A few more thoughts on how engineers can prevent or rapidly fix stalls in AI application projects.
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, much-hyped new concept into a practical engineering capability.
To read the first article in this two-part series, click here.
Skills shortages and training deficiencies
The successful application of AI in engineering demands multidisciplinary collaboration. Data scientists understand the algorithms, but not necessarily the physical principles governing structures, materials, or thermodynamics. Conversely, engineers possess domain expertise but may lack proficiency in machine learning, statistics, or data visualization. These gaps create communication barriers and implementation bottlenecks, stalling progress.
To bridge the divide, organizations promote “AI literacy” among engineers and “engineering literacy” among data professionals. Cross-functional teams, comprising engineers, data scientists, and IT specialists, are often the key to translating technical concepts into practical outcomes. Continuous professional development programs, partnerships with universities, and in-house training initiatives all contribute to building expertise. The future of engineering will belong to those professionals who can interpret both finite element analysis and neural network outputs with equal fluency.
Gaps in the computing infrastructure
With the explosion in data volumes and the high resource demands of AI applications, the computing infrastructure that supports engineering groups can be overwhelmed. That leads to storage shortages, poor performance, and unplanned outages. These issues stall engineers’ productivity.
Engineering organizations can add to the capacity of their computing infrastructure by:
- Investing in additional on-premise capacity.
- Moving some AI applications from servers to high-performance workstations.
- Moving some applications to the cloud that is operated by a cloud service provider (CSP).
- Moving some applications from on-premises to the computing infrastructure of a Software as a Service (SaaS) vendor.
Organizational resistance and cultural barriers
Engineering culture is grounded in precision, accountability, and safety. These values, while essential, can also foster skepticism toward new technologies. Some engineers question the validity of AI-generated recommendations if they cannot trace their underlying logic. Project managers may hesitate to delegate decisions to AI systems they perceive as “black boxes.” Such caution and resistance stall progress.
Overcoming this resistance requires transparency and inclusion. AI models used in engineering should emphasize explainability—demonstrating how inputs lead to outputs. Involving engineers in AI model development builds trust and ensures the results align with physical realities. IT leadership must communicate that AI is a decision-support tool, not a replacement for engineering judgment and expertise. By framing AI as an enabler of better engineering rather than an external disruptor, organizations can foster acceptance and enthusiasm.
Lack of ethical, legal, and safety considerations
Engineering operates within strict regulatory and ethical frameworks designed to protect public safety. AI introduces new dimensions of risk, such as:
- Discrimination and toxicity
- Privacy and security
- Misinformation
- Malicious actors and misuse
- Human-computer interaction
- Socioeconomic and environmental
- AI system safety, failures and limitations
If an AI-driven application predicts incorrect stress tolerances or misjudges maintenance intervals due to one or more of these risks, the consequences can be catastrophic. Because AI project teams are new to these risks, they may become overly cautious, which can stall progress.
To mitigate these risks, engineering firms establish rigorous model validation procedures, document decision processes, and ensure compliance with industry standards and safety regulations. An AI ethics and safety review committee should evaluate new AI applications before they are deployed. Transparency and accountability are not optional in engineering—they are fundamental responsibilities.
Underestimating the complexity of change management
Introducing AI is not simply an IT upgrade—it is a transformation of the organization. Engineering workflows, approval hierarchies, and performance metrics often require reconfiguration to incorporate AI insights effectively. AI projects stall when leadership underestimates the organizational change necessary to operationalize AI results.
A conscious people change management approach is essential. It includes stakeholder engagement, pilot demonstrations, and training for every rollout. By delivering multiple tangible improvements—such as reduced maintenance costs or faster design cycles—AI projects build momentum for broader adoption and ultimately achieve a lasting impact.
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 stall when skills are in short supply, the organization is resistant, or workflows resist adaptation. Success demands high-quality data, transparent governance, and an organizational culture that embraces continuous learning. Engineering has always been about solving complex problems through disciplined innovation. Implementing AI effectively is the next evolution of that successful tradition.
The organizations that combine engineering rigor with AI insights will not only overcome today’s barriers but also define the future of that organization.
