When predictive AI is useful: five essential criteria

Predictive Artificial Intelligence (AI) can help organizations make better decisions and improve processes. Yet, the technology isn’t suitable for every situation. Its success depends on a number of fundamental conditions. This article outlines five criteria that determine whether predictive AI can be deployed responsibly and effectively, along with practical recommendations.

1. Data must be representative

A model can only make reliable predictions if the dataset reflects all relevant outcomes.

• Good use: training on both sick and healthy patients.

• Incorrect use: only data from hospitalized patients.

Recommendations:

• Conduct a data audit to ensure all relevant groups are included.

• Actively collect missing data, e.g., from patients not admitted.

• Document decisions on data selection to ensure transparency.

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2. There must be a real pattern to discover

AI is effective when patterns exist that experienced professionals already recognize.

• Good use: senior doctors notice subtle signals that AI can reinforce.

• Incorrect use: if even experts can’t identify a pattern, there is likely only noise.

Recommendations:

• Focus on areas where human expertise already adds clear value.

• Use AI to amplify existing patterns, not to analyze random noise.

• Test in advance whether human decisions are sufficiently consistent.

3. Patterns must be stable over time

Predictive models rely on consistency to be reliable.

• Good use: training on recent, stable data.

• Incorrect use: using outdated data in rapidly changing contexts.

Recommendations:

• Establish policies for regular model retraining.

• Monitor external factors that could affect patterns (e.g., pandemics, market shifts).

• Use version control and document dataset changes.

4. Patterns must be socially acceptable

AI must not reinforce existing biases.

• Good use: bias audits and equal treatment of all groups.

• Incorrect use: training on biased historical data.

Recommendations:

• Conduct bias testing before deploying a model.

• Involve diverse stakeholders in evaluating model outcomes.

• Set up an ethics committee or board to assess social impact.

5. Some predictions are more valuable than others

Not every prediction has the same impact.

• Good use: small improvements in critical diagnoses deliver significant value.

• Incorrect use: improvements in trivial predictions have little meaning.

Recommendations:

• Prioritize applications where accuracy directly improves outcomes or reduces costs.

• Quantify the value of predictions in a business case.

• Avoid investing in models with limited practical impact.

Predictive AI is not a universal solution. It only works when data is representative, patterns exist and are stable, outcomes are socially acceptable, and predictions add real value. Combining these criteria with practical recommendations increases the likelihood that AI will not only succeed technically but also contribute sustainably and responsibly to your strategy.

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