PLM welcomes its new AI collaborators.
In a rapidly evolving landscape where artificial intelligence (AI) often takes center stage, the realm of PLM stands as no exception. In a previous article, I elaborated on the top ten AI-powered PLM opportunities to drive efficiency, innovation and productivity.
But will these improvements displace the jobs of engineers and PLM experts? The dilemma surrounding AI and humans lie in finding the delicate equilibrium between automation and human expertise. Striking the right balance involves redefining job roles to complement AI, upskilling the workforce and ensuring that humans remain integral in areas where ethical, creative and contextual decision-making is paramount.
Broadly speaking, synergies between AI and human expertise can be endless—not merely a coexistence but a collaborative effort to harness the full spectrum of PLM’s capabilities. AI contributes its data-driven precision, automation and predictive power, while human engineers bring contextual understanding, ethical judgment and creative problem-solving to the table. Together, they can navigate the dynamic PLM landscape, ensuring that products are not only technologically advanced but also adhere to the highest standards of quality, ethics and innovation.
This collaboration heralds a promising future where the fusion of AI and human intelligence drives superior product outcomes. In this post, I take a closer look at the integral role of human engineers as AI increasingly becomes the driving force behind PLM workflows.
AI in PLM: Where We are Now
Before we delve into the evolving role of human engineers, let’s quickly recap the current state of AI in PLM. AI is transforming PLM through applications like design optimization, predictive maintenance, demand forecasting and quality control. These technologies are already enhancing product development, supply chain management and manufacturing efficiency.
Aligning PLM and AI holds the promise of transformative business benefits. It streamlines product development, optimizes manufacturing and enhances supply chain operations. This alignment fosters innovation, reduces costs, accelerates time-to-market and ultimately positions organizations for sustained growth. In short, AI-powered PLM creates a competitive advantage in today’s dynamic business landscape.
Paving the Way for AI Integration
Organizations must take certain steps to ensure a seamless integration of AI into PLM workflows. High-quality data is the bedrock of AI, so organizations must ensure data is well-structured, connected and easily accessible. Engineers can optimize this PLM and AI integration by ensuring data flows seamlessly across business functions and platforms. They must also clearly identify where AI can make the most significant impact within PLM workflows.
Whether it is automating routine tasks or optimizing product designs, understanding the use cases is key. Equip teams with the skills to work effectively with AI by Training engineers and designers to understand AI models, interpret their outputs and fine-tune them when necessary. Collaboration between PLM experts and AI specialists is invaluable. AI experts can help tailor AI solutions to specific PLM challenges, ensuring they align with organizational goals.
The Evolving Role of Human Engineers
As AI continues to automate PLM workflows, the role of human engineers will transform. Human engineers will continue to validate AI recommendations and make final decisions. Their domain expertise and contextual understanding are essential to ensure AI-driven decisions align with organizational goals.
But the difference is that humans become a stop-gap that oversees AI systems to ensure ethical considerations are met. This includes preventing or compensating for AI bias and making decisions that align with organizational values. AI models require continuous learning and adaptation; human engineers will be responsible for monitoring those processes. To do so, they will control AI systems, refine them and ensure they adapt to evolving product standards and customer preferences.
Human-in-the-Loop: Striking the Balance
The concept of human-in-the-loop is central to AI-PLM integration. It emphasizes that human engineers remain integral to the decision-making process, ensuring AI models continuously learn and adapt.
Establishing this feedback loop allows human engineers to provide input into AI systems. These iterative cycles help AI models understand when their recommendations are successful and when they need refinement. AI can then learn how humans adjust models, parameters and constrains. This ongoing training between AI and engineers ensures they both understand their capabilities and limitations. This empowers them to collaborate effectively.
Implications for End-Users: How to Keep Your Job
To remain relevant in this landscape, engineers and designers need to not only make peace with the fact that AI will become more integrated into PLM workflows, but also that they need to support it. To do so, they will need to acquire new skills, including AI model interpretation, interaction and fine-tuning. They must learn how to feed the models with robust data and continuously enhance data models and generative engines. This diversification of skills will make them more versatile PLM professionals.
Roles may shift, with engineers taking on more strategic tasks that require creativity, problem-solving and complex decision-making. Routine tasks will be automated, freeing up time for higher-level work. Beyond “feeding the machine with more data,” end-users will have AI as a valuable decision support tool, providing data-driven insights and recommendations to inform their work.
Collaborative Future: AI and Human Engineers
In the future of PLM, AI and human engineers are poised to collaborate closely, driving innovation, efficiency and product excellence. AI offers a wealth of capabilities, but it thrives when complemented by human expertise, creativity and ethical oversight.
The journey toward this harmonious partnership has already begun, with organizations laying the foundation for AI integration. During these steps, they must foster collaboration between AI and PLM experts to ensure human engineers remain at the heart of the decision-making process. Ethical councils and regulators will also need to be created to act as guardrails and produce compliance rules—and engineers must be a part of this too.
Elaborating on this topic, McKinsey highlighted in its article The analytics academy: Bridging the gap between human and artificial intelligence, that “AI technologies are constantly evolving, and technical experts need to stay up to date on AI techniques, tools and supporting technologies. Talent comes and goes, and new hires need to acquire institutional knowledge quickly. As transformations march forward, cross-functional teams find better ways of working together, which must ultimately be fed back into the ecosystem.”
By embracing AI as a transformative force while preserving the essential role of human expertise, the future of PLM shines with promise and possibility. Together, AI and human engineers will navigate this dynamic landscape, ensuring that products are not only technologically advanced but also align with the highest standards of quality, ethics and innovation. Overall, let’s hope that AI will contribute to bring PLM up to the next level.
