Where AI fits within the pharma value chain
Elliott Charbonneau Elliott Charbonneau
July 22 5 min

Where AI fits within the pharma value chain

As investments in research and development lead to the emergence of innovative, much-needed new medicines around the world, it is increasingly important that we understand the role AI stands to play across the pharma value chain.

In this blog, we will look at the core components of this value chain examining pain points experienced by pharmaceutical companies across the world and highlight a few areas where AI can increase both productivity and quality. This blog post marks the first part of a broader series where we will dive deeper into the role of AI at each segment of the pharma journey.

Getting to grips with the pharma value chain

The term “pharma value chain” relates to the full set of industry activities from upstream active pharmaceutical ingredient (API) manufacturing through to when a drug is dispensed to end patients. Understanding what each stage entails is a vital first step to enabling the identification of areas ripe for disruption and improvement with AI down the road.

At a high level, the pharma value chain is made up of four key segments:

  • Research and Development (R&D)
  • Manufacturing
  • Distribution
  • Dispensing

Each portion of this chain is highly susceptible to disruption and AI can potentially serve as a catalyst for this disruption, touching everything from the enablement of more effective drug discovery to optimizing supply chains to ensure the right medicines are available at the right times for the right patients. In the next section, we’ll take a look at various pain points within the value chain that pharmaceutical across the globe are experiencing.

Pain points experienced by pharma companies

Over the last several decades, the complexity of the pharmaceutical industry has increased immensely. Today, large pharmaceutical companies deal with higher product volumes, more diverse SKUs, and increasingly advanced manufacturing methodologies than their peers of the past. As a result of these changes, pharma companies face a number of challenges:

  • Supply chain complexity - more products, more SKUs and more diverse production methods have necessitated the development of intricate, interconnected and increasingly fragmented manufacturing and supply chains. With this complexity comes the challenge of managing these networks and the disruptions that occur within them.
  • Quality management - the growth rate of quality incidents has outstripped the growth rate of the pharma industry at large. The ability to effectively monitor and track not only the symptoms but address the root causes of quality issues with a more diverse set of players and processes is paramount to ensuring effective patient outcomes.
  • Manufacturing flexibility - emerging gene therapies and personalized medicines give rise to the need to efficiently service smaller, more targeted patient groups with small batch drugs. To accomplish this, manufacturing facilities need to be configured in a flexible manner to handle more diverse and specialized SKUs.

Fortunately, across the value chain, AI can be a powerful enabler for addressing a number of the pharma industry’s pain points to drive efficiency improvements and better patient outcomes.

Fitting AI into the value chain

AI can play a vital role at every stage of the pharma value chain:

Research and development

Pharmaceutical manufacturers are sitting on huge amounts of disparate data. From patents to historical project reviews, pharmaceutical companies rely on internal expertise to guide their research and new product development.

This work can greatly benefit from the knowledge or past experience of colleagues who may or may not be available to contribute. Identifying the right internal expert across geographies is crucial, and tools like Element AI Knowledge Scout can aggregate data in a way that forges a connection between users and knowledge contained in an organisation. With just a simple query, research scientists can use AI to identify the right expert, reducing effort and potential overlap of work.

Manufacturing

The ability to investigate deviations and make informed decisions quickly relies on access to accurate information. With the right intelligence, quality personnel can understand the full context of what’s happening and how to resolve issues in manufacturing.

With the help of AI-powered decision-support products, employees can benefit from faster access to the information they need to tackle complex issues. These product deviations can cause bottlenecks and hold ups in the production process delaying access to key medicines — AI keeps the process running smoothly ensuring drug availability for those in need.

Distribution and dispensing

From the factory to the pharmacy, AI-driven solutions can interpret a wide array of signals, from weather and traffic data to shelf sensors. This means they can be used to help predict, prescribe, recommend, and orchestrate activities across all points of pharmaceutical companies’ supply chains. Harnessing these diverse data streams, today’s systems can now help to optimize supply chains, counter disruption, and improve profit margins.

This valuable AI-powered assistance can streamline a pharmacy’s processes and maximise employees’ productivity, engagement, and satisfaction. As well as reducing time spent on storeroom or back-office tasks and freeing up their time for more customer-facing activities.

Start your AI journey with Element AI

Most pharmaceutical manufacturers understand that it’s not necessarily a question of whether or not to deploy AI solutions but rather a matter of where or when to get started. At Element AI, we have a cross-functional team of experts that can guide you through the process of driving real business value with AI. Our goal is to help pharmaceutical companies achieve the most effective value chains, so they can innovate and bring the vital drugs of the future to market without delay.


From identifying areas of innovation that require further research in the pharma value chain to deploying sustainable AI business applications at scale, we can help you map it all out. To find out more, get in touch with a member of our team today.