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Thanks to artificial intelligence (AI), pharma marketers now view time as a product. Success hinges on how well a company uses this technology to speed up and optimize research and development (R&D), drug discovery, clinical trials, or marketing. Since time can be easily measured, the impact of AI is palpable across the organization, often leading to broader digital transformations.
In this post, the Viseven team examines AI in pharma. We will separate hype from reality, tackle major challenges, and dive into some real-world use cases. If you are considering bringing AI into your pharma business, keep reading!
AI in pharma involves leveraging AI-powered systems to enhance and optimize drug discovery, clinical trials, manufacturing, and marketing. This technology helps companies find drug candidates, improve molecular design, develop precision medicine, eliminate manufacturing waste, and create content that resonates with target audiences.
Yet, despite these tempting benefits, AI adoption in life sciences has been slower compared to other sectors, mainly due to strict regulations. While the average AI adoption rate across industries is 5%, the pharma sector lags at 2%.
AI adoption across industries and domains
Based on our experience, there are some myths about AI that keep popping up. They shape the way many pharma stakeholders think about the technology, leading to project failures or low adoption rates. It is high time to debunk them.
Being aware is almost the same as being prepared. You should know your challenges well to ensure a smooth adoption process.
Security is a key challenge when introducing any advanced technology. In 2023, 77% of companies reported data breaches in their AI systems.
The last thing you want is to be part of that statistic this year. So, what can you do beyond the usual strategies like two-factor authentication and encryption?
First, identify who your vendor partners with. Ensure it is a trusted provider like OpenAI or Amazon. This should be the companies that prioritize their reputation and take all necessary precautions to protect your data from exploitation by third parties or their own systems.
Let’s not overlook that the greatest weapon in a hacker’s arsenal is often people. Employees who forget or disregard security measures can expose their organization to significant legal and financial risks. Your goal is to ensure they understand security protocols and know how to identify and respond to potential threats.
Poor data costs any organization a lot. On average, companies lose about $12.9 million every year because of it. This leads to higher maintenance costs, system crashes, and ineffective decision-making –– a situation no one wants to face.
In the highly regulated pharma industry, low-quality data is especially harmful. For example, pharma companies must avoid delivering biased or inaccurate content to patients, as it could have serious consequences for their health. That is why they train large language models on their own databases and retrain them when there are tone, style, or context issues.
AI has made its way into every domain of the pharmaceutical industry. Let’s take a closer look at how it is being used.
A drug discovery process is costly and time-consuming. Machine learning (ML) speeds up the discovery of new molecules by scanning through the vast amount of chemical and biological data.
Since new drugs need to be approved before they hit the market, pharmaceutical companies must conduct research and trials during discovery. AI plays a big role in automating quality assurance, making sure the entire development process meets high standards.
Clinical trials always involve massive amounts of data. As the data gets more complex every year, processing it manually becomes harder, and the risk of human error grows.
The beauty of AI is that it can handle massive data sets in record time. Pharmaceutical companies turn to AI to help identify potential drug candidates. AI can collect and process data like existing health conditions, demographic details, infection rates, and other key factors to help create the right testing group for a successful trial.
AI enables quality control and predictive maintenance and improves supply management. Moreover, AI-driven tools can optimize production by handling the most complex tasks, ensuring everything is done with surgical precision.
Life sciences companies leverage AI to reduce manufacturing errors, ensure compliance, and eliminate waste during this phase. The technology allows for more agile and resilient manufacturing processes, reducing downtime and costs.
This section will take a more technical approach and highlight the leading AI models that support drug development and clinical trials.
R&D is understandably the main priority for pharmaceutical companies, but marketing is getting more attention lately, especially with AI advancements. Let’s examine how this technology improves essential commercial advantages, like time, and moves the needle in the sector.
Localizing pharma content for diverse markets used to demand a lot of effort and time. Translating texts correctly was only a part of the equation. Companies needed to ensure the content met local regulations, reflected cultural nuances and matched their branding. This was no small task until generative AI came along.
Today, content experience platforms like eWizard enable companies to fine-tune models with pharma-specific vocabulary, brand guidelines, and regional regulations. This fine-tuning can also incorporate cultural nuances, allowing the model to generate local idioms and appropriate formal or informal language. A key perk is that marketers can manually refine the content, and the system learns from these adjustments.
eWizard auto-tunable translation
AI has become a go-to solution for life sciences brands to manage their extensive knowledge bases. The eWizard virtual assistant, for instance, helps users kill two birds with one stone: assisting in employee training and onboarding, and retrieving critical documents.
AI makes it easier for new employees to understand projects and products, so pharmaceutical companies do not have to spend as much on experienced trainers. Plus, marketers can use virtual assistants to pull up the materials from the database and create content that resonates. This way, they improve customer experience and minimize costs at the same time.
Pharma companies must engage early with customers but also stay compliant with MLR guidelines. Personalized outreach requires perfect timing, but approvals take up to two months.
The good news is that MLR compliance is always rule-based by its essence, which makes it a perfect fit for AI applications. eWizard MLR acceleration engine can analyze content against key rules such as references, approvals, verifications, grammar, and modifications. The report shows whether references are intact, traces the source of modules and approval statuses, and reveals any alterations.
eWizard MLR acceleration engine
The tool also provides the likelihood of content approval and suggests changes that can improve this score. You can select specific users to train the model, allowing it to become more accurate over time.
AI has left no part of the pharma industry untouched. With this tool, we can identify ideal molecular structures, choose the right trial candidates, and optimize timing to connect with healthcare providers. This technology grants life sciences brands the most valuable resource they could ever have –– time. In a field where time influences survival rates, it is incredibly powerful.
Viseven offers eWizard, an AI-driven content experience platform that empowers users to create, localize, modularize, check, and distribute content, all in one place. With a range of AI features for video creation, content generation, or MLR acceleration, eWizard optimizes your content processes from the project start to finish.
If you want to learn more about our platform and the way it’s AI capabilities can empower your pharma marketing efforts, please reach out through this form.
