The Pharmaceutical Industry's AI Journey And Digital Transformation

Forbes

26-06-2025

Andreas Eschbach is the founder and CEO of eschbach, a software company that helps production teams work smarter and enhance collaboration.
The pharmaceutical industry stands at a critical juncture. To navigate the complexities of sophisticated drug development and manufacturing, a fundamental shift toward AI-powered technologies is not just desirable but essential.
Pharma's future hinges on embracing digital transformation and leveraging the power of AI to streamline operations, enhance problem-solving and root cause analysis, drive continuous improvement and mitigate risks within the pharmaceutical regulatory and compliance landscape.
The AI-Powered Future Of Pharma
AI is projected to generate between $60 billion and $110 billion in annual economic value for the pharmaceutical sector. Such untapped potential stems primarily from AI's ability to improve productivity across the drug lifecycle. Digital tools that can be tailored to complex processes and operational workflows, in addition to being more easily personalized to users' needs, will be key.
The pharmaceutical industry is estimated to invest $4.5 billion in its digital transformation initiatives within five years. The main manufacturing focus for digitalization will be on the optimization of operations, increased productivity and enhanced quality. Raw material conversion costs will decrease, and reliability with delivery will improve.
However, often due to issues in data quality, costly connectivity and maintenance challenges, as well as regulatory complexities, not all AI initiatives have been able to meet expectations. Success in AI initiatives can't happen without taking into account the needs of the organization's people, nor if the initiatives are not integrated into existing workflows. The most valuable gains from AI will likely result from the empowerment of people and using enhanced data analysis, knowledge management and augmented decision making.
The Key To Data Liberation
Too often, critical data is siloed across multiple systems that support pharmaceutical manufacturing—whether it be MES, historian or LIMS —and data is buried within unstructured sources like shift notes and maintenance logs. Such data silos cause fragmentation, hindering information transparency and accessibility as well as making it nearly impossible to deliver the right information to the right people at the right time.
Manufacturing facilities produce vast quantities of data from both automated sensors and human-generated notes (e.g., shift notes, compliance reports, etc.). However, much of this valuable information remains hidden or difficult to access, leading to inefficiencies and missed opportunities. Employees may spend hours searching through multiple data sources for information relevant to their current problem or task if they are able to find it at all.
AI, however, can help to bring buried pharmaceutical operational information to the surface by pulling data from shift notes, maintenance logs and other sources of structured and unstructured data. Utilizing natural language processing and machine learning enables people to ask questions in plain language, delivering the most relevant results in seconds rather than minutes or hours. This dramatically improves efficiency and access to valuable information.
Unlocking Historical Knowledge
Many manufacturing issues have historical precedents, but the solutions are often hidden in unstructured notes or lost when experienced workers retire or move on. The deep institutional knowledge required for informed root cause analysis and swift problem resolution is often scattered or missing. This leads to delays in problem resolution, as employees must either spend hours searching for relevant information or reinvent the wheel entirely each time a problem is encountered.
AI-powered tools can comb through past notes and data to identify historical precedents that can inform current issues. Using that data, it can then suggest potential root causes and present potential solutions for better decision making through AI's ability to run through similar past incidents. Production teams can then quickly get back on track. The technology will link directly to relevant notes and events so employees can see exactly what happened, who was involved, the most likely root cause and the impacts of actions taken.
Building A Culture Of Continuous Improvement
AI is a powerful driver of continuous improvement (CI) in pharmaceutical manufacturing, enabling teams to proactively identify opportunities for efficiency, quality and performance gains. AI applications can facilitate CI by capturing critical knowledge in real time, enabling communication within and between teams and giving access to the information people need to make more effective operational decisions.
AI supports continuous improvement by providing enhanced decision making. Data-driven insights help teams make faster, more informed decisions using real-time data. Streamlined knowledge sharing can happen when AI applications capture relevant knowledge, making valuable information accessible to all managers at every level of the organization. Improved collaboration occurs by consolidating data and automating communication. AI strengthens teamwork and information flow across shifts and departments.
Under the umbrella of continuous improvement is consistent process optimization. AI analyzes workflow patterns and bottlenecks over time, uncovering opportunities for ongoing improvements in production quality. Data-driven problem-solving can make a significant impact on a pharmaceutical company's profitability. For instance, AI surfaces actionable insights from past experiences, enabling quicker and more effective root cause analysis and more efficient problem solving.
Empowering Human-AI Collaboration
For AI tools to drive continuous improvement, they need to be rooted in the practical needs and workflows of the people using them. The next progression for pharma manufacturing is Industry 5.0, which emphasizes collaboration between humans and machines.
Industry 5.0 is the natural evolution from Industry 4.0, where automation and data management are partnered with human know-how. This symbiosis of humans and technologies allows humans to be the creative brain, resulting in faster and more innovative decision making.
When AI tools are designed to support employees' tasks and decision making, they empower teams to work smarter, adapt faster and create a sustainable culture of continuous improvement. By pinpointing specific areas where AI can make a quick impact, like root cause analysis, predictive maintenance or knowledge management, the value can be immediate while keeping the enterprise rollout manageable.
Taking the time to plan the rollout carefully, with a focus on training and creating a solid adoption plan, will make the transition smoother and encourage long-term use. The journey toward an AI-powered future in pharma requires a certain kind of strategy, one that focuses on a human-centered approach as well as provides continuous improvement.
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