AI Opportunities
Most AI programmes do not fail on technology. They fail because nobody agreed on which problem was worth solving, what it was costing today, or how the result would be measured.
Updated 3 September 2026
This hub covers the discovery end of AI: how to surface candidate opportunities from real workflows, how to score them consistently, and how to decide what to run first — and what to deliberately not do.
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.
Identify AI opportunities by starting from workflows rather than tools: list the processes that consume the most repetitive human hours, isolate the decision or…
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.
An AI opportunity assessment is a structured review that identifies where AI could change the economics of a specific business, quantifies the value range for e…
A transparent scoring model for prioritizing AI opportunities across value, confidence, effort, risk and readiness — plus how to sequence the first twelve months.
Prioritize AI opportunities with an explicit score across five dimensions — annual value, estimate confidence, implementation effort, residual risk and organisa…
The Merjora assessment maps your workflows, scores each opportunity and quantifies the value range before you commit budget.
Discover your AI opportunitiesEditorial standard. Merjora publishes analysis, frameworks and publicly documented examples. We do not publish invented statistics, unattributed benchmarks or unverified customer stories.