Let’s think a little more about how engineers can prevent or quickly fix AI application project outages.
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.
Click here to read the first article in this two-part series.
Lack of skills and training
Successful applications of AI in engineering require multidisciplinary collaboration. Data scientists understand algorithms, but they don’t necessarily understand the physical principles that govern structures, materials, and thermodynamics. Conversely, engineers may have domain expertise but lack proficiency in machine learning, statistics, or data visualization. These gaps create communication barriers and implementation bottlenecks, slowing progress.
To bridge this gap, organizations promote “AI literacy” among engineers and “engineering literacy” among data professionals. Cross-functional teams of engineers, data scientists, and IT specialists are often the key to turning technical concepts into real-world results. Continuing professional development programs, university partnerships, and in-house training initiatives all contribute to building expertise. The future of engineering belongs to experts who can interpret both finite element analysis and neural network output with the same fluency.
Computing infrastructure gap
The explosive growth in data volumes and the high resource demands of AI applications can overwhelm the computing infrastructure that supports engineering groups. This results in storage shortages, performance degradation, and unplanned outages. These issues slow down engineer productivity.
Engineering organizations can add capacity to their computing infrastructure in the following ways:
- Invest in additional on-premises capacity.
- Move some AI applications from servers to high-performance workstations.
- Move some applications to a cloud operated by a cloud service provider (CSP).
- Migrate some applications from on-premises to a software as a service (SaaS) vendor’s computing infrastructure.
Organizational resistance and cultural barriers
Our engineering culture is based on precision, responsibility, and safety. Although these values are essential, they can also foster skepticism toward new technologies. Some engineers question the validity of recommendations generated by AI if the underlying logic cannot be traced. Project managers may be hesitant to delegate decisions to an AI system that they perceive as a “black box.” Such vigilance and resistance slows progress.
Overcoming this resistance requires transparency and inclusiveness. AI models used in engineering should emphasize explainability, showing how inputs lead to outputs. Involving engineers in the development of AI models builds trust and ensures that the results match physical reality. IT leaders must communicate that AI is a decision support tool and not a replacement for engineering judgment or expertise. By viewing 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 takes place within a strict regulatory and ethical framework designed to protect public safety. AI introduces new risk dimensions, including:
- discrimination and harm
- Privacy and security
- false alarm
- Malicious actors and abuse
- human-computer interaction
- socio-economic and environmental
- AI system safety, failures, and limitations
If an AI-driven application incorrectly predicts stress tolerance or misjudges maintenance intervals due to one or more of these risks, the consequences can be catastrophic. AI project teams are unaccustomed to these risks and can become overly cautious, slowing progress.
To mitigate these risks, engineering firms establish rigorous model validation procedures, document decision-making processes, and ensure compliance with industry standards and safety regulations. AI Ethics and Safety Review Boards are required to evaluate new AI applications before they are introduced. Transparency and accountability are not options in engineering, they are fundamental responsibilities.
Underestimating the complexity of change management
Deploying AI is more than just an IT upgrade; it’s an organizational transformation. Effectively incorporating AI insights often requires reconfiguring engineering workflows, approval hierarchies, and performance metrics. AI projects stall when leaders underestimate the organizational transformation needed to operationalize AI results.
A conscious staff turnover management approach is essential. This includes stakeholder engagement, pilot demonstrations, and training for each deployment. By delivering multiple tangible improvements, such as lower maintenance costs and shorter design cycles, AI projects build momentum for broader adoption and ultimately have lasting impact.
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 stall when skills are lacking, organizations resist, or workflows resist adaptation. Success requires high-quality data, transparent governance, and an organizational culture that embraces continuous learning. Engineering has always been about solving complex problems through disciplined innovation. Effectively deploying AI is the next evolution of a successful 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.
