Planning for 2027:
Which AI Projects are worth the investment?

AI project discussions often begin with a technical idea. Someone suggests a chatbot for technical documentation. Another department wants to use machine learning for production planning.

The operational problem should be understood before choosing the technology.

For predictive maintenance, relevant factors include the frequency of unplanned downtime, its costs, and existing maintenance procedures. If failures are rare, the financial benefits of a predictive solution are smaller. If they regularly cause significant costs, improving maintenance planning can make a substantial difference.

The same applies to generative AI. A document assistant used daily by many employees can save valuable working time. If the information is only needed occasionally, improving the existing search function may be the better starting point.

A clear problem definition provides the foundation for evaluation.

For automated quality inspection, the baseline could include the current costs of manual checks, scrap rates, customer complaints, and costs resulting from defects.

An AI system can reduce these expenses. At the same time, cameras, infrastructure, integration, maintenance, and manual checks of uncertain results introduce additional costs.

Time savings also need to be assessed correctly. If an application speeds up quality inspection, it creates financial value when the available capacity can be used productively or actual costs decrease.

For project evaluation, it helps to distinguish measurable benefits from assumptions that have yet to be validated.

Reduced scrap or energy consumption can be calculated using existing metrics. Faster decisions and better access to knowledge require different evidence.

A credible estimate with documented assumptions is more useful than an apparently precise ROI figure.

A company can have years of sensor data and still struggle to develop a predictive maintenance model.

Failures must also be documented adequately. Maintenance events should be linked to the correct machines and time periods. Sensor measurements need to contain information that helps identify an impending failure.

In production planning, the challenge often lies in fragmented data. Information is stored across ERP systems, Excel spreadsheets, and local planning tools. Inconsistent definitions and data versions make it difficult to combine.

A limited data assessment can often establish whether an idea is technically feasible or requires preparatory work.

A project with substantial business potential and insufficient data can still be worth pursuing. For budget planning, the initial investment would focus on establishing the necessary data foundations.

A production planning model may generate better schedules from exported ERP data. In daily operations, it also needs to consider machine availability, staffing, material shortages, and last-minute order changes.

The results then need to enter the existing planning process. This requires interfaces, current data, and suitable approval procedures.

This integration effort is often underestimated during early project evaluations.

A document assistant connected to a well-maintained knowledge base is easier to integrate than an application influencing decisions across several production sites.

Budget planning should therefore account for the complete operational solution, including integration, security, and ongoing maintenance.

An AI model identifies potential quality problems several hours in advance. Its predictions are reliable, but adjustments to the relevant machines are only permitted during scheduled maintenance windows.

In this situation, the additional information does little to improve production.

The employees who will use the system understand these restrictions best. Their experience should inform the evaluation from the beginning.

Ongoing operations also require clear responsibilities. We explore model monitoring, updates, and maintenance in more detail in our article AI in Production: Who Takes Care of It After Go-Live?

Five criteria provide a useful starting point for comparing AI applications.

Criterion

Key question

Business value

What measurable improvement is realistic?

Data readiness

Is the required data accessible and suitable?

Implementation effort

What will development, integration, and operation cost?

Operational readiness

Can business teams use the results?

Risks and uncertainties

What could prevent successful implementation?

Risks include information security, regulatory requirements, and dependence on external providers. Certain applications also need to account for the EU AI Act.

A simple low, medium, or high assessment is often enough to make differences visible. There is no need to combine everything into a numerical score that suggests more precision than early project ideas can support.

Predictive maintenance for a critical production asset

Unplanned downtime is expensive. Sensor data is available, but the quality of historical failure records is unclear.

The next step is a feasibility assessment focused on data quality and available failure examples.

An internal AI assistant for technical documentation

Documentation is stored centrally. Employees regularly spend time searching for information.

A limited pilot can show how reliably the assistant responds, whether people use it in their daily work, and how much time it saves.

AI-based production planning across several sites

The potential benefits are significant. However, planning procedures differ between sites, and important information is partly maintained manually.

Data definitions, responsibilities, and integration with existing systems should be clarified first.

All three projects could offer business value. Their current level of readiness calls for different next steps.

Investment planning should distinguish between projects ready for implementation, unresolved feasibility questions, and necessary preparatory work.

That might mean funding one clearly defined pilot, conducting two feasibility assessments, and postponing another project until data quality has improved.

Shared requirements also deserve attention. Better data infrastructure or standardized interfaces can support several future applications.

At craftworks, we work with industrial companies to evaluate the business and technical requirements of their AI projects. This includes assessing available data, examining feasibility, and accounting for existing systems.

For 2027, a manageable number of well-prepared projects is often a better investment than an ambitious portfolio that never moves beyond the pilot stage.

Planning your AI investments for 2027?

We help companies evaluate potential applications, assess technical feasibility, and develop solutions for real-world operations. Let's discuss which projects are worth pursuing.