Most companies do not have an AI problem. They have a prioritisation problem: too many possible use cases, no agreed baseline, and no defensible way to say which one is worth funding first.
An AI opportunity assessment is a structured review that identifies where AI could change the economics of your business, quantifies what each candidate is worth against today's baseline, scores it on feasibility, data availability and risk, and produces a sequenced shortlist. The output is a decision document: what to run first, what to run later, what to reject, and what each judgement assumed.
Why valuable AI use cases are hard to identify from the inside
The people who know where the work is painful rarely know what is technically feasible, and the people who know what is feasible rarely know what the work costs today. The result is a list that mixes trivial wins with projects nobody can staff, ordered by whoever argued most persuasively.
Vendors do not resolve this, because the shape of their product determines the opportunity they find. An assessment is only useful when the method is independent of what gets built afterwards.
How the assessment runs
The assessment works from your real workflows rather than a capability checklist. Each step is described in terms of volume, handling time and cost before any technology is discussed, so every later value claim has a baseline behind it.
Map the workflows that carry commercial weight: revenue, cash, fulfilment, service
Isolate the specific steps where time and error cost accumulate
Establish the baseline — how often, how long, at what loaded cost
Generate candidate interventions, including the non-AI ones
Score each candidate on value, feasibility, data readiness, risk and organisational absorption
Sequence the shortlist and write down the disqualified candidates with reasons
How opportunities are scored
The five dimensions applied to every candidate
Dimension
What it asks
Why it changes the ranking
Business impact
What is the annual value range against the baseline?
Separates material opportunities from tidy ones
Feasibility
Can this be built with available systems and access?
Stops projects that stall at integration
Data readiness
Does the input exist, in usable form, in enough volume?
The most common cause of failed pilots
Risk
What happens when the output is wrong?
Determines the review design and the regulatory exposure
Absorption
Can the team adopt this alongside its day job?
Decides sequencing more often than value does
What quantification means here
Value is expressed as a range with the assumptions written next to it, never as a single confident figure. Hours released, cost avoided and cash effect are kept separate, because only some of them will ever appear in a budget line — and finance will ask which.
Where the evidence is thin, the assessment says so and assigns a confidence level rather than filling the gap with a benchmark from another company's industry.
From shortlist to roadmap
The final section sequences the shortlist into a practical order: one opportunity that can be delivered quickly to establish the measurement discipline, then the larger structural ones. Each entry carries an owner, the systems it touches, the review point, and the metric that will decide whether it worked.
What you receive
A workflow map of the processes assessed, with baselines
A scored opportunity portfolio with value ranges and confidence levels
A prioritised shortlist with a recommended sequence
An explicit rejected list, with the reason for each rejection
A business case outline for the first opportunity, structured for finance
Business impact
Budget goes to the opportunity with the strongest evidence rather than the loudest sponsor
The baseline exists before the change, so realized value can be measured later
Rejected candidates stop being re-proposed every planning cycle
Delivery starts with a defined review point and a named owner
Where this is the wrong fit
Companies that have already selected a vendor and need the business case written backwards
Organisations looking for a technology audit rather than a commercial one
Teams expecting a ranking without providing volumes, handling times or cost inputs
Frequently asked questions
What is an AI opportunity assessment?
A structured review that identifies where AI could create measurable value in a specific business, quantifies each candidate against today's baseline, scores it on feasibility and risk, and produces a sequenced shortlist with an explicit rejected list.
How do you identify AI use cases?
By starting from workflows rather than technology. We inventory the steps that consume repetitive time or generate error cost, quantify them, and only then consider which of them AI, automation or simplification could improve.
How do you calculate the ROI of each opportunity?
Volume multiplied by handling time and loaded cost gives the baseline. Against that we model a value range with stated assumptions, subtract implementation and running costs including review time, and report a payback period rather than a single return figure.
How long does an assessment take?
It depends on how many processes are in scope and how quickly volume and cost data can be gathered. A focused assessment covering two or three core processes moves considerably faster than an organisation-wide review.
What information do you need from us?
Process volumes, approximate handling times, the systems involved, and access to people who actually run the work. Perfect data is not required; documented assumptions are.
Can Merjora also implement the recommended opportunities?
Yes. The assessment is independent of implementation, and it is deliberately possible for it to conclude that nothing should be built.