The efficiencies and insights offered by artificial intelligence (AI), machine learning (ML), predictive analytics, and other emerging technologies present abundant opportunities for laboratories. A McKinsey report indicates that over a quarter of companies with proactive AI strategies attribute at least 5 percent of their top-line profits to AI. It’s not just the financial bottom line that benefits — significant enhancements can be observed in turnaround times, lab throughput, and labor costs due to successful AI implementation.
However, despite these advantages, many labs are not fully capitalizing on AI's potential. Numerous institutions are yet to adopt the technology, and those that have often approach it in ways that are likely to result in failure. A Gartner study found that nearly half of CIOs planned to implement AI, yet up to 85 percent of these initiatives may fail due to biases in data, algorithms, or the teams managing them. The core issue lies not in the technology itself, but in the strategic approach taken.
To assist clients in leveraging digital transformation and preparing for the lab of the future, LabVantage Solutions has developed a five-step process for successfully implementing and benefiting from AI across the enterprise. Additionally, the company offers an AI-readiness workshop to help organizations harness this technology effectively.
But first, what is AI?
According to Accenture, AI is defined as “a constellation of many different technologies working together to enable machines to sense, comprehend, act, and learn with human-like levels of intelligence.” This definition emphasizes that AI is an extension of human capabilities rather than a replacement.
While many envision robots exhibiting human-like traits, AI realistically encompasses solutions that utilize data and digital assets to enhance processes. In a laboratory context, this could involve using data from connected systems to make decisions, predict outcomes, or alleviate bottlenecks.
What is ‘lab of the future’?
Although the ‘lab of the future’ is not yet explicitly defined, many individuals have diverse visions of what it entails. Central to this concept is digital connectivity, where all instruments are interconnected, allowing for the potential of a “digital twin” of the physical lab. This setup facilitates predictive modeling, optimization, automation, and, beyond that, autonomation — automation that incorporates intelligence enabling human interactions with automated processes.
Monitoring and visualizing lab activities will enhance control; this, in turn, fosters the ability to optimize lab performance and scientific research, ultimately creating opportunities to automate and autonomize more lab functions. As labs become digitally empowered, they along with the scientists within them will play critical roles in addressing significant humanitarian challenges such as climate change, unmet medical needs, and food safety.
To achieve this, organizations must commit to technologies that align with the lab of the future.
Commitment precedes success
True success in AI begins with a genuine commitment to the process. Many labs are drawn to the allure of new technology but underestimate the effort required to realize long-term benefits.
Labs that perceive AI through a testing lens rather than a commitment lens are setting themselves up for failure. Mastering AI requires a developmental mentality, not merely a research-focused one. Instead of experimenting with the technology, organizations should commit by addressing the following questions:
- Can success be clearly defined?
- Is there a plan for achieving this success?
- What resources are necessary to execute the plan?
- How will the technology be deployed in production?
- How can this technology be integrated into existing business plans?
- How will it be streamlined into business processes?
- Who can partner with the lab to access needed resources?
Essentially, labs that ‘experiment’ with AI often achieve results similar to those who ignore the technology entirely. In fact, a purely experimental approach can lead to negative outcomes, wasting time and resources.
The five steps to successful AI transformation
Image Credit: LabVantage Solutions
1. Start with a use case
The first step toward commitment is selecting a clear use case. Rather than merely “playing around,” developing a use case establishes a defined purpose for AI implementation, along with associated ROI and expected business outcomes. A clearly defined use case allows for proper budgeting, action plan development, and roadmap forecasting. AI adopters with a proactive strategy tend to achieve significantly higher profit margins than those that simply experiment with technology.
Designing the ideal use case
There is no one-size-fits-all use case for AI; the most effective case for a lab will be highly relevant to its operations and reflect the specific business outcomes that matter most.
A common goal for labs beginning their AI journey is to leverage functions that yield the highest return with minimal effort or risk. Several functions fit this description, such as lab performance analysis, integrated modeling, and predictive formulas. Each of these areas offers substantial value to the organization. A thorough AI-driven performance analysis enables labs to sift through the complexities of their operations and swiftly identify hotspots causing issues, whether related to quality, turnaround time, or overall performance. Integrated modeling allows for statistical modeling, such as calibration curves and stability studies, without the loss of valuable information from data transfers. Finally, employing AI to derive formulas from existing data can significantly reduce the number of physical studies needed.
Examples of potential use cases include:
- Instrument data analysis: Establishing a real-time data ingestion pipeline for lab instruments, facilitating downstream data analysis and predictive maintenance.
- Lab resource scheduling: Enhancing the utilization of lab resources (raw materials, equipment, and manpower) through operational research modeling.
- Quality management: Implementing statistical process control and quality analytics to pinpoint drivers of poor quality and recommend real-time intervention strategies.
- PK-PD modeling: Accelerating pharmacokinetic and pharmacodynamic studies using statistical tools and machine learning models for sophisticated analyses.
- Immunogenicity analyses: Facilitating cut point analyses and calculations using parametric and non-parametric approaches through a set of out-of-bag models.
- Formulation studies: Leveraging AI-driven algorithms on existing data to predict formulations that utilize specific raw materials and meet desired specifications.
2. Get a handle on your data
Data continues to pose a significant challenge for many labs. Successful AI implementation necessitates a commitment to sourcing the right data and transforming it into useful, readable formats.
Most labs often perceive data issues as technology problems, but these challenges typically stem from higher up in the organization. They are often issues of corporate vision rather than lab technology. Improved data management starts with better stewardship and design. Appointing a single individual responsible for resolving data-related friction and helping to design a data ontology that represents the organization’s best interests is crucial.
Many labs have yet to integrate their LIMS, ELNs, and other digital assets with financial and production systems; data must flow through a network that mirrors the organization inherent in business systems. Understanding these connections and building an ecosystem to facilitate data flows is vital for successful AI adoption. Organizations need a clear understanding of what is being measured and the parameters that define those measurements. Most importantly, they must grasp the metrics that truly matter.
The technology required to manage data already exists; what is often lacking is the business acumen and structure to apply the data effectively. Clients are encouraged to appoint a project leader and collaborate across the organization to create a ‘digital twin’ of their laboratory — digitally recording everything to enable monitoring. With clean, up-to-date, and well-prepared data, labs can efficiently progress to the next step.
3. Assess techniques and technologies
The next step in successful AI implementation involves identifying suitable AI tools and collaborating with the right talent to fill existing capability gaps.
Navigating the realm of AI independently can lead to project failure. The technology landscape evolves rapidly, making it challenging to stay informed about the latest tools and technologies. A total commitment to AI requires partnering with knowledgeable experts who can guide organizations through technology, models, methodologies, and the language associated with their projects.
Organizations should seek partners with extensive technical experience in AI and analytics, combined with a thorough understanding of their specific industry. AI service providers are emerging rapidly, but a partner knowledgeable about the unique instruments, processes, and use cases in a particular sector can significantly influence success. When selecting a partner, it is essential to ensure they possess the industry experience necessary to connect technical implementation with specific business objectives. Additionally, verifying that data quality, privacy, and security protocols are adequate is crucial. Again, a trusted partner can make a substantial difference.
4. Integrate AI into workflow
The best results from AI arise from identifying points within the lab’s workflow where AI can add the most value. This may require organizational changes and some degree of training and up-skilling.
From there, organizations should seek ways to incorporate AI models into workflows by integrating them into existing software and tools or developing new complementary interfaces. Optimizing the human/machine interface is also important. Look for platforms that provide user-friendly, intuitive interfaces and dashboards, making it easy for teams to execute tasks with AI assistance.
Utilizing hardware tools such as mixed reality, robotics, and digital assistants can help bridge the human/machine divide, assisting in various lab functions, including training, onboarding, and manufacturing.
As organizations continue to work with AI, it’s recommended to monitor the effectiveness of digital workflows regularly. This approach will help identify ways to optimize and reconfigure for the best results continually.
5. Adapt a culture for AI
Often, the most significant challenge lies not in technology but in human nature. Today’s cultural resistance to AI mirrors the resistance faced by previous technologies (telephones, computers, the internet) that have become integral to daily life. In many respects, these technologies arrive like waves; those who embrace the technology adapt successfully, while those who resist may struggle.
AI has the potential to replace many tasks currently handled by humans in laboratories, leading to fears of job displacement. Organizations are encouraged to foster an open, collaborative culture that educates staff about the opportunities presented by this new technology and reassures them of their ongoing value in the workplace.
Labs will experience greater benefits from AI when they focus on up-skilling their teams, ensuring a complementary relationship between computing and human labor. While AI may now handle some of the more manual and tedious processes, teams can concentrate on higher-level, strategic work.
As with most new technologies, it may take time and education for teams to fully accept and trust insights generated by AI. However, the advantages of AI will become evident over time.
By following these five key steps, laboratories can position themselves to successfully implement AI, harness its full potential, and navigate the future of scientific innovation with confidence.
References
- Mckinsey & Company (2021). Global survey: The state of AI in 2021 | McKinsey. (online) McKinsey. Available at: https://www.mckinsey.com/capabilities/quantumblack/our-insights/global-survey-the-state-of-ai-in-2021.
- Gartner (2018). Gartner Says Nearly Half of CIOs Are Planning to Deploy Artificial Intelligence. (online) Gartner. Available at: https://www.gartner.com/en/newsroom/press-releases/2018-02-13-gartner-says-nearly-half-of-cios-are-planning-to-deploy-artificial-intelligence.
- Manning, C. (2020). Artificial Intelligence Definitions. (online) Stanford University. Stanford University. Available at: https://hai.stanford.edu/sites/default/files/2020-09/AI-Definitions-HAI.pdf.
About LabVantage Solutions
LabVantage Solutions, Inc. is the leading global laboratory informatics provider. Our industry-leading LIMS and ELN solution and world-class services are the result of 35+ years of experience in laboratory informatics. LabVantage offers a comprehensive portfolio of products and services that enable companies to innovate faster in the R&D cycle, improve manufactured product quality, achieve accurate recordkeeping and comply with regulatory requirements.
LabVantage is a highly configurable, web-based LIMS/ELN that powers hundreds of laboratories globally, large and small. Built on a platform that is widely recognized as the best in the industry, LabVantage can support hundreds of concurrent users as well as interface with instruments and other enterprise systems. It is the best choice for industries ranging from pharmaceuticals and consumer goods to molecular diagnostics and bio banking. LabVantage domain experts advise customers on best practices and maximize their ROIs by optimizing LIMS implementation with a rapid and successful deployment.
Sponsored Content Policy: News-Medical.net publishes articles and related content that may be derived from sources where we have existing commercial relationships, provided such content adds value to the core editorial ethos of News-Medical.Net which is to educate and inform site visitors interested in medical research, science, medical devices and treatments.