C-Suite Perspectives On AI: Joel Dolisy Of WellSky On Where to Use AI and Where to Rely Only on Humans
An Interview With Kieran Powell
Scope the problem to solve: Trying to solve large problems that are ill defined with AI is bound to fail. Remember that AI is a tool that can be great, but not all problems are nails. Like right now, just asking AI to build you an application that does anything useful is not a reality, but asking AI to help you write parts of it faster is doable.
As artificial intelligence (AI) continues to advance and integrate into various aspects of business, decision-makers at the highest levels face the complex task of determining where AI can be most effectively utilized and where the human touch remains irreplaceable. This series seeks to explore the nuanced decisions made by C-Suite executives regarding the implementation of AI in their operations. As part of this series, we had the pleasure of interviewing Joel Dolisy.
In his role as chief technology officer, Joel Dolisy manages both product development and information technology for WellSky. Before joining WellSky as CTO, Joel was CTO of Kinnser Software, where he led the company’s engineering and product organizations. Before Kinnser, Joel was SVP, CTO, and CIO for SolarWinds, a leading provider of IT management software. With more than 20 years of experience in product strategy and software engineering, Joel is a leader in developing and delivering commercial software products to market.
Thank you so much for your time! I know that you are a very busy person. Our readers would love to “get to know you” a bit better. Can you tell us a bit about your ‘backstory’ and how you got started?
I got hooked on computers and software when I was 14 and started writing commercial software when I was still in college for a small company in Brussels, Belgium. I love applying technology to solve problems. For most of my career I have been fortunate to build software or build teams and organizations that deliver software. My first job was to work in the multimedia industry, building things like encyclopedias and atlases that are now available for free online. I also was involved in video game development, where I learned a lot about maximizing resource usage and real-time systems concepts. I later moved into enterprise software, delivering system management and network management solutions to clients across many verticals. I finally moved to healthcare 7 years ago and have not looked back.
It has been said that our mistakes can be our greatest teachers. Can you share a story about the funniest mistake you made when you were first starting? Can you tell us what lesson you learned from that?
Even though I was passionate about computers and software, I took a detour during my first two years of college through business school. It wasn’t really made for me, but for some reason I was dead set on wanting that degree. Finally, after two years, I made the switch to computer science and never looked back. That detour taught me some valuable lessons around rebounding around failure and it also gave me great foundation in micro and macroeconomics.
Are you working on any exciting new projects now? How do you think that will help people?
At WellSky we are delivering software and services that help healthcare providers, payers, and patients daily. In addition to the successful solutions that we are bringing to market, we are also working on some interesting applications of generative AI within our different solutions. To maximize the speed to market, we have spun up a small incubation team that is researching how to best integrate those new capabilities to solve some of the more complex use cases. One of those is helping to reduce the burnout among nurses and clinicians by ensuring that the tools they interact with integrate more seamlessly into their workflows and do not require them to spend additional time typing in data that we could have gathered differently.
Thank you for that. Let’s now shift to the central focus of our discussion. In your experience, what have been the most challenging aspects of integrating AI into your business operations, and how have you balanced these with the need to preserve human-centric roles?
WellSky has integrated AI based capabilities into its solutions for the past 6 years, well before generative Ai exploded. We have relied on traditional machine learning (ML) based predictive analytics and robotic process automation (RPA) for years. This has given us time to think about how to ensure that we think about those capabilities holistically in the context of the people using them. In that approach, we have always put the clinicians at the center of the process and that has meant early on being transparent about how the system got to the recommendation it is making. We have also put a lot of emphasis on understanding what bias may be in any of the datasets that we use to train our models, to ensure a responsible approach to integrating those technologies in our solutions. We did not get there immediately; it is the result of an iterative and incremental process on continuous improvement. We are building on this foundation to accommodate the new challenges brought in by generative AI.
Can you share a specific instance where AI initially seemed like the optimal solution but ultimately proved less effective than human intervention? What did this experience teach you about the limitations of AI in your field?
AI is like any tool; if you look at it as a hammer you can then have everything look like a nail that you can use it on. The reality is that applying AI does require you to focus closely on a specific problem to solve and the more specific you are the more likely you will be successful finding an application for AI. Understanding where AI is good compared to where humans are is a great starting point. Computers and AI are great at sifting through a lot of data and identify patterns that would be almost impossible for humans to identify. On the other side humans can apply critical thinking that machines currently can’t, even though they fake it better than ever. As I think about those, I always try to use AI in places where I know I can leverage its processing advantages and keep the human in charge of the final decision.
How do you navigate the ethical implications of implementing AI in your company, especially concerning potential job displacement and ensuring ethical AI usage?
At WellSky, we do not believe that a future where people are replaced by AI is around the corner. However, we do believe that AI is giving a set of new capabilities to people to do things more efficiently and effectively, to some extent giving them superpowers. It is like how accountants that used to use pen and paper in the 80s had to adapt and start using spreadsheets. Those spreadsheets did not replace people directly but helped make them more productive and remove some of the tedious work.
I see a same evolution for EMRs and other solutions, where we can make those more seamless, more intelligent, by presenting the right information at the right time, without needed to be asked, therefore allowing clinicians to spend more meaningful time with patients, instead of spending time in the system.
WellSky is committed to the following responsible AI principles in the design, development, deployment, and review of our AI systems, ensuring we respect the rights and interests of our users and the wider community: fairness, reliability and safety, privacy and security, inclusiveness, transparency, accountability.
Could you describe a successful instance in your company where AI and human skills were synergistically combined to achieve a result that neither could have accomplished alone?
We have started rolling out AI based tools in our development organization to help developers be more productive by generating code automatically. Software engineers are typically skeptical about tools that generate code automatically. We rolled out the tool to about 120 of our engineers and after a few months of usage we ran an internal survey. 98% of the participants felt an increase in productivity by using the AI based tool and the tool helped them save 10–20% of the time they would have spent before doing the same task. This is a good example of tools integrated within our workflows and helping people do their job, not replacing them. It is a good example that shows leveraging AI for its capacity to deal with patterns and generating content, while leaving the human in the loop and deciding what to use of the proposed code.
Based on your experience and success, what are the “5 Things To Keep in Mind When Deciding Where to Use AI and Where to Rely Only on Humans, and Why?” How have these 5 things impacted your work or your career?
- Scope the problem to solve: Trying to solve large problems that are ill defined with AI is bound to fail. Remember that AI is a tool that can be great, but not all problems are nails. Like right now, just asking AI to build you an application that does anything useful is not a reality, but asking AI to help you write parts of it faster is doable.
- Approach it responsibly: The advent of generative AI has pushed the question of adopting AI responsibly to the forefront. Those technologies are so good at creating content, that they almost look like operating with magical power. That is where it is important to ask questions about how the technology was trained, what datasets were used, is there any inherent bias in those datasets, whether the answer provided is rooted in facts or is made up (hallucinations). WellSky has put in place an AI review board that is now reviewing any proposal to integrate AI in our solutions and asking those questions.
- Humans in the loop: Humans are at the center of what we do, whether they are patients or clinicians, and it needs to continue to be that way. Ensuring that we understand that AI is there to support and not supplant people is how we approach everything. People need to be able to override a proposed choice made by an AI system, and they need to be able to see why the AI system is making its recommendation.
- Understand how your data is used: AI systems rely on data to be useful. AI models need to be trained on large datasets to be useful and then they need to execute on input data to deliver their predictions. People and companies need to understand how their data is going to be used by the model provider. Is the input data going to be used to train and refine the model? If you train a derived model, do you own it or does your provider own it? Getting answers to those questions typically requires partnering with your legal and compliance teams as well as with your AI technology vendor.
- Put a small team to focus on AI: Most organizations are typically not structured to be able to perform research while they are building products or delivering projects. It is imperative to have a small team of technologists to partner with different business stakeholders and incubate the adoption of Ai within the organization. Having a separate small team focused on it will ensure that this effort is not being disrupted by existing teams’ dynamics within the organization. I have had good experiences with this approach in different parts of my career to help put focus on an initiative and make quick progress toward adoption.
Looking towards the future, in which areas of your business do you foresee AI making the most significant impact, and conversely, in which areas do you believe a human touch will remain indispensable?
As I look in the short to medium term, it is undeniable that with the advent of generative AI, a new technological step has been made that will have a lasting impact on how we think about making software solutions more seamless to use. That said, like with every new innovation, a lot of questions arise and most of them are valid. But while those are being answered, it is important for me to think how WellSky can already adopt those capabilities in a responsible manner today. I see a lot of opportunity in areas where product support can provide more meaningful and faster answers to user requests. Content creators will continue to benefit from better tools to help them generate better content, faster. This will be true in Marketing, UI designs and other code development.
For our users it will continue to help make our software simpler and more intuitive to use by surfacing the right information about a patient at the right time, with the right level of details.
As I look further out, I believe that those AI technologies will have more reasoning capabilities, that will come closer and closer to how humans reason about certain problems, making them better suited to solve complex, multi-step, multi-variable problems. I still believe that humans will be in the loop of accepting those solutions and questioning their validity for the foreseeable future.
You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger. 🙂
I believe that food and water scarcity is one of the major problems around world and even in local communities I wish I could do more to help at a larger scale solve those issues.
How can our readers further follow your work online?
The best way to follow me is to follow either WellSky or my personal LinkedIn profile.
This was very inspiring. Thank you so much for joining us!
About The Interviewer: Kieran Powell is the EVP of Channel V Media a New York City Public Relations agency with a global network of agency partners in over 30 countries. Kieran has advised more than 150 companies in the Technology, B2B, Retail and Financial sectors. Prior to taking over business operations at Channel V Media, Kieran held roles at Merrill Lynch, PwC and Ernst & Young. Get in touch with Kieran to discuss how marketing and public relations can be leveraged to achieve concrete business goals.
C-Suite Perspectives On AI: Joel Dolisy Of WellSky On Where to Use AI and Where to Rely Only on… was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.