Chapter 1
Digital transformation in the age of AI and ML
Digital transformation continues to be a driving force in the pharmaceutical industry, with artificial intelligence (AI) and machine learning (ML) at the forefront.
Our survey revealed a fascinating perspective on the expected return on investment (ROI) from these initiatives. A significant 65% of respondents anticipate seeing ROI within 2-3 years, highlighting a patient yet optimistic outlook.
Interestingly, only a small fraction (3%) expect ROI within the next 1-2 years, suggesting the industry acknowledges the complexity and time required for successful AI/ML implementation. When asked about the areas with the greatest potential for improvement through AI/ML, supply chain visibility and risk management emerged as the clear leader (25%).
This indicates a strong desire to leverage AI to enhance transparency and resilience in the face of increasing uncertainties. Manufacturing planning and optimisation followed closely at 17%, emphasising the drive for efficiency and precision in production processes.
It's noteworthy that personalised medicine and patient engagement, research and development, and regulatory compliance received significantly lower responses. This suggests that while AI/ML holds immense potential in these areas, its current application and perceived impact may be less pronounced compared to supply chain and manufacturing.
What is your expected return on investment (ROI) timeframe for your AI and ML initiatives?
2-3 years
3-5 years
1-2 years
“We are expecting to see an ROI in 2-3 years. We expect our timeframes for AI and ML solutions to match any other IT project. As things stand however I do think there are other solutions which can give a higher return on investment and sufficient intelligence than AI solutions currently can. That's normal for newer technologies though, as the first users are always paying higher costs."
Stefano Chiei, Director Operations EMEA/EE, Advanced Bionics
Which area of your pharmaceutical supply chain do you believe has the greatest potential for improvement through AI and ML technologies?
Supply chain visibility and risk management
Manufacturing planning and optimisation
Logistics and distribution
Quality control and assurance
Demand forecasting
Inventory management
Supplier relationship management
Research and development (R&D)
Regulatory compliance
Personalised medicine and patient engagement
"The quality control use case is very interesting for AI, especially with cold chain. AI can be used to monitor batches in and out of ambient zones and predict whether a batch is going to breach its ambient limit, giving the manufacturer enough time to bring it back within range by reducing ambient time later in process, thereby increasing yields which is a huge value add."
Shirell James, Vice President, Blue Yonder
How would you rate your organisation's current maturity level in leveraging AI/ML within your supply chain?
(1=Nascent, 5=Mature)
0%
One
Two
Three
Four
Five
“For us, AI still needs to be embedded in our core routine. Right now, we use that technology to resolve important issues, but not the strategic ones. So, we need to embed AI decision-making into the core business. This is what we're working on, but it's going to take some time."
David Ruiz, Head of Digital Supply Chain Strategy & Execution
