Enterprise AI buyer’s guide cover

Enterprise AI buyer’s guide

How to evaluate enterprise AI platforms for accurate work, verifiable results, and lasting business value.

What’s inside

  1. Establish the requirements

    Assess security, privacy, administrative controls, and information access. Confirm that a platform can reach approved sources without bypassing the permissions that protect them.

  2. Test the work, not just the features

    Evaluate source quality, model strategy, and agent workflows using real business tasks. Check the finished output, supporting evidence, and controls around actions.

  3. Measure value beyond the license

    Compare time to first useful output, sustained adoption, and cost per useful result, including the effort required to verify and correct the work.

Summary

Enterprise AI buying decisions should center on the work employees need to produce. Security, integrations, and price matter, but so do the accuracy of the output, the evidence behind it, and the effort needed before it is ready to use.

This guide sets out eight criteria for evaluating enterprise AI platforms, covering security, information access, verification, model strategy, high-value work, agent controls, adoption, and cost. Practical checklists and evaluation exercises help buying teams test vendors against the same requirements.

Explore how to structure trials around representative workflows, trace important claims to original sources, and assess the full cost of a useful result. The guide closes with considerations for choosing among Perplexity, Claude, ChatGPT, Microsoft Copilot, and Gemini based on organizational needs

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