Building Better Workflows

When Forbes Coaches Council asked a few of us experts how to design smart workflows for ambitious goals, the answers revealed something fascinating: we all agree workflows matter, but we rarely discuss what makes them actually work under pressure. Among the 19 perspectives shared, one principle stood out—workflows must be self-improving, built with regular review and adaptation elements that make them efficient, repeatable, and resilient to change.

This idea of self-reinforcing systems deserves deeper examination. Most workflow failures stem not from poor initial design, but from treating implementation as a one-time event rather than an ongoing practice. We design elaborate systems in calm environments, then wonder why they collapse under operational pressure. The problem isn’t the workflow—we assume that understanding equals capability.

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History provides instructive patterns. Benjamin Franklin tracked his personal virtues daily for decades, not to achieve perfection but to build a self-correcting system for incremental improvement. He marked violations honestly, focused on one virtue per week for deep practice, and repeated quarterly cycles for years. His insight was profound: “I never arrived at the perfection I had been so ambitious of obtaining, but I fell far short of it, yet I was, by the endeavor, a better and a happier man than I otherwise should have been.” Progress through consistent tracking, not achievement of perfection.

What these self-improving systems share is progressive practice under increasing difficulty. Workflows aren’t static procedures we follow—they’re dynamic systems we practice until responses become automatic. Research shows that behavioral change takes at least 66 days to form new neural pathways, and automaticity requires 6+ months of consistent practice. Yet most organizational workflows expect adoption in days or weeks.

The key lies in starting impossibly small. Don’t design the complete workflow. Design one element so simple it can be executed in 30 seconds, practiced until 80% automatic, then build from there. This micro-habit approach bypasses willpower depletion and creates compound effects over time.

Self-improving systems also require measuring what matters before outcomes manifest. Leading indicators predict success or failure long before goals are achieved or missed—track adoption consistency, not completion rates. Monitor connection quality before isolation becomes turnover. Measure psychological safety through disagreement frequency before innovation stagnates. These early signals enable course correction while adjustment is still possible.

Perhaps most critically, we must acknowledge that workflows don’t work equally for everyone. Power dynamics shape whose voice matters in design, whose vulnerability is accepted during execution, and whose boats rise first when the tide comes in. Self-improving systems must explicitly account for these dynamics, tracking which workflows are actually working and adjusting based on available data.

The competitive advantage isn’t having better initial workflow designs—it’s building systems that learn and adapt through consistent practice over extended timelines. Organizations that commit to 18-24 month implementation cycles, practice under progressive stress, measure leading indicators, and adjust based on real behavior under pressure achieve 311% increases in financial returns compared to traditional approaches.

That’s our focus on implementation science, and it’s how smart systems teach themselves to get better.

Expert Panel. (2025, October 30). How to design smart workflows to help teams meet goals. Forbes. https://www.forbes.com/councils/forbescoachescouncil/2025/10/30/how-to-design-smart-workflows-to-help-teams-meet-goals