AI : Going Beyond Efficiency and Cost Reduction.
2024 has seen an early majority of enterprises begin the adoption journey for AI in the enterprise . The statistics are telling when you compare AI adoption in 2023 vs 2024: (See Side Bar 2)
Yet a lot of the initiatives are about saving costs. While efficiency is important, an overemphasis on cost-cutting misses the bigger opportunity—AI as a driver of new services, products, and revenue streams.
In the 19th edition of Mindvista, we explored how AI could create new business models for Apple, Marriott, and LinkedIn. The 14th edition introduced the Bi-Modal AI Enterprise framework, differentiating between AI-Enhanced (incremental improvements) and AI-Driven (new, disruptive innovation).
A January 2025 McKinsey report adds weight to this shift. Titled “Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential,” the research presents compelling insights:
AI’s potential extends beyond operational improvements—it’s about unlocking entirely new opportunities.
AI Tech Innovators in Pharma:
Let us now look at AI first Pharma innovators. Without being comprehensive , but to be illustrative of the scope and impact of AI, here are a list of six very interesting AI tech innovators in Pharma to showcase real world successes:
1.Insilico Medicine - End-to End AI led Drug Discovery
Insilico Medicine stands at the forefront of AI-driven drug discovery, focusing on the biology of aging and related diseases. They employ generative AI to sift through complex biological data, identifying and designing new drug candidates for conditions like fibrosis and Alzheimer’s.
Their use of AI allows for rapid iteration in drug design; their platform, Pharma.AI, uses deep learning to generate molecules with high efficacy potential. The outcomes of their work include the first AI-designed drug, INS018_055, entering Phase II trials, in 3 years vs 6-7 industry average. If sustained this is a significant acceleration of the traditional drug development timeline and potentially bring new therapies to market much faster.
2. Recursion Pharma - Cellular Digital Twins
Recursion Pharma harnesses AI to revolutionize drug discovery, not limiting itself to specific diseases but rather exploring treatments across a wide spectrum, including rare diseases and those affecting the elderly. They analyze millions of cellular images to uncover drug-target interactions, making drug discovery more data-driven and less dependent on serendipity.
Instead of relying on the conventional teams led by MDs or PhDs, Recursion is pioneering a model where quantitative biologists take the lead. This shift allows for a more systematic and scalable approach to finding new treatments, leveraging AI to analyze vast amounts of biological data to pinpoint potential drug-target interactions, thus opening up new avenues for drug discovery.
3.Unlearn.AI - Synthetic Control Arms
Unlearn. AI uses AI to create digital twins for clinical trials, simulating patient responses to new treatments across all disease types. This technology allows for more efficient trial designs by reducing the need for large patient cohorts and cutting down on both time and cost.
Their AI-driven approach has been FDA-cleared for trials in complex areas like ALS, showing potential reductions in trial duration by 40% and costs by 30%. This could lead to faster market entry for new drugs, particularly for diseases like Alzheimer’s where time is of the essence.
4.BenevolentAI - Innovating Drug Repurposing with AI
BenevolentAI applies AI to discover new uses for existing drugs, focusing on a wide array of health issues including those prevalent in aging populations. Their AI builds knowledge graphs that connect vast amounts of medical and biological data, enabling the identification of drug candidates for repurposing.
The results of their work include identifying over 20 drug candidates for various conditions, including baricitinib for COVID-19. This not only accelerates the drug development process but also offers cost-effective solutions for treatment, particularly for conditions where new drug development might be prohibitively expensive.
5.Exscientia - Generative AI for end-to-end drug design
Exscientia is pioneering end-to-end AI drug design, covering everything from target identification to candidate molecule selection across multiple therapeutic areas. Their focus on oncology is notable, but their technology is versatile, applicable to cardiovascular, metabolic, and other diseases related to aging.
Their AI algorithms optimize drug design by predicting how molecules will interact with biological targets, resulting in more effective and less harmful drugs. With six clinical-stage candidates, including work on ALS, Exscientia demonstrates how AI can streamline drug discovery design drugs in 1 year vs 4-5 year on average in pre-clinical stages, potentially reducing the time and cost to bring new treatments to patients.
6.Verge Genomics - Human tissue-based AI target discovery
Verge Genomics uses AI to analyze human tissue data, focusing on neurodegenerative diseases like ALS and Parkinson’s, which are significant in aging populations. Their approach involves using machine learning to predict drug targets from complex genomic data.
Through partnerships, including with Eli Lilly, and with ongoing clinical trials, Verge Genomics is validating their AI-driven approach avoiding animal model biases. Their work could lead to breakthroughs not just in neurodegenerative diseases but also in understanding the broader mechanisms of aging and disease, potentially influencing treatments for a wide range of conditions.
Regulated AI: Pharma Shows The Way:
The success of AI-first pharma innovators in bringing their drug candidates through clinical trials and regulatory approvals underscores their ability to meet the FDA’s rigorous standards for safety and efficacy. Despite the complex regulatory landscape, companies like Insilico Medicine and Exscientia have made significant strides, demonstrating the potential for AI to drive innovation while complying with existing regulations.
As the FDA continues to engage with the AI community and evolve its regulatory frameworks, the achievements of these pioneers serve as a positive signal for the broader adoption of AI in the pharmaceutical industry. Their examples highlight the successful close collaboration among AI innovators, large enterprises and regulators to ensure the responsible and impactful application of AI for accelerated innovation and business value.
Learnings for Leverage
The AI-first pharma innovators mentioned in the draft showcase the wide-ranging potential of AI in drug discovery and development. From designing novel drug candidates to streamlining clinical trials and repurposing existing drugs, these companies demonstrate how AI can be applied at various stages of the pharmaceutical pipeline to accelerate innovation and improve patient outcomes.
Their success stories offer few valuable examples for businesses across industries.
AI in Big Pharma is unlike AI in Big Finance
In the 33rd edition we saw examples like (BlackRock Life Path , Block’s Goose) of established FS firms demonstrate agency using AI for value creation and there are more from JP Morgan Chase (Lending), Citigroup (FX) , Morgan Stanley (Wealth advisory).
With few exceptions we have not seen AI led innovation in Big Pharma and these could be because there are structural differences:
However tech startups not having the legacy or burden of Big Pharma, have taken a AI first approach in drug discovery and development. According to a Dec 2023 Statistica survey, 75 percent ‘AI-first’ biotech companies stated they use artificial intelligence in drug discovery substantially.
Seeing the progress and traction by AI first innovators, Big Pharma realizing the value and benefit has adoptedt the partnership route with these AI innovators as seen with some examples below:
Interesting isnt it, to see the difference between Big Finance and Big Pharma?
Takeaways For Your Consideration
The 2025 Mckinsey research notes that f 92% of companies plan to increase their investment in the next 3 years but also expect that AI should deliver more than 5% revenue growth . Today only 20% achieve that and this is where AI led Accelerated Innovation (AI for AI) matters.
AI tech and infrastructure is constantly and rapidly evolving in its capabilities and support super agency to deliver revenue and strategic value.
Being efficient is one and being disruptive is another. Both are paths to pursue.
However to do the disruptive, the key questions you should ask/continue to ask are:
PS: As we pointed the public health impact of AI in Pharma , as you explore the potential of AI in your own industry, also consider how it can contribute to improving lives and creating a more equitable future for all. By aligning business goals with societal needs, we can unlock the full potential of AI for social good.
Final Word
In previous editions we saw a how small team at Deepseek disrupt AI itself and real examples in disruption from using AI in FS and Pharma. No doubt we will find impactful and exciting ones as we cover key industries in the upcoming editions.
In democratized AI, Any one, any team, any where can do any thing if they can, if they have the courage, intelligence and tenacity.
And the time to do is now.
What a promising start to 2025—the first year of the next quarter-century.
Explore, join and stay tuned for more!
Love to hear your comments, thoughts and ideas.
Best wishes
II. Questioning / Asking
Good conversations flow from well‑sequenced questions—topical, simple, coherent, cohesive.
LLM Conversation Example 1
Q: What are empirical judgments?
A: Empirical judgments are based on observation, experience, or experimentation.
Q: What are moral judgments?
A: Moral judgments are based on ethical principles and values.
