Dimitrios Skaltsas Of Intelligencia AI: How AI Is Disrupting Our Industry, and What We Can Do About It

Thank you so much for joining us in this interview series. Before we dive into our discussion our readers would love to “get to know you” a bit better. Can you share with us the backstory about what brought you to your specific career path?

My career path has been anything but straightforward or conventional. I didn’t grow up knowing that I wanted to start an AI company. Nor did I know that it would be a company that helps the pharmaceutical industry manage the risk of drug development. What I did know, though, was that I wanted to do something meaningful, something that would impact peoples’ lives in a positive way.

By the time I co-founded Intelligencia AI I had received my law degree, practiced for a few years, went back to school for engineering, which I never graduated from, and worked at McKinsey in various roles. I spent some time in Africa with a nonprofit and eventually earned my MBA from INSEAD. Throughout this journey, I even dabbled in the food world as I set up a spinach pie business to bring this beloved and authentic Greek delicacy to the U.S.

My current role and work bring together my value system and personality traits. Making a meaningful, positive impact on people’s lives. Being entrepreneurial, with an urge to bring forth new ideas. Thriving when working in teams. Building for the long-run. I am in awe of the amazing advancements in science, and I am grateful to have the opportunity to contribute.

What do you think makes your company stand out? Can you share a story?

I will focus on two aspects — our mission and our people.

At some point in our lives, we have or will most likely take pharmaceutical drugs for medical reasons. Many of you reading this may not realize just how risk-laden, long and expensive it is to develop a drug from the research stage all the way through to approval by regulators, such as the FDA and EMA. There are different stats out there but ballpark numbers are 10 years and over $2B to develop a single drug. Making this worse is the fact that the vast majority of drugs that start down this long and expensive path never make it out the other end — only about 10–15% actually do. These failures greatly diminish focus and can delay bringing the most-promising drugs to patients.

At Intelligencia AI, we focus on improving the probability of success (PoS) of new drugs, by helping pharma companies better understand and manage risk in the drug development process. We’re on a mission to empower life sciences companies to accelerate clinical development and de-risk business decisions with AI-driven solutions. If a pharma company can more accurately predict the chances of drug approval, they can make more informed decisions early on and focus on developing promising drugs faster. We were the first company (as far as I know) to apply AI in assessing the probability of success of new drugs. I know that what we do is innovative and pushing boundaries in our space, but of course I am a bit biased.

A pivotal point was when we were working with a major pharmaceutical company and they updated their workflow to embed our technology into their decision-making process. Another validation of our work was recently receiving a patent for core parts of our technology. This further validates that what we are doing is truly special, and we are in fact pioneers and leaders in the space.

Our people are at the core of our success. We have an amazing, interdisciplinary group of individuals who bring their expertise in fields as vastly different as data science, medical research, engineering, product development, commercialization and business operations. It is not just the expertise and the advanced degrees — it is teamwork, a common culture and a sense of belonging. It’s that desire to succeed together and the ability to work alongside people with varied educational and cultural backgrounds that have led us to develop such an impactful solution and weather the challenges of scaling up the company.

You are a successful business leader. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?

Perseverance. It takes a lot of grit to attain goals in life — even more so in pharma and biotech which are definitely not the easiest industries to realize fast success. A book that I almost always carry with me is the Odyssey by Homer — the story of a man returning home after 20 years in wars and endless travels to worlds unknown and overcoming real and imaginary obstacles. Entrepreneurship is a gift in the form of a journey where one realizes dreams and also meets one’s fears. Without perseverance, chances are that one will be lost. In the Odyssey, Ulysses’s companions are devoured by monsters, drown in the deep sea and get stranded in alluring places far away from home. I am grateful for the road I have traveled so far at Intelligencia AI, and it has only been possible because of perseverance. Love and perseverance, combined with some healthy optimism and a willingness to make some sacrifices are needed. It also takes some luck — it always takes a bit of luck.

Creative Itch. Intelligencia AI started from scratch, literally ground-zero, and now has become an award-winning organization of more than 100 people, working with top pharmaceutical companies. I love coming up with radically new ideas and concepts, as well as the design phase. And I have come to appreciate the arduous process of building as well. Designing and building are integral to all parts of the company — from building a company, a product, and meaningful customer relationships. I have channeled much of my creative energy into the company. However, I still manage to make time for myself and time with family, friends or co-workers, as well as to paint, play music, cook and celebrate how life and the world shapes us and how we can shape it as well.

Care. I am moved by a sense of deeper purpose, as most people that I have had the privilege to meet in life sciences seem to be as well. I care about helping people live better, longer lives. This was a primary driver for creating Intelligencia AI. I have directly experienced disease and loss from a young age and joining forces with others to build a healthier future is fundamental to my work. At the same time, care extends more broadly, and is one of our core company values. I care about my colleagues by fostering professional growth opportunities and an environment that promotes work life balance. I care about our customers, to help them achieve their goals. And I care about the people in the company’s broader ecosystem, investors and advisors. There is a rational and, primarily, an emotional part to caring.

Let’s now move to the main point of our discussion about AI. Can you explain how AI is disrupting your industry? Is this disruption hurting or helping your bottom line?

With all the hype about artificial intelligence (AI), it’s not easy to paint a full picture of this evolving technology. It’s important to keep in mind that change in drug development is slow and for a good reason: people’s lives and health depend on this process being well-understood and regulated. This industry doesn’t make hasty changes nor does it try out new technologies and approaches on a whim. Precision and accuracy matter. It is by no means an overstatement to say that AI has already started to change how drugs are discovered and developed at pretty much every stage of the value chain. AI is likely to truly revolutionize and completely change healthcare and specifically the pharmaceutical industry over the next decade for the better.

At Intelligencia AI, we are proud to be part of this ongoing disruption. AI lies at the core of our technology and business model. Even more so with the latest development in Large Language Models (LLMs), where we can now source the data that feeds our predictive AI models at an even larger scale and faster pace.

Risk is the elephant in the room in our industry, and we focus on better assessing it and addressing it by applying AI. When we started in 2017 — a time when AI was more of a buzzword than a reality — we took a bet that AI would be a powerful tool to help de-risk drug development and that the pharma industry would adopt it. It was a bet, yet a calculated one, because AI excels at the tasks necessary to have a better handle on risk: using huge amounts of data — billions and billions of data points — to detect and make sense of patterns humans or even conventional computational methods are unable to understand and doing so in a quick and objective way. We help complement human intuition and judgment with the predictive accuracy and objectivity that AI offers when trained on appropriate data.

Which specific AI technology has had the most significant impact on your industry?

The answer to this question is pretty much all of them, plus what has yet to be invented.

Drug discovery and development is an extremely complex process. To simplify, it starts with understanding a disease better. What goes wrong in a person’s body to make them sick? Once that is understood you need to find a “target,” a gene or a protein that you can activate, deactivate, up- or downregulate, etc. that will either cure the person or alleviate their symptoms. Then you need to find a molecule that interacts with the target and causes the right reaction. To do so, scientists screen millions of different chemicals on cells and eventually in animals.

Once that is all done, the longest and most expensive part begins: testing the drug in clinical studies. In three phases, hundreds of volunteers receive the drug under close medical supervision to determine whether the drug helps the patient and doesn’t have negative side effects.

Throughout the drug development cycle, there are a myriad of different opportunities for AI to be used. Generative Adversarial Networks and Convolutional Neural Networks can help design new drugs in a way that they will bind to the target, taking the trial and error out of this process. Long Short-Term Memory Networks can be used to evaluate drug efficacy. Transformer models are used to extract information from the scientific literature, patent databases, and clinical trial data, enabling researchers to make informed decisions in drug development.

During the entire drug development process there are many decision points of great consequence. Arguably the most important ones are during clinical development:

• Do the results of my phase 1 or 2 trials look good enough to take the next step?
• Which of several similar drugs has the best chance of success?

These are just two of the many questions we are helping to answer with our AI solutions, especially with machine learning (ML). ML is excellent at generating novel insights after ingesting a lot of data and therefore supporting these decisions with fact-based, unbiased information.

The advent of LLMs is also critical. They can help extract and summarize information from written documents e.g., academic papers, or doctors’ notes and generally help us automate the time-consuming task of finding and cleansing data.

Can you share a pivotal moment when you recognized the profound impact AI would have on your sector?

My ‘aha’ moment came in 2016 when I started as the Domain Lead in Big Data and AI for Pharmaceutical R&D at McKinsey New Ventures — a newly developed area of the business. I was transitioning from consulting to building AI and software solutions, and it was here where my intrigue with this then little-known world of AI shaped into fervent enthusiasm about its possibilities. We were getting better answers to complex industry challenges at a faster pace, in a more reproducible manner. This served as the bridge to Intelligencia AI, which I co-founded with a former colleague and friend. On one hand I saw huge gaps and opportunities in building higher quality data, an absolutely necessary foundation to any successful AI model. And on the other hand, AI was very promising for better assessing risk, which I had so often seen delivered in a haphazard manner.

How are you preparing your workforce for the integration of AI, and what skills do you believe will be most valuable in an AI-enhanced future?

At Intelligencia AI we live and breathe artificial intelligence. It is at the heart of our technology platform. We have a core team of data scientists and a broader cross-functional team that is well versed in AI. We also have ‘translators,’ our subject matter experts with cross-functional expertise who drive the diffusion of knowledge to customers and colleagues. Product development teams serve as the business translators — they work with data scientists and engineers — and take the technical expertise to build our product based on industry needs. They keep passing this knowledge on to educate and inform other teams like marketing and business development (BD) for instance. And those commercial groups — marketing and BD — help inform and explain just how we’re going to tell the world about our products and how they work. Each person and function has a role to play. This isn’t anything groundbreaking, but the importance of communication and explaining complex topics in more simplistic terms has been elevated at Intelligencia AI to make the greatest impact.

At the same time, AI such as LLMs continues to increase in its use to enhance efficiencies across our business functions. We embrace AI and see the value it can bring not just in our solutions for our customers but to us, too. We are actively using LLMs in software engineering and data science to write code faster, in business operations to streamline our processes, in BD to synthesize and update information, in customer success to generate insights faster, in marketing to ideate on headlines, content topics, etc. AI has a place in all of this.

What ethical considerations does AI introduce into your industry, and how are you tackling these concerns?

This is a very relevant, complex and important topic especially in the pharmaceutical industry where people’s health and lives are at stake and where equity as well as privacy are huge concerns. There are ongoing discussions within organizations like the FDA and the WHO about ethical considerations. I want to focus on three common concerns that are especially relevant to the work we do and ones we help mitigate.

Disinformation. The old principle of “garbage in, garbage out” applies to AI in full force. If you “feed” your algorithms incorrect, incomplete or biased information the results will produce incorrect, incomplete and biased answers. This is even more apparent with the advance of LLMs (e.g. ChatGPT), where AI can make up facts and serve lies — it hallucinates. This is never good when querying data that inform important decisions. Maybe one day, AI will be smart enough to detect these issues independently, but currently humans still have a huge role to play in training the AI models. We go to great lengths to make sure what the algorithms digest is correct, complete, and free of bias. Our data is carefully curated by domain experts to make sure we have an extremely solid data foundation for AI. AI is not a replacement for people’s expertise. It’s another, very advanced tool we use, but not without close human supervision.

The Black Box. AI works well, but in many use cases it is most helpful when used to augment human expertise. AI alone is often perceived to be a mysterious black box which hinders adoption. On one end a lot of information goes in and on the other end an answer comes out. How did we get from here to there? How do we know we can trust those results? Developing trustworthy AI is key. For us at Intelligencia AI, it is important to help our users understand which factors inform the results AI calculates for them. That’s why the AI explainability we provide is very important to eliminate this mysterious black box. If people understand which factors the AI algorithms relied on mostly in coming up with its results, it allows for human agency and oversight and gives people more confidence in the output.

Privacy. AI is trained on data, and much of the data in our space includes patient data. The industry and regulations are constantly evolving so that data privacy is being protected. At Intelligencia AI we follow GDPR guidelines and apply strict internal protocols on how to access and use any data provided by our partners and keep a close watch on evolving changes.