AI application projects stall easily due to these common issues.
Artificial intelligence (AI) is rapidly reshaping the engineering profession. From predictive maintenance in manufacturing plants to design in civil and mechanical projects, AI applications promise to increase efficiency, enhance innovation, shorten cycle times and improve safety.
Yet, despite widespread awareness of AI’s potential, many engineering organizations struggle to progress beyond the pilot stage. AI implementations often stall for various reasons—organizational, technical, cultural, and ethical. Understanding these barriers and fixing them is crucial for leaders who aim to advance AI from an intriguing and much hyped new concept into a practical engineering capability.
Lack of clear AI application objectives
Engineering organizations often approach AI from a technology-first perspective rather than a business or technical problem-first mindset. Senior management may task a team to “use AI to improve productivity” without specifying a more focused aspect of productivity. Overly broad examples include reducing design rework, optimizing supply chain logistics, or forecasting equipment failures. This ambiguity stalls progress by diffusing effort, yielding uneven results, and wasting resources.
Practical AI projects in engineering will be more successful if they begin with tightly defined business and technical objectives. For example:
- A civil engineering firm might aim to reduce project delays caused by material shortages by using enhanced AI-driven demand forecasting.
- A mechanical engineering team could target reduced downtime through more sophisticated AI-driven predictive maintenance analytics.
- An electrical engineering work group could utilize AI to simplify circuit board designs, thereby improving manufacturing quality.
Aligning AI projects with measurable engineering outcomes—such as higher throughput, improved energy efficiency, or longer asset lifespan—creates both focus and accountability. Without such clarity, AI projects remain academic exercises rather than operational solutions.
Insufficient data quality
Engineering operations generate immense quantities of data—from versions of design drawings, manufacturing sensor readings, to field inspection reports. However, this data is rarely standardized or integrated. Legacy systems store data in incompatible formats, while field-collected data is often incomplete or inconsistent. Moreover, in many engineering environments, critical data resides in siloed applications, on isolated local servers or externally with partners. Sometimes, the impediment is that digital data transformation has not advanced sufficiently. Poor data quality and incomplete data lead to unreliable models, eroding confidence among engineers who depend on sustained data accuracy. These data issues stall progress until the data quality is improved.
AI models require reliable, high-quality data to produce accurate insights. Addressing this issue demands robust data governance — defining ownership, standardizing data formats and values, simplifying accessibility, and ensuring traceability. For large-scale engineering enterprises, implementing centralized data lakehouses or data warehouses can provide a unified data foundation. Without disciplined data management, even the most advanced AI applications cannot deliver actionable results.
Unrealistic expectations
Sometimes, engineering teams, in their enthusiasm, over-promise what they can deliver. Examples include:
- More functionality than their AI model and the available data can achieve.
- Overly aggressive AI project timelines.
- Underestimated required resources and related project budget.
These issues lead to management disappointments and a reluctance to commit to additional AI application projects, which stall progress.
Setting and managing expectations is never easy. Promising too little will not create enthusiasm and support. Promising too much is guaranteed to lead to disappointment. Here are some techniques that have proven successful for engineers:
- Mockup expected results with visualizations using PowerPoint slides.
- Start with an exploratory prototype.
- Conduct an AI pilot project with sufficient scope to enable the follow-on project to deliver a production-quality AI application.
- Conduct an AI risk assessment, share the results with management and incorporate mitigations into your project plan.
Inadequate 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 exposes integration challenges. For example:
- An AI model that predicts equipment failure may not easily link to existing maintenance scheduling systems or supervisory control and data acquisition (SCADA) platforms.
- AI-generated design recommendations might not align with CAD software data standards or quality assurance protocols.
- AI-generated recommendations that alter supply chain vendors or order quantities may be challenging to implement within the existing suite of applications.
These integration issues frequently stall progress. Engineers may see AI as disruptive or unreliable if it requires substantial changes to established processes or applications.
The solution to AI integration issues lies in collaborative systems engineering. This concept, which facilitates smoother integration, consists of:
- Designing AI applications that complement, rather than replace, existing systems.
- Building application programming interfaces (APIs) that integrate new AI applications with existing systems.
- Adopting a more modular system architecture that creates simpler integration points.
- Integrating AI incrementally because it allows for easier absorption by the organization compared to a sweeping replacement.
- Ensuring backward compatibility where feasible.
AI has immense potential to revolutionize engineering practice. It can enhance design optimization, improve maintenance predictability, and elevate overall manufacturing efficiency. However, realizing that potential requires more than algorithms—it requires alignment, trust, and integration.
AI projects often stall when data is fragmented, objectives are unclear, or integration is overly complex. Success demands clear business objectives, high-quality data, and determined leadership. Engineering has always been about solving complex problems through disciplined innovation. Implementing AI effectively is the next evolution of that tradition.
Organizations that combine engineering rigor with AI insights will not only overcome today’s barriers but also define the future of that organization.
