Is the Pharmaceutical Industry Ready for AI?

Evgeny Maslov Client Manager at Cinimex When it comes to artificial intelligence in the pharmaceutical industry, the most high-profile topic is undoubtedly the breakthrough potential in drug development, including molecule generation and clinical trial modeling. At the same time, there is significant potential to transform the industry not in research laboratories, but in companies’ day-to-day operations — for example, in manufacturing, logistics, and marketing, where AI can deliver rapid efficiency gains and a competitive advantage.

But how ready are Russian pharmaceutical companies for widespread adoption of these technologies? Today, we see a significant gap between initial experiments and the large-scale deployment of AI across operational processes. Many companies have already explored the technology, testing both free and leading commercial foreign and domestic models for specific tasks — from content generation to the initial analysis of documents and knowledge retrieval. A new stage has now begun: process automation with AI agents. It is precisely here that the key problem becomes apparent — the lack of a systematic approach.

In addition to the ongoing challenges related to data infrastructure and data quality, the adoption of AI agents is hindered not only by a lack of in-house expertise in developing AI pipelines, but also by a shortage of methodological expertise. Companies often lack dedicated teams capable of managing the full cycle — from auditing business processes and identifying pain points with the highest automation potential to calculating ROI and developing an implementation roadmap.

At the same time, another equally serious barrier is becoming apparent: the lack of widespread internal demand “from the bottom up.” Employees on the ground — whether accountants, lawyers, HR specialists, or line managers in field operations — often do not see how AI tools can become their “exoskeleton,” significantly enhancing their productivity. They are not accustomed to viewing routine operations as tasks that can be automated, while IT departments do not always proactively propose such solutions, leaving significant potential untapped.

It is also worth mentioning companies that rushed to adopt AI at the height of the hype. They quickly agreed to external agencies conducting AI implementation audits, only to achieve virtually no results — which was understandable at the time given the limited number of actual implementations. At Cinimex, we still encounter the negative consequences of this experience among clients. Many are categorically opposed to conducting another assessment of their operations, even though the approaches and practical experience in this field have advanced significantly since then.

Therefore, the main challenge today is not technological but managerial and cultural. The technological readiness of platforms is already there, and the economic incentives are clear. However, moving from isolated experiments to widespread adoption requires strong strategic commitment from management. Companies need to establish cross-functional teams at the intersection of business and Data Science, invest in employee training, and, most importantly, make a culture of optimization an integral part of the organization.

The ability to overcome this organizational gap will become a key factor in competitive advantage in the coming years. The result will be more than just optimized costs — it will be a new operating model for the pharmaceutical business.

It is now important to objectively assess the internal barriers to AI adoption and consider bringing in external expertise. This is particularly relevant because IT integrators, including Cinimex, that work with the pharmaceutical industry are investing resources in developing solutions based on AI-agent platforms and have already accumulated practical project experience in this area.