Leadership Development Beta MVP

CEOWorks: JTBD Mastery Mobile & Web Application

A beta MVP exploring framework productization: Can one element of the Talent To Value process be gamified into a mobile/web app that teaches clients to write better JTBDs?

Client CEOWorks
Industry Leadership Development
Project Type Beta MVP / Pilot
Technologies Claude Code, Expo, Railway, AppStore

MVP Deliverables

*Metrics shown are indicative and represent estimated outcomes based on engagement scope.

Mobile & Web

Cross-Platform Beta App

3 Modes

Learn, Build, Critique

AI-Powered

Feedback & Suggestions

AppStore Ready

Pilot Testing Phase

The Exploration

Consulting frameworks delivered exclusively through consultant time hit capacity ceilings—can productizing a single element (JTBD writing) into a gamified app create self-service value?

  • Capacity constraints: Teaching JTBD writing skills consumes consultant time that could focus on higher-value strategic work
  • Reinforcement gap: Clients need ongoing practice applying learnings to new hiring/succession decisions post-engagement
  • Scalability question: Can interactive learning replace traditional delivery, or does the framework require human expertise?

The Learning: Beta MVPs test productization hypotheses before major investment—validating whether frameworks can scale through software or need consultant delivery.

The MVP

Full-stack mobile/web application with three-mode gamified learning (Learn-Build-Critique) and AI-powered feedback to test framework productization viability.

MVP Deliverables:

  • iOS & web deployment — Single codebase (Expo) published to AppStore + web for accessibility testing
  • Three-mode learning — Learn Mode (10 Fortune 500 examples), Build Mode (guided creation), Critique Mode (AI feedback)
  • Offline-first design — Local storage, no sign-in required, works without internet for learn mode
  • Production-ready infrastructure — Railway backend, Claude Code API, progress tracking, support site

Strategic Approach:

  • Fast time-to-market (weeks not months) using Expo + Railway + Claude Code for rapid full-stack development
  • Beta pilot approach tests hypotheses (gamified learning, self-service viability, AI sufficiency) before scaling investment
  • Single-element productization focuses on ONE teachable skill (JTBD writing) vs. attempting to productize entire framework

The Learning: Framework productization requires hypothesis validation through working MVPs. Beta pilots reveal whether self-service works, AI feedback suffices, and whether isolated elements create value—informing scale/pivot decisions.

MVP Value Delivered

Testable Hypotheses

  • Working product deployed to iOS AppStore and web for pilot testing
  • Self-service model testable with three-mode learning (Learn-Build-Critique)
  • AI feedback functional to evaluate whether human expertise remains necessary
  • Monetization foundation enables testing subscription vs. free tier models

Rapid Development

  • Weeks not months to production using Expo + Railway + Claude Code
  • Single codebase for iOS and web reduces maintenance complexity
  • Full-stack capabilities including backend API, storage, progress tracking
  • Production-ready infrastructure with support site and feedback channels

Framework Productization Learning

  • Single-element approach validated focusing on one teachable skill
  • Gamified learning architecture tests whether interactive modes work
  • Real business scenarios embedded (hiring, succession, key initiatives)
  • Pilot data collection will inform scale/pivot decisions

Strategic Positioning Potential

  • Post-engagement touchpoints testable for ongoing client relationships
  • Market education tool that demonstrates methodology credibility
  • Scalability without hiring if self-service model proves viable
  • IP protection through software productization of framework

Exploring Framework Productization for Your Firm?

Learn how we can help you build MVP mobile/web applications to test whether your consulting frameworks can be productized—validating hypotheses before major investments.

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