Checklists and decision guides for organisations assessing a use case, choosing a solution pattern or moving a prototype into production.
The guides are intentionally tool-independent. They help structure questions and make decisions traceable; they do not replace technical and domain assessment of a specific initiative.
The guides provide the overview. These articles explain individual decision points with examples, limitations and concrete questions.
An AI use case is worth pursuing when it measurably improves a frequent or important problem, suitable data and clear ownership are available, and potential errors can be detected and controlled with reasonable effort.
A proof of concept tests a central technical assumption, a pilot evaluates the solution with real users in a limited real-world workflow, and a production solution also meets binding requirements for security, reliability, operations and ownership.
Buying makes sense when a market product covers most of a standard process. Integration connects existing models or platform services with the organisation's data and workflows. Custom development is worthwhile for differentiating requirements that standard products cannot meet appropriately and that the organisation can operate over time.
First compare models on the same representative tasks and against explicit quality limits. Then assess provider terms, data flow, security, availability, integration, cost and portability. The best model is the option that reliably serves the specific purpose as part of the complete solution.
A managed cloud service often fits when rapid access, current capable models and flexible scaling matter more than complete infrastructure control. Self-hosted or local models can make sense for offline operation, very low latency or particular control requirements—provided that hardware, updates, security, evaluation and operations are covered for the long term.
The cost of an AI solution includes product or model use, data and infrastructure, integration and development, quality assurance, security, operations, support and human handling of outputs and exceptions. Costs become comparable only when expressed per successful business outcome.
The ROI of an AI project compares measurable additional value against a documented baseline with the full investment and operating cost. Financial effects should be reported alongside time, quality, risk, adoption and effects on employees.
Choose an AI platform against prioritised use cases and binding requirements for models, data, identity, integration, quality, security, operations, cost and portability. Test the shortlist with internal cases and a real integration before making a broad commitment.
An AI solution is production-ready when it demonstrates sufficient quality for its intended context, controls data and permissions, has passed its legal and contractual release gate, can be operated reliably and observably, has clear intervention paths and is owned across its full lifecycle.
Seven criteria make ideas comparable without creating false precision.
Assess each criterion with a short rationale. A scale may support comparison, but documented assumptions are more important than the total score.
Which concrete outcome improves, and for whom does that improvement matter?
How often does the task occur and how many cases or people does it affect?
Can normal cases, exceptions, desired outputs and ownership be described?
Is relevant data accessible, current, sufficiently complete and lawful to use?
Which systems must read, write or approve an action?
What happens when an answer or action is wrong, and can the error be corrected?
Who owns domain quality, process change, approval and user feedback?
Record the rationale and open assumptions for every criterion
Never consider high impact separately from risk and data readiness
Choose a first pilot based on learning value and ability to bound scope
Update the assessment with evidence from analysis or a pilot
A simple assessment can make ideas comparable. It does not replace feasibility work or produce an automatic investment decision.
Rules, AI-assisted workflows, knowledge assistants, platforms and custom software differ in task and accountability.
The right solution pattern follows the workflow. Combinations are common, but every component should have a clearly named purpose.
Best when inputs, checks and actions can be described as stable rules and do not require language interpretation.
Best when selected steps require classification, extraction or language processing but actions must remain controlled.
Best when approved knowledge must be findable and answers need traceable citations.
Best when several user groups, sources, models, tools and workflows need a shared foundation.
Best when a specific internal or customer-facing workflow is not adequately covered by standard products.
Does the system only provide information, or must it also act?
Are rules stable, or is interpretation required?
Does one user group need one capability, or do several teams need a shared foundation?
Can an existing product be configured or integrated before custom development is considered?
A specialist application may combine knowledge retrieval, deterministic checks and selected AI steps. Clear boundaries between components matter most.
Twelve checks separate a convincing demo from a responsible working solution.
A prototype proves a core idea. Production additionally requires explicit quality, permissions, error paths, integration and ownership.
Domain owner and users are identified.
Success and acceptable errors are defined.
Representative test cases and expected outcomes exist.
Data sources, freshness and access are documented.
Roles and permissions follow least privilege.
Human review and escalation are defined.
Error, stop and fallback states are understandable.
Integrations handle retries and partial failures safely.
Privacy, retention and logging are addressed.
Quality, cost and latency are monitored.
Support, runbook and ownership exist.
Model, data and workflow changes trigger reassessment.
Production readiness is not a single technical step. It emerges when domain ownership, evaluation, integration and operation are addressed together.
Fourteen questions help assess data control, model choice, quality, integration, operation and portability before a decision.
Feature lists show only part of the picture. A sound decision also requires clear data flows, access, evaluation, integration, operation and a path for changing providers.
Where are inputs, outputs and logs stored?
Who can access them and how are existing permissions preserved?
How are sources, permissions and freshness handled?
Which models and providers can be used or changed?
How can quality be evaluated on your own cases?
How are sources, uncertainty and limitations of the results made visible?
In which applications and workflows should staff use the platform?
Which existing systems need to provide information or receive results?
Which interfaces are available for this—in real time, periodically or manually?
Which actions may the platform perform in these systems, and where is approval required?
How are errors, cost and latency monitored?
Can data, configuration and documentation be exported?
What happens when the provider or contract changes?
Who owns domain quality and day-to-day operation?
A good platform decision explains not only what is possible today, but also how the platform fits into existing systems and who controls quality, operation and change tomorrow.
We structure the open questions and create a sound basis for the next step.
