Knowledge Base
Every framework, use case, industry analysis, definition and report in one searchable place.
40 results
Hubs
A practical hub on finding AI opportunities in a real business: discovery methods, scoring models, prioritization frameworks and department-level opportunity maps.
Hubs
How to calculate AI ROI honestly: cost models, value drivers, payback periods, business case structure and the measurement discipline that keeps the number credible.
Hubs
Enterprise and mid-market AI strategy: a workable framework, readiness assessment, operating model choices, governance and a sequenced implementation roadmap.
Hubs
Department-level AI use cases with the detail that matters: where AI helps, complexity, data requirements, risks, KPIs and ROI considerations for each workflow.
Articles
A repeatable method for finding AI opportunities: start from workflows and cost, not from technology. Includes signals to look for, a discovery sequence and disqualifiers.
Articles
What an AI opportunity assessment covers, how it differs from an AI readiness assessment, the method behind it, and what a credible output document contains.
Articles
A transparent scoring model for prioritizing AI opportunities across value, confidence, effort, risk and readiness — plus how to sequence the first twelve months.
Articles
A complete method for calculating AI ROI: value drivers, the full cost model, a worked example, payback period and the measurement discipline that keeps the number honest.
Articles
A section-by-section AI business case structure: problem baseline, proposed change, value range, cost model, risk, governance, measurement plan and decision request.
Articles
The real cost structure of an AI implementation — build, integration, evaluation, change management, inference, human review and maintenance — and how each line behaves over time.
Articles
A five-part AI strategy framework — economic thesis, portfolio, operating model, governance and measurement — designed for mid-market companies rather than global enterprises.
Articles
The dimensions of AI readiness — data, process, systems, skills, governance and change capacity — how to score them, and how readiness should influence sequencing.
Articles
A twelve-month AI implementation roadmap for mid-market companies: what happens in each quarter, what gates each stage, and the signals that you are moving too fast.
Use cases
Where AI genuinely helps in customer service — triage, drafting, knowledge retrieval and summarisation — with complexity, data requirements, risks, KPIs and ROI considerations.
Use cases
Practical AI use cases in sales — lead qualification, follow-up, research, proposal drafting and CRM hygiene — with data requirements, risks, KPIs and ROI considerations.
Use cases
AI use cases across finance operations — invoice processing, reconciliation, expense review, reporting and forecasting support — with controls, risks, KPIs and ROI notes.
Use cases
AI in operations and back office: intake, scheduling support, exception handling, supplier communication and reporting, with complexity, risks, KPIs and ROI considerations.
Use cases
How AI document processing works in practice — classification, extraction, validation and review — including accuracy expectations, data needs, risks, KPIs and ROI drivers.
Use cases
AI across the claims lifecycle — intake, triage, document handling, fraud signals and status communication — with regulatory controls, risks, KPIs and ROI considerations.
Industries
Where AI creates value in insurance — claims, underwriting support, service, fraud signals and document handling — with regulatory constraints, ROI drivers and documented examples.
Industries
AI in construction: document control, estimating support, project reporting, safety observation triage and risk signals — with data constraints, ROI drivers and documented examples.
Industries
AI in healthcare administration and operations: documentation support, scheduling, coding, prior authorisation and patient communication — with clinical safety and GDPR constraints.
Glossary
An AI agent is a system that pursues a goal across multiple steps using tools. Definition, business relevance, an example, and the misconceptions that cost money.
Glossary
Generative AI produces new content — text, code, images, structured data — from learned patterns. What it means commercially, with an example and common misconceptions.
Glossary
AI readiness is the organisational ability to deliver, operate and govern AI-supported workflows. The six dimensions, why it matters, and how it should affect sequencing.
Glossary
AI ROI is net annual benefit divided by total investment. The formula, what belongs on each side, and the mistakes that make published AI ROI figures unreliable.
Glossary
AI automation applies models to steps that previously required human judgement about unstructured input. How it differs from RPA, with an example and misconceptions.
Glossary
An AI business case documents the quantified problem, the proposed change, the value range, the full cost model, risk, governance and measurement plan.
Glossary
A large language model predicts text from context, enabling classification, extraction, summarisation and drafting. What it means for business processes.
Glossary
RAG grounds a language model in your own documents by retrieving relevant passages before generation. Why it matters commercially, and where it goes wrong.
Glossary
AI governance is the set of controls determining how AI systems are approved, overseen, documented and monitored — including EU AI Act obligations.
Reports
A practical opportunity report on AI in European insurance: claims triage, underwriting support, document intake, and the operating conditions that decide whether value is realised.
Reports
Where AI creates measurable value in real estate agencies and property companies: lead response, listing production, document handling and transaction coordination.
Reports
An opportunity report on AI for private clinics and healthcare groups: scheduling, patient communication, documentation support, and the boundaries that must not be crossed.
Reports
The full methodology behind Merjora's AI value ranges: baseline construction, value drivers, adoption discounting, cost modelling, confidence levels and measurement after go-live.
Product
How Merjora surfaces AI opportunities from the way your business already works — workflow by workflow, with no technology shopping list.
Product
How Merjora turns an opportunity into a defensible value range: baseline, value drivers, implementation cost and payback period.
Product
The scoring model Merjora uses to rank AI opportunities: value, confidence, effort, risk and dependency — applied consistently across every candidate.
Product
The report Merjora produces for a decision meeting: the ranked opportunities, the numbers behind them, the assumptions and the recommended next step.
Product
How Merjora turns an approved opportunity into a sequenced roadmap: pilot scope, data preparation, integration, oversight and the measurement that proves value.