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Artificial intelligence (AI) is making a lot of headlines right now. Tools like ChatGPT and visual art generators are shocking audiences with their creativity, lucidity and function. Investment in everyday business-focused AI and machine learning is accelerating exponentially, with more sophisticated options appearing in the market.
AI is poised to shift the way companies operate in many industries. As a product executive for a life sciences company, I’m often asked when AI will revolutionize our industry. The answer: Realizing the wider benefits from AI for quality and manufacturing professionals may be out of reach until critical changes are implemented.
AI can be a transformative and valuable tool for healthcare companies today—most, however, aren’t ready to adopt helpful solutions. For one thing, functional AI requires large quantities of digitized data, and many life science practitioners are still storing data in paper notebooks, logbooks and binders. There’s much that would need to change before companies of all sizes could benefit from AI.
The following are key first steps before AI can be effective without putting companies and consumers at risk. Digital maturity is the foundation of life science quality and manufacturing benefitting from AI. This can be a big step for smaller companies that still utilize paper records in daily work.
AI will provide the most accurate findings, predictions and recommendations only when it has access to wider, more complete data about your manufacturing and quality processes. For example, an AI model designed to understand which portions of your manufacturing process are contributing to product defects can only consider the data within its view.
For a comprehensive view of variables, you’ll need to ensure the model has access to data about your suppliers, materials, equipment, quality processes and each step of your manufacturing process. When stripped of the ability to consider the right data factors, an AI model will draw false correlations and incorrect conclusions.
Connecting your data from across systems within your company will help ensure that you (and your AI) have an accurate and comprehensive view. Utilizing master data management will help ensure that data is uniform, consistent, understandable and appropriately modeled, providing the right information for your analytics and AI initiatives.
For example, if the materials used in your product manufacturing have different names in each software system, the AI won’t understand that each ID represents the same material. This prevents the model from having a complete understanding of the role that material plays in your overall process.
Overcoming the sizable challenge of digitizing and connecting data right now may be enough to disqualify many life science companies from achieving value from AI.
Develop a clear vision of the problems you want to solve.
When life science companies are thinking about adopting AI, one of the most important considerations in the implementation is that the margins of error in production are minuscule. Any mistakes don’t just lead to a product recall or downturn in business—they could be life-threatening.
Developing a clear understanding of your company’s challenges and where AI can help is an important early step toward implementation. In many industries, it can work to adopt a technology tool first and then identify problems and use cases appropriate for the solution.
However, the inherent risks and challenges in manufacturing for life science companies make that approach unwise. For AI to provide benefits, we must identify areas with a high volume of relevant data and a reasonable margin for error.
For example, AI can evaluate data resulting from your manufacturing process, proactively monitoring and detecting production runs and environments in which the risk of potential defects is higher than the norm. Then, it can make recommendations for further investigation and testing.
Human oversight and intervention can initially validate findings and ensure that the model is performing well and becoming more accurate over time. In this way, AI can be used to minimize risk on the manufacturing floor in a safe and controlled way.
It’s also important when utilizing AI in life sciences to ensure that models are explainable, with mechanisms for clearly demonstrating its conclusions. And in the highly regulated world of life science manufacturing, it’s also critical to ensure that models are utilizing data in compliance with industry regulations. By first identifying areas in which AI can provide real value without heightening risk, companies can get the most out of technology investments while maintaining high safety standards.
Embrace the benefits and limitations of AI.
AI is an advanced technology, but it still has limited understanding—it only “knows” the information to which you’ve given it access. This means an AI model oriented to making recommendations will do so even without sufficient data—that’s how it’s been programmed. To avoid false findings and conclusions, you must ensure that the data consumed by your AI models is complete and offers a holistic view of the problem.
When given access to the appropriate data, AI can help recognize patterns in manufacturing, supplier and quality data that aren’t detectable to the human eye. For example, a human might notice that more defects come out of a batch when an oven is set to 375 degrees Fahrenheit rather than 360 degrees. However, an AI model might be able to recommend that 363 degrees is the optimal temperature for the lowest number of defects. Or, when a substitute material from a specific supplier is used, the temperature actually ought to be 366 degrees.
AI opens a world of potential benefits for life science companies. To make the leap, most life science companies will have foundational steps to take before their efforts can bear fruit. Companies that are serious about attaining these benefits must determine what they hope to achieve with AI, ensure that the relevant data exists in their digital ecosystem and then take initial, cautious steps with an eye toward minimizing risk.
