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  • Gartner AI Techniques Header

Gartner® Report: How to Choose the Best AI Techniques per Manufacturing Use Case

AI is reshaping manufacturing faster than any previous wave of digitalization. New techniques emerge every quarter, GenAI is entering industrial workflows, and agentic AI is beginning to automate decision‑making. Yet the core challenge remains the same: which AI technique is the right fit for each manufacturing use case?

Our learning from the latest Gartner® report shows how manufacturers can evaluate AI techniques based on real use cases, value impact, and implementation readiness.

Download the full Gartner report

Practical groupings of AI techniques

It is essential to use clear definitions of different AI techniques in order to facilitate collaboration with business stakeholders and AI vendors.

To implement AI effectively in manufacturing, decision-makers need shared definitions, a clear evaluation framework, and alignment across IT/OT, engineering, and operations. This report provides a structured view of the most relevant AI techniques for today’s production environments.

Definitions of practical groupings of AI techniques

blue digital binary data on computer screen

Traditional AI techniques (pre-ChatGPT)

Rule-based systems, classical machine learning, statistical and pattern recognition techniques, early natural language processing, neural networks, deep learning.

High clarity workflow where datum is structured in a remote chain assisting approval and planning as a major contract solution forms

Generative AI (post-ChatGPT)

Generative AI (GenAI) can generate new derived versions of content, strategies, designs, and methods by learning from large repositories of original source content.

AI artificial intelligence technology concept. Machine learning, deep learning, industry 4.0 automation. Robotic arm in factory, CPU chip on circuit board. Future of manufacturing innovation.

Agentic AI

Agentic AI is an approach to building AI solutions based on the use of one or multiple software entities that are classified, completely or, at least partially, as AI agents. AI agents are autonomous or semiautonomous software entities that use AI techniques to perceive, make decisions, take actions and achieve goals in their digital or physical environments.

Select AI techniques based on business outcomes

Manufacturers increasingly face pressure to demonstrate ROI, scalability, and technical feasibility of AI deployments. Gartner emphasizes that technique selection must be anchored in concrete value creation, not trend chasing.

The motivation for selecting AI techniques should not be simply to do anything with disruptive AI techniques, but rather to derive their meaningful use from a business context, since a concrete business case for implementation is only required after the cost-effective creation of a prototype.

This perspective supports a structured evaluation of use cases along dimensions such as: 

Applying AI techniques in practice with iTAC.CATi

Once the appropriate AI technique is selected, manufacturers need solutions that can apply these methods reliably in operational environments. iTAC.CATi is designed to support several technique families referenced in the report; including traditional AI methods, machine learning, and selected GenAI-driven workflows; with a focus on root cause detection, knowledge retrieval, and contextualized decision support on the shopfloor. 

iTAC.CATi helps manufacturing teams translate AI insights into operational decisions aligned with the evaluation principles described in the Gartner report.

iTAC.CATi enables practical AI-driven insights for key manufacturing scenarios.

Disclaimer

Gartner, How to Choose the Best AI Techniques per Manufacturing Use Case, By Alexander Hoeppe, Pedro Pacheco, 6 February 2026 Gartner is a trademark of Gartner, Inc. and/or its affiliates.