2026 February | Gunnar Øyvin Fredrikson

Feb

The Red Ball Method

A high-velocity innovation framework for parallel exploration, disciplined convergence, and scalable delivery

Author: Gunnar Øyvin Jystad Fredrikson
Version: Draft for professional and academic review

Abstract

The Red Ball Method is a structured innovation methodology designed for high-speed, interdisciplinary teams operating under uncertainty. It combines parallel exploration (each participant pursues a distinct concept) with a deliberate convergence mechanism (the team selects one concept to scale), while preserving psychological safety and individual ownership. This white paper formalizes the method, provides a theoretical grounding in established research on team effectiveness, psychological safety, divergence–convergence processes, sprint-based experimentation, and parallel prototyping, and proposes evaluation criteria suitable for both practitioners and academic scrutiny.

Executive Summary

Many innovation initiatives stall because they either (a) broaden indefinitely without convergence, or (b) converge too early and kill promising options before they have been explored. The Red Ball Method addresses this tension by separating exploration and convergence into explicit phases with a small interdisciplinary team (≤7 including the process leader), rapid parallel work, and a receiver-driven governance gate.

The method is particularly suited to:
• new product or service concepts in uncertain markets
• migration strategies (sunsetting products and moving users)
• business model discovery and reframing
• rapid concept validation and early-stage delivery

The result is a scalable deliverable or a portfolio of well-articulated options, supported by retained learning assets and traceable decision logic.

1. Why “Red Ball” and why a ball?

1.1 The Red Ball as an attention mechanism

The method uses a tangible metaphor to create shared focus and individual ownership at the same time.

Red signals salience: it is the attention color—easy to spot, hard to ignore, and cognitively “sticky.” In practice, it represents the idea that each participant selects something that feels meaningful enough to hold attention through uncertainty and criticism.

Ball signals motion, transfer, and momentum: a ball can be carried, passed, dropped, picked up again, refined, and made bigger. That maps directly to the method’s behaviour: ideas move between team members, evolve through exploration, and survive even when they are not selected as the “one ball.”

In other words, the Red Ball is not a decoration. It is a deliberate coordination artefact that supports speed, autonomy, and convergence.

1.2 Why not “persona”, “concept”, or “prototype”?

“Concept” is abstract and tends to flatten ownership. “Prototype” implies a specific artefact. “Red Ball” is deliberately playful but precise—it invites exploration while establishing an expectation of movement and eventual convergence.

2. Conceptual and research foundations

The Red Ball Method does not replace established methodologies; it recombines compatible mechanisms into a distinct process logic.

2.1 Psychological safety as a prerequisite for speed

Team learning and candid exploration require an environment where interpersonal risk-taking is safe. Psychological safety is consistently linked to learning behaviour, error reporting, and adaptive performance in teams (Edmondson, 1999).

2.2 Small-team effectiveness and coordination overhead

Keeping teams small reduces coordination cost and increases speed. This aligns with widely used agile guidance that teams should remain small enough to be nimble (e.g., Scrum Guide: “typically 10 or fewer”), and with broader team-effectiveness research emphasizing enabling structures and clear direction (Schwaber & Sutherland, 2020; Hackman, 2002).

2.3 Divergence and convergence as the core innovation rhythm

The method follows an explicit divergence–convergence rhythm comparable to the Double Diamond model: explore broadly, then focus and commit (Design Council, Framework for Innovation).

2.4 Parallel prototyping to avoid fixation and increase quality

Parallel exploration reduces “design fixation” and increases breadth and quality of outcomes. Empirical research in HCI shows parallel prototyping yields better results and greater divergence than iterating a single early option (Dow et al., 2010).

2.5 Validated learning and minimum viable delivery

The build–measure–learn loop and MVP logic support rapid learning under uncertainty (Ries, 2011). The method’s “minimum value solution” phase is an execution-oriented counterpart to the exploration stage.

2.6 Sprint logic as time-bound acceleration

Timeboxing and structured facilitation enable fast decisions and prototyping, consistent with sprint-based problem solving (Knapp et al., 2016).

3. The Red Ball Method: process overview

3.1 Design objectives

The method is designed to:

maximize idea throughput without early elimination
preserve individual ownership while enabling group alignment
convert exploration into a scalable deliverable or an actionable portfolio
retain learning assets to reduce waste

3.2 Roles

Process leader (facilitator):
designs the cadence, protects psychological safety, manages convergence.
Team members (≤6):
pursue one “ball” each, critique constructively, and contribute to convergence.
Receiver (decision owner):
provides context, constraints, and makes continuation decisions when convergence is ambiguous.
Builders (optional, later):
execution-focused contributors who transform the selected ball into a testable deliverable.

4. The eight-step method (formal specification)

Step 1: Recruit and compose the team

Requirement: interdisciplinary expertise and diversity of perspectives.
Constraint: maximum 7 participants including the facilitator.
Rationale: reduce coordination overhead while maintaining cognitive diversity.

Step 2: Introductions and team contract

Goal: establish psychological foundations and norms.

Operating principles:

participation is voluntary
respect and open-mindedness are non-negotiable
neurodiversity is encouraged and supported through structured autonomy

Output: lightweight “team contract” (norms, communication, decision rules).

Step 3: Explore and anchor the problem space

Even vague inputs must be anchored to:

a future owner (receiver)
a strategic objective
a boundary condition (time, market, constraints)

Output: a one-page problem frame: context, desired direction, constraints.

Step 4: Rapid parallel ideation

Run a short ideation session to generate options.
Rule: each member chooses one idea they want to pursue—no early idea killing.
Output: each member declares their initial Red Ball in one sentence.

Step 5: Run with the ball (parallel exploration)

Each participant explores their ball independently.

Allowed moves:

deepen your own ball
swap balls
challenge someone else’s ball by exploring weaknesses or new angles
replace your ball if it becomes unpromising

Key rule: no ball can be blocked, removed or cancelled – challenging a ball involves adding to it not removing.
Expected artefacts: sketches, value hypotheses, risks, minimal prototypes, narrative framing, quick tests.

Step 6: One ball to rule them all (disciplined convergence)

The group converges on one ball to scale.
Key rule: no ball is “lost.” All explored balls become retained assets.
If no convergence: each member presents their preferred ball; the receiver decides what continues.

Step 7: Keep the ball rolling (handoff and continuity)

The chosen ball moves into delivery.
Rule: continuation is voluntary; the team can be refilled.
Output: a continuation plan: who continues, what must be built, what will be tested.

Step 8: Expand and build (minimum value solution)

The core team strengthens:

value proposition
target users / customers
success metrics
key assumptions and risks

Builders create a testable, measurable artefact.
Decision gate: continue scaling, pivot, or stop based on evidence.

5. Governance and evaluation

5.1 Convergence criteria (recommended)

To reduce politics and increase repeatability, use explicit criteria:

strategic alignment
customer value potential
feasibility and scalability
time-to-test
risk and dependency profile

5.2 Evidence package (what the receiver receives)

chosen ball: narrative + value hypothesis
alternatives considered (retained balls)
key risks and assumptions
proposed test plan and success metrics

5.3 Measurement and learning

Suggested metrics:

cycle time from framing to test
number of parallel options explored
degree of convergence (time and clarity)
quality of evidence at decision gates
downstream adoption or scaling success

6. Strengths, limitations, and academic critique

6.1 Strengths

Parallel exploration reduces fixation and increases the probability of identifying higher-quality options (aligns with evidence from parallel prototyping research).
Ownership is protected, which can increase motivation and persistence.
Convergence is explicit and disciplined, reducing endless divergence.
Retained assets reduce waste by building organizational “innovation memory.”
Adaptive team architecture separates exploration from execution by design.

6.2 Limitations and failure modes

Facilitation dependency: weak facilitation can lead to dominance effects, fragmentation, or stalled convergence.
Attachment risk: ownership can become identity; convergence can become political.
Context drift: without anchoring, parallel exploration may diverge too far.
Regulated contexts: where compliance gates must precede exploration, adaptations are required.

6.3 Mitigation strategies

formal convergence criteria
explicit team contract and timeboxing
receiver involvement early (context and constraints)
structured critique formats (e.g., “steelman” + risk mapping)

7. Comparison with adjacent methodologies

7.1 Design Thinking

Design Thinking excels at empathy and structured exploration, but can become ceremonial and slow in high-velocity contexts. The Red Ball Method compresses cycles and increases ownership while maintaining a divergence–convergence rhythm.

7.2 Lean Startup

Lean Startup is strong when hypotheses are identifiable. The Red Ball Method is stronger earlier, when hypotheses are not yet clear and exploration must remain broad.

7.3 Agile / Scrum

Agile is optimized for delivery once a concept exists. The Red Ball Method sits upstream, generating and converging concepts that are ready for agile execution.

7.4 Design Sprints

Sprints are powerful timeboxed sequences for prototyping and testing. The Red Ball Method is more flexible in how exploration is distributed, and adds a distinct convergence and asset-retention mechanism.

Publishing posture: what to validate next

To strengthen publishability, the next step is to operationalize the method for empirical study.

Suggested study designs:

comparative case studies across innovation programs
controlled team experiments (parallel vs serial exploration)
longitudinal studies of retained-asset reuse

References (starter set)

Edmondson, A. C. (1999). Psychological Safety and Learning Behavior in Work Teams. Administrative Science Quarterly, 44(2), 350–383. https://doi.org/10.2307/2666999
Hackman, J. R. (2002). Leading Teams: Setting the Stage for Great Performances. Harvard Business Review Press.
Design Council. Framework for Innovation (Double Diamond). https://www.designcouncil.org.uk/our-resources/framework-for-innovation/
Dow, S. P., Glassco, A., Kass, J., Schwarz, M., Schwartz, D. L., & Klemmer, S. R. (2010). Parallel Prototyping Leads to Better Design Results, More Divergence, and Increased Self-Efficacy. ACM. https://doi.org/10.1145/1879831.1879836
Schwaber, K., & Sutherland, J. (2020). The Scrum Guide. https://scrumguides.org/scrum-guide.html
Ries, E. (2011). The Lean Startup. Crown Business. (MVP definition and validated learning)
Knapp, J., Zeratsky, J., & Kowitz, B. (2016). Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days. Simon & Schuster.

Feb

Beyond Competitive Advantage: Strategic Continuity in the Infinite Business Landscape

Modern strategy literature is saturated with the language of advantage: competitive advantage, first-mover advantage, sustainable advantage. Yet the historical record suggests something more sobering: advantages erode.

Technologies decay.
Markets reorganize.
Institutions mutate.

From a Darwinian perspective, survival is not secured by dominance, but by adaptation. From a thermodynamic perspective, systems naturally move toward entropy unless energy is continuously applied. From a game-theoretic perspective, equilibrium is rarely permanent. If we accept that business operates in a dynamic, non-equilibrium environment, then perhaps the central strategic task is not to win — but to remain viable.

This article proposes a structured model for what I call strategic continuity: the capacity of an organization to survive the death of its own services while remaining coherent in identity and direction.

The Infinite Game as Structural Premise

The distinction between finite and infinite games, articulated in philosophy and later applied to organizational theory, offers a useful lens.

• A finite game has known players, fixed rules, and a defined endpoint.
• An infinite game has shifting participants, evolving rules, and no terminal victory condition.

Most individual services are finite games. They launch, compete, generate returns, and eventually decline. But the organization itself participates in an infinite game. There is no final win-state. There is only continued participation or exit.

Game theory formalized by John Nash demonstrates that equilibrium conditions depend on player expectations and strategic interaction. In real markets, players continuously enter and exit, altering the payoff matrix. Stability is provisional.

From evolutionary biology to dynamic systems theory, the pattern repeats: systems that survive are those capable of adaptation under changing constraints. The question therefore becomes:

How do we design organizations structurally capable of continuity in an infinite landscape?

The Infinite Service Continuity Model

To answer this, I propose a layered model. Imagine the organization as a structured system composed of four concentric layers.

Infinite Vision (Core Identity)
At the center lies Infinite Vision: purpose, identity lock points, moral anchors, and sustainability commitments. In philosophy, identity persists through change if core properties remain stable. In constitutional law, enduring principles anchor evolving interpretation. In finance, long-term value presumes consistent underlying thesis. Vision must survive product cycles. If a company’s identity collapses when a service fails, the service was mistaken for the organization.
Endless Strategy (Governance and Continuity Logic)
Surrounding the core is Endless Strategy: governance structures, cultural norms, legal compliance, and value systems. Ronald Coase demonstrated that firms exist to reduce transaction costs within markets. Governance, therefore, is not administrative overhead; it is structural logic. In legal theory, durable institutions balance flexibility with rule-based constraint. Endless Strategy defines how the organization moves without dissolving its identity. It is the stabilizing field around the core.
The Time Spectrum (Strategic Awareness Across Horizons)
Strategy is often collapsed into annual plans. Yet time itself is multi-layered.
The Time Spectrum includes:
• Reflective – Institutional memory and post-mortem learning
• Actual – Present operations
• Focus – Immediate directional prioritization
• Tomorrow – Forecast based on available evidence
• Future – Scenario exploration beyond current models
• Endless – Long-arc existential positioning
Physics reminds us that systems are path-dependent. Economics shows that expectations shape behavior. Psychology, particularly Daniel Kahneman’s work on cognitive bias, demonstrates that short-term focus often overrides long-term reasoning.
The Time Spectrum corrects for temporal blindness by institutionalizing multiple horizons simultaneously.
Finite Services (Orbiting Activations)
Beyond the strategic ring are finite services. Each service is an activation:
• It has a birth.
• It generates value.
• It accumulates entropy.
• It eventually declines.
Joseph Schumpeter’s concept of creative destruction captures this dynamic: innovation dismantles previous structures. In finance, portfolio theory (Markowitz) spreads risk precisely because individual assets are volatile. Services are not identity. They are portfolio elements. An organization that understands this can allow services to die without existential panic.
The Ecosystem Layer (Experience and Data)
Surrounding everything is the ecosystem:
People.
Processes.
Market structures.
Regulation.
Technology.
Data.

Claude Shannon’s information theory reminds us that data is not meaning, it is signal. Meaning arises through interpretation. Data precedes technology. It may exist as archival documents, lived experience, or mathematical models. Technology, including artificial intelligence, is a retrieval and transformation mechanism.

Within this model, AI systems – including systems that simulate “personalities” – function as advanced tools for engaging with data. They are not identities. They are structured interfaces for navigating complexity, enabling interdisciplinary teams to interrogate archives, simulate scenarios, and detect patterns at scale. Used correctly, AI becomes a cognitive amplifier within the ecosystem layer. Used incorrectly, it becomes a distraction from structural design.

Finite Innovation Inside Infinite Structure

Continuity requires renewal. High-velocity innovation frameworks for rapid convergence and scalable outcomes operationalizes innovation as a finite, repeatable cycle within the infinite structure. Small interdisciplinary teams pursue ideas in parallel. Psychological ownership is given and preserved to ensure process momentum. Convergence is structured. Key concepts advance to scalable testing; others remain archived.

This resembles scientific hypothesis testing: multiple models compete, one survives provisional validation, but none claim final truth. Thomas Kuhn’s work on paradigm shifts reminds us that even dominant frameworks eventually yield. Innovation methods that allows organizations to generate new service orbits continuously, at low cost and high velocity, without destabilizing the core are critical to keep evolving and adapting into an ever changing business ecosystem.

Cross-Disciplinary Foundations

This model can draw implicitly from multiple enduring fields such as:

• Natural Sciences: Evolutionary adaptation and entropy.
• Mathematics: Game theory and equilibrium instability.
• Psychology: Bounded rationality and cognitive bias.
• Finance: Portfolio diversification and risk distribution.
• Law: Institutional continuity through rule structures.
• Philosophy: Identity persistence through structured change.
• Marketing: Brand coherence amid tactical variability.

None of these fields alone explain continuity. Together, they converge on one insight:

Stable systems are those that separate core identity from adaptive components.

Who This Framework Serves

This architecture is not optimized for early-stage startups struggling for viability. In the earliest phase, focus must dominate. The Infinite Service Continuity Model becomes relevant when an organization already occupies space in the landscape – when it is already playing the game.

It is particularly relevant for:
• Established technology firms navigating platform transitions
• Media institutions adapting to digital ecosystems
• Public agencies balancing regulation and innovation
• Mature organizations managing multiple service portfolios

These entities face a different problem than survival through growth. They face survival through transformation.

Beyond Advantage

Competitive advantage is a finite concept. Strategic continuity is infinite.

Advantage may secure temporary dominance. Continuity secures participation across generations of services, technologies, and market structures. In the infinite business landscape, the objective is not to defeat competitors permanently. It is to design an organization capable of replacing itself, repeatedly, without losing itself. The only true failure is structural inability to adapt. And that, unlike market fluctuations, is a choice.