The McKinsey Global Institute (MGI) has estimated that generative AI technology could generate a staggering ​​$60 billion to $110 billion a year in economic value for the pharma and medical-product industries. However, a recent study from MIT's NANDA initiative, The GenAI Divide: State of AI in Business 2025, revealed a stark reality: 95% of AI generative AI pilots fail, leaving only 5% to achieve rapid revenue acceleration. This low success rate presents a conundrum for all industries, involving the decision of building versus buying AI technology and implementing it across organizations. 

One particular insight from the study was that 5% of companies that achieve rapid revenue acceleration through AI choose just one area to implement the technology in. At Reliant AI, we are proud to enable our partners to be part of this 5%. Our product team focuses on building tools to accelerate literature reviews, a particularly tedious and costly area for both rapid and systematic reviewers. We have been instrumental in several successful AI implementations in the life sciences industry. 

In this guide, we’ll delve into the everyday challenges like fragmented data, layered workflows, and complex change management that can make AI adoption a barrier for life sciences companies. 

Understanding the Core Challenges in AI Implementation

Learning Gaps And How Reliant AI Fills Them 

Change management, especially around AI and processes, can be complex for organizations and their workforces. In an interview with Fortune Magazine, Aditya Challapally, the lead author of the report, stated, “While executives often blame regulation or model performance, MIT’s research points to flawed enterprise integration. Generic tools like ChatGPT excel for individuals because of their flexibility, but they stall in enterprise use since they don’t learn from or adapt to workflows.” 

Key Obstacles:

• Generic models aren’t fit for specialized domains, but are familiar to individuals. 
• Enterprise adoption requires higher levels of service and usability.

How Reliant Trains and Onboards Customers: 

Reliant AI takes a different approach to supporting clients. Every license to our software includes access to former life sciences strategy consultants and epidemiologists. We also offer tiered services, including virtual and in-person onboarding and training sessions, as well as ongoing support and updates, to ensure our clients are always at the forefront of AI technology. 

We have collaborated with firms and customers to rework their onboarding materials, so every time a new employee onboards, there is a Reliant AI training session to ensure their work processes incorporate AI at the most opportune touch points.  

Meet Reliant AI's Expert Team

Meet a few of our experts — Brielan Smiechowski and Sam Mardini — and feel free to reach out with any questions you may have about Reliant AI. Our team is here to support you in your AI adoption journey. 

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Data Silos: The Hidden Cost of Fragmentation

One key challenge in the life sciences is managing data silos. Additionally, in medical literature, information is often presented in profoundly different ways, including PDF tables, images, and charts that are difficult to extract information from, plain-text descriptions, and difficult-to-parse nuance. As a result, professionals spend countless hours wrangling data from sources, often involving spreadsheets and countless browser tabs.

Key Obstacles: 

• Data professionals allocate significant bandwidth to data wrangling, reducing capacity for high-value model-building.
• Fragmented datasets risk introducing bias and incomplete insights, which undermines both research integrity and regulatory confidence.

How Reliant Approaches Fragmented Data: 

Reliant AI simplifies data segmentation by compiling key life sciences data, such as FDA labels, PubMed, and Clinical trial data, and allows users to query it with plain text inputs, getting results in minutes, not hours. 

Incorporating AI Into Complex Workflows Without Sacrificing Scientific Rigor 

Literature reviews and scientific evidence synthesis are both well-documented processes, but practitioners often have their own workflows and tools they like to use. All literature reviews involve screening and extracting information from documents, articles, and abstracts. At Reliant, we’ve chosen to embed AI into these specific steps of literature review to use technology to augment processes at their most tedious points. Additionally, our team of experts guides users on the best places to use our tools. 

For example, we encourage professionals to design and analyze studies themselves, while using AI to screen and extract data, thereby automating the manual tasks and focusing on generating insights post-study qualification. 

Building a Scalable and Compliant AI Ecosystem

AI adoption is not a “set and forget” activity. Long-term advantage requires scalable systems, mature data practices, and ongoing compliance.

Your AI Implementation Success Framework:

• Unified Data Infrastructure: Ensure the product you’re building or buying has the data you need, so you don’t run into the same problems you are looking for AI to solve. 
• Implementation and Training Strategy: Expand proven pilots into additional business lines with clear KPIs and milestones, with options for virtual or in-person onboarding and training.
• Continuous Quality Control: Implement regular performance reviews, process audits, and stakeholder feedback loops.

Transforming Adoption into Sustained Impact

Reliant Tabular positions organizations at the forefront of life sciences innovation. Through deliberate pilot programs and robust integration strategies, leading teams use our literature review and competitive intelligence tools to turn isolated innovation into enduring business and scientific value.

Discover how we can empower your organization to join the 5% of companies leveraging AI to drive rapid revenue growth. Contact our team today.