Converge Methodology artefact
Ten elements, scored for every idea: the convergence scorecard
Comparing AI opportunities fairly needs a common scale. Every idea raised in discovery is assessed against the same ten elements, so that any two, in any function, can be compared like for like, and so the output drops straight into your own investment process without rework.
What every idea is scored on
Scored for every cluster before the convergent workshop, as a straw man the room corrects, so any two opportunities in any function compare like for like.
Priority
High, medium or low, as calibrated in the room, not imposed from outside.
Strategic fit
How directly the opportunity serves the business's stated objectives and current priorities.
Technical feasibility
Whether the capability exists and is proven, and whether it can be made to work in your environment.
Complexity
Integration, change and dependency load, from switch-on to multi-system programme.
Estimated cost
A three-band scale (£ / ££ / £££), relative and business-calibrated, firmed up at business-case stage.
Estimated benefit
Scale and reach of the value, including the "one licence, many benefits" effect of consolidated capabilities.
Risks
Safety, compliance, data protection, reputational and delivery risks, with the mitigations they imply.
Data position
Low dependency, in hand, siloed, partial or absent. The element that most often decides sequencing.
Vendor options
Candidate products from a landscape of over a hundred vendors across twelve functions; candidates, not selections.
Recommendation
Do now, prepare, research, or park, with the adoption pathway and pyramid layer attached.
A straw man the room corrects
Ahead of each convergent workshop, the ideas from the divergent session are pre-clustered into related groups and pre-scored on the ten elements. The scores are deliberately offered as a straw man, an "Aunt Sally", for the people who do the work to challenge, correct and force-rank rather than accept. This matters: the scores in the final report are not an external assessment imposed on the business, but the business's own view, calibrated in the room, which is what makes the roadmap one the organisation will act on.
Status is read at a glance throughout: a red-amber-green scale for readiness and risk (green ready to proceed, amber proceed with conditions, red blocked or not yet ready), the three-band cost scale, and a five-point deliverability rating per theme. These are relative, business-calibrated indicators for prioritisation, not precise financial estimates, and they are firmed up as each use case moves into its business case.
Written to drop into your own investment template
Most businesses of any size already have a technology-investment or innovation process with its own template: a general description, strategic fit, a value tree, a total-cost-of-ownership breakdown, an evaluation framework and a prioritisation matrix. The ten elements are chosen to map onto that structure, so each consolidated use case can be lifted into your process without rework. Where your template asks for detail not fixed in the workshop, such as a full cost breakdown or named accountable owners, the field is flagged for the use-case owner rather than guessed.
We also recommend four governance fields on every use case, which most templates lack and the EU AI Act makes useful: risk category, data protection, human oversight and transparency.
About the scorecard
Who does the scoring?
We pre-score; your people correct and force-rank in the convergent workshop. The final scores are the business's own, with our recommendation set out alongside where it differs.
Why consolidate ideas before scoring them?
Many ideas call on the same capability, the same licence, the same knowledge platform, and differ only in who uses it and for what. Scoring them individually understates their value and overwhelms the assessment process. Consolidated, one funded capability returns benefit many times over, and one well-made case is stronger than a dozen thin ones. Every individual idea stays recorded and traceable beneath it.
Is the data position audited?
In discovery it is assessed from what participants report. The first scoping task of every data-dependent use case is a data audit that turns the position into a measured baseline.
Score your own ideas on the same scale
Discovery produces the scorecard for every idea your people raise.