The fusion of automation and AI is reshaping industries and the workforce. Senior IT leaders face the challenge of spearheading this transition, balancing technology adoption with workforce adaptability. Key strategies involve upskilling, ethical technology deployment, and fostering a culture of lifelong learning. How can IT leaders harness AI’s potential to optimize operations while cultivating an agile, future-proof workforce amid rapid innovation?

Contributor

• • Kristin Myers, Executive Vice President, Chief Digital and Information Officer, and Dean for Digital and Information Technology (IT), Mount Sinai Health System

Transcript (AI-Driven Future)

Sanjog Aul [00:00:00]:
Hello and welcome to CTN. To learn more about the show, please visit ciotalknetwork.com and the topic for today is AI Driven Future Workforce Evolution In The Automation Era. So what are we talking about here? So we’ve had this fusion going on between the automation, robotics and AI and frankly it’s reshaping many industries and also workforce makeup is also shifting. So when we are trying to have the IT leaders spearhead this transaction, transition into from a regular worker to digital plus human workers plus automation, we got to figure out how much of that technology do we introduce and at what pace and in what ways so that the workforce can adopt to it. So we have challenges and or at least some hurdles to get over. You got upskilling that you need to do to learn all these new things, how to exploit them. Then at the same times when you’re deploying technology, you got some ethical concerns. And then since we are bringing so much innovation and so much newness into the very mix of how work will get done, you got to have learning happen on an ongoing basis.

Sanjog Aul [00:01:27]:
And that requires a fundamental building of culture of that lifelong learning that everyone must adopt for us to stay afloat. So while on one hand AI shows great potential and organizations are trying to use it to on one hand optimize operation, on the other side they are trying to do innovation. But at the same time you also want to make sure that you build a workforce not only which works for today, but actually it is a future proof kind of workforce which is agile and meets the demands and the needs of what the organization wants. Well, not an easy task. And for that I’ve invited Kristin Myers, who’s the Executive Vice President, Chief Digital and Information Officer and the Dean for Digital and Information Technology for Mount Sinai Health System. Hey Kristin, how are you?

Kristin Myers [00:02:18]:
Well, how are you?

Sanjog Aul [00:02:20]:
Very good. It’s an honor to have you on our show. Now, the topic is not new, it’s I think top of our mind. Every day we are seeing innovation happening. When you took my first question is to kind of set the stage. So yes, a lot of newness is happening. Automation is a little older, I would say. Yes, we are still making strides there and now we are bringing AI into the mix as well when we are trying to look at human machine collaboration

Sanjog Aul [00:02:48]:
let’s talk about that as a context. How cryptic is it for someone to determine what to give to machine or what to strive to give it to a machine versus human and how they will run point on these things? What you do in a business Look.

Kristin Myers [00:03:04]:
I think that senior technology leaders need to work very closely with their teams and the stakeholders in the business to understand existing processes and functions in order to identify the value, what is suitable for automation or AI integration and there are areas that have quite frankly higher potential. You need to look at repetitive high volume and manual processes like data entry or processing or other user interface interactions between applications. And I think that robotic process automation is a great technology to leverage for this type of automation. And we have a program at Mount Sinai focused on this and have been able to save around 75,000 hours annually just through looking at automations on these repetitive high volume manual processes. Also looking at data analysis and insights. So the ability to leverage AI and ML algorithms to analyze large volumes of data to analyze and identify patterns and make predictions and generate insights which can be used across many areas. And we have a clinical innovation data science team that builds and operation decision support applications to improve clinical quality, safety and the patient experience.

Kristin Myers [00:04:29]:
I also think software testing, so the ability to generate test cases, simulate user interactions and identifying bugs to help streamline the testing process. Cybersecurity and fraud detection can help with anomaly detection, threat identification and response. AI algorithms can continuously monitor networks, detect suspicious activities, and assist in mitigating cybersecurity risks and fraudulent activities. We think about the world that you know of, Retam and we see AI powered chatbots and virtual assistants that can handle support inquiries and troubleshoot technical issues and we’ve been able to implement that in the healthcare setting here. And I also think that programming anything that can help with code generation and other simple coding tasks are probably examples of areas that have high potential.

Sanjog Aul [00:05:29]:
Now when you talk about you’ve actually very carefully and very neatly compartmentalized that this we can give to AI versus this we keep to humans, which essentially was to some extent also done when we started this whole RPA and automation things, where we said, okay, so we can automate this part, which will relieve humans to do something more with their time or better with their time. Now, automation is one thing, but AI is a different animal again, because there we are not just relying to make it an aid, but fundamentally to take over, because we are essentially saying there is some more intelligence built in. Would you say that is this a clear compartmentalization, a good idea, because AI is evolving and if you start compartmentalizing and accordingly start building skills in the workforce which you know could soon be replaced with AI, then that is a throwaway work and a lot of resources wasted if you don’t know where AI is going and you hold back on people building their skills in that area because we were wrong about what AI will evolve to. That means that’s a lost opportunity because suddenly these humans are not able to perform at the speed of business. What have you done or what are you trying to do to get become near accurate? You cannot have a crystal ball, but what can you do to bring more accuracy to this understanding of evolution so that you can carefully and more practically build capabilities in AI and or whatever is coming your way in terms of the evolution that’s happening in this space? And perfect pair it with the human capability development.

Kristin Myers [00:07:19]:
Yeah, I think that there are many things that technology leaders need to do to maximize the potential of the human machine collaboration, quite frankly, to drive organizational success. It all starts with the strategy having a clear vision, objectives in alignment with the enterprise goals. And we have to understand, identify, prioritize and measure the value of that AI human machine collaboration opportunities. And again, this isn’t just about technology. You have to collaborate with the business and this is a change in how we work. And we have to obtain support, buy in and full adoption from our stakeholders. It’s really important to ensure we can maximize results. You also have to promote a culture of innovation.

Kristin Myers [00:08:12]:
So fostering an environment where people are open to adopting new technologies and encouraging really that there are AI solutions out there and other innovative technologies and solutions we should be looking at. We also have to invest in training. Technology is changing so rapidly, it’s keeping keeping up is difficult. So we have to invest and we are investing in upskilling our employees to be able to understand AI in more detail and also topics around data literacy and we have to be able to train our teams to work alongside automation and AI systems. Because all of our employees, including the executives, which is myself, need to have a continuous learning mentality. We have to be able to stay relevant and ahead. I also think that we have to ensure ethical use.

Kristin Myers [00:09:09]:
So again, when we talk about AI, this is extremely important. We need to make sure that these technologies are used ethically, responsibly and safety. There have to be governance controls, guidelines in place to ensure the appropriate use of these technologies. And we’ve had an AI ethics committee in place for the last year that reviews all models that go through the intake process, whether it’s vendor models or whether it is internally developed models and then you have to measure the value realization. So measuring the impact, the value and other key performance indicators is critical to identify areas of improvement, tracking progress, monitoring performance to make sure that there are expected value and outcomes and that they’re actually being achieved.

Sanjog Aul [00:10:01]:
Now, what you just mentioned here, one is the strategy, another is on the ground when you’re trying to do it. What challenges are you facing with this calibration, if you will?

Kristin Myers [00:10:11]:
Again, I think that it’s a change management exercise. I think it’s a change management exercise within my own department, the technology department. And it’s also a change management exercise with our team members, our clinical teams, who are starting to adopt this technology. And we need to make sure that as this technology is being deployed, that they’re understanding what it is and how it’s going to be useful to them and that it works correctly and I think that with implementations around this, you really have one shot to make sure that the implementation goes well and that it’s actually adopted. The worst thing that can happen is you can invest in technology and deploy it and either nobody uses it or the results are not there from a value realization perspective and there’s no adoption.

Kristin Myers [00:11:12]:
So I think that the challenge is really around the change management piece, the human factors.

Sanjog Aul [00:11:20]:
So interestingly, the human factor is big issue, as this is the only fuzzy thing left for us to tackle, because we have kind of built science around the people, the process and the technology part. People are only the fuzzy science. And now here, because you’re dealing with humans and we are doing change management, is there something that you’ve tried to do in the recent past or as the automation came into the mix, I think a couple of years ago, so that we are not just tinkering, we start producing value from it, or is it still in the labs and sandbox?

Kristin Myers [00:11:57]:
No, this is not in the labs and sandbox. This is out in production. I think that again, being metrics based is extremely important and looking at the return on investment and being able to present that in a way that no our Chief Strategy Officer or a Finance Officer can understand and we have many predictive models in place, whether it be sepsis, malnutrition, predicting falls and we’ve been able to generate and show that these predictive models, when used, generate a return on investment. So again, I think that technology leaders need to work with the business but just being very clear about measuring the value is extremely important.

Sanjog Aul [00:12:51]:
If you had to take a step back and say, now the ROI metric that we are talking about, right, because you are making investments, so people are. The executive leadership will be saying, okay, you’re going to invest. Like any other traditional form of investment, I would need an ROI metric. But do you think realistically are we at a point yet in this adoption where something which is so unprecedented that you actually put a number and live up to it?

Kristin Myers [00:13:20]:
Yes. I mean, you have to, in my view. Otherwise, implementing technology for technology’s sake, I don’t agree with. I think that it needs to add value. And as a technology leader, you need to be able to articulate what that value is going to be in a very clear and concise way. And it also, you need to be able to show is there going to be a financial roi? I mean, these are the questions that from my perspective, need to be answered. And healthcare, as you’re probably well aware, is in a very challenging place post Covid and 60 to 65% of all health systems across the country have a negative margin.

Kristin Myers [00:14:09]:
So being a. It’s more and more important to be able to articulate what a return on investment is with technology across the board and I think AI is no different.

Sanjog Aul [00:14:21]:
And when you are trying to make this metric in or the ROI calculation, are you presenting to your executives and the board a soft metric which says, okay, it brings less stress to my people, or does it come up as a hard metric also where you say, okay, I created a better patient outcome, I got this person or this team to do projects sooner, which is essentially money saved, which is money earned. What kind of give us an idea about the kind of metric that you’re using to demonstrate the ROI.

Kristin Myers [00:14:54]:
Yeah, look, I mean it can be soft metrics around either patient experience or workforce experience, reducing burnout or and actually not an OR is the financial, the hard ROI and I think that it’s important to be able to articulate both and so we have a process where the technology team work really closely with our finance team and our chief strategy office team to make sure that we’re all very clear on what the ROI is going to be and what is going to be presented moving forward so that we can all stack hands on it and say, yes, we are in agreement with this and I think that’s important. It should not be just an isolated ROI completed in the technology group. You need that collaboration between teams, especially the finance team and the business team, that the value that we’re saying we’re actually harvesting is actually happening.

Sanjog Aul [00:16:01]:
Okay, so let’s take a quick break, Listers, we’ll be right back. And let’s talk about specifically what reskilling or upskilling or rather to for this workforce, for them to get truly ready to go hand in hand, shoulder to shoulder with AI to create the most value. What would that look like so? Let’s discuss that more when we come back. Please stay tuned.

Sanjog Aul [00:17:16]:
Welcome back. So in your context, let’s paint a context, Kristin, about like the kind of skills that you truly need among the workers that you are at least dealing with as an organization and where have you identified specific tweaks in terms of reskilling and upskilling which will be to make them more compatible with the AI enablement or the digital workforce, I.e. the AI tools and bots?

Kristin Myers [00:17:47]:
Yeah, sure. I think it requires a very thoughtful approach. I think that organizations have to be able to develop appropriate policies and guidelines around the use and management of the technologies that are safe and ethical, responsible. And as we upskill and reskill our employees, we need to also include ethical considerations at the training topics like AI ethics, privacy, data protection and responsible use of technology. I will say promoting that culture of ethical awareness and responsibility through training and raising the awareness around potential biases or unintended consequences and just encouraging that critical thinking about the ethical implications of their work. We also have to encourage employee feedback and engagement through different channels to share concerns about AI ethical using, key challenges and I think that being transparent and having that bidirectional communication really allows us to be able to address issues and concerns effectively.

Sanjog Aul [00:19:03]:
Now so the things that you mentioned are the ethical issues and that is more training so that we get them to become conscious of things they might unknowingly or unintentionally do. Now that’s something which is very important, as you rightly said, to kind of have them start behaving in the right manner but then if you were to look at the reskilling and upskilling specific ones, where do you think a worker today in healthcare, whether technology worker or a business worker, is doing things which could very well be done with AI, but then now that they’ll have some breathing room they could do something different and or their current position would be rendered redundant but they’re good workers, they’re keepers. So you’re finding ways to get them to redefine their career path and or career goals and then help them reskill or retool them to make them productive and effective as a part of the workforce. Which are the top candidates there? Let’s start there.

Kristin Myers [00:20:11]:
Sure. Look, I think that AI is truly amazing, but at this point, they’re working side AI is working side by side with humans. It’s not taking over full roles and I think that we have to emphasize qualities of our employees that AI cannot do, which is pretty critical in the communication and messaging around this. So skills around problem solving, critical thinking, empathy and what employees bring to the table and I think so AI is really meant to augment their work and capabilities and I think that’s extremely important to continuously emphasize.

Kristin Myers [00:20:59]:
You have to define the roles and responsibilities and be very clear about what the technology does and what the human worker is expected to do. So establishing clear boundaries and expectations I think will lead to better outcomes. And again, training and education is critical to employees and a better understanding of the technology. I think that fear and distrust will reduce. And we just recently, last week even had a tech talk on GPT just and brought in Microsoft really to ensure that we’re providing education to our employees on the latest technology and we have monthly meetings with our employees on the latest technologies that we’re deploying, etc and I think that again, that’s really important from a communication and change management perspective. People are very interested.

Sanjog Aul [00:22:00]:
So if you were to go ahead and start making investments, what does your proposal look like to your CFO on one hand, CHRO on the others, when you talk about upskilling and reskilling because I’m sure there is some collaboration that’s going on. Let’s take for example, your own department. So for your department, what is your proposal and how is that being met and or considered? What feedback and or pushback are you getting from your executive management when you’re saying, okay, this is what my vision is for my team with respect to bringing AI and human workers on the same page in order get them to work together properly?

Kristin Myers [00:22:40]:
We have a huge amount of support in the organization around doing this. As I said, we have been trying to automate less complex processes for a very long time. And again, in terms of steps, it’s always about transparent communication and making sure that you, it’s critical to have clear explanations expectations, support mechanisms and what the next steps are and I think that helps alleviate anxiety and fear because I think the first question really is, well, is this going to take my job or what will I be doing in the future? So I think that you’ve got to be able to clearly articulate that again, employee engagement and feedback. So making sure you’ve got multiple channels for employees to reach out, providing training, so training again, making sure that employees understand AI, its role and how it impacts the work and equipping employees with the knowledge and skills I think reduces fear and uncertainty. Making sure that we’ve got support resources in place if people want counseling services or employee assistant programs. Again, conducting engagement surveys, so continuously assessing the impact of transition through surveys and focus groups and check ins and other channels.

Kristin Myers [00:24:06]:
So again, having a, I think a comprehensive strategy that executives are working with their HR department is extremely important. But it can’t just be implementing the technology without the change management plan that goes around it.

Sanjog Aul [00:24:26]:
If you were to say the top concerns and or pet peeves that the humans are raising besides the potential job loss in terms of you trying to have them agree and accept that hey, AI is going to be part of our lives and that will become a part of the workforce which you’ll have to work collaboratively with in whatever way, not just we look at it as a tool. What is it that you are hearing? When you talked about change management, what are you managing when it comes to the pet peeves and the pushbacks and the resistance, a passive and aggressive resistance from the humans that you have in your workforce?

Kristin Myers [00:25:04]:
Look, I think the first, which you mentioned, the top one is always about the jobs. I think that’s like the number one priority and I think once you get past that, it’s around AI ethics, privacy, data protection, responsible use of technology, just making sure that there isn’t bias in the models and making sure that they’re accurate. I think that’s the other concern is ensuring that these technologies are working appropriately. So again, I would say it’s around the ethics, but also are these going to work practically and improve their world?

Sanjog Aul [00:25:48]:
You had to go ahead and of course when you had started this initiative with AI coming into play and where you are today, you think you’re kind of done with. I know change management is ongoing, but are you at a stable state or there is some turbulence which you still have to tackle.

Kristin Myers [00:26:05]:
Look, technology is changing every single day. So for example, when GPT was announced, large language models. Again, this has created another area where we need to and we are updating our entire AI governance committee making sure that it’s not just the ethics we’re looking at, we’re looking at the specific use cases around large language models. So my point would be is that technology is changing so rapidly that you’re never done. You’ve got to be nimble. You’ve got to make sure that you’re constantly evaluating your intake process, your overall governance and the change management that’s appropriate for the technology and making sure that it’s up to date.

Sanjog Aul [00:27:02]:
Now one last question for you which is around like at a commercial level, people are doing what they’re doing. As, individual organizations trying to make the most of this technology. What do you think is your appeal and or first of all let’s take what is the current state of where the government is and the industry standards are with healthcare because that’s a regulated industry. So with respect to bringing AI, especially when lives are at stake and what’s your appeal and or what’s your expectation from these policies and standards in terms of where do they get to in terms of evolution that they can realistically be safely and consistently applied for the best outcome for all parties involved?

Kristin Myers [00:27:48]:
Yeah, I think technology leaders need to actively engage in policy discussions and industry collaborations to really help shape the government policies and make sure that we have a fair and responsible transition towards an AI driven future of workforce and some areas from my perspective are really around data and security. So being able to contribute to the formulation of policies and regulations regarding that industry collaboration, so participating in industry collaborations and consortiums that focus on AI ethics and workforce implications, developing best practices, industry wide standards and frameworks and guidelines, policy advocacy, so making sure that we’re advocating for policies that promote responsible AI adoption and addressing potential workforce challenges. Being able to participate from a thought leadership perspective around expertise and experiences related to AI and DEI advocacy, so advocating for policies and initiatives that are promoting diversity and inclusion in AI development and adoption. So making sure that there really is no bias in the models. So I think that technology leaders really need to get involved here to help the government put the standards in place.

Sanjog Aul [00:29:22]:
Once again, thank you so much Kristin for sharing your insights about how the government, the academia, the commercial institutions, enterprises are able to collaborate so that we are able to bring AI related or AI driven future to a reality, get the workers to start working alongside the AI enabled capabilities and bring the absolute best outcome for the commerce and the community. So thank you so much.

Kristin Myers [00:29:51]:
Thank you for having me.

Sanjog Aul [00:29:54]:
And listeners please connect with us on social media subscribe to our podcast. Once again, thank you for listening to CTN. This is your host, Sanjog Aul. Till next week, take care and god bless.

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