Practical AI decisions
Use artificial intelligence only where it produces a worthwhile, testable result and the likely errors can be controlled. This cluster helps organisations choose AI tools, protect information, evaluate outputs, calculate cost and set proportionate rules for responsible use.
Start with the task and consequence, not the AI label. Define what a useful output looks like, which mistakes matter, what information the system will receive, who will review its work and how the organisation will stop or reverse the use if it causes problems.
Follow the complete AI decision route
AI tools can draft, summarise, classify, search, predict, recommend or take actions. Those capabilities create different benefits and different risks. A low-consequence writing aid should not be governed in the same way as a system influencing employment, credit, health, safety or access to essential services.
Decide whether AI fits
Define the task, user, benefit, error consequence and human role before comparing products.
Set requirements
Record workflow, data, accuracy, security, accessibility, integration, support and exit needs.
Test real outputs
Build representative test cases and evaluate usefulness, factuality, consistency, omissions and harmful failures.
Protect information
Understand what users submit, what the provider retains and how data may be used, accessed, exported or deleted.
Set workplace rules
Create a usable policy covering approved tools, prohibited data, review, disclosure, records, incidents and accountability.
Pilot and monitor
Introduce the tool through a bounded pilot, training, acceptance tests, monitoring and a defined stop route.
A simple risk ladder
Lower consequence
Ideas, internal first drafts or formatting where a person checks the result and an error is easy to correct.
Material consequence
Customer communication, analysis, recommendations or workflow actions where mistakes affect money, trust, privacy or service.
High consequence
Uses affecting rights, employment, finance, health, safety or essential access. These require specialist legal, technical and domain review.
Primary frameworks used as question-setting resources
- NIST AI Risk Management Framework and its Generative AI Profile.
- OECD AI Principles on human-centred values, transparency, robustness and accountability.
- OWASP 2025 Top 10 risks for LLM and generative AI applications.
- ICO guidance on AI and data protection.
- European Commission AI Act information and application timetable.
These resources do not certify an Attach Planet page or replace legal, security, privacy or professional advice for a particular use.
Artificial intelligence decision FAQs
What should I check before using an AI tool?
Define the exact task, users, information involved, expected benefit, plausible errors and consequence of failure. Then test the tool with representative examples and set human review, access and stop controls.
Can an AI tool be accurate but still unsuitable?
Yes. A tool may perform well on average but fail on the cases that matter, create unacceptable privacy or security risk, be inaccessible to users, cost too much to supervise or lack a workable exit route.
Does Attach Planet rank AI products?
Not yet. Product rankings require a defined audience, inclusion method, direct testing, current pricing basis, commercial disclosure and clear limitations. These guides help users build that evidence first.
Continue your AI decision
Use the next guide that matches the question or risk you still need to resolve.