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AI for business: choosing a first task and evaluating its value

Choosing an AI pilot: a specific task, available data, a baseline, quality criteria, human review and actual costs.

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ALL IT Agency team
Practical IT guides for business

Evaluate AI through a specific action such as classifying requests, drafting text or finding a passage. A general desire to add AI does not define an outcome. A pilot should establish process fit considering errors, data and review costs.

Choose a bounded task

Describe input, expected output and its user. Establish current workflow and time costs. Compare AI with simpler rules or search to justify its role.

For example, a system suggests a request category and staff confirm it before routing. This separates suggestion quality from operational effects. Automatically executing every subsequent action may need different controls.

Prepare data and permission to use it

Review sources, freshness, completeness and sensitivity. Do not transfer customer or personnel material externally without an agreed procedure. Define retention and access for models, integrations and logs.

Select representative examples, including difficult and incomplete ones. Convenient demonstrations do not establish real quality. The process owner approves expected outcomes and human handoff conditions.

Define quality criteria and acceptable errors

Evaluate task-specific outcomes: category correctness, grounded facts, completeness or draft usefulness. Errors have different costs. A false urgency flag and a missed critical request need different treatment.

Include human review time in value assessment. A fluent answer requiring lengthy rewriting may not speed work. Generative output should not be promised error-free; use sources and controls appropriate to purpose.

A pilot should end in a decision

Compare baseline and new workflows using agreed examples. Include service, integration, support and review costs. Define pilot boundaries and conditions for expansion, a changed approach or stopping.

After implementation, monitor quality and data changes. Model or instruction updates also need validation. Hand over owners and limitations so AI remains a managed process component with observable value.

Sources and further reading

Key takeaways

  • AI pilots start with tasks and a baseline.
  • Value includes quality, human review and total costs.

Frequently asked questions

Not necessarily. First assess existing capabilities, search and simpler approaches against task criteria.

Do not make that promise. Use validation, visible limitations and appropriate human oversight.
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