01
Define value
What result is the customer buying? Which outcome creates value for the organisation?
Business Value & AI Alignment
Align AI with the real problem.
MoreSight clarifies the organisation’s value proposition and priority problem hypotheses, then evaluates the conditions under which a current or proposed AI initiative should be started, fixed, scaled or stopped.
Approach
Define the value and the decision before the technology.
MoreSight is not designed to produce a long list of AI ideas. It examines how the organisation creates customer and internal value, identifies the most important decision point, and determines what role AI could play and what evidence is required to evaluate it.
This public page shows the high-level logic. The detailed question architecture, assessment rules and analysis templates are applied within the engagement.
02
Build the problem hypothesis
Separate the symptom from constraints in process, information, ownership, incentives or measurement.
03
Establish the evidence level
Which information is reported, documented, calculated or validated through a controlled pilot?
04
Align the AI role
Should AI detect, predict, explain, recommend, simulate or generate?
05
Set the decision gate
Define the evidence and thresholds required for a Start, Fix, Scale or Stop decision.
Evidence ladderNot every finding carries the same level of confidence.
ReportedInformation provided by a participant
DocumentedSupported by a document or record
CalculatedDerived from supplied data
ValidatedSupported after a pilot or implementation
AI Decision Cases from the Field
From the real problem to a more defensible AI decision.
Seven cases, drawn from more than two decades of operational experience and anonymised for confidentiality, move from a visible business problem to a value-aligned AI opportunity, the evidence required, and a Start, Fix, Scale or Stop decision.
Select a case to open its dedicated detail page.
When the Factory’s Real Constraint Is the Promise, Not the Machine
A delivery date promised without a capacity check creates a decision problem that must be fixed before AI-enabled planning can create value.
Open the case→
When AI Forecasting Solves the Wrong Problem
A general demand forecast can look sophisticated while missing the component-level decision and structural information-flow problem that actually creates risk.
Open the case→
AI Proposes. The Engineer Decides.
A governed AI decision-support system can accelerate quotations while limiting substitution risk through codified knowledge and mandatory human approval.
Open the case→
When the Algorithm Has No Rule to Automate
When the historical process has no consistent decision rule, an AI model may scale the ambiguity instead of reducing freight cost.
Open the case→
When a Loyalty Program Starts Leaking Margin
AI can help quantify and prioritise suspected abuse, but the deeper value comes from fixing the programme incentives that make leakage rational.
Open the case→
When an AI Pilot Cannot Prove Whether It Worked
Without a protected control, pre-agreed thresholds and a single owner, neither positive nor negative pilot results support a defensible AI decision.
Open the case→
When Retention, Not Ad Tuning, Is the Real LTV Lever
The highest-value AI opportunity may sit in early retention and content sequencing rather than in another round of ad tuning.
Open the case→
Disclosure: These cases are drawn from more than two decades of hands-on operational, manufacturing and consulting experience across manufacturing, supply chain, aftersales and digital product organisations. Organisation characteristics, events and figures have been altered and combined to protect confidentiality, so no case represents a single identifiable organisation. The decision logic shown is the same logic applied in a MoreSight review.
Your next decision
Is your AI project connected to the right problem?
Assess a current or planned initiative against business value, evidence readiness and implementation conditions.