AI Consul­ting
for Successful AI Projects

Optimising maintenance processes is one of the most tangible AI applications in the MRO sector because the benefits are directly reflected in reduced aircraft downtime. The bottleneck is rarely the individual model itself, but the number of use cases that need to be qualified in parallel. Each contributes to cost reduction, but each also requires its own evidence and validation. Organisations that underestimate the rollout phase may gain processing efficiency only to lose those gains in approval procedures.

Export controls and security requirements limit infrastructure options. Standard cloud-based services are often not viable. Where a tool is used in a certification-relevant process, its qualification must also be demonstrated.

In many medium-sized automotive suppliers, quality inspection still relies on physical gauges and templates that must be manufactured for each component design. This visual inspection process is not only repetitive but can also contribute to scrap. AI-powered image recognition can perform these inspections and learn from the same pass-fail criteria previously represented by physical templates. The benefits extend beyond reduced error rates, as some physical inspection tools can be eliminated entirely.

Skills shortages make recruitment for quality assurance particularly difficult in the automotive supply chain. At the same time, standards such as IATF 16949 require complete traceability, meaning that an AI solution must not only replace existing inspection logic but also provide auditable documentation.

Bringing together weather, market and grid data is a technical challenge, but rarely the main bottleneck. Complexity arises when forecasts influence automated operational decisions or binding market commitments, making them both commercially and operationally critical. At this stage, average accuracy alone is not enough. What matters is how the model performs during the rare periods when forecasting errors become costly, and who is accountable when commitments are missed.

Systems used in grid operations are subject to dedicated approval and testing procedures. This lead time should be included in project planning from the outset, not left until the final stage.

A KPMG study in the United States modelled the potential EBITDA impact that generative AI could unlock in the banking sector through productivity gains alone, independent of new products or revenue growth. According to the authors, this impact is not automatic. It depends on how consistently tasks are actually shifted to generative AI, rather than on whether a pilot project is running somewhere within the organisation.

Without reliable metrics demonstrating value creation, the business case remains weak. According to the study, banks identify measuring return on investment as one of the biggest barriers to AI adoption.

Large language models can help pre-structure study and quality documentation, consolidate information from disparate sources and standardise formats, reducing the manual effort currently required from specialist teams. However, every output must remain fully traceable back to its source, otherwise it cannot be used in a validated environment. The real efficiency gain therefore lies not in producing the initial draft, but in the review process that follows: validation becomes significantly faster when every statement is linked to supporting evidence from the outset.

Computerised systems are subject to validation requirements. Any model change requires supporting evidence and documentation, which must be planned from the start.

Regardless of sector, the same pattern emerges. AI adoption has become common, but measurable business impact remains the exception. The difference is rarely the model itself. It is whether workflows are redesigned around the outcome or whether the AI tool is simply added alongside existing processes. Organisations that achieve measurable results typically do both: they define a focused use case and establish operational ownership before deployment.

Around two-thirds of organisations have not yet begun scaling AI beyond individual initiatives. A common scenario is a convincing proof of concept without an operating model, leaving ownership, data maintenance and approval processes unresolved in day-to-day operations.