Artificial intelligence is becoming a practical tool for laboratory operations, with AI-driven asset management offering clearer visibility into how equipment is used, maintained, and optimized. As connected devices generate increasing volumes of real-time data, many labs are beginning to explore how AI can transform that information into actionable insights that support efficiency, reliability, and long-term planning. In this Q&A, Stephen Miller, director of product design at MachineQ, discusses emerging capabilities, early wins, and what lab leaders should consider as they integrate AI into their operational strategy.
Q: Could you walk us through a real-world example of how AI is being used to track and optimize lab equipment utilization or maintenance?
A: One real-world example comes from a global pharmaceutical company that’s using an AI-driven smart summary feature to help improve lab equipment utilization and maintenance. The feature leverages AI to synthesize millions of IoT data points—from asset location and utilization to equipment alerts and sensor readings—into clear, actionable insights in seconds.
AI helps lab managers summarize any number of lab assets to rapidly identify patterns and anomalies across their lab environment, highlighting underused equipment, flagging potential maintenance needs, and surfacing opportunities to optimize asset performance and space planning. What once took their operations team days of manual data analysis now happens in a matter of seconds, reducing alert fatigue common with threshold-based systems.
Q: What are some early “quick wins” labs typically experience after adopting AI-enabled asset monitoring?
A: Labs that implement AI-enabled asset monitoring often see measurable benefits. One of the first “quick wins” is the ability to uncover utilization inefficiencies across lab equipment. For example, AI can help identify underused assets or redundant resources that can be redeployed to help improve productivity or decommissioned to manage costs.
As a result, AI-enabled asset monitoring is helping lab operations teams streamline maintenance planning, improve asset uptime, and provide a more complete, real-time view of their operations. The boost in operational clarity can build confidence in AI adoption and lay the foundation for broader optimization.
Q: What kinds of data are most valuable for AI-driven asset management, and how should labs approach collecting and cleaning that data?
A: For AI-driven asset management, the most valuable data is both comprehensive and context-rich. It captures not just where assets are, but how they’re performing over time. This typically includes real-time IoT data, such as equipment location, utilization rates, operational status, environmental conditions, and alerts from sensors and monitoring systems. When combined, these data points create a holistic picture of how lab assets are being used and maintained.
However, the real value comes from data quality and consistency. Labs should focus on establishing automated, continuous data collection through connected devices rather than relying on manual entry or fragmented systems. Plus, standardizing data formats can help create a clean, unified dataset that AI can process effectively.
Q: What are the most common challenges you see during adoption, and how can lab leaders mitigate them?
Lab Quality Management Certificate
The Lab Quality Management certificate is more than training—it’s a professional advantage.
Gain critical skills and IACET-approved CEUs that make a measurable difference.
A: One of the more common challenges that labs face during AI adoption is change management and getting teams to trust and operationalize AI-driven recommendations.
To manage this challenge, lab leaders should start by creating transparency around how AI works and what insights it delivers. When teams understand the “why”, adoption rises quickly.
Successful organizations typically start with well-defined use cases, such as optimizing asset use or reducing equipment downtime. Then, they expand use cases as they see measurable value. This phased approach helps ensure internal buy-in, setting the stage for scalable AI impact across the lab.
Q: Given the sensitive nature of pharma R&D, how do organizations ensure data security and compliance when deploying connected AI systems?
A: Data security and compliance are paramount when deploying any technology in pharmaceutical environments. These organizations operate under stringent regulatory frameworks, so every layer of the solution—from device to cloud—must be designed with security in mind.
An approach that combines end-to-end encryption, secure device onboarding, and role-based access controls is ideal to ensure sensitive information always remains protected.
Q: What emerging capabilities are most exciting or realistic for labs in the near term?
A: The combination of AI and IoT is allowing lab teams to move from reactive to proactive management thanks to intelligent summarization of complex IoT data.
We’ll see greater adoption and advancement of AI over time as AI models grow smarter through machine learning (ML), enabling richer, context-aware outputs at scale.
As a result, AI will continue delivering real operational impact, helping labs achieve greater efficiency, reliability, and agility as they advance innovation.
Q: Finally, what advice would you give to a lab manager just beginning to explore AI as part of their operational toolkit?
A: For lab managers just starting to explore AI, the key is to think big but start small. Begin with a clear, measurable use case where AI can deliver tangible value quickly. Early wins build trust and create momentum for broader adoption.
It’s also important to view AI as an enabler, not a replacement. The goal isn’t to remove human expertise but to enhance it and free teams from manual data work, so they can focus on higher-value tasks.
