Updated October 14, 2026
Machine learning uses patterns learned from data to produce outputs such as predictions, classifications or generated content. For a business buyer, the useful question is which approach can perform a defined task reliably enough to justify its cost. Google’s introduction to machine learning provides background on the main model types.
Start by naming the decision or piece of work you want to improve. Forecasting demand, routing an invoice and drafting a customer response require different capabilities, evidence and controls.
Match the approach to the work
Rules-based software follows conditions that people specify. It is worth considering when the policy is stable and the inputs are predictable, such as routing an invoice above an approval limit.
Predictive machine learning estimates an outcome from examples. It may suit demand forecasting or identifying records that need investigation, provided useful historical data and a credible evaluation are available.
Generative AI is a form of machine learning that produces content. It can support drafting and summarisation, but fluent output still needs checking. A workflow may combine all three: a model extracts information, rules check required fields, and a person handles exceptions.
Establish a baseline before choosing a model
Document how the work performs today. Measure completion time, errors, rework and the effort required to resolve exceptions. Compare candidate systems against that baseline on the same representative cases.
Include a simple alternative. A straightforward rule or statistical model may be sufficient. A 2022 benchmark of tabular datasets found tree-based models competitive with, and often stronger than, the neural networks tested. That result supports testing alternatives; it does not settle which model will work best on your data.
Treat data quality as an operating requirement
Check where the data came from, whether you may use it, and whether it reflects the population and conditions the system will encounter. Review missing values, inconsistent labels and information that would only become available after the decision.
Keep evaluation cases separate from training and tuning. Include unusual, incomplete and difficult examples. Where outcomes arrive later, decide how you will collect them and assess performance over time.
More data does not guarantee higher accuracy. Research on language-model scaling describes relationships between training resources and a particular measure of model performance. It does not establish unlimited accuracy across business applications. Data relevance, task design and evaluation still matter.
Measure errors and total cost
Ask which errors matter most. Missing an urgent request may be more consequential than sending an ordinary request for extra review. Report those errors separately rather than relying on one average score.
For generated content, check factual support, omissions and compliance with the task’s instructions. Include human review, integration, data preparation, computing, monitoring and rework in the cost estimate. Compare cost per successfully completed task at the expected workload.
Define what happens when the system fails
Before a pilot, name an operational owner and specify when work must be referred to a person. Give reviewers access to the relevant evidence and authority to reject an output.
Agree on acceptable performance, a fallback process and conditions for pausing the system. Recheck after material changes to the model, data or workflow. A useful buying decision ends with evidence about the complete service, including the people and controls needed to run it.
