At one point, the “Lab of the Future” was focused on automating instrument technologies like autosamplers, liquid handling systems, sample preparation, PCR and more. Nowadays, the Lab of the Future is more focused on the integration of artificial intelligence (AI) and machine learning (ML) into the everyday lab.
Regardless of the how, the why of the Lab of the Future has always been the same: optimize workflows, streamline operations and reduce costs. However, while many organizations embrace new technologies, only a small number fully leverage their potential to improve overall laboratory productivity and accuracy.
For example, nearly 60% of lab professionals report significant downtime due to equipment failures, missed calibration schedules and difficulty locating lab assets—all of which can be accomplished simply with automated technologies. This is according to a MachineQ’s recent “Lab of the Future” survey, which gathered insights from more than 400 U.S.-based laboratory professionals at life sciences companies with over $1 billion dollars in annual revenue. The survey, conducted by independent research firm Censuswide, focused on current practices in lab operations, the challenges professionals face, equipment management, and the adoption of emerging technologies like Internet of Things and AI.
Asset tracking
According to survey respondents, significant gaps in asset tracking technology led to more downtime. Forty-one percent of respondents cited time-consuming manual processes as a significant hurdle, indicating a need for automation and improved asset location data. The need for more investment in real-time tracking technologies was repeatedly cited by lab professionals.
Of the laboratories surveyed, 32% rely on manual tracking methods or do not track equipment at all. Meanwhile, 25% utilize real-time tracking systems (e.g., Bluetooth, Wi-Fi, UWB); 22% use passive tracking technology (e.g., RFID, barcode/QR, NFC); and 20% rely on a third-party service provider.
Manual tracking methods can be cumbersome and increase the chance of human error, Passive RFID is more automated than manual tracking, however, challenges such as costly infrastructure and manual scanning processes can limit lab managers.
“The survey data suggests the opportunity is ripe with nearly two-thirds of respondents indicating that improved tracking could mitigate downtime,” write the authors of the survey report. “Additionally, inventory management data, such as asset quantities and locations, was most frequently ranked as the most valuable data source for improving lab operations.”
Asset utilization
In addition to tracking, the survey revealed asset utilization is also a key hurdle, with 44% of respondents ranking it a top 3 challenge. This lack of insight can lead to inefficiencies and wasted resources, exacerbating the difficulties in managing lab operations.
Similar to companies’ varied approaches to asset tracking, respondents revealed monitoring asset utilization in a variety of ways:
- 56% track asset utilization manually via reservations, spreadsheets or staff input
- 30% track asset utilization using real-time monitoring technology
- 14% have no system/process in place
Interestingly, survey data shows a notable correlation between the methods used for tracking asset use and the respondents’ confidence in their data. For those using real-time monitoring like as IoT technology, 70% said they have the necessary data to optimize usage. In contrast, only 58% of respondents employing manual tracking methods had this confidence. Furthermore, among those who do not track asset utilization at all, this figure drops to 42%.
Additionally, the survey reveals that confidence extends to justifying asset purchases. For example, 62% of respondents using real-time monitoring feel equipped to justify their asset investments, compared with only 55% of those tracking manually. For those not tracking asset utilization, just over half (51%) say they have adequate data to justify purchases.
“Overall, these findings demonstrate that implementing real-time monitoring technology can enhance data confidence and operational effectiveness, ultimately positioning organizations for better asset management” reads the survey report.
Asset investment
More than 55% of respondents ranked managing and reducing expenses as one of their top three concerns, with nearly a quarter citing it as their number one challenge. On a positive note, the survey indicates a strong inclination toward increasing expenditures across various operational facets over the next 24 months.
Over a third of respondents (37%) plan to increase spending on lab equipment, yet 17% of them admit they don’t have the data needed to justify these purchases. Additionally, 62% of those planning to increase equipment budgets believe they could reduce equipment spending if they made better use of their existing assets.
However, less than 30% of the respondents said they plan to increase their investments in digital transformation.
“The time for investment is now,” reads the report. “The survey stresses the vital need for labs to embrace the future and invest in emerging technologies.”
Currently, IoT is the most widely adopted technology with 29% of respondents saying they use it. AI/ML is not far behind, with 28% of respondents already using these technologies in their labs. Looking ahead, both technologies show significant growth projections, with 56% of respondents planning to implement IoT and 59% intending to adopt AI/ML over the next two years.
“Labs must focus on adopting advanced technologies, building stronger data architectures, and fostering a culture of continuous innovation,” concludes the survey report.