A complete walkthrough of how DeciMetrics transforms competing strategic narratives into a single, mathematically defensible recommendation — with every step auditable and every assumption visible.
Methodology
AHP + DEMATEL
Robustness Testing
1,000 Monte Carlo Trials
Time to Report
Under 90 Minutes
Shown Using
Real Enterprise Case
01 / 07
Input
The strategic mandate arrives as it always does — in prose.
"The board wants a prioritised roadmap to €10M ARR. We have five initiatives, capital for two, and 26 months of runway."
No structured templates. No spreadsheets pre-filled by a consultant. You paste the actual mandate — a board memo, a CEO brief, a strategy document — directly into DeciMetrics. The platform reads it the way a senior analyst would: extracting initiatives, identifying hard constraints, and flagging tensions that the document itself may not surface explicitly.
Raw Input Extract — SpectrumOps Series B
Sanitized Case
"We are SpectrumOps, a B2B SaaS company providing AI-powered supply chain visibility... closed our Series B nine months ago. The board expects a clear path to 10M euros ARR within 24 months.
Runway: 26 months at current burn. Aggressive hiring across multiple initiatives could compress this to 16–18 months.
Engineering team (28 people) is already at approximately 85% capacity on core product.
Initiative 'AIEdge': Develop advanced predictive capabilities... requires hiring 3–4 senior ML engineers in a highly competitive talent market. Estimated investment: 2.5M euros over 18 months..."
SYSTEM SIGNAL
The platform identified two binding constraints — runway compression risk and engineering capacity — that would later determine the entire ranking outcome. Neither was labelled as a constraint in the original document.
02 / 07
Framework Design
Defining what success actually means — before anyone votes on it.
Generic frameworks produce generic decisions. The criteria must be derived from the constraints of this specific organisation, at this specific moment.
The AI analyses your brief to generate a bespoke set of evaluation criteria — mutually exclusive, collectively exhaustive. Because the brief mentioned engineering capacity and talent scarcity, "Execution Feasibility & Resource Availability" was surfaced automatically as a top-tier criterion. This matters: it ensures you are evaluating options against the constraints that are actually operative, not the ones that are generically important.
AI-Generated Evaluation Criteria
7 Accepted / 0 Overridden
Execution Feasibility & Resource Availability
Constraint-Derived
Strategic Alignment & Market Positioning
Board Priority
Market Size, Growth Rate & Competitive Landscape
Financial Upside & Return on Investment
Risk Exposure & Mitigation Complexity
Time to Revenue & Implementation Horizon
Sustainable Competitive Advantage & Moat Creation
HUMAN-IN-THE-LOOP
All 7 criteria were accepted without manual amendment — a signal that the AI's reading of the brief was precise enough to require no correction. The human remains in control: every criterion can be edited, reweighted, or removed.
03 / 07
System Dynamics — DEMATEL
Criteria are not independent. The system maps who drives whom.
"Financial Upside" is not a goal you can pursue directly. It is an output — downstream of market conditions and your capacity to execute. Treating it as an independent driver is a category error.
Using DEMATEL (Decision Making Trial and Evaluation Laboratory), the platform constructs a causal map of how your criteria influence each other. This step is where most decision frameworks fail: they treat all criteria as parallel inputs and weight them independently. DeciMetrics reveals the actual dependency structure — which factors are root causes and which are downstream effects — so that the final weighting reflects the system's true architecture.
DEMATEL Cause-Effect Map
Influence Threshold > 0.30
Root Drivers (Causes)
Market Size & Growth Rate
Execution Feasibility
Strategic Alignment
Drives
Downstream Outcomes
Financial Upside & ROI
Risk Exposure
Time to Revenue
STRATEGIC IMPLICATION
Any initiative with high projected ROI but low execution feasibility will not produce that ROI. The upside is theoretical until the capacity to execute is confirmed. This single insight separates rigorous prioritisation from optimistic planning.
04 / 07
Bias Elimination — AHP
Priorities set by pairwise logic, not by who speaks loudest.
The question "how important is feasibility relative to market size?" has a defensible mathematical answer. The Analytic Hierarchy Process extracts it from your own stated preferences — without the politics.
AHP (Analytic Hierarchy Process) converts qualitative judgements into a mathematically consistent weight distribution. Rather than asking stakeholders to assign percentages — a process prone to anchoring bias and political negotiation — it asks a series of simple pairwise questions: "Is criterion A more important than criterion B, and by how much?" The resulting weights are verifiable: a Consistency Ratio below 0.10 confirms the judgements are internally coherent.
AHP Weight Distribution
CR = 0.087 (Consistent)
Execution Feasibility & Resource Availability
29.9%
Strategic Alignment & Market Positioning
21.6%
Market Size, Growth Rate & Competitive Landscape
16.5%
Financial Upside & Return on Investment
10.9%
Risk Exposure & Mitigation Complexity
7.4%
NOTABLE OUTCOME
Financial ROI weighted at only 10.9% — less than a third of execution feasibility. This is not a devaluation of returns; it reflects the DEMATEL finding that ROI is a downstream outcome. Maximising a downstream variable directly is not a viable strategy.
05 / 07
Transparent Scoring
Every score cites the exact clause that produced it.
A black-box score that says "AIEdge scores 3/10 on feasibility" is useless in a boardroom. A score that cites the sentence in your own brief that caused it is not.
Each initiative is scored against each criterion by the AI — but the scoring is fully auditable. The system extracts the specific passage from your original input that informed the rating, states the inference it drew, and renders a verdict. Every score can be manually overridden. This transparency is not optional: it is the mechanism by which the recommendation earns trust with stakeholders who did not participate in the process.
Sample Audit Trail — AIEdge vs. Execution Feasibility
Score: 3 / 10
Source passage extracted from input
"Requires hiring 3–4 senior ML engineers in a highly competitive talent market... engineering already at ~85% capacity... 3–6 month recruitment delays."
AI inference & Verdict
The initiative requires scarce specialist talent (senior ML engineers) at a moment when the engineering function is already at near-maximum capacity. Hiring delays of 3–6 months on a 26-month runway make this initiative high-risk to initiate in the near term. Rated 3/10 on execution feasibility.
Total AI Ratings
35 scores generated
Manual Overrides
0 — All accepted
GOVERNANCE
Zero manual overrides across all 35 scores is a strong signal of input quality — but the override mechanism exists precisely because the human retains authority over every judgement. The AI advises; the decision-maker decides.
06 / 07
Robustness — Monte Carlo
A recommendation that holds under pressure is the only kind worth presenting.
A board will not accept "our model says so." They will ask: what if your weights are wrong? What if conditions shift? Does this recommendation survive those questions?
The platform runs 1,000 Monte Carlo simulations, each introducing random perturbations to the criteria weights (±15%) and initiative scores (±0.75 points). The purpose is to test whether the winning recommendation is a robust finding or an artefact of a specific parameter set. If the same initiative wins across nearly all simulations, the recommendation is resilient. If the rankings fluctuate, the decision requires deeper analysis before it can be presented with confidence.
Monte Carlo Robustness Results — 1,000 Trials
Weights ±15% / Scores ±0.75
CustomerFortress — Rank #1 Frequency
993 / 1000 trials
99.3%
#1
CustomerFortress
Recommended
6.71
#2
AIEdge (Predictive Analytics)
5.61
#3
PlatformPlay (Partner Ecosystem)
5.29
#4
GeoLaunch (SE Asia Entry)
4.96
#5
DeepExpand (Enterprise Pivot)
4.69
CONFIDENCE LEVEL
A margin of 1.10 points over the runner-up, sustained across 99.3% of 1,000 randomised simulations. This is not a close call. The recommendation is robust to significant shifts in both priorities and assessments.
07 / 07
Output
The work ends where it must — with a document the board can act on.
A rigorous analysis that cannot be communicated is not yet complete. The final deliverable translates every mathematical step back into language a board can interrogate, challenge, and approve.
The platform generates a structured advisory report automatically. It includes the executive summary, the methodology with consistency checks, the full alternative comparison, a phased implementation roadmap, and a contingency plan for the runner-up — complete with specific activation triggers. The document is designed to withstand scrutiny, not just to summarise a conclusion.
Generated Report — Contents
14 Pages · PDF Export
Executive Summary
Bottom-line recommendation with confidence metrics
Methodology
AHP weights, DEMATEL maps, CR validation
Alternative Evaluation
Scored comparison across all 5 initiatives
Implementation Roadmap
5 phases, Month 1–24, with go/no-go gates
Contingency Plan
AIEdge activation triggers and hiring sprint protocol
Robustness Appendix
Monte Carlo distribution, sensitivity tables
TIME SAVED
The complete workflow — from raw brief to board-ready report — was completed in a single session. The same analysis, conducted through a traditional consulting engagement, typically requires 4–6 weeks and a significant advisory fee.
The decision is made. Make it defensible.
DeciMetrics is built for organisations where strategic decisions are high-stakes, the methodology will be scrutinised, and a well-reasoned wrong answer is more valuable than a right answer nobody trusts.