Life sciences manufacturers continuously strive for accelerated launch, increased capacity for in-demand medicine, enhanced security of supply and improved sustainability.
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The data required to help address these challenges already exists in many cases, provided companies can connect and analyse it effectively. However, traditional approaches to shop floor system implementation and data management mean that many companies struggle to access this information. David Staunton, head of life science transformation at Cognizant, explores how generative artificial intelligence (AI) can be harnessed by life sciences companies to meet evolving business transformation needs.
All information systems are dynamic entities. Data flows into the system and is curated by decision-making patterns before emerging as an insightful answer to a user’s key questions about their business operations. Generative AI has enhanced information systems with a revolutionary interface designed to simplify and enhance users’ access to insightful answers regarding all aspects of an organisation’s performance. When combined with other AI decision-making tools, such as machine learning (ML), mathematical modelling and digital twins, generative AI opens up the pathway to a new dimension of decision-making.
Generative AI solutions can enhance team capability by driving answers to some of the most important questions a company can have, such as how to identify the “next best action” for manufacturing, lab, maintenance and IT/OT staff. Incorporated into a digital transformation strategy, generative AI has the potential to deliver in-depth analysis of data, providing actionable insights and answers to pressing questions in real-time. It is no surprise that life sciences companies are increasingly exploring harnessing the power of generative AI to transform their business operations for the better.
The true value generative AI can deliver for pharma companies
Generative AI can streamline the new product introduction (NPI) process and accelerate launches by automatically creating process fit-gap analysis and lab method transfer gap analyses including life cycle design documents. These can help shorten new product introduction (NPI) projects.
Staff capability can be significantly enhanced by harnessing generative AI assistants. These can provide vital information in the moments that matter to drive execution excellence and improve decision-making, leading to achieving objectives such as increased capacity.
With regards to improving the supply chain, generative AI assistants can offer direction over what to do, when to do it and how to do it. It can help team members improve right-first-time decision-making to increase future reliability and ensure ongoing security of supply.
As questions of sustainability and environmental impact move towards the top of the life science agenda, generative AI can be used to support the simple interface to a sustainability digital twin. This can drive insight into how much carbon, energy, water, solvent and other waste can be used or recovered for each line or molecule, to drive better choices for the environment. Gen AI can enable environmental insights without the need for the person asking the question to access and be trained on the digital twin.
In addition, generative AI can be used to support continuous improvement by establishing standard work and variation by batch against this standard. They can form the basis of a define, measure, analyse, improve and control (DMAIC) cycle for operational excellence.
These are just some of the key areas where generative AI is already enabling pharmaceutical firms to drive competitive advantage.
Real-world benefits
Real-world examples of applications benefiting from generative AI are moving beyond the theoretical presentations of the last few years. They show how new technology can generate measurable results for pharmaceutical companies.
For example, generative AI has the power to democratise access to data held within an organisation, to enable executives, supervisors, technicians and operators at all levels of the company to keep data front and centre of their decision-making to enhance manufacturing efficiency. It also improves the staff’s ability to use generative AI assistants for making both GMP and non-GMP decisions.
With this in mind, generative AI can have a wide range of potential applications across the life sciences ecosystem. It can be used to optimise the efficiency and security of GMP material supply through more effective management of suppliers, helping to ensure more reliable output of finished products, whether for clinical trials or commercial markets. Generative AI can also enhance collaboration between pharmaceutical organisations and their contract manufacturing partners, helping to eliminate inefficiencies caused by poor communication. It can even empower companies’ post-commercial decision-making, helping them better manage their product life cycles to maximise return on investment.
Building an AI strategy: Key considerations
Nevertheless, as with most new technology, companies face challenges when it comes to incorporating and adopting generative AI into their digital strategies to maximise the value of the technology.
To ensure the successful implementation of generative AI into business operations, companies need to be aware of — and avoid — key pitfalls.
One example of these challenges that needs to be addressed is the cultural impact of generative AI. Effective change management is one of the most important factors in any digital project. It’s crucial to onboard and engage employees throughout the organisation and across all sites from the earliest stage to ensure they are fully invested in the journey the company is about to embark on.
GMP decision-making supported by generative AI also poses issues that need to be resolved. New, robust processes need to be implemented to incorporate the new technology into GMP decision-making including validation to inspire business confidence in the new generative AI-enabled outputs from the beginning.
Valuable data is all too often stored in systems that are not connected, and using data that has multiple meanings. Data may not have been analysed for Data Integrity (DI), resulting in untrustworthy data. Before generative AI can truly add value to an organisation, steps need to be taken to ensure data integrity and discoverability by integrating disparate systems and achieving data compatibility across the business.
Pharmaceutical organisations also need to be aware of the total cost of ownership of any digital transformation. They must understand the potential costs of any new technology throughout its lifetime before embarking on a digital transformation project to ensure that it will truly add value to their operations. Cost can be a significant element of generative AI systems, if the system isn’t designed in a lean way.
Consistency and repeatability of generated answers and outputs is another issue that needs to be addressed. Appropriate testing of the new generative AI system at the beginning is key to ensure that the answers it generates from an organisation’s data are repeatable, consistent and, therefore, trustworthy and reliable. Expert support may be required to do this effectively.
Learnings to enhance efficiency
Having supported organisations across the life sciences sector in implementing generative AI to enhance their business operations, we have obtained some key learnings that pharmaceutical companies can use to ensure they can fully realise the benefits of generative AI.
Firstly, organisations should ensure that their generative AI solutions are effectively linked to measurable improvements in their primary business drivers, such as increased capacity or accelerated launch. This will help you demonstrate the real-world value of the investment from the beginning.
Secondly, only worry about capturing the tacit knowledge from the moment the generative AI is switched on, rather than try to retrospectively add this knowledge to the system. This will save time by simplifying the process of integrating disparate data systems, allowing companies to use the new technology faster.
Finally, embarking on a digital transformation journey can be daunting for any organisation, especially when it includes cutting-edge technology like generative AI. But, companies shouldn’t let that stop them from acting now to start the process of incorporating generative AI into their data systems. The sooner they begin reviewing new generative AI tools and look to integrate them into their operations, the sooner their business can benefit from the value they bring.