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Research

How we evaluate AI integration fit

A practical framework InfoWebPlus uses before recommending AI features—focused on measurable workflow impact, not hype.

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Research

Published June 1, 2026·Updated July 12, 2026

  • ai
  • product-engineering

InfoWebPlus

Product engineering studio

Key findings

Summarised outcomes from our research—not marketing headlines.

  1. Finding 1:

    1.Most AI requests fail a simple ROI check when baseline workflow time is not measured first.

  2. Finding 2:

    2.Teams that document the current manual process before scoping AI ship faster and spend less on rework.

Methodology

How we evaluated sources, assumptions, and limitations.

We reviewed 24 client discovery calls from 2024–2025 where AI was requested. For each, we scored whether baseline workflow time, error rate, and handoff steps were documented before a solution was proposed.

Projects with documented baselines were 2.1× more likely to reach a thin vertical release within the agreed timeline. Limitation: sample is biased toward product-engineering engagements, not pure ML research.

Full analysis

This sample article demonstrates the article document type. Replace with production content in Sanity Studio.

Frequently asked questions

When should we skip AI entirely?

When rules, better UX, or a small integration solve the problem with lower operational risk.

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Related reading

Jun 15, 2026·George Barbu

LLM Hallucination in Business Contexts: Risk Profiles by Use Case

Risk is not uniform across AI use cases. The same model producing the same rate of hallucination presents very different business risk depending on how errors propagate in each deployment context. A framework for evaluating consequence over rate.

  • ai-implementation
  • llm-risk
  • hallucination
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