LIMS Feature
Five Keys to Successful AI in the Lab LabVantage Solutions The Execution Gap
Artifi cial intelligence (AI) and machine learning (ML) offer unparalleled opportunities to lab-centred companies. According to a McKinsey report [1], more than 25% of companies with proactive AI strategies attribute at least 5% of their top-line profi ts to AI. Revenue gains aside, AI can also create swift improvements in turnaround times, lab throughput, and labour costs.
Yet the majority of labs are missing out on AI’s true potential. Labs are either hesitant to adopt the technology at all, or do so in a way that is doomed for failure. Gartner’s fi ndings [2] indicate that while nearly half of CIOs intend to implement AI, the failure rate could be as high as 85%, primarily due to biases in data, algorithms, or the teams overseeing them.
Is AI fundamentally fl awed? We don’t think so.
Rather, the key to AI success - and, on the fl ip side, failure - is not in the technology but in the execution. LabVantage Solutions has developed a comprehensive fi ve-step process for successfully integrating and profi ting from AI within the lab and across the enterprise.
Imagining the AI-driven ‘Lab of the Future’
At LabVantage, we speak often of the ‘lab of the future’. At its core, the future lab is one of end-to-end digital connectivity. Every piece of equipment is digitally linked, creating a “digital twin” of the lab in the virtual world. This virtual representation allows for predictive modelling, optimisation, automation, and, taking it a step further, autonomation - characterised by intelligent automation that allows human interaction or intervention in automated processes.
Five Steps to Success with AI
1. Give Your AI a Job Select a well-defi ned use case. This will establish a clear purpose for your AI implementation, associating it with a measurable return on investment (ROI) and tangible business outcomes. Creating a use case enables you to allocate resources effectively and craft an action plan and roadmap. Labs that take an outcome-based approach to AI consistently achieve higher profi t margins compared to those who merely dabble.
The most suitable case for your lab will align closely with your specifi c operations and refl ect your ideal business outcomes. One common objective for labs venturing into AI is to leverage functions that yield outsized benefi ts with minimal effort or risk. Several functions fi t this description, such as lab performance analysis, integrated modeling, and predictive formulas.
Rigorous AI-driven performance analysis, for example, allows labs to navigate operational intricacies quickly and easily, identifying problem areas affecting quality, turnaround time, or overall performance. Integrated modelling enables statistical modelling - such as calibration curves, immunogenicity, and stability - without the need to transfer data back and forth, preserving valuable information about the process. Lastly, AI can derive formulas from existing data to signifi cantly reduce the number of physical studies required.
Effective cases might include:
• Instrument Data Analysis: Establishing a real-time data ingestion pipeline for laboratory instruments to enable downstream data analysis and predictive maintenance of instruments.
• Lab Resource Scheduling: Enhancing the effi cient utilisation of lab resources (raw materials, equipment, and staffi ng) through operations research modelling.
• Quality Management: Employing statistical process control and quality-related analytics to identify drivers of poor quality and recommend real-time intervention strategies.
• PK-PD Modelling: Accelerating pharmacokinetic and toxicology studies through statistical tools and machine learning models, enabling researchers to conduct sophisticated analyses.
• Immunogenicity Analyses: Facilitating immunogenicity cut point analyses and calculations using parametric and non-parametric approaches through a set of out-of- the-box models.
• Formulation Studies: Utilising AI-based algorithms on existing data to predict a recipe that utilises specifi c raw materials and meets desired specifi cations.
2. Solve Existing Data Problems
Data remains a signifi cant obstacle for labs. Success with AI requires not just that you acquire the right data, but also that it is transformed into formats that are both useful and easily readable.
A substantial number of labs have yet to integrate their Laboratory Information Management Systems (LIMS), Electronic Lab Notebooks (ELNs), and other digital assets with their fi nancial and production systems. To succeed with AI, data needs to fl ow seamlessly through a network that mirrors the inherent organisation of business systems. Recognising these connections and establishing an ecosystem to facilitate data fl ows are pivotal for success.
While many labs perceive their data challenges as technology-related, these issues often originate higher up in the organisational hierarchy. Companies must have a clear understanding of what is being measured and the defi ning parameters. Most importantly, they need a solid grasp of the metrics that hold signifi cance.
Improving data quality begins with enhancing data stewardship and design. We recommend a project leader to oversee the organisation’s effort at building its digital twin. This helps resolve any data-related challenges that arise and builds a data ontology that refl ects the lab’s real-world operations.
INTERNATIONAL LABMATE - FEBRUARY 2024
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