per employee per year spent on meetings
Otter.ai and Prof. Steven Rogelberg, UNC Charlotte, 2022
In a 2022 study of 76 companies published by Harvard Business Review, cutting meetings by 40% lifted employee-rated productivity 71% and employee satisfaction 52%. A regulated enterprise cannot simply cancel those meetings, because that is where judgment gets applied and signed. WisdomTwin is designed to bring that judgment to the decision, so the meeting becomes optional. Your pilot measures it.
Published research, not a WisdomTwin result.
I spent twenty-eight years in enterprise rooms where Tuesday's prepared work waited for Thursday's meeting. I am building WisdomTwin.ai so the decision can move with evidence, permissions and human authority intact.

Watch a Judgment Twin get a decision to the person who can put their name on it.
It answers on iMessage, WhatsApp, Telegram, Signal, Slack, email and Zoom, with the evidence attached. Not a person. A named human still decides.
Watch an Analyst Judgment Twin answer a 2:14 a.m. question.
Book a callBeforeImportant decisions still wait for people, calendars, and reconstructed context.
AfterBring the right judgment into the decision while the question is live.
Stop waiting for judgment. Start moving at AI-native speed.
These are intended operating changes.
Give regulated enterprises their meetings back.
We make the judgment of your best people available on demand, so the decisions that wait for a meeting can move while the question is live, with a named human still in charge.
The credit exception is ready on Tuesday. The person who can approve it is free on Thursday. The committee meets next month. The facts are searchable in seconds. The judgment lives in a few senior people's calendars, and regulated teams cannot paste the work into a public chatbot. So the meetings stay, and the bill keeps growing.
per employee per year spent on meetings
Otter.ai and Prof. Steven Rogelberg, UNC Charlotte, 2022
of that on meetings employees say they could skip
Otter.ai and Prof. Steven Rogelberg, 2022
a year in unnecessary meetings at companies with 5,000+ employees
Otter.ai and Prof. Steven Rogelberg, 2022
a year consumed by one weekly executive committee meeting at one large company
Bain & Company, 2014
Published research, not WisdomTwin results.
AI made answers instant. Enterprises still make decisions at calendar speed.
A paragraph can explain a situation. A workflow still needs a typed decision: a bounded action, evidence, uncertainty, policy result, authorization and a named human release.
WisdomTwin.ai is being built around that contract. The model proposes a bounded recommendation. A policy layer applies explicit rules. The accountable human keeps the call.
This is the difference between a paragraph that sounds plausible and a decision a regulated workflow can inspect.
I spent twenty-eight years selling into P&G across 21 brands, Apple, Microsoft, General Motors, Marriott and Wave, across 15 countries and 5 continents. The work was ready Tuesday. The calendar said Thursday. Those companies were my classrooms, not WisdomTwin.ai deployments.
Decisions should not wait for the room. We don't replace judgment. We remove the wait.
The wait is a prepared decision sitting until the next meeting. Governance, required approvals and named human authority remain.
Read Roman’s founder storyThe answer may already exist somewhere. The judgment does not. It is fragmented across senior people, meetings, policies, precedent, exceptions, inboxes and documents.
WisdomTwin.ai is designed to reduce the wait between a consequential question and an accountable decision. Experts remain in authority. Their role-specific judgment can be made available beyond the limits of their calendars, with governance built into the workflow.
The operating thesis: reduce avoidable decision latency without removing human authority.
A chat answer can be fluent and still be operationally unusable. A governed workflow also needs an allowed action, supporting evidence, explicit limits and a named person authorized to release the decision. WisdomTwin.ai is designed around that decision contract.
Open the decision when it arises and convene the roles it needs, without creating another calendar dependency.
Bring forward the authorized policy, precedent, thresholds, exceptions and escalation rules a role uses to exercise judgment. An AI Judgment Twin is not a cloned person and does not act with independent authority.
Designed to keep provenance, source permissions, version history, human review, escalation and audit records in the decision path.
Decision outcomes: Recommend. Escalate. Safe Decline.
The model recommends. Policy controls enforce the boundary. The accountable human keeps the call.
Connect the evidence a role is authorized to use, inside the enterprise's data and identity boundaries.
Structure that evidence into role-specific principles, thresholds, exceptions, precedent and escalation paths.
Use that judgment in a One-Press Huddle or asynchronous decision thread. The system recommends, escalates or safely declines, while a named human retains authority.
Designed for on-prem, on-device, and customer private cloud patterns. Patterns are scoped in pilot. None is claimed as a certified production air-gap today.
BeforeCredit exceptions wait for committee availability.
AfterThe decision package is assembled while the question is live.
BeforeClaims wait while context and policy limits are reconstructed.
AfterClaims move with evidence, limits, and escalation attached.
BeforePolicy interpretation waits for the next governance meeting.
AfterPolicy interpretation arrives with its evidence, for administrative decisions.
BeforeProcurement determinations wait between reviewers.
AfterDecision support starts immediately, with authority still named.
BeforePartners become the bottleneck, and recurring determinations wait.
AfterInstitutional judgment is available, and exceptions reach a human.
BeforeA limit or exception request waits for the credit authority to be free, and the file is re-explained in the next meeting.
AfterThe role judgment, applicable thresholds and documented exceptions arrive with the request, and a named approver releases or escalates it.
BeforeAn interpretation question sits until the next committee, while the business guesses or stalls.
AfterThe governing policy, precedent and limits are assembled when the question arises, with escalation defined and the review recorded.
BeforeA vendor decision waits on reviewers in different functions, each rebuilding context from prior submissions.
AfterOne governed decision thread carries the evidence, the role perspectives and the escalation path, and authority stays named.
BeforeAn access or data-use request queues behind the few people who know the standing rules and their exceptions.
AfterThe authorized rules, boundaries and prior determinations are present in the request, and a human owner decides on the record.
WisdomTwin.ai is building the sovereign judgment layer for regulated enterprises. Roman Bodnarchuk is Co-Founder and CEO. Stella Cabrera is Co-Founder, Governance (Dubai).
Pilots available. Book a Judgment Assessment.
Start with a short, scoped, read-only evaluation of one decision type. We discuss scope and terms directly, with evidence rights and start dates confirmed before contracting.
Bring one recurring, high-consequence decision loop. We map the before state, the target operating state, and what governed evidence it would take to move it.