/spark

Modern Product Discovery

Purpose: guide Spark through modern discovery frameworks that improve opportunity selection before proposal writing.

Contents

• Opportunity Solution Tree
• Continuous discovery cadence
• Shape Up
• Outcome-Driven Innovation
• AI-assisted discovery
• Discovery input checklist

Opportunity Solution Tree (OST)

Map Spark phases to OST:

Best practices:

• decompose large opportunities into smaller sub-opportunities
• test assumptions, not only full solutions
• update the tree weekly as a living artifact

Continuous Discovery Habits

Core rule:

• discovery is a weekly rhythm, not a kickoff ceremony

Recommended cadence:

• Monday: review learnings and update the OST
• Tuesday to Thursday: 2-3 customer interviews per week plus assumption tests
• Friday: synthesize and choose the next opportunity

Avoid:

• batch discovery only at project start
• PM-only discovery silos
• jumping to solutions before synthesis
• optimizing only existing areas without exploration

Shape Up

Key concepts:

How Spark uses it:

• treat the proposal as the pitch
• define appetite before expanding scope
• combine appetite with RICE Score to avoid overcommitment

Outcome-Driven Innovation (ODI)

Opportunity Score:

Opportunity Score = Importance + max(Importance - Satisfaction, 0)

Opportunity zones:

Spark integration:

• extract job steps from existing data and workflows
• estimate Importance and Satisfaction
• prioritize Underserved opportunities during IGNITE

AI-Assisted Discovery

Good use cases:

Warnings:

• AI summaries may miss 20-40% of important details
• AI is additive, not a replacement for speaking with users
• human review is mandatory for synthesized insights

Discovery Input Checklist

Quantitative inputs:

• underused features or dormant datasets from Pulse
• funnel drop-off points
• error-rate and support-ticket trends

Qualitative inputs:

• research synthesis from Field
• feedback clusters from Voice
• competitor gaps from Compete

Opportunity evaluation:

• map insights to the OST opportunity layer
• use ODI scoring where possible
• confirm technical feasibility with Scout or Lens

Discovery Discipline (Core Contract rationale + sources)

Extended rationale and sources for the Spark Core Contract discovery rules:

• Name by the user problem, not the solution — discovery starts with pain points, not feature shapes. [Source: productboard.com — product discovery framework; herbig.co — product discovery guide]
• Outcomes, not outputs — define the behavioral change or business impact, not just the feature shape. [Source: itonics-innovation.com — outcome-oriented development trend 2026]
• OST → OKR alignment — the OST metric must align with a KPI from your OKRs; only initiatives that can move that metric warrant active investigation. [Source: producttalk.org — Teresa Torres CDH framework]
• Fail Condition — teams are overly lenient with success criteria, but a fail condition (the measurement that disproves the hypothesis) forces intellectual honesty. [Source: kromatic.com — Lean Startup validation]
• Weekly discovery rhythm — Torres's minimum cadence is weekly customer touchpoints (interviews, 5-second tests, prototype probes). If a proposal rests on research older than ~4 weeks, refresh at least one evidence source before handoff — evidence decays. [Source: producttalk.org — Continuous Discovery Habits; maze.co — continuous product discovery]
• Progress, not activities — frame customer jobs as progress sought, not activities. "Users want to generate reports" is an activity; the real job is the progress it unlocks ("demonstrate progress to stakeholders" or "cover myself in an audit"). Activity framing produces feature shapes; progress framing reveals opportunities. [Source: kaizenko.com — JTBD framework; productschool.com — JTBD framework]

Default Opportunity Patterns

Recurring shapes worth checking at IGNITE: dashboards from unused data · smart defaults from repeated actions · search and filters once lists exceed 10+ items · export/import for portability · notifications for time-sensitive workflows · favorites, pins, onboarding, bulk actions, and undo/history for recurring friction.

Non-Consumption & Workarounds

The most overlooked competitor is "nothing." Include non-consumption and workarounds in competitive framing:

• Airbnb found 40% of guests would not have traveled at all without it; they were competing with non-consumption, not hotels.
• Compensating behaviors (manual spreadsheets, email threads, copy-paste workflows) are hiring signals that reveal unmet jobs.

[Source: Christensen Institute — Non-consumption is your fiercest competition; thrv.com — Jobs-to-be-Done]

AI-Assisted Discovery (2026 addenda)

Extends the AI-Assisted Discovery section above:

• Encode quality gates so AI-assisted automation (feedback theme analysis, opportunity backlogs linked to user goals, story-map slices reflecting technical constraints, comparisons against prior work) is helpful but never unaccountable. [Source: storiesonboard.com — AI agents in PM 2026]
• Methodology-first, not prompt-first — AI output quality depends on structured inputs (explicit OST node, persona, hypothesis, fail condition), not prompt cleverness. 94% of enterprise PMs use AI daily; the gap between transformative and merely-helpful traces to input quality, not tool choice. Feed Pulse/Voice/Compete findings through OST/JTBD framing before asking AI to synthesize. [Source: productboard.com — AI product discovery; ainna.ai — AI product management 2026]
• Collapse low-value steps, not judgment steps — AI is strong at interview transcription, theme clustering, and surface-level synthesis. Keep persona selection, fail-condition definition, and cross-opportunity trade-off reasoning human-led; AI-generated versions anchor to training-data averages, not the current customer. [Source: producttalk.org — 2026 roadmap / AI-powered discovery]