Context
An interactive decision dashboard built on public datasets, designed to answer where AI adoption investment should go — not which tools to buy, but which sectors, use cases, and sequences make the strongest business case.
This is a consulting and methodology deliverable with no runtime AI and no live inference. The analytical value is in the framework and the data. The live demo may take a few seconds to load on first visit.
Problem
AI adoption decisions are frequently made on vendor claims or trend reports rather than structured market analysis.
The dashboard applies established business-case frameworks — TAM/SAM/SOM, PESTLE, Porter's Five Forces — to the AI adoption decision specifically, producing a comparable and inspectable view rather than a static presentation.
Workflow
The dashboard layers three analytical views: market opportunity sizing (TAM/SAM/SOM), competitive and macro-environment analysis (PESTLE, Porter's Five Forces), and use case scoring across five dimensions.
A sequenced implementation plan organizes the ten use cases by composite score, so a decision-maker can move from analysis to prioritized action.
01
TAM/SAM/SOM
Market opportunity sizing
TAM/SAM/SOM analysis on public datasets establishes the addressable market context for AI adoption investment.
02
PESTLE + Porter's
Environment analysis
PESTLE and Porter's Five Forces frame the macro environment and competitive dynamics relevant to AI adoption.
03
10 × 5 matrix
Use case scoring
Ten AI adoption use cases are evaluated across five dimensions in a structured, inspectable scoring framework.
04
Sequenced plan
Implementation sequencing
Use cases are ranked by composite score into a sequenced implementation plan for prioritization decisions.
Architecture
Built with Python, Plotly Dash, and pandas on public datasets. Deployed as a Hugging Face Space (always-on canonical demo). Docker and gunicorn handle containerized serving.
- Plotly Dash for interactive chart rendering.
- pandas for dataset processing and scoring calculations.
- Docker + gunicorn, deployed to Hugging Face Spaces.
Data layer
Public datasets processed with pandas provide the market sizing and scoring inputs. No proprietary data, no live inference.
- Public datasets
- pandas processing
- Transparent scoring
Dashboard layer
Plotly Dash renders TAM/SAM/SOM charts, PESTLE/Porter's views, use case scores, and the implementation sequence interactively.
- Plotly Dash
- Interactive charts
- Inspectable framework
Deployment
Containerized with Docker and served via gunicorn on a Hugging Face Space. Always-on canonical demo.
- Docker
- gunicorn
- Hugging Face Spaces
Governance
All analysis is sourced from public datasets. No proprietary client data, no live AI inference, no model calls at runtime.
The scoring framework is fully transparent — every dimension weight and use case score is visible in the dashboard.
Metrics
Ten AI adoption use cases are evaluated across five scoring dimensions. TAM/SAM/SOM sizing provides market context. PESTLE and Porter's Five Forces frame the macro and competitive environment.
The sequenced implementation plan ranks use cases by composite score to support prioritization decisions.
- Use cases evaluated
- 10
- Scoring dimensions
- 5
Each scored across five dimensions in a structured decision framework.
Framework dimensions applied to each AI adoption use case.
Roadmap
The dashboard is complete and deployed. The Hugging Face Space serves as the canonical live demo; the code repository is available for inspection and adaptation.
Completed
Dashboard and framework
TAM/SAM/SOM, PESTLE, Porter's Five Forces, ten use case scores, and sequenced implementation plan are all live.
Available
Code repository
Full source code is publicly available for inspection, adaptation, and extension.
Reflection
Structured business-case frameworks — TAM/SAM/SOM, PESTLE, Porter's Five Forces — apply directly to AI adoption decisions when paired with a consistent use case scoring model.
An interactive dashboard is a more durable analytical deliverable than a static report: the assumptions and scores remain inspectable after the presentation is over.
Technical depth
System assumptions and operating controls.
Architecture diagram
Python, Plotly Dash, and pandas on public datasets. No live AI inference. Containerized with Docker and gunicorn, deployed as a Hugging Face Space. The analytical value is in the framework: TAM/SAM/SOM, PESTLE, Porter's Five Forces, and a five-dimension use case scoring model across ten use cases.
01
Public data ingestion
pandas loads and processes public market and sector datasets for TAM/SAM/SOM sizing and use case scoring inputs.
02
Scoring framework
Ten AI adoption use cases are scored across five dimensions using a transparent, configurable weighting model.
03
Plotly Dash interface
Interactive charts render market sizing, PESTLE/Porter's analysis, use case scores, and the sequenced implementation plan.
04
Deployment
Docker container served by gunicorn on a Hugging Face Space. Always-on with no cold-start delay.
System component reference
Tool
Plotly Dash
Purpose
Render interactive charts for all analytical views.
Input
Processed dataset and scoring outputs
Output
TAM/SAM/SOM charts, PESTLE/Porter's views, use case scorecards, implementation sequence
Guardrail
All inputs are from public datasets — no live inference or model calls.
Tool
pandas
Purpose
Load, process, and score public market datasets.
Input
Public datasets
Output
Processed scoring inputs for the dashboard
Guardrail
Data sources documented in the code repository.
Tool
Docker + gunicorn
Purpose
Containerize and serve the Dash application on Hugging Face Spaces.
Input
Application code and dataset
Output
Always-on web dashboard
Guardrail
No runtime model calls — static serving only.
Evaluation metrics
Use case coverage
10 use cases scored across all 5 dimensions
Dashboard visual review confirms all use cases and dimensions are populated.
Framework completeness
TAM/SAM/SOM, PESTLE, Porter's, and use case scoring all rendered
Dashboard visual review confirms all analytical views are present and interactive.
Risk and failure scenarios
Dataset staleness
Market sizing inputs no longer reflect current conditions.
Data sources documented in the repository; update cadence is the responsibility of the user.
Hugging Face Space unavailability
Live demo is inaccessible.
Code repository provides full source for local deployment as a fallback.
Human review checkpoints
Scoring weight review
Analyst or decision-maker
Review and adjust scoring dimension weights before using the implementation sequence for investment decisions.