AI application projects can easily stall due to these common issues.
Artificial intelligence (AI) is rapidly reshaping the engineering profession. From predictive maintenance in manufacturing plants to the design of civil and mechanical projects, AI applications promise to increase efficiency, enhance innovation, reduce cycle times, and improve safety.
However, despite widespread recognition of AI’s potential, many engineering organizations struggle to move beyond the pilot stage. AI implementation often stalls for a variety of reasons, including organizational, technical, cultural, and ethical. Understanding and remediating these barriers is critical for leaders looking to evolve AI from a sexy, much-hyped new concept to a practical engineering capability.
Lack of clear goals for AI applications
Engineering organizations often approach AI from a technology-first perspective, rather than a business or technical problem-first mindset. Senior management may task their teams to “improve productivity using AI” without specifying more focused aspects of productivity. Very broad examples include reducing design rework, optimizing supply chain logistics, and predicting equipment failures. This ambiguity slows progress by scattering efforts, producing uneven results, and wasting resources.
Practical AI projects in engineering are more successful when they start with tightly defined business and technical goals. for example:
- Civil engineering companies may aim to use enhanced AI-driven demand forecasting to reduce project delays caused by material shortages.
- Mechanical engineering teams can aim to reduce downtime through more advanced AI-powered predictive maintenance analytics.
- Electrical engineering working groups can use AI to simplify circuit board design and thereby improve manufacturing quality.
Aligning AI projects with measurable engineering outcomes, such as increased throughput, increased energy efficiency, and extended asset life, creates both focus and accountability. Without this clarity, AI projects remain academic exercises rather than operational solutions.
Insufficient data quality
Engineering operations generate vast amounts of data, from engineering drawing versions to manufacturing sensor readings to field inspection reports. However, this data is rarely standardized or integrated. Traditional systems store data in incompatible formats, and data collected in the field is often incomplete or inconsistent. Additionally, in many engineering environments, critical data resides in siled applications, isolated local servers, or externally with partners. In some cases, a lack of advanced digital data conversion may be an obstacle. Poor data quality and incomplete data lead to unreliable models and undermine the confidence of engineers who rely on sustained data accuracy. These data issues will slow progress until data quality improves.
AI models require reliable, high-quality data to generate accurate insights. Addressing this issue requires robust data governance, including defining ownership, standardizing data formats and values, simplifying accessibility, and ensuring traceability. For large engineering companies, implementing a centralized data lakehouse or data warehouse can provide a unified data foundation. Without disciplined data management, even the most advanced AI applications cannot produce actionable results.
unrealistic expectations
In their enthusiasm, engineering teams can overpromise what they can deliver. Examples include:
- More than what AI models and available data can accomplish.
- Overly aggressive AI project schedules.
- The required resources and associated project budget are underestimated.
These issues can leave executives disappointed, reluctant to take on additional AI application projects, and slow progress.
Setting and managing expectations is never easy. Promising too little will not generate enthusiasm or support. Promising too much is sure to lead to disappointment. Here are some techniques that have proven successful for engineers.
- Use PowerPoint slides to visualize and mock up expected results.
- Start with an exploratory prototype.
- Conduct AI pilot projects with sufficient scope so that subsequent projects can deliver production-quality AI applications.
- Conduct AI risk assessments, share results with management, and incorporate mitigations into project plans.
Poor integration with existing engineering workflows
Unlike software-driven processes, engineering workflows are deeply intertwined with physical processes, regulatory compliance, and long-established methodologies. Introducing AI into these workflows often reveals integration challenges. for example:
- AI models that predict equipment failure may not be easily linked to existing maintenance scheduling systems or supervisory control and data acquisition (SCADA) platforms.
- Design recommendations generated by AI may not match the CAD software’s data standards or quality assurance protocols.
- AI-generated recommendations that change supply chain vendors or order quantities can be difficult to implement within an existing application suite.
These integration issues often stall progress. Engineers may view AI as disruptive or unreliable if it requires significant changes to established processes or applications.
The solution to AI integration problems lies in collaborative systems engineering. This concept, which facilitates smoother integration, consists of:
- Design AI applications that complement, rather than replace, existing systems.
- Build application programming interfaces (APIs) to integrate new AI applications with existing systems.
- Adopt a more modular system architecture and create simpler integration points.
- This is because integrating AI in stages makes it easier for organizations to absorb it than replacing it entirely.
- Ensure backward compatibility where possible.
AI has immense potential to revolutionize the practice of engineering. This enhances design optimization, improves maintenance predictability, and improves overall manufacturing efficiency. However, realizing the possibilities requires alignment, trust, and integration, not just algorithms.
AI projects often stall when data is fragmented, objectives are unclear, or integration is overly complex. Success requires clear business objectives, high-quality data, and decisive leadership. Engineering has always been about solving complex problems through disciplined innovation. Effective implementation of AI is the next evolution of that tradition.
Organizations that combine the rigor of engineering with the insights of AI will not only overcome today’s barriers, but will define their future.
