Teams bolt LLMs onto products, run demos, declare victory—while workflows stay a mess. Real shift: end-to-end AI workflows that save time and prevent errors.
The story
AI trend we’re seeing in dev teams right now:
Leaders are under pressure to “do something with AI,” so they:
– bolt an LLM onto an existing product
– run a few flashy demos
– declare victory
Meanwhile, the actual workflows? Still a hot mess of copy‑paste, swivel‑chair integrations, and humans babysitting brittle scripts.
From our vantage point at Creative Robot, the pattern is clear:
Old way [Feature-Driven]:
– Isolated AI widgets
– One “wizard” engineer nobody can replace
– No clear metric beyond “it looks cool”
Emerging way [Workflow-Driven]:
– End‑to‑end AI *workflows* that save real minutes and prevent real errors
– Teams who understand prompting, evaluation, and failure modes
– Non‑technical users talking to systems in plain language and trusting the outcomes
This is where Scott Howard Swain comes in.
Scott blends senior‑level full‑stack engineering with AI strategy, UX thinking, and a frankly nerdy obsession with human judgment. He doesn’t just *advise* your team; he codes with them, reviews architecture, and coaches people to think in terms of entire AI-powered workflows.
In practice, that looks like:
– Auditing current processes to find high‑leverage automation
– Designing AI agents that fit your existing stack
– Pair‑programming and code reviews so your devs level up
– Raising data ethics, bias, and transparency questions early instead of in the post‑mortem
After 30 years of building production software, Scott’s specialty is multiplying a team’s abilities, not replacing them.
