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A feature needs AI only if it measurably improves a defined user or business outcome compared with simpler software or manual control. Start with the problem—not the technology—and compare rules, existing tools, traditional AI, and generative AI. Then test the benefit and decide what happens when the system is wrong.
Start with the outcome, not the AI
Write down the user’s problem and the result the feature is supposed to improve before selecting an implementation. “Add an AI assistant” is a proposed solution, not an outcome. A more useful goal might be reducing the time people spend finding an answer, helping them locate relevant items, or processing a defined type of request more effectively.
Google Cloud recommends deciding whether the expected result calls for generative AI, another kind of AI, or no AI at all. Google People + AI Research similarly advises confirming that a product or feature requires AI or would be enhanced by it. Google People + AI Research, Patterns and Google Cloud’s use-case guidance both put the need ahead of the technology.
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AI can be useful for recommendations and personalization, prediction, understanding natural language, or recognizing images. But those capabilities do not automatically make AI the best choice. Rules or heuristics can be preferable when outcomes need to be predictable and transparent, and a manual control may be better when users want to make the choice themselves. An automated suggestion that makes a task harder—or takes control away from the user—does not create value merely because it uses AI.
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Before building a custom feature, check whether an existing product or a deterministic rule already meets the need. Microsoft’s AI Decision Framework recommends defining the desired outcome and experience, then considering whether an existing tool can do the job. Microsoft AI Decision Framework
Match the approach to the task
“AI” covers different capabilities. The input, the output, and how much predictability the task requires help determine what kind of system—if any—to use. Traditional predictive AI is particularly suited to structured data; generative AI is suited to tasks such as summarization, content generation, advanced transcription, and working across modalities such as text, images, video, or audio. Classification and detection may be handled by a pretrained traditional model if it meets the requirements.
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| Approach | Often fits | Key question |
|---|---|---|
| Rules or heuristics | Known conditions with outcomes that should be consistent and explainable | Can the cases be expressed clearly as rules, and are users comfortable with the automation? |
| Traditional predictive AI | Prediction or classification based on structured data; some recognition and detection tasks | Is there suitable data, and does the model meet the required accuracy, latency, and control needs? |
| Generative AI | Summarization, content generation, advanced transcription, or interpreting and producing varied content | Is open-ended output useful, and can its variability and errors be managed? |
| Combined approach | Workflows where a predictive capability and a generative interface serve different parts of the task | Does each component add value, and can the whole workflow be evaluated? |
The choice can also depend on training data, control, time to market, latency, and model metrics. Google Cloud’s guidance distinguishes generative AI from traditional AI and notes that some applications combine prediction with generative interfaces. Google Cloud: When to use generative AI or traditional AI
Distinguish AI behavior from ordinary software
Conventional software is often rule-based and deterministic: it follows specified instructions and remains fixed until someone changes those instructions. AI may instead use data to predict, generate, recognize complex patterns, or adapt to context. These are practical indicators, not a universal legal or technical definition. A particular policy or jurisdiction may classify systems differently and impose its own oversight requirements.
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For a jurisdiction-specific explanation of these indicators, see Digital NSW’s guide to identifying AI.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test measurable value in the real workflow
Set a baseline before launching a pilot or feature, then measure the outcome that matters to the people or business using it. A convincing demonstration is not evidence that a feature improves a real workflow. Google Cloud’s support-chatbot guidance lists possible measures including operational costs, inquiry volume handled, agent hours, time to resolution, escalations, first-contact resolution, and customer satisfaction. These are candidate metrics, not reported results or promises of improvement. Google Cloud’s use-case guidance
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- Choose measures that correspond to the stated problem rather than metrics that are easy to collect but do not show user value.
- Compare the feature with the current process or a simpler alternative.
- Include operating and integration effort, latency, and data availability in the assessment.
- Decide in advance what result would justify continuing, changing, or stopping the feature.
The cited guidance does not establish a universal threshold for worthwhile AI or report achieved chatbot performance. The threshold has to be set and tested for the product, users, and constraints in question.
Decide who handles errors and approvals
Assess how repeatable the task is, how serious an error could be, whether a person can detect it, and how time-sensitive the decision is. Microsoft emphasizes that delegating work to AI does not transfer accountability: people remain responsible for appropriate direction, validation, and approval. Microsoft Support’s guidance on choosing Copilot or an agent
Use the answers to decide how much oversight the feature needs. Low-impact suggestions may need a different review path from outputs that affect important decisions. Where mistakes are consequential or hard to detect, require appropriate human review and approval before the result is used.
Quick Recap
Use a decision check before building
- State the outcome: Name the user or business problem and how you will measure improvement.
- Check simpler alternatives: Try manual control, clear rules, or an existing tool where they could meet the need.
- Identify the task and inputs: Decide whether the work is structured prediction, classification or detection, open-ended generation, or a combination.
- Weigh constraints: Consider predictability, transparency, user preference, available data, latency, and integration effort.
- Plan for mistakes: Assess error impact and detectability, and define when a person must validate or approve the output.
- Test against a baseline: Measure the feature in its intended workflow and continue only if it delivers the defined benefit.
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