Twenty years ago, lab automation was the “hot” topic with manufacturers debuting a variety of instruments with automated functions to make lab operations less laborious and available 24/7. While some were quick to adopt automated technologies, it took the majority of the industry years and years to come around—with some still not employing automation to the level that was forecast in the early 2000s.
Fast forward to today and artificial intelligence (AI) and machine learning (ML) are now the hot topics. There’s a glaring difference through—society in 2025 is much different than 2000. While that was the beginning of the technological boon, today’s young lab professionals grew up with phones in their pockets and data at their fingertips.
Still, there is a learning curve when it comes to AI in the laboratory—even for the younger generation of scientists. In January 2025, Titian Software and Labguru surveyed the 155 professionals in the life sciences industry to get a better understanding of the trends and innovations that matter most in digital lab operations.
According to the survey results, while machine learning and AI are expected to be major drivers of transformation in lab operations, many labs aren't quite ready to fully harness its potential. Foundational issues remain, with inventory management and the automation of manual processes taking precedence. In fact, 65% of survey respondents identified inventory management—specifically of reagents and supplies—as the top technology they’re interested in adopting. A strong majority—77% of respondents—believe automation will be the primary driver of change by 2026, underscoring the urgent need to address manual processes before the broader adoption of AI and machine learning.
AI potential
According to the survey, as it currently stands, 15% of labs are fully digitized, while 50% still have significant manual processes. When asked about the most significant role AI will play in lab operations, the top choice, selected by around a quarter of respondents (24%), was managing the massive volumes of data generated from experiments, instruments and other sources—a trend we have seen lately as manufacturers work to integrate more and more data handling and processing software into their instruments. This was followed by accelerated drug discovery (19%) and improving data documentation and integration (17%).
While 45% of survey respondents plan to implement next-generation lab technologies—such as AI and advanced robotics—within 2 years, 25% stated that they either have no current plans or expect it to be longer than 5 years to implement these technologies.
“The results signal the pressing need to address operational inefficiencies before labs can scale into more advanced technologies. The results were consistent across every type of lab, from big pharma to start-up,” Titian-Labguru said in a press release announcing the survey results. “The gap highlights a critical period of transition, where foundational improvements must be prioritized before the full promise of AI can be realized.”
Data deluge
As indicated in the AI questions and answers, data overload is the biggest challenge currently for labs. In the survey, 54% of respondents said data overload/data management was the challenge most likely to drive change in the lab. A close 53% of respondents said funding and cost pressures, while 49% picked automation of manual processes, reflecting on a challenge that the industry has faced for some time.
“AI’s greatest promise lies in making sense of the overwhelming volume and complexity of lab data,” said Titian-Labguru. “It’s not just the quantity of data that’s straining labs—it’s the complexity across diverse modalities, creating added pressure around storage, automation, acquisition, compliance, and regulatory requirements.”
Data management
According to Titian-Labguru, it’s the additional modalities that are causing complex data management challenges. The survey revealed an astounding 76% of respondents feel they are only somewhat prepared to handle this increase in sample types.
Currently, 55% of labs utilize inventory management software; however, these systems are varied. While one lab may use LIMS exclusively, another may use ELNs, screening data analysis, substance registration software, workflow management, or even vendor-specific (non-agnostic) software.
The survey revealed 65% of labs say they have an interest in adopting new inventory management systems. While demand for more sophisticated systems is a positive outlook, this situation can negatively affect AI. For AI, fragmented datasets can lead to a lack of proper context and understanding of the data—which can eventually make AI jump to the wrong conclusions.
“If we recognize data as being the fuel for AI, and the precursor to AI adoption, then a significant challenge that labs of all sizes need to address is the disconnect of platforms that they have in place today,” said Keith Hale, Group Chief Executive Officer at Titian Software and Labguru. “Better data practices and smarter sample and inventory management are essential not only for improving day-to-day operations but also for setting the stage for more advanced capabilities. AI cannot deliver real, meaningful benefit without connected and well-managed data.”
Trust and timeline
Whether or not to trust AI is a societal question much larger than the laboratory. But what labs do have to take into account is regulatory compliance. Like everything else in the laboratory, AI will be heavily scrutinized and subject to regulations before it fully seats itself within the lab environment. For example, survey respondents chose “implementation and oversight of AI tools and their output” (52%) as the second-most likely lab operation to see enforced changes due to regulatory pressures (preceded slightly by sample tracking and chain of custody). Also notable is that 39% said they think enhanced requirements for reproducibility of results will be enforced by regulatory and market pressures.
“To move forward confidently with AI in the lab and to trust it with lab data, organizations will need clearer guidance, ethical frameworks, and validation processes to ensure that AI complements the rigorous scientific standards. It’s imperative that any new technologies coming into the lab don’t alter the quality management and reproducibility of results,” said Titian-Labguru.
There’s no doubt AI is coming—now, it’s more a question of when. While 15% of survey respondents said they plan to implement next-generation technologies like AI within the next 12 months, the majority see it being a longer-term timeline—with 30% stating they plan to implement AI in 1 to 2 years, and another 30% saying 2 to 5 years. The remaining 25% don’t have any plans or think it will be longer than 5 years.
Looking at organization type, the majority of pharma and biotech companies—regardless of size—plan to implement AI within the next 5 years.
“What we’re seeing is a clear preference for AI to augment and support operations, not replace human expertise. AI is viewed as a tool, and something that will work with human oversight and, until trust is built, the timelines for AI adoption may be extended,” said Titian-Labguru.
More key survey insights
When asked to select the three trends that will drive the most change in lab operations, 77% said automation will drive change by 2026, while 75% also stated AI/machine learning will play a big role in driving change next year.
Regardless, fixing inventory management is the highest priority, with 65% of respondents staying it is the top technology they’re interested in adopting moving forward in 2025 and 2026.
Process-wise, survey respondents felt automation in the lab benefits repetitive tasks the most (58%), followed by sample workflow tracking (57%), data capture/recording (48%), inventory updates (43%), report generation (36%) and data analysis & interpretation (30%).
While today’s lab leverage lab automation mostly for liquid handling and sample prep/handling robotics, tomorrow’s lab are forecasted to augment this automation with AI and machine learning.
“These new tools will revolutionize data workflow automation, supporting the integration of instruments in the lab, while orchestrating data to ensure it is usable and accurate,” said Titian-Labguru.
The focus on automating work that is time consuming and takes away from science is recognized by over 60% of respondents, while over 50% also see the potential that AI tools have for planning and scheduling.
“If lab technology solution providers choose to take this into account, we’ll likely start seeing more AI-based automation coming into play, helping to unify and integrate data across platforms,” predicts Titian-Labguru.