A new category of business system

Build Outcome Machines for your most important business goals.

Not another AI pilot or point solution. An Outcome Machine is built around one business goal you own — connecting the signals, decisions, approvals and workflows that move it — and measures the incremental value it delivers against an agreed baseline.

Plan your first machine→ See how it works
Business signalRecommendationHuman approvalMeasured result
The core insight

Point solutions solve a slice.
Outcome Machines own the result.

Enterprises are piling up copilots, agents and pilots — each solving one piece of a problem, none accountable for the number. An Outcome Machine is built around one goal, end to end: from signal, to decision, to action, to measured result.

AI pilots & point solutions

  • × A copilot here, an agent there
  • × Each solves one slice of the work
  • × No one owns the business result
  • × Stuck in pilot, never in production
  • × Value claimed, never proven

Outcome Machines

  • ↑ Built around one goal you own
  • ↑ Connects signal, decision, action and result
  • ↑ An accountable owner approves key actions
  • ↑ Runs in production against your KPI
  • ↑ Incremental value measured against a baseline
The method

Outcomeering: run AI like a P&L, not a pilot.

Outcomeering is the method behind every Outcome Machine — a discipline for turning a business goal into accountable decisions and measured results. Six principles set it apart from point solutions.

01

Start from the number

Name one business goal and the KPI that measures it before choosing any technology.

02

Agree the baseline first

Decide how success will be measured before anything is deployed.

03

Map what moves it

Understand the activities, decisions and causal drivers behind the KPI.

04

Name an accountable owner

Every consequential decision has a person who approves it.

05

Measure incremental value

Credit only the improvement over baseline that the actions produced.

06

Learn from every result

Feed each measured outcome back into the next decision.

The Outcomeering loop

From signal to accountable action.

This is Outcomeering in practice. The machine works with the relevant business data and context, a person approves consequential actions, and each completed action is measured against the baseline.

01

Monitor

Watches the goal continuously using the relevant business data and context.

02

Explain

Investigates likely drivers and shows the evidence behind them.

03

Recommend

Proposes an action with its evidence, expected effect, and policy constraints.

04

Approve

Pauses for the accountable owner to approve or reject the proposal.

05

Execute

Runs the approved action through an inspectable workflow.

06

Learn

Compares the observed result with the baseline and improves the next decision.

Illustrative machine cycle

A stockout becomes a decision.

A sample Revenue Recovery cycle shows the information an operator gets before action, and the result the machine would measure afterward. The scenario is illustrative, not a customer result.

94.5-second illustrative walkthrough · Demo data

  1. 01 · Signal

    Cover is running low

    A fast-moving SKU falls below its inventory-cover threshold in a high-demand region.

  2. 02 · Investigation

    Find the constraint

    The machine checks store-level demand, available inventory, replenishment timing, and supplier fill rate.

  3. 03 · Recommendation

    Move stock where needed

    It proposes a transfer from lower-velocity stores, with the expected effect and supporting evidence.

  4. 04 · Approval

    Keep an owner in control

    A merchandising or supply-chain owner reviews the proposal before the transfer workflow starts.

  5. 05 · Outcome

    Check what changed

    Recovered sales and stockout rate are compared with a baseline over the agreed measurement window.

Illustrative decision record

Transfer stock to the constrained region

Awaiting approval
Signal
Inventory cover is below the agreed threshold for a fast-moving SKU.
Evidence to review
Store demand, available stock, replenishment timing, and supplier fill rate.
Proposed action
Transfer units from a lower-velocity store to the high-demand region.
Policy check
Keep the sending store above its minimum cover; confirm the transfer is permitted.
Decision owner
A merchandising or supply-chain owner can approve, reject, or request a revision.
Measurement plan
Compare stockout rate and recovered sales with a baseline after the transfer. The record stays open until an observed result is available.
The catalog

One machine per outcome.

Each machine focuses on one goal and the decisions that move it. These are example operating models; the signals, policies, and measures are configured for your organization.

OM-01 Explore model

Revenue Recovery

Finds stockouts, supplier delays, and allocation gaps while there is still time to act.

GoalRecover sales lost to operational gaps.
SignalInventory cover falls as demand rises
DecisionReallocate stock or accelerate replenishment
MeasureRecovered revenue
See how it works
OM-02 Explore model

Collections

Prioritizes accounts and field actions using repayment, risk, and intervention history.

GoalImprove recovery from overdue accounts.
SignalBroken promises to pay increase
DecisionChange contact strategy or assign a visit
MeasureCollection rate
See how it works
OM-03 Example model

Customer Retention

Connects service, engagement, and renewal signals to a timely retention decision.

GoalKeep customers at risk of leaving.
SignalRenewal risk rises in a customer segment
DecisionPrioritize a service or renewal intervention
MeasureRetention rate
OM-04 Explore model

Sales Productivity

Separates lead quality, follow-up, coaching, and territory effects before recommending a change.

GoalImprove conversion across the sales team.
SignalConversion falls despite steady activity
DecisionAdjust coaching or lead allocation
MeasureLead-to-sale conversion
See how it works
OM-05 Example model

Branch Performance

Finds where branch outcomes diverge and which operating practices may explain the gap.

GoalImprove performance across locations.
SignalComparable branches begin to diverge
DecisionInvestigate staffing or operating practice
MeasureOutcome per branch
OM-06 Example model

Procurement

Connects demand, price, supplier reliability, and inventory to sourcing decisions.

GoalReduce avoidable supply and spend loss.
SignalSupplier fill rate deteriorates
DecisionEscalate or shift supply allocation
MeasureCost per supplied unit
Control and measurement

Every action has a reason, an owner, and a result.

An Outcome Machine should make the path from evidence to action inspectable. Teams set the policy and approve consequential decisions; the machine records what happened next.

01 · Evidence

Show the reasoning

Each recommendation identifies the signal, supporting data, and assumptions behind its expected effect.

02 · Approval

Name the decision owner

Policy names the human approver, sets the action boundaries, and determines which workflow can run after approval.

03 · Outcome

Measure against a baseline

The team sees the action taken, the observed result, and where the model needs correction.

Under the hood

Built on a digital twin of your organisation.

Before an Outcome Machine can act, it needs to understand your organisation — the goals and KPIs at every level, the activities people run to achieve them, and the causal relationships between them. We build and maintain that model as a digital twin, so each machine knows not just what is happening, but why, and what to do about it.

The book

The book behind the method.

Outcomeering sets out the method behind every Outcome Machine — written for operators and P&L leaders who need AI to deliver results they can measure, not more pilots.

The book · Early access
Outcomeering
How intelligent enterprises learn, decide and improve.
Request early access
Insights / Outcome Intelligence

Ideas for systems that learn from results.

Amit Rathore writes about the shared data, governed decisions, and measured results that make an organization a learning machine.

Explore Insights
Founding essay · 4 min read

What Is Outcome Intelligence?

Read the essay
Start with one outcome

Pick a goal.
Plan a machine.

Bring one business outcome. In an Outcomeering session, we'll map the metric, the decisions that affect it, the data those decisions need, and a practical first deployment path.