Strategy · Evolved from Opportunity Solution Tree
Opportunity Solution Tree (AI-Branched)
Structure work as outcomes → opportunities → solutions → experiments. Extended with explicit branches for AI-native, agentic, and human-in-the-loop solution paths, each with distinct risk and evaluation requirements.
Executive Guidance
How to hold this framework as a leader
The opportunity solution tree makes strategy legible. It forces a linear chain from outcome to opportunity to solution to experiment, and refuses to let a favored solution skip the reasoning above it. That property is what makes it useful in a room with senior stakeholders.
In the AI era every tree carries at least one AI-native branch. Executives should read the branch structure as a portfolio: which opportunities are being pursued with UI changes, workflow automation, assistive AI, or autonomous agents — and the risk each branch carries.
When to Apply
- Translating a strategic outcome into a portfolio of bets
- Deciding whether a problem is best solved with UI, workflow, or agent
- Communicating trade-offs to executives without over-committing to solutions
AI-Era Notes
Tag each solution branch by autonomy tier and reversibility. AI-native branches inherit additional preconditions: eval set, guardrails, monitoring, and rollback authority.
Key Trade-off
Makes trade-offs visible; without autonomy tagging, AI branches inherit disproportionate downstream risk.
Operating Sequence
The order in which to install the framework
- 01Anchor the tree on a single outcome that ties to a strategic objective.
- 02Enumerate the opportunities that would move the outcome, sourced from discovery.
- 03For each opportunity, sketch multiple solution branches including at least one AI-native option.
- 04Tag every AI branch with autonomy tier, reversibility, and evaluation preconditions.
- 05Attach the assumption tests that would falsify each solution before it is built.
Key Artifacts
The documents this framework produces
Outcome Statement
The single measurable outcome the tree pursues this cycle.
Owner: Product + Executive Sponsor
Opportunity Tree
Structured decomposition from outcome to experiment.
Owner: Product
Branch Risk Register
Autonomy tier and evaluation requirements per AI branch.
Owner: Product + AI Governance
Operating Checklist
What "good" looks like when installed
Structure
- The tree has exactly one outcome at the root.
- Every solution is traceable to an opportunity, and every opportunity to the outcome.
- No solution branch lacks at least one falsifying experiment.
AI Branches
- Each AI branch is tagged by autonomy tier.
- Reversibility of each AI branch is classified.
- Evaluation preconditions are documented for each AI branch before build.
Common Antipatterns
- Trees that stop at solutions and skip experiments
- Uniform treatment of AI and non-AI branches
Boardroom Questions
- Where in the tree are we betting most heavily on unproven AI capability?
- Which opportunities are unaddressed by any branch?
- What experiment, if it failed next month, would change our strategy?