AI Use Cases in Industry: Where to Start to Maximize ROI?

Industrial companies now all have an artificial intelligence (AI) roadmap. Yet, according to the 2026 benchmark conducted by Sia and La Ruche Industrielle de Lyon, only around one third of the use cases discussed during interviews actually make it to the shop floor. Bridging the gap between strategic intent and production deployment has therefore become one of the main challenges for industrial, logistics and operations leaders.

This does not call the reality of the benefits into question. The benchmark shows that AI ROI in industry is real, but takes longer to materialize than in support functions. Several factors explain this lag: the availability and quality of data, change management among operational teams, and integration into field processes.

For mobility and logistics players, these challenges take a particular form. Their data comes from multiple, often heterogeneous systems: warehouse management, transport planning, onboard telematics and maintenance tools. Operations also run under tight time constraints, meaning an inaccurate recommendation can quickly affect the entire chain. The key question is therefore no longer whether to adopt AI, but where to start.

The benchmark draws on interviews with more than 20 companies in chemicals, healthcare, nuclear, construction materials, aerospace and transport. More than 500 use cases were reviewed. Of these, 70 were selected for their ROI potential and then classified into seven business categories spanning the full value chain.

These seven categories include transactional automation, knowledge assistants, vision-based inspection, forecasting and scoring, optimization under constraints, process control, as well as simulation and digital twins. Their value lies less in their technological novelty than in the operational problems they help address.

Prioritization is based on three criteria. Speed of ROI refers to the time needed to observe measurable benefits. Reliability refers to the consistency of those benefits over time and under operational variability. Finally, scalability measures the ability to reproduce the results across other production lines, sites or flows.

The benchmark also identifies three industrial profiles: standardized, multi-product and highly heterogeneous. The right starting point differs depending on the profile. This framework applies directly to logistics networks: a network of sites with homogeneous processes cannot be managed in the same way as a group of facilities inherited through successive acquisitions, with disparate systems and practices.

The benchmark is supplemented by around a dozen detailed use case sheets. They cover the full value chain, from R&D to production, quality control, logistics, distribution, after-sales service, maintenance and support functions. Each sheet outlines the context, business objectives, expected benefits, ROI estimate and success factors.

For mobility and logistics operators, several categories address well-known operational challenges. Forecasting and scoring can be applied to anticipating volumes or equipment failures. Optimization under constraints supports route, load and resource planning. Vision-based inspection can be used to check goods or equipment.

The benchmark raises a critical question for these sectors: why does a use case that works on one production line succeed—or fail—at another site? Across a network of platforms or depots, the value of a use case depends precisely on its ability to be deployed everywhere. A high-performing pilot at a single site, if it relies on local data or practices, delivers only a one-off benefit.

This requirement for replicability means assessing, from the scoping phase onwards, the consistency of data across sites, team maturity and system compatibility. It also requires involving end users—planners, team leaders and maintenance technicians—in the design of the tool. AI enhances their expertise; it does not replace it, and accountability for operational decisions remains human.

The choice of the first use case should not be based on how innovative it is. It must meet four conditions: demonstrable business value, proven technical and data feasibility, realistic adoption by teams, and the ability to scale. A use case that meets only some of these conditions will, in most cases, remain a pilot.

Our experts therefore recommend starting with the existing portfolio rather than creating a new one. Each use case should be assessed against the benchmark’s three criteria, the organization should be positioned within one of the three industrial profiles, and a limited number of use cases should then be selected whose ROI can be measured in the field.

The question to ask the executive committee on Monday morning is simple: among the use cases currently underway, which ones could be deployed at a second site within the next six months?

Sia supports industrial companies from AI strategy through to production deployment: awareness-building and training, evolution of the target operating model, use case identification and prioritization, roadmap development, proofs of concept and minimum viable products, followed by industrialization. This approach makes it possible to move from pilot to production in six to eight weeks.

The value of AI is measured in the field, not in the roadmap.

Sia’s experts are available to present the full benchmark, with insights tailored to each organization’s industrial profile and challenges.