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136: AI and EHS Software
321 Plays10 days ago
Do you have questions about AI and the role it’s playing in EHS? Is it a trustworthy source, and how would you know if it is? What are the benefits and implications? Jill sits down with three of HSI’s top executives who are leading the charge in AI technology development for EHS software. Join Jose Arcilla, Chief Executive Officer, John Hambelton, Chief Technology Officer, and Mike Case, Vice President of Product, as they break down what’s working with AI, precautions you need to know, and how they’re creating tools that grow with the people who rely on them. You’ll hear how they’re designing tech that learns from real work, minimizes risk, and supports better choices, all while keeping humans firmly in control. From guardrails around ethics and human oversight, and questions to ask before selecting AI-powered solutions, this episode pulls back the curtain on AI development, what’s coming next, and why it matters for EHS professionals.
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Transcript
Introduction to AI in EHS
00:00:09
Speaker
This is the Accidental Safety Pro brought to you by HSI. This episode is recorded in January of 2026. My name is Jill James, HSI's Chief Safety Officer.
00:00:20
Speaker
And today i want to discuss artificial intelligence as it impacts our profession. ai seems to be everywhere. If AI were a person, we'd be bumping into one every day.
00:00:31
Speaker
Sometimes as a helpful stranger who gave us good directions and other times as a know-it-all that took us down the wrong path. AI is moving so fast, it's nearly dizzying.
00:00:44
Speaker
Look over here, look over there. It can do what? And some of it's fun. Some of it's frenetic, some of it's kind of freaky. And for our profession, it can be all of that and a great help aid. Yet, how do we know what is a good help aid?
00:01:01
Speaker
What can we trust? As EHS professionals, we excel in identifying and mitigating risks. That's our job. Yet, with
Meet the Guests
00:01:10
Speaker
this emerging frontier, what are the risks we don't yet know because of the speed with which it's all moving?
00:01:17
Speaker
For all of those reasons and more, I have three guests with me today who are on the front lines of AI for the EHS profession, and they happen to be my colleagues.
00:01:27
Speaker
I promise you this is not an infomercial or a sales pitch. You know that I wouldn't do that to this audience. Rather, i want you to hear how the sausage is made from the people who make it and the care with which they do the work.
00:01:41
Speaker
My guests today are Mike Case, Vice President of Product at HSI, John Hambleton, Chief Technology Officer at HSI, and Jose Arcilla, Chief Executive Officer at HSI. They're joining us today from Washington, Oregon, and Texas.
00:01:59
Speaker
Welcome to the show. Now, as is tradition with the Accidental Safety Pro, I'd like to start with your origin stories. Jose, can you tell us a little bit about yourself? How did you come to this work in helping the EHS profession and specifically in technology?
00:02:14
Speaker
Yeah, absolutely. and And thank you for having me on the show. um First of all, hopefully I don't confuse too many people with Jose and then the English accent. I i grew up in the UK. obviously going through school and college, I had a very big focus on computers, technology, um the sciences. And so that really got me into yeah working with technology companies. And so I spent all of my career really in technology, software providers.
00:02:45
Speaker
um which naturally over the course of time led me to the US, of course, as a huge hub for technology innovations. um And so that got me to Texas, where obviously it led to HSI.
00:02:58
Speaker
and But there is a connection actually to um workplace safety. My mom used to work at a hospital in the yeah UK called Stoke Handable Hospital. It was in the town where I grew up.
00:03:11
Speaker
And it's world renowned for spinal injuries. And so at a very early age, I used to go to the hospital with my mom. They do movie nights. And I got to meet a lot of the patients there and I got to speak to them. And whilst many were flown in from around the world, whether it was to receive treatment or to give their families a bit of respite.
00:03:34
Speaker
um I remember talking to a gentleman that had a workplace incident. um And it kind of like stuck with me at an early age because it was one of those situations where you never used to think about workplace safety and compliance.
00:03:46
Speaker
um You know, it was a case of someone who had gone to work, and doing his job, had an accident, and he was left paralyzed from the neck down.
00:03:58
Speaker
And all of a sudden you start to think about your own parents and my dad who used to work in a factory, you know, when he goes to work, what could potentially happen? And it's kind of like sits with you ah for the rest of your life as you grow up. So it was an opportunity that when hsi came up and the nature of its work, it really resonated to me.
00:04:19
Speaker
I have not heard
Journeys into EHS and Technology
00:04:20
Speaker
that story from you before. Thank you for sharing that. That's beautiful. John, how about you? Yeah, my career as a software developer started at a really young age. At the ripe age of 13, I was brought into a three-year grant project to flip the classroom where we were teaching educators how to integrate technology into their classrooms in the state of Oregon.
00:04:46
Speaker
From there, I had the privilege of working for several web startup companies during the dot-com era. following that a brief time as an instructor and advisor for our local colleges, and then really growing in my career here at HSI with the initial opportunity to pioneer emergency care online training, but really spending the last 10 years moving beyond the training and creating solutions addressing the complex data workflows and logistics of yeah EHS professionals. Uh,
00:05:18
Speaker
Technology and education and growth is my passion. Again, being able to teach teachers how to use technology integrated in the classroom, ah pioneering some of the early online training technology within this space and other K-12 spaces. I really have a belief that technology can grow and develop individuals and groups. And there's so much opportunity today to continue to do that.
00:05:43
Speaker
ah at a young age, I wanted to be a doctor. That was kind of like my little child, hope and dream
HSI's AI Journey Begins
00:05:50
Speaker
to be able to go and to save lives and help people be healthy.
00:05:55
Speaker
ah I'm really grateful that my skills in the technology space where I was able to combine that with those original goals to be able to actually put work towards protecting our workforces, individuals, and operate and help people in the continuous effort of of growing themselves. It's a super rewarding industry to be a part of, and I'm really grateful to be a part of it in in this weird secondary way.
00:06:25
Speaker
Totally recognize my background is is quite a bit different than the EHS professional, but having the ah had the opportunity to meet with several EHS professionals during my career at HSI,
00:06:38
Speaker
can have really come to appreciate the parallels between our work within risk management, compliance, managing a complex network of individual difficult control variables, and then always managing that balance of of speed, quality, and cost. I feel like I share these issues with the EHS professional in the, in the software development space and happy to to do it in that context. so I'm grateful to be here and looking for the discussion.
00:07:09
Speaker
Thank you, John. Since age 13, holy cats. I did not know that about you either. Mike, you and I have known one another longest. Let's see if you can surprise me. Tell us about yourself. Sure. So actually, John and I have very similar um very similar backgrounds.
00:07:25
Speaker
I started growing up with computers. My high school that
AI Solutions for EHS Challenges
00:07:30
Speaker
I went to in the mid-90s was one of the very first in the state of California to get wired for the internet. And I still remember i'm logging in and and checking the weather using a service called Gopher, the weather for the Minneapolis airport.
00:07:46
Speaker
And just thinking how cool it was that like you could just get that information in real time. i am You know, from there, i I, you know, like John worked at several internet startup companies in the in the dot com era. And that was exciting and fun. And I learned a lot.
00:08:03
Speaker
i But by the time 2006 rolled around, I was looking for something a little more more stable. And I ended up taking a job with a company that was building online safety training.
00:08:14
Speaker
um We had catalog of online safety courses and the learning management system. And i worked with that company to help transition them through several kind of technology evolution cycles, i'm you know embracing im broadband internet and then ah mobile and you know the modern internet technologies that that we take for granted these days.
00:08:43
Speaker
um The common thread for me through all of it is is that at the end of the day, what we do keeps people safe, i helps... sir reduce injuries, i'm and I think makes a meaningful impact on people's lives. That's that's been i'm kind of a positive thing about where where I've worked and for all these years. and And now having been kind of adjacent to the EHS space for for almost 20 years and and spending time with the customers and and at trade shows, I feel like we know the space pretty well. And and and I think it's a pretty...
00:09:25
Speaker
pretty rewarding area to be. Yeah, yeah. The reasons that drew you to the company is what drew me in as well. Meaningful work for the profession, for the people. Jose, wondering, would you mind sharing with the audience where the HSI AI journey started?
00:09:47
Speaker
Yeah, absolutely. um Look, AI has been around and hopefully, you know, a bit of education comes in now. AI has been around for a long, long time. um Yeah, you can go all the way back, you know, to the first mechanical machines ever made.
00:10:04
Speaker
um For me, that journey, when I think about AI really starting like the 1950s, you know, when the Turing test came out about how can you tell if there's, you know, artificial intelligence with, you know, some of these new modern ah technology devices were coming out.
00:10:20
Speaker
And so, Long history. And I think growing up, certainly in the 80s, you know, with the advancements of computers, always got really excited. You can call me a nerd. You love gadgets, love technology. And so joining HSI, I spent a lot of my initial time working with John and Mike on the technology side.
00:10:42
Speaker
And as we were obviously growing through acquisition, um as well as internal development, part of the conversation
Human Oversight and AI Reliability
00:10:50
Speaker
naturally turned to, well, how do we supercharge what we do for the customers?
00:10:55
Speaker
um And that really led to the whole discussion around AI, because there was a lot of new advancements around large language models. And so obviously part of my job is, you know, hopefully to have some really good ideas, but obviously Mike and John are the ones that turn those ideas into reality.
00:11:15
Speaker
And so all the way back in 2023, said that like it was a long time ago, but like for for AI, I guess it was. Yeah. Back in 2023, we had undertaken a lot of work to really build this holistic platform.
00:11:30
Speaker
um But we obviously needed to supercharge the capabilities of the safety professional, because I think we can all appreciate that safety departments are obviously obviously, or sometimes I should say, overlooked in terms of the budgets they need, the resourcing they need.
00:11:47
Speaker
um to really have a meaningful impact. And so you can digitize, you can build a lot of capabilities to really help them. But you know how do you build something that really makes them far more effective and efficient and then starts to turn the discussion of a safety culture onto being more proactive and predictive?
00:12:06
Speaker
to really mitigate issues. And that's where AI came into the journey. And so we spent a lot of time looking at the possibilities of AI, how we could use our system, our data, um to truly make it something that would help the safety professional. And of course, we partnered with other groups to help us learn about AI and
Consumer Expectations and AI Evolution
00:12:30
Speaker
to really help bolster our capabilities.
00:12:33
Speaker
But it was always it was always founded in the desire to how do we empower the safety professional to do more, to be more effective, but to really move beyond the reactive into that proactive approach.
00:12:47
Speaker
um you know And the rest really was just the you know the determination of the team when they all saw the vision and the value of what we could what we could really do for a safety professional.
00:12:57
Speaker
Yeah, good. Good. i would i would like us to talk about, you know, what you were just ending on, Jose, about how we consider the EHS professional in what we're building. And and so would um some or all of you mind talking about, like, how do we connect with the EHS professional? How do we kind of get into their heads and figure out what they need? um I mean, I know you guys...
00:13:22
Speaker
I'll ask me a lot of questions, but let's talk more about let's talk more about how we do that. So we're not just creating things in a silo. Go ahead, Mike. Yeah, so I'm I'll go ahead and talk about our customer advisory board to kind of build on what Jose was saying. I'm i Back in 2023, when we're really starting to take a hard look at the the new tools and capabilities that we're rapidly advancing i'm onto the scene, we had one of our customer advisory board meetings.
00:13:53
Speaker
And we used some of our time with with our on customer advisory board members to ask them about how they number one, we're already using AI. And it was interesting to hear, even even back back in 2023, how those safety professionals were already kind of exploring ways that they could use it to to be more productive.
00:14:16
Speaker
But then to ask them where they would where they would trust deploying AI into the into the everyday things that they were that they were doing. we definitely heard... and we definitely heard you know a lot of stories about accuracy concerns and being being suspicious about um the capabilities of AI to to provide i am not just an answer, but an accurate answer. and We've seen AI be confidently wrong many times now. um So we listened to that.
00:14:53
Speaker
i think the common theme that came back was that they were looking for ways to use AI that expanded their reach, that helped them i am be more present, more available, i am even when they weren't physically able to be you know to be in a place, to to be able to leverage their expertise to do things like find hazards or understand what the safe you know working method was for a job that was about to be performed, to help answer some of those questions
00:15:24
Speaker
because they can't be everywhere all at once. um We certainly recognize that the ah the safety pro wears a lot of hats and and is often kind of stretched thin.
00:15:35
Speaker
So coming out of those conversations, we started thinking about what are some ways that that the AI... i technology that we were looking at could help, help expand that reach to, to help, um, confidently extend the safety pros, uh, knowledge and experience in ways that could, um, make the, i the jobs that workers were performing, safer and more efficient.
00:16:02
Speaker
Yeah. John or Jose, anything you want to add to that? There are also a lot of low hanging logistics that are not necessarily exclusive to the EHS professional, but are common to software and the normal logistics of, of dealing with risk management and, and data and the flow of data. This is very much 2023 example, but it's one of the areas that I still feel really passionate about. And that is in data summarization. It seems like such a simple thing.
00:16:37
Speaker
it It seems like a, a technology
AI in Hazard Recognition
00:16:40
Speaker
that's that's table stakes across software these days, right? Most like even this conversation that we're having right now is being recorded. ai summarizations are being are being generated as we speak.
00:16:53
Speaker
But for the EHS professional, just being able to simplify the logistics of the day-to-day and we see those comments coming back to us, challenges to be able to solve problems like being able to share data to different audiences. We spend so much time gathering data, making sure that the right people see the data, making sure that it's all accurate.
00:17:16
Speaker
um One of the things that we have not been able to historically scale is simply the ability to provide really data dense objects. Let's just take like an incident record, for example, um that has there's so much data that comes into a single incident and being able to make that consumable to executive audiences, for example. A.I. allows us to turn that into a button click. So there are just a lot of common logistical pieces where we can trust those pieces because they're properly referenced. All the source material is still there and available. But now we're able to scale the output of those. So we're getting both trust and speed. which has been super important for us in developing some of these initial features, as well as applying ai to our own internal workflows.
00:18:14
Speaker
okay who I mean, Jose, you said we you were a nerd. I think we're a company of nerds here, and I think it's good nerding at this point.
00:18:27
Speaker
Absolutely. Yeah. Look, and ah yeah the Mike and John yeah give you yeah great insights in how we're able to engage yeah and and understand yeah through things like the cab in terms of needs and technology. And obviously John is always looking from a technology perspective.
00:18:45
Speaker
I think, you know, very small adage to that is yes looking at the amount of customers we engage with, looking at, you know, best practices, working with yourself, Jill, you know, getting your insights from your background. You know, there's also that component of listening to, you know, what's of importance, not necessarily connected to AI.
00:19:07
Speaker
And then figuring out how we can deliver that, you know, through the advancements of AI, you know, so customers truly get the benefit. Because sometimes you really don't know what you need. You just know you' you're trying to tackle a problem. And obviously our goal, you know, Mike and John's, myself and the entire company is always how can we solve that problem? Yeah. How can we be more effective and more efficient?
00:19:29
Speaker
Right. Right. I mean, that's one of the key things we do when we meet with our customer advisory board. They may not be coming to us saying, hey, can you do X, Y, and Z with AI? More so, we're listening to the problems, to your point, in Jose, that people are trying to solve for. And then Mike's and John's ears are like, what?
00:19:48
Speaker
oh, that sounds like something we could solve for using you know their expertise and background. So it's's it's a fun interplay when we're together with customers. you know i I
Ensuring AI Safety and Compliance
00:20:00
Speaker
started out in the introduction talking about you know AI is everywhere and it can take you to good places and it can take you to ah not such good places. i' like I'd like to have you share a little bit of cautionary tales if you if you would. you know like What are the problems with with ai
00:20:21
Speaker
Or what yeah what should people know about? Like, John, you were talking about data and data sourcing earlier. yeah So really early on in applying AI to my own day-to-day, again, going it's the same story. It's the same technology. Very early on, we started taking advantage of AI note takers within our meetings. We're constantly meeting about anything and everything. And ah we had an opportunity to have several discussions where a team member was out and we got to provide summarizations ah to that team member later on. And again, this was several years ago.
00:21:01
Speaker
ai hallucination is still very much a thing today. It was worse back then. Uh, and one of the first times I actually shared an AI fully AI generated asset with another team member, that asset inside of it, not having reviewed it thoroughly, actually had a hallucinated call out that was very negative for, for that person that we shared that information with. I was very lucky to be able to go back, speak with that individual and point out, Oh no, here's the moment in time where it made the mistake. Here's what was actually said. And we were able to resolve that. Um,
00:21:35
Speaker
But that actually was a foundational learning experience for me in applying AI, right? Human in the middle is still very important. Building trust, but also having inherited trust from the AI and its outputs, but also within the work within your own workflows, having opportunities to treat that as you would human-generated data. There's going to be human error. There's going to be ai error and that there's checks and balances and receipts all along the way.
00:22:07
Speaker
We can't just expect the magic to happen and trust it wholeheartedly. At the same time, it can do a lot of powerful things. It can scale a lot of problems and it can do a lot of, give us a lot of new opportunity to consume data in in ways that we couldn't before.
00:22:27
Speaker
either because of cost scale issues, what have you. But again, from a trust standpoint, and and again, we apply this internally within our own day-to-day interactions with AI and within the development of you know the services that we build is there has to be a very regimented set of logs around what the AI is able to do provide evidence of why it made the decisions it did, come with those receipts, and then you can effectively come in and apply those to logistics where in the past you may not have trusted. it
00:23:11
Speaker
All of those things are growing and the trust is growing on a day-to-day basis. So we absolutely have to take it from risk-first standpoint, but absolutely think about trust as you're coming into it. But there are steps and there are logistics that you can bring in that will help you build that trust and prevent risk.
00:23:32
Speaker
Yeah, you know, we, I mentioned the term data sourcing, and I know it's something that, you know, internally, we talk a lot about as we're developing things like where did this information come from? In case a listener hasn't heard that before, can one of you maybe talk about like, what does that mean? And maybe give an example of, of where we are sourcing things internally, or how we do that?
00:23:58
Speaker
Yeah, look, i'm I'm happy to take a crack at that and then Mike and John can correct me. um It's very typical they do that. So look, I think as John was talking about, um obviously AI, certainly in the in the very early days, far more common, far more talked about.
00:24:15
Speaker
um But the AI ah systems would have hallucinations. That was a term that's used or used today um in terms of how it was providing a response. and That's really because what it's doing is its it's data that it's pulling from, in essence, is the internet. and so Whatever data out there exists um is what it believes to be true. It can't make a judgment call per se on itself. It's certainly not there yet today.
00:24:43
Speaker
um But it can't really interpret, is that data correct? you know Where's its data sources? And so that can cause risk and concern on the on the validity of the response you get from AI.
00:24:55
Speaker
It's one of the reasons, as John said, you know human in the loop is really, really important. Because
Challenges and Future of AI in EHS
00:25:00
Speaker
you want to make sure that whatever AI is presenting to you you can look, review, and approve any actions that you therefore want to take on what AI is presenting. um Now, yeah from our perspective, our goal is always to use known sources, known data, information that we know is factually correct.
00:25:21
Speaker
And so when we use AI, we use it on our library of content that we've built with industry experts. um All of the content has been validated, you know whether it's through regulatory specifications, regulatory requirements. yeah Again, people like yourself, Jill, experts who validate the content to be true and factual. that becomes the source library of our AI system.
00:25:49
Speaker
yeah So it's not just the information in the platform itself, but our content. We do pull in third-party data from like regulatory bodies ah you know and from partners who have validated sources of data.
00:26:02
Speaker
And that's how we're able to respond with AI with a level of confidence. But we still always like to have you know that human in the loop. as an extra safety step. Because certainly what we don't want to do in the area of workplace safety and compliance, you know, is certainly provide some kind of response to an incident, to an issue, to recommend some kind of training to a potential customer and their employees, which is actually incorrect because that leaves them exposed to all manner of issues, you know, up to and including injury on the workplace. And we certainly don't want to do that.
00:26:39
Speaker
It's
Final Thoughts on AI's Impact
00:26:40
Speaker
why, you know, we now consider data to be king again, you know, for that content to be king, because it's what it's what feeds the engine. If anyone is going out and just using the internet as their source, who knows what who provided that information, where it's coming from. It could be someone who thinks as an expert, creating content online about, you know,
00:27:03
Speaker
the best safety practices and procedures, which could be completely, you know, and factually incorrect. Right. It's, it's, um, are you getting the junk food or are you getting the nutritious stuff? Right.
00:27:17
Speaker
Exactly. Yeah. Yeah. Thanks for, thanks for sharing, um, that. I mean, it's really what we do to mitigate risk and, um, build trust, uh, for our customers. Um, I know,
00:27:32
Speaker
In terms of you know how AI has changed the landscape on what consumers expect and how we interact with computers, um can can one of you talk about, like, what have you seen? i mean, you've all talked about your deep history, but what have what are we seeing today in terms of expectations when people pull up to software today? What's changed?
00:28:01
Speaker
I'll take that one. I think i am it's been interesting to watch how chat type interfaces like chat GPT are reshaping the way people expect to interact with, with software.
00:28:17
Speaker
i am to me, it's been kind of an interesting social experience to to kind of see how I've changed the way I interact with the internet. If I think back to a few years ago, when I wanted to learn about something, I would go to you know, Google or my search engine of choice, I would type in you know, a search query, you know, I want to, I want to learn about, you know,
00:28:38
Speaker
ah what it means to be ah to to be safe in in this kind of situation. And i would get back a list of sites and I could then open up those sites and read you know one by one the information that you site you know each site had. i am It was kind of a slow process.
00:29:00
Speaker
Now, when I run that same search, David Price – Google will summarize give me an AI summary of of that information I'm looking for David Price – Well it's of course good to go verify the sources and read the underlying source material for a lot of things that I'm doing I don't need that level of accuracy and so.
00:29:25
Speaker
Many of the searches that I run now end with me just reading the AI summary, right? Just a quick answer, you know, maybe to a a conversation that my kids and I are having at the dinner table, we'll look something up and I no no longer need to read, you know, two or three different websites to get the answer. I i just get the answer back in the response.
00:29:44
Speaker
Those chat interfaces are reshaping the way that we expect to to interact with stuff. And it's making the traditional graphical user interface that that you know most of us are familiar with, pointing and clicking to navigate around, it's making that less important because we're retraining people to expect that they're going to be able to find the information they're looking for, not through a series of menus and clicks,
00:30:11
Speaker
but just by typing in your question and getting a direct answer to the thing you're looking for or asking an action to be performed and then seeing that action performed on your on your behalf.
00:30:24
Speaker
I think um it's one of the more i am transformational technologies from ah a user interface standpoint that we've seen in a very long time. And it's it's exciting to see that play out as a technology enthusiast.
00:30:39
Speaker
And in parallel to how you're interacting with data and interfaces and doing discovery. You also, individuals now also have the ability to generate technology to solve tasks more than they ever have before, where you can have someone who may not necessarily know how to code or work with complex software, be able to perform tasks for themselves or on behalf of their teams that they would typically have to either send to an engineering team, send to a technologist or purchase ah a piece of third-party software in order to ah to resolve. ah
00:31:20
Speaker
A recent experience for me was I had a really large payload of data that I knew had ah answers to a question But previously, I would have had to to write something in order to process that data and provide the outputs.
00:31:36
Speaker
I may have needed to purchase maybe some advanced BI software to process and store all that data and then start to work through that individual pieces of software's interface to gather insights, um where now I can, through the same chat interfaces that Mike was talking about, I can simply provide the data and start talking through my requirements and start getting discoveries and working with that data and chatting back and forth and either generating software or letting these interfaces that have access to other software behind the scenes help me through those problems where those problems before required either code knowledge or or some type of other technical knowledge in order to solve those problems. So that's another really exciting piece for us too. Our engineering teams are doing this on a daily basis now. Other departments within HSI, lots of exciting opportunities to process problems in new ways because there's new technology just through a chat interface at our fingertips to be able to solve those individual problems. And that's only going to ah evolve as software evolves and integrates um those capabilities into their own software.
00:32:52
Speaker
and And we're thinking about that on the daily. Payload of data. I'm certain no one has ever said that on this podcast before. That's fantastic. That's fantastic. hey bo I want to get ah into a a discussion for our listeners about some things specifically that we've built and the care with which...
00:33:14
Speaker
you all did that work for reliability and you know risk avoidance and all of the things that we're talking about. But before we dig into that, i wanted i wanted you all to talk about you know, how Jose, you started out talking about how we really got into this journey in 2023. But when you decide from all of your seats at the table to start building something, how do you know it's not going to expire, you know, tomorrow or the next month or whatever? How did, how did you thoughtfully decide, like, how are we going to do this to future proof it, I guess? Yeah.
00:33:55
Speaker
Sure. and So look, it's that's a really difficult proposition to try and answer. Certainly when sitting down with the development team and you know all of the yeah all of the heads talking about, look, how do we take this new technology and start to build it? yeah For us, it wasn't necessarily about future-proofing the feature sets or necessarily the tools that we're using or implementing. Mm-hmm.
00:34:24
Speaker
It was really about how do we make sure that whatever we build into the system you know is almost like you know Lego. It's interchangeable. you know If we're using you know the latest and greatest AI capabilities and AI engines today,
00:34:39
Speaker
Well, I think we all have an appreciation that it's moving so quickly that could change in yeah six months to a year, um yeah to two years. So how do we make sure that we're able to yeah transition to the latest and greatest technology? And so it's really having that conversation about re-architecting our platform to ensure that we built the appropriate layers and orchestration layers that say, this is how we want AI to interact with our system, with our data,
00:35:09
Speaker
um to make sure that yeah we could interchange that as we needed it to. So even with some of the technology we have, and I'm i'm sure John is squirming in his seat listening to me explain it, um you know for us it's really a case of we treat our own AI capabilities and features like third-party systems. So even though it's ours, we build it, we treat it that way because we know in the future Our AI capabilities will change and they already have changed since we started.
00:35:40
Speaker
um yeah This is really a learning exercise for us as well when we started all the way back in 23. um But it could be, again, how are people going to interact with our system? As as John mentioned, you know being able to no longer use that graphical user interface, you know, no longer typing and searching through lists, but really having a conversation is what we have to keep in mind. And so we certainly spent time thinking about, well, someone else's AI agent could at some point be connecting to our system.
00:36:12
Speaker
You know, when AI becomes a lot more of that personal agent. yeah and We could have other systems connecting to us you know to analyze our data or to get feedback from us. um It kind of like reminds me of a film.
00:36:26
Speaker
don't know if you guys have ever seen it or your listeners have have seen it. It's called Her. I think it was back in 2013. It was about a new operating system that came out and it had an AI agent.
00:36:38
Speaker
And that AI agent was kind of like transitioning from the desktop to um the actor's handheld device. And it went everywhere with them. And it was more about the conversation.
00:36:51
Speaker
know, there was no longer typing. It was just talking to your AI agent and your AI agent, providing you with insights, having that conversation, giving you the data you need. It's an incredible film. If you get a chance, certainly watch that.
00:37:04
Speaker
But that's kind of like where we are today. um a few months ago, i was ah I was playing around with the new voices that came out on both ChatGPT and Gemini.
00:37:16
Speaker
And i was in the car waiting for my wife. And yeah I was playing around with it. I switched it on and I left it on listen mode. And of course, yeah the wife comes in, jumps in the car seat and says, so what are we doing now?
00:37:29
Speaker
And the device piped up and said, no idea, but I can make some recommendations if you want, because there's a great brewery just down the road. And I was like, wow, amazing. Of course, the wife told me to shut it down and that i should only listen to her.
00:37:44
Speaker
So, um yeah, interesting. But again, it just talks to the fact that it's evolving so quickly. And what we do you know today is going to look very different tomorrow.
00:37:58
Speaker
Yeah, that's beautiful. The subject matter expert in the car was your wife is what we're hearing. Correct. And that's never going to be challenged by AI. Right. That's awesome. I i really want to echo what what Jose said here, because I think it's really important. Not only have we applied this to how we're integrating AI into our software and our daily workflows, but But any technology decision maker, any individual who's looking to scale themselves and solve problems with AI, being nimble has to be a top priority. We've we've gone to extremes in this where we may we may have a problem that we want address with AI.
00:38:40
Speaker
And day one, we're putting multiple irons in the fire because things are moving so quickly. To Jose's point, the landscape is going to be different tomorrow. You have to be nimble for when your first decision may have been the wrong one or that vendor may just not exist tomorrow or that technology integration point may be disproven for now given you know accuracy or or the current technology of the day.
00:39:06
Speaker
But being able to rapidly move from one place to the next ah has been really important for us to be able to take advantage advantage of the early wins. So just really wanted to echo that point. I think it's super important to realize, and especially in this industry where it's so regulatory driven and compliance based, and there's a lot of red tape between the initial idea and the final implementation of the decision, have multiple items pending during, through that whole pipeline of decision-making so that if the first idea doesn't pan out, you've got the next one already
00:39:44
Speaker
partway through the process, ah ready for the next stage. Yeah, beautiful. Yeah, I i really would like to share um with with the listeners, you know and ah like you know, the story of a journey of some out of out of all many of the solutions that we have, and we have a lot. um You know, i I mentioned in the opening, like how the sausage is made. I'm wondering if y'all could talk about kind of the journey of our hazard recognition
00:40:16
Speaker
solution that we have in in terms of the the care with which you designed it you know the how do you get good information how can we trust it you know how how does how did that one happen if someone maybe want to start wants to start with you know what is it to start with i I can kick that off. we are have So image hazard recognition is a tool that we've built that that allows a user to take a photo of something and have ai
00:40:51
Speaker
view the image, and identify potential hazards it sees. right So think about it. I'm i'm showing up at a work site. Before I get started, i just want to make sure that there aren't any any hazards that that you know I'm not aware of.
00:41:06
Speaker
I can take a photo, run it through this process. It will give me a list of potential hazards that it sees. And then I can, from there, either confirm or reject, again, human in the loop.
00:41:19
Speaker
on the the identified hazards. ah we I think it's a great example of how we approach most problems. right we We want to start with the problem. In this case,
00:41:31
Speaker
you know we're thinking about how can we expand the reach of of the EHS Pro, right? How can how can our on site supervisor be at all the all the the places work is happening at once? i am This solution kind of comes from that that problem and recognizing that there's a ah new kind of opportunity with with AI and this new technology this new technology to to apply i am
00:42:02
Speaker
to apply to that problem. And so from there, we work with the the technology team, so John and his team, to to say, all right, um we see ah an opportunity to solve this problem with with AI.
00:42:16
Speaker
i This is kind of the vision that we have in mind for how it could work. Where do we go from here? Mm-hmm. Mm-hmm. You know, i'm I'm, John, I can hear you want to speak. I wanted to talk about this particular, this particular solution has a recognition that Mike is talking about. I remember when the two of you, John and Mike, you set a meeting on my calendar and you're like, we have this idea of this thing we want to do, but we need your help.
00:42:47
Speaker
And, you know, they come to me and they're like, we want to be able to identify hazards in a photograph. Do you have anything like, photographs that you could give us that you know for sure have hazards in them and can you tell us exactly what's in the photograph so that we can is am i am i so am i am i sharing too much about how the sausage is made guys before i go on Not at all.
00:43:08
Speaker
Okay. and And I remember both of you were so excited to get these photographs for me and, you know, from from my career, I shared them. And John, you were so excited about one in particular, which was of ah of a giant metalworking bandsaw. what excited you about it was that the background in the photograph was all gray and so was the machine.
00:43:34
Speaker
And you're like, oh, this is going to be such a good test. I mean, you know, Jose said we're nerds. We are. um But, you know, like, that's how the baby part of this particular thing started. And then, John, please, please go on. oh I love being part of it. I knew you were going to mention that one. I was like, oh, please talk about the bandsaw one. Because, yeah, and I still get kind of goosebumps over it Because there's there's magic there, right?
00:43:57
Speaker
um the The technology we were using, it's it's even better... When we first started this that experiment, it's so much better even now.
00:44:07
Speaker
um What it was able to capture and differentiate um was just and was just incredible. right um We were kind of expecting, OK, this will catch the obvious. this will This will do the things that can be done at a moment's um glance. But when we started seeing the the outputs of from the AI and the feedback from a hazard recognition standpoint and the bulleted list of things that it was going through, and then backing that up with, again, those data sources that Jose has talked about, those curated data sources that Jill, you and and your team and our content experts have oversight on and make all those correlations, that was one of those first real moments for me where i was like, okay, we've really we've really got something here.
00:44:57
Speaker
My other really favorite part about image processing is it checks three of the boxes for me for making decisions about where to start inserting AI in your day-to-day processes, right? You want to give it tasks that you don't want to do. You want to give it tasks that are difficult to scale and you want to give it tasks that have oversight, but still you can insert human in the loop and grow it from there.
00:45:23
Speaker
Image processing, hazard recognition from images and image processing in general checks all those boxes, right? You don't, again, going back to that data payload, right? We'll so we'll say it twice in this podcast. So EHS professionals, they have...
00:45:43
Speaker
payloads of image data that is potentially coming in um whether that's video capture you know daily inspections the potential for a library of images to exist within a ehs professional's day-to-day life is immense right and but they don't have the human capital or um or even the desire to go through and look at all of those and analyze them.
00:46:08
Speaker
um And it's really, really difficult and expensive to scale. We're now all of a sudden, not only we can process those and do things like recognize what's in the image, validate what is there and even generate that as data. So it's more searchable, right? Like tell me what this picture even represents and turn that into searchable data. um It's doing that at scale.
00:46:30
Speaker
And now it's even solving more complex questions that are relevant to the EHS professional like hazard recognitions. And then it's generating all this data and putting it away that um you now have oversight on. So we've generated a lot of AI information, EHS professionals and users still has oversight.
00:46:51
Speaker
And now we can start doing more advanced workflows as we gain trust over that system over time, right? So today we're just going to capture and recognize.
00:47:03
Speaker
ah Then we're going to start recommending actions um to do to do based off of what we captured. And then in the future, we're going to start automating those actions and or assigning actions to other people automatically to go perform action against what has been what has been processed. And then we multiply that complexity over time. And that's where we see all of this heading.
00:47:29
Speaker
Yeah, I mean, the scalability you're talking about, John, is exactly what got me excited as a professional. Because anyone who's listening who's been an EHS professional for any amount of time, you know that our eyes are uniquely trained to identify and see hazards. And we we're not scalable, right? It's just our two eyes. And maybe we've trained a safety committee somehow to see certain types of hazards, or maybe we've trained some supervisors or managers to do that. I just saw this solution in particular as our one example we're giving today.
00:48:02
Speaker
as scaling those eyes of the EHS professional so that then as a profession, we can come in and do the exception management to say affirmed or wow, I didn't even think about that or see that sort of piece. um And it's exciting and fantastic. And also I'd like to hear about, you know, in terms of, um,
00:48:24
Speaker
you know, the precautions to make sure we're we're doing things well and we're not leading people down a stray path. What sort of safeguards do you all have in place when you're building something like this?
00:48:37
Speaker
There's a whole new set of compliance controls um that our teams are responsible for when building out these solutions. This is this is one of the, especially a few years ago, one of the scarier parts about how technologies and and hype grows for something like this. because organizations are gonna move forward, technology is gonna move forward before the compliance and oversight does.
00:49:02
Speaker
um And thankfully there have been people who've recognized that and invest a lot of time in helping us understand the additional controls that we need to put in place when bringing in technology like LLMs.
00:49:18
Speaker
um There's a lot of common sense that happens as well, right? We develop a piece of software, That software is going to go through hundreds of different controls from initial inception to production delivery. ne So all of those still apply to any AI technology.
00:49:36
Speaker
you know I talked about vendor management and all the security control oversight that has to go through with just bringing on a new partner. um But in addition to that, there are very specific controls around AI that is specific to AI that we have to now consider as well. um The industry is is trying to keep up with it as best we can. Their ISO is coming out with this has come out with a standard which is which is great progress.
00:50:05
Speaker
But anytime we implement AI into any piece of our technology, right, at an absolute minimum, we're making sure that it only has access to the data that it's allowed to have, right? um We go through a stringent round of tests around hallucination and there's a and there's a very big round of tuning that happens any single time we come in We're always learning and adjusting how it interacts with those pieces to be as accurate as possible.
00:50:36
Speaker
Every single interaction, every single decision, again, comes with logs and receipts. So any piece of data that's generated from the ai we know why it generated that. And we can go back and ask that question. And more importantly, we can monitor deviations over time.
00:50:55
Speaker
um one of the One of the things that we're most proud of when we really first started implementing these into our technology is we created a baseline that set a standard set of questions and any single, anytime we changed our integration or change how we interface with AI, we would always check it against the baseline and we can monitor deviation even as AI, the LLM um versions, right? People are familiar with, you know, chat GPT moving from version four to version five. Anytime those changes happen, we're monitoring how that changed, how that AI is actually changing our baseline and then constantly adjusting. So it is a moving target is a way to put it nicely. Um, but, uh,
00:51:44
Speaker
Yeah, theres there's so much additional control oversight and it feels really intimidating. And as as you're making decisions, again, going back to that, how do I, what decisions do I make in terms of implementing AI into any particular task, right?
00:52:01
Speaker
Give it the tasks that you don't want to do. Give it the tasks you can't scale, but also give it the tasks that it can start its role with simply oversight and then build your trust from there. You follow that pattern. And those are the patterns that we followed. And you're going to be able to walk along the way, get gains and efficiencies day one, but really grow them into those more hype-based magical ones as time goes by.
00:52:27
Speaker
Yeah, beautiful. Jose, I know that you are excited to talk about something. I was, but now I just feel like that was an advert for the entire development team when I keep squeezing them to do more.
00:52:41
Speaker
um Why they they slow it down. No, look, you know, I, you know, I just because what the team have done is is just phenomenal. You know, I kind of like want to go back to that image hazard, you know.
00:52:54
Speaker
It's incredible to be able to take that image, um to analyze that image to you know to the points you and John made, yeah really identify everything and then make recommendations to take action or provide those actions to the safety professional. it's just It blows my mind. and it's It's one of those things as I was testing that because, of course, everything always gets loaded into my sandbox and I'm always prodding and and testing, which is...
00:53:21
Speaker
Hopefully not untowards, given my position, but I just, again, i said I was a nerd. you know I say that in a loving way for all nerds. It's just we love the technology.
00:53:32
Speaker
But I know John and Mike, I'm sure, were frustrated. ah So were many people in the office because I would literally walk around and flip over tables and take a picture. and The system would come back and say, yes, there's a hazard. There's a table blocking the entryway. Oh, and by the way, you're missing this sign on the wall. And like, I didn't even notice. it um it's It's just incredible. And I do just want to say, you know, take take the opportunity online on your podcast to to give these guys a big high five and a thank you because it's incredible work. And more importantly, it's incredible work for you know the the safety professional because it really does you know turn them into superheroes.
00:54:15
Speaker
Thank you. It's been, it's, it's so fun to work with this team. I, I do have to say it's so fun. And I think that, you know, Mike, John, you're also patient with me. I know recently I brought you some idea.
00:54:28
Speaker
like it goes both ways, right? You bring me things. I bring you things sometimes. And then they're like, uh, I mean, I don't know anything about technology, but I'm like, could you do something that would do this? And you take me seriously. And it's so fun to work together on that.
00:54:44
Speaker
Yeah. So closing thoughts, um as we established from the beginning, the pace of this change and the numbers of ai solutions on the market, you know, is dizzying.
00:54:58
Speaker
For our listeners, what questions do you all think safety professionals should be asking themselves? before they're buying or maybe if they're using something right now um you know what what should they be asking in terms of risk mitigation what should raise a red flag to them if they're asking questions what kind of answers should they expect um things like that you know cautionary tales buyer beware things that you absolutely would would ask
00:55:31
Speaker
And I'll take a first very minor stab at this, but then I'll turn it over to to Mike and John. Certainly for me, before people start delving into this tool versus that tool, i guess for me, the first question is always, what am I trying to achieve? What is my goal?
00:55:49
Speaker
um And obviously base the decisions thereafter on achieving that goal. Because I think sometimes, you know, people can get too tied up. i certainly do on the technology. You know, what's the most fanciest way? What's the newest way of doing something when really it's about What am I trying to achieve and what is the best way of achieving that? You know, not just for what I'm my goal, my role, my responsibilities, but for the good of my organization.
00:56:15
Speaker
And so that to me is always the first question, the why. I think thereafter, and this is where you started to hear from John about, you know, the sanity checks. you know, the the the checks and balances, it's really about, you know, who can I partner with, um you know, that has the the pedigree and that he is going to be there with me for that journey moving forward. Because, again, what you don't want to do is start down a path and then realize, okay, well, there's a dead end, you know, in my line of sight, and I now need to pivot or go back, go do something chaos, because,
00:56:49
Speaker
that can be really frustrating, really painful. And of course, it can certainly be very costly. um And so for me, you know working with those partners that are going to be there for that journey and those partners that are going to be transparent as well, because that's the other key thing.
00:57:04
Speaker
Certainly with AI, we're all learning at the same time. We're all learning at the same pace. And so making sure that you have a partner that's going to be there with you and talk about, look, here's what we're seeing. What are you guys seeing? How can we work together? is absolutely critical.
00:57:20
Speaker
with With AI, there, again, we are in a season of a lot of excitement and a lot of promises and a lot of change. i I don't think we can recognize enough ah Jose's earlier comments around how today is today and tomorrow's another day and the landscape is going to be completely different tomorrow. And that scenario really has been playing out now since um the excitement around open AI's, you know initial LLM offerings started coming into into play and those became accessible to the everyday person.
00:57:57
Speaker
um We're still navigating such a aggressive series, a season of change that it feels intimidating to navigate. And there's a lot of promises, both, fulfilled and broken.
00:58:12
Speaker
um i think that whether from a like a software development standpoint, where I think about organizational compliance and proper software development practices, or that EHS professional who is implementing new ways to consume their data, keep their workforce protected and and safe and healthy, that there are, like I mentioned before, Common sense practices, all the technology practices that you have today are available and completely applicable.
00:58:45
Speaker
You don't have to boil the whole ocean day one. You can build trust with this technology. You just need to respond in being equally as aggressive in terms of adopting it, experimenting with it. And yes, that takes time and that does take cost.
00:59:04
Speaker
But we've seen here at HSI within our own internal business practices, as well as our products, that that time is we're now seeing the payouts of those opportunity with really exciting things like we talked about with image hazard recognition to examples internally where we're just able to consume such large payloads of data and learn from them and keep our systems safer and keep our develop and bring up our development quality All of these things are things that AI is creating individual point solutions for that are starting to come together as more of a holistic augmented process. It's it's something I'm really excited about. I think it's absolutely applicable to the day-to-day EHS professional who is simply trying to scale out their oversight to keep their work workplace safe. And to make sure that people are working smarter and there's just so much opportunity out there.
01:00:07
Speaker
And I absolutely think it's applicable right now. Yeah, that's great. So what i'm what I'm hearing from all of you is asking questions like to companies that you're that you're working with or considering to work with, you know, are you building to scale? You know, how are you considering change in the future? Or or is your niche really being a point solution? Where are you getting your source data? Where does that come from? how do you How do you test what it is that you're building? Are you using subject matter experts for it? What sort of compliance gates are you using in the development? And then if you're actually using them something to commit to using it, to your point, John, so that the data only gets better. Did I miss anything?
01:00:53
Speaker
ah that's That's a great summary. Nailed it. Okay. All right. Well, gentlemen, I appreciate you coming on the show. Listeners, I hope you find this helpful. You know, we don't get a lot of feedback from the podcast, but if you certainly have questions for us or something that you'd like us to dig into more with our nerddom here, we'll happily come back and do that. But but Mike, John, Jose, thank you so much for being here today. Appreciate it.
01:01:25
Speaker
Thank you, Jill. Thank you. Thanks. and And thank you all for spending your time listening today. And more importantly, thank you for your contribution toward the common good. May our employees and those we influence know that our profession cares deeply about human well-being, which is the core of our practice. If you aren't subscribed and want to hear past or future episodes, you can subscribe in iTunes, the Apple podcast app, or any other podcast player that you'd like. Or if you prefer, you can read the transcript and listen at hsi.com.
01:01:55
Speaker
We'd love it if you could leave a rating and review us on iTunes. It really helps us connect the show with more and more yeah EHS professionals. Special thanks to Emily Gould, our podcast producer. And until next time, thanks for listening.