01
The solution has been chosen before the problem is understood
A platform, chatbot, or training programme is announced before the team knows what people actually need.
Service 06
Turn an inclusion challenge into a testable direction without pretending every problem needs an app, an AI feature, or a large platform.
Discuss this work
What experience teaches us
Communities have seen many promising pilots arrive, collect stories, and disappear when funding or attention moves on. We ask who will own the work, what it costs to maintain, whose knowledge shapes it, and what happens when connectivity or support is limited.
When this service helps
01
A platform, chatbot, or training programme is announced before the team knows what people actually need.
02
Ownership, maintenance, staffing, data, accessibility, and long-term cost were never part of the experiment.
03
People are invited to validate a nearly finished idea rather than shape the direction and trade-offs.
What the work can include
01
We separate assumptions from evidence and define the people, context, constraints, and decision that must be made.
02
We compare technology and non-technology options, including access, safeguarding, data, maintenance, and ownership.
03
We define what should be tested first and what evidence would justify further investment.
04
Leaders receive a clear recommendation, risks, dependencies, and next steps rather than a vague innovation report.
Possible deliverables
The final scope depends on the problem, people, timeline, evidence, and budget. A proposal should state what is included and what is not.
Access commitment
Disabled people should have influence over decisions that affect them. Participation needs clear purpose, accessible communication, consent, recognition, and a route for people to see what changed because they contributed.
Questions worth asking
We can support discovery only or continue into design and development when that is the right next step. The recommendation does not assume we must build it.
Yes, with attention to the actual use case, data, bias, accessibility, privacy, human oversight, maintenance, and whether AI adds enough value to justify the risk.
No. Inclusive practice improves wider technology and service decisions, especially where users differ in language, confidence, device, connectivity, disability, or institutional access.
You do not need a finished brief. Explain the people, the task, where it breaks, and what needs to be different.
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