

When agents work well for one developer, others can adopt the same configuration in minutes, not weeks. The whole team can access proven patterns.

Keep working as tools evolve

When better editors or models emerge, your skills and configurations move with you. Adopt new tools immediately without rebuilding your setup.

Getting Started

Ready to accelerate AI adoption on your team? Explore Nori Skillsets to get started with battle-tested agent configurations, or contact us to discuss your team's specific needs.