Digital engineering: reshaping pharmaceutical innovation

Authored by Ramji Vasudevan, Head of Life Sciences and Adam Caplan, President of Digital Business and AI at Altimetrik

Historically cautious about adopting new technologies, the pharmaceutical industry is experiencing a remarkable transformation. Driven by competitive pressures and compelling early successes, pharma companies are increasingly embracing artificial intelligence and digital engineering to reimagine their approach to drug development, manufacturing and patient care. Pharmaceutical companies have traditionally been slower to adopt AI technologies compared to other sectors. In recent years, however, the industry has made concerted efforts to catch up and maximise AI implementation across their operations.

Breaking through regulatory barriers

The industry's hesitation has largely stemmed from regulatory concerns. Operating in a highly regulated environment, pharmaceutical companies have struggled to implement AI solutions while remaining within compliance parameters. Advancements in explainable AI are helping overcome these obstacles by making AI decisions more transparent and interpretable. This technological progress coincides with a significant mindset shift. Companies are gradually abandoning legacy systems and outdated beliefs in favour of more open attitudes towards data and AI's potential. Nevertheless, scaling beyond initial proof-of-concept projects remains challenging, primarily due to disorganised data landscapes that hinder wider implementation.

From concept to application

The pharmaceutical industry's engagement with AI spans both ambitious future applications and practical solutions delivering immediate value. AI-powered drug discovery perhaps represents the most transformative potential application. Major pharma companies are investing heavily in technologies like AlphaFold and various generative AI solutions to accelerate the identification of promising molecules and compress development timelines. Alongside these headline-grabbing initiatives, numerous practical AI applications are already yielding tangible benefits. Call centres now deploy AI agents to augment human representatives, enhancing efficiency without eliminating the human element. Most companies wisely implement a "human in the loop" approach rather than exposing AI directly to customers.

Marketing compliance teams have discovered AI's value too. The compliance review process for marketing materials often faces significant backlogs, creating an ideal opportunity for AI-powered compliance bots to perform initial reviews before human officers get involved.

Measuring real-world impact

The true value of AI in healthcare will be measured by its impact on patient outcomes, a metric that is still evolving but already shows significant promise. Much like operational efficiency improvements which yield straightforward measurements. When building employee efficiency use cases and saving staff hours, the benefits are easily quantifiable.

Patient health outcome improvements, though more challenging to measure, potentially carry greater significance. Supply chain optimisations ensure critical medications reach patients within tight expiration windows. This was evident during the pandemic, with the development of the COVID-19 vaccine serving as a powerful example of how data sharing and AI can accelerate scientific progress. This acceleration extends to patient onboarding, where early results are especially promising. Showing improvements in insurance submissions, appointment scheduling, clinical trial preparation and medication access, all of which enhance patient experience while reducing administrative barriers to care.

Redefining manufacturing practices

Pharmaceutical manufacturing involves complex, multi-stage processes from raw materials to finished medications. Traditional "make, test, release" cycles are typically progressed through paper-driven, time-intensive procedures. The inefficiency can be staggering. When a deviation appears in test results during batch production, the resolution process takes approximately 30-34 days for some manufacturers. These delays represent not merely inefficiency, but missed opportunities for quality improvement.

Digital tools and AI now compress these decision cycles dramatically. Using database-driven decision making and AI, companies can analyse where these delays occur and then shorten the entire cycle. Many manufacturers aim to reduce deviation resolution from over a month to just a single day, a transformation that would radically enhance manufacturing agility.

Personalised care and integrated systems

AI promises to transform patient engagement through customised treatment plans tailored to specific conditions and needs. While regulatory considerations currently limit direct patient applications, healthcare providers increasingly use AI-generated analyses to inform patient interactions. Wearable technology opens new frontiers for patient monitoring and decentralised clinical trials. Future applications might combine wearable data with AI to create personalised health insights and symptom reporting tools. Large language models now appear throughout pharmaceutical organisations, from HR and marketing to legal and clinical departments. Yet their true value emerges only when connected to internal company data, transforming generic AI into specialised tools with deep domain expertise. Yet, challenges persist, particularly "hallucinations" - AI-generated responses that appear plausible but contain factual inaccuracies. In highly regulated pharmaceutical environments, such errors could have serious consequences. Better data and robust feedback mechanisms offer a path forward.

A new frontier

Conversational analytics represents an exciting frontier, moving beyond traditional dashboards to provide genuine insights through natural dialogue with data. This trend is growing rapidly across industries, particularly within pharma. On the other hand, multi-agent orchestration is gaining traction, with specialised AI agents working collaboratively to manage specific tasks. Some summarise data, others verify the work of peer agents, while others take concrete actions. This approach improves accuracy by having specialists focus on narrow tasks while an orchestrator agent manages their interactions. This is a vital component, especially in regulated environments where errors are unacceptable.

The road from technological hesitancy to digital transformation in pharmaceuticals continues to unfold. Pharmaceutical companies that embed AI effectively into their operations are set to move ahead of those taking a more tentative approach. Success in tomorrow's pharmaceutical landscape will increasingly depend on how effectively companies blend traditional scientific expertise with cutting-edge digital tools. However, progress remains uneven across the industry, with data quality and regulatory navigation presenting ongoing challenges. Nevertheless, the direction is unmistakable. The ultimate beneficiaries of this technological revolution will be patients, who should experience faster drug development, more personalised treatment approaches and improved access to life-changing medications. This transformation, perhaps the most significant since the advent of modern pharmaceutical practices, promises to reshape healthcare delivery for generations to come.