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Connect Bias № 113 · Last updated 6 June 2026

Pro-Innovation Bias.

"The new thing will obviously win — we underestimate the old thing's reasons."

01Overview

Pro-innovation bias is the assumption that innovations are universally good, will be adopted smoothly, and face little legitimate resistance. Change management costs, learning curves, and users who preferred the old way are underestimated.

Product culture rewards shipping. Pro-innovation bias paints skeptics as laggards and ignores switching costs — muscle memory, integrations, trust. Designers ship parity gaps labelled "beta" and expect gratitude.

02Detailed explanation

Bias appears in roadmaps and launch narratives:

  • Mandatory redesigns without migration tools — "users will love it."
  • Feature removals justified by visionary narrative, not usage data.
  • Adoption forecasts ignore training and support load.
  • Survivorship from past launches — only winners remembered — inflates confidence.

Appeal to novelty is the rhetorical wing — new therefore better. Pro-innovation bias is the planning wing — new will be easy and universally welcomed.

03Why it exists

Innovators are rewarded socially and economically. Bias aligns with identity.

Teams suffer from exposure to internal excitement users never see — false consensus that everyone can't wait.

The short version

What legitimate reason might someone have to keep the old way — and did you design for it?

04Effects on users

Users face unbudgeted learning time, broken workflows, and removed features — pro-innovation bias externalises those costs as user failure to adapt.

Power users with efficient old paths become detractors when innovation serves median at expense of expert workflows without choice.

05Effects on designers & teams

Teams bake bias into launch playbooks:

  • No rollback plan. Confidence without escape hatch.
  • Underfunded education. Release notes as sole change management.
  • Dismissing opt-out requests. Labeled resistance to progress.
  • Ignoring integration debt. APIs, exports, automations break silently.

06Practical takeaways

  • Inventory switching costs. Time, money, habit, social — before ship.
  • Ship migration, not only novelty. Import tools, dual-run periods.
  • Measure adoption curves realistically. Not launch-week only.
  • Offer temporary legacy paths. Sunset with data, not decree.
  • Listen to expert workflows. Innovation can add without deleting.
  • Pre-mortem resistance. List why rational users might say no.

07Design examples

Redesign

Mandatory new IA

Navigation overhaul ships without map. Power users lost. Support spikes. Team surprised — pro-innovation bias assumed delight.

Feature removal

Everyone uses the new one

Legacy export removed; 8% of revenue cohort relied on it. "Old way is dead" narrative ignored legitimate workflow.

Enterprise

Beta as promise

Feature parity gap labelled beta for a year. Procurement expected finished product — adoption stall blamed on customer conservatism, not gap.

Forecast

Hockey stick adoption

Internal model assumes 80% uptake month one. Reality 22%. Support and training budget absent — pro-innovation planning.

08Ethical risks

Forcing innovation without migration harms users with least time to retrain — caregivers, shift workers, low-literacy users.

Removing old paths for growth metrics without consent is coercion dressed as progress.

Self-test: Who loses capability in your next launch — and have you counted their switching cost as real?

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