AI-assisted operating rhythm · Interactive prototype
Align: From scattered input to shared decisions
When a decision spans a leadership team, the positions already exist. They are just scattered across threads, calls, and side conversations, so the tradeoff never gets resolved in one place.
Explore the interactive prototype(opens in a new tab)Context
Align is an interactive prototype built with a fictional company and sample data. It shows how source-linked stakeholder positions, disagreements, proposed resolutions, decisions, owners, and readiness can live in one workflow. It does not describe an employer's internal operations and makes no claim of adoption, integration, or measured impact.
The problem
Stakeholder positions are scattered across conversations, so nobody can see who holds which view or why. Meanwhile the unresolved tradeoffs sit quietly in the middle of the plan and block the downstream work that depends on them.
The design decision
Put the whole chain in one place: each position linked to its source, the disagreements named plainly, a proposed resolution, the recorded decision with an owner, and a readiness read on whether the organization can actually absorb it.
- Collect each stakeholder position and link it to the conversation it came from.
- Name the competing positions on a tradeoff instead of averaging them into agreement.
- Show the supporting evidence beside each position, including what is missing.
- Propose a resolution for the facilitator to accept, challenge, or replace.
- Record the decision with an owner, the follow-through it unblocks, and a readiness read.
Explore the example
Sample content: every name, figure, and quote below is fictional demo data from the prototype. Nothing here reflects a real company or a real decision.
Position A · Sales leader (sample persona)
Weight the plan toward new growth. The incentive should follow where the company wants the year to go.
Evidence cited: Sample pipeline mix for the fictional company, plus two quotes drawn from the demo's sample conversations.
Position B · Finance leader (sample persona)
Protect the existing revenue base first. A hard tilt puts predictable renewal revenue at risk.
Evidence cited: Sample renewal concentration figures and a sample retention risk note. The demo flags that no measured downside exists yet.
Proposed resolution · AI synthesis
Split the weighting rather than choosing a side, start at the lower end of the growth tilt, and revisit once renewal risk has been measured. Suggested next step: get the retention read before the weighting is locked.
Recorded decision · Facilitator
Accepted with a change. The facilitator rejected the proposed start point as unsupported by the evidence on hand, set the split at the conservative end, named the revenue owner as accountable, and marked the retention read as the condition for revisiting. Readiness: not ready to commit further until that evidence lands.
AI produced the synthesis and the suggested next step. The judgment, the change, and the commitment are the facilitator’s.
What the work demonstrates
The human judgment stays with the facilitator: weighing evidence, challenging the synthesis, resolving the tradeoff, and confirming that people have actually committed. AI contributes synthesis and suggested next steps. In the prototype, the ingestion and scoring behavior is simulated with sample data rather than connected to live systems, and the interface is written to say so.
Scope and limits
Scope: an interactive prototype with a fictional company and sample content. It is not connected to messaging, meeting, or planning tools, it produces no real readiness score, and it is not evidence of adoption or business outcomes. A real deployment would need its own data handling review and adaptation to a team's decision rights.