Navigate uncertainty in strategy execution through hypothesis-driven development

When formulating a strategy, a large number of assumptions about market trends, customer behavior, and the competitive landscape are made, often on the basis of incomplete information. Rather than blindly accepting these assumptions as truths to guide feature implementation, however, they should be treated as hypotheses to be rigorously tested and validated. Similar to the methodology used in scientific research, hypotheses are subjected to empirical testing and require data-driven validation. 

Each assumption carries with it a probability of occurrence, similar to a bet with associated risks, potential rewards, and resource investments. Interestingly, while organizations traditionally incorporate probabilities into risk assessments, the same rigor is often lacking when evaluating strategic initiatives. 

There are many good and different ways to describe and handle feature requests out there. Due to its emphasis on the uncertainty aspect we recommend hypothesis-driven development. It is an approach to product development in which teams formulate hypotheses, or educated guesses, about how certain changes or features will affect user behavior or achieve desired outcomes. Instead of continuing development based solely on assumptions or intuition, teams design experiments to test these hypotheses and gather empirical evidence to validate or invalidate their assumptions.

Here's how it typically works:

1. Formulate hypotheses: Teams identify specific assumptions or beliefs about user behavior, market dynamics, or product features in their area of responsibility that they want to test and might later develop. These hypotheses are framed as statements that can be proven true or false through experimentation. The responsible team establishes clear, measurable criteria for success or failure of the hypothesis.

1. Design experiments: Teams create a minimal experiment to collect data relevant to the hypotheses, often in the form of an MVP (Minimum Viable Product) or A/B test. These experiments can take many forms, including A/B testing, user surveys, prototype testing, or market testing.
2. Execute the experiments: Teams execute the experiments and collect data on user behavior, feedback, or other relevant metrics. They ensure that experiments are conducted in a controlled environment to minimize confounding variables and bias.
3. Analyze results: Once the experiments are complete, teams analyze the data to determine whether the hypotheses were supported or refuted. They look for patterns, trends, or statistically significant differences in the data to draw conclusions. Based on the results, they decide whether to persevere with the idea, pivot to a new approach, or abandon the hypothesis.
4. Iterate and learn: Based on the results of the experiments, teams iterate on their product or feature, making adjustments based on the insights gained from the experimentation process. If a hypothesis is validated, teams can continue development or scale the feature. If a hypothesis is invalidated, teams can pivot or change course based on the new information. The actual process is cyclical, involving strategic alignment, hypothesis generation, prioritization, experiment design, execution, analysis, learning, and strategic review.

Hypothesis-driven development should be used at every level of an organization, from strategic initiatives at the top level to individual stories at the team level. At the strategic level, it enables leaders to derive or reformulate hypotheses about market dynamics, customer needs, and the competitive landscape from the strategy that sets the direction of the organization. These hypotheses serve as the basis for strategic decisions, ensuring that initiatives are based on empirical evidence rather than unfounded assumptions. Similarly, at the team level, hypothesis-driven development enables agile teams to create user stories based on hypotheses about user behavior, feature effectiveness, and product outcomes with domain teams taking primary ownership of hypothesis generation and testing within their areas of expertise.

Set in the context of strategy execution:

1. Align with strategic goals: Ensure that hypotheses are tied to high-stakes challenges and strategic goals.
2. Create hypothesis backlogs: Maintain a prioritized list of hypotheses to test, aligned with strategic priorities on the organizational, domain and team level.
3. Establish an experimentation framework: Establish tools and processes for rapid experimentation and data collection.
4. Define decision thresholds: Establish clear criteria for when to persevere, pivot, or stop based on experiment results.
5. Integrate with existing processes: Incorporate hypothesis testing into sprint planning, reviews, and retrospectives.

We describe the exact process of how to prioritize and manage hypotheses across domains and teams in detail in chapter xxx

Overall, hypothesis-driven development emphasizes a systematic and data-driven approach to product development, enabling organizations, domains and teams to make informed decisions, reduce risk, and increase the likelihood of building products that meet user needs and achieve business goals.