Automation, search, RAG, agents, and predictive ML solve different workflow problems.
The useful question is not “which AI tool should we buy?” It is “what kind of work must the system perform, under what controls, and how will we know it works?”
Choose by the core job the system must perform.
Use deterministic automation for stable, repeatable logic with structured inputs.
Use enterprise search when users need approved records, documents, or passages.
Use RAG when an answer must be generated from governed, changing source material.
Use an agent only for bounded tool use with observable state and recoverable actions.
Use predictive ML when historical data can support a forecast, score, or anomaly signal.
Use no AI yet. Fix the workflow definition, ownership, or data problem first.
Search retrieves; RAG synthesizes.
Enterprise search should return relevant source material directly. RAG adds a generative layer that composes an answer from retrieved context. That can make information easier to use, but it also adds failure modes: weak retrieval, unsupported claims, lost nuance, and citations that do not support the answer.
Prefer search when exact source discovery is sufficient. Add RAG only when synthesis creates meaningful workflow value and can be evaluated.
Do users need the document, or do they need a defensible answer assembled from several documents?
An agent is an operating model, not a chat interface.
An agent observes state, chooses a step, calls a tool, and continues toward an objective. That creates value when the work is multi-step and variable. It also creates risk because the system can change records, contact people, trigger processes, or compound an earlier error.
Start with narrow permissions, explicit checkpoints, complete action logs, and a reliable recovery path. If those controls cannot be designed, keep the system advisory.
What is the most consequential action this system can take, and can a person detect and reverse a bad action?
Predictive ML needs outcomes, not document prompts.
Forecasting, propensity scoring, anomaly detection, and risk estimation depend on representative historical data and a measurable target. A language model may explain or present a prediction, but it does not replace the need for outcome definition, backtesting, threshold design, and drift monitoring.
Use predictive ML when the decision depends on a pattern in data. Use RAG when the decision depends on retrieving and interpreting source knowledge.
Can the team define the outcome, assemble relevant history, and measure whether the signal improves a real decision?
Write a one-page workflow brief before evaluating vendors or models.
Input, output, user, frequency, exceptions, current baseline, and desired business effect.
Approved sources, tool permissions, human checkpoints, prohibited actions, and privacy boundaries.
Offline test set, acceptance threshold, failure categories, operational monitoring, and escalation path.
Business owner, technical owner, source-data owner, and the person accountable after launch.
Validate fit before committing to architecture.
The Workflow Suitability Framework provides the complete selection map. If the workflow itself is not yet clear, begin with the AI Readiness Assessment.