How Bridge Uses AI to Remove Workplace Communication Friction

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[00:00:31] How many hours does your business lose each week because somebody missed a name, misunderstood an instruction, or left a meeting with a completely different version of what was agreed? Well, recent research shared by a company called Bridge says one in six working adults experiences hearing loss.

[00:00:52] And 37% of employees with hearing loss lose over five hours each week to communication gaps. So, today on AI at Work, I'm going to be joined by Paul Lee. He's the CEO of Bridge and InnoCaption. And together, we're going to discuss why communication friction deserves a place beside automation in every AI strategy.

[00:01:18] And Paul will explain how real-life captioning, transcription, translation, meeting summaries, and meeting intelligence can actually help people understand one another and turn conversations into usable company knowledge. I will also discuss the curb cut effect, multilingual teams, AI return on investment, and why the best workplace tools can remove repetitive effort while also leaving judgment with people.

[00:01:47] So, if you've ever left a meeting wondering whether everyone attended the same meeting, and I think we've all been in one of those, today's conversation is for you. But enough from me. Let me introduce you to my guest right now. So, a massive warm welcome to the show. Can you tell everyone listening a little about who you are and what you do? Thanks for having me, Neil. My name is Paul Lee. I am the CEO of InnoCaption and Bridge.

[00:02:15] At InnoCaption, we serve people who are deaf, hard of hearing, or have a speech disability by providing access to something that a lot of people take for granted, the phone call. And it's a consumer service that brings best-in-class AI and human-powered real-time captioning, plus predictive text-to-speech to phone calls. And it's a service that's funded by the FCC, so it's offered at no cost to eligible Americans. And we're 10 years in that business, and we've captioned over 30 million calls.

[00:02:44] And then now Bridge is part of a new sister company that we've set up, and we're taking the learnings from the InnoCaption experience and now looking to break down workplace communication barriers. And we're doing this through a suite of AI-powered technology products. And today, that starts with real-time captioning for workplace phone systems, and we're expanding from there. And I've been building accessibility technology products for the past six years.

[00:03:10] And before that, I spent over a decade in the finance and investment world, where I then decided to come and help my father, who actually founded InnoCaption, build on his legacy, and make sure the technology that he invested decades of his life into saw its full potential. And it's such important work that you're doing here.

[00:03:29] And on this podcast, I try and bring to life how AI is impacting, yes, the workplace, and indeed entire lives around the world in ways that many people don't automatically think about. And AI productivity, that conversation focuses on automating tasks and generating content. And that's what fills up our news feeds and many AI conversations that we see online. But you argue that communication friction is one of the biggest hidden drains on workplace productivity.

[00:03:59] So tell me more about that and what our business is overlooking. Sure. So a lot of, I think, the modern AI productivity conversation tends to center around taking humans out of the loop by automating tasks and generating content. But I think that as tasks get more automated, human connection and sharing knowledge are going to become increasingly important. And so that means that communication friction will also become an increasing drain and competitive disadvantages.

[00:04:27] And it's something that really businesses should look to proactively address because this is going to cause longer term losses for them. And so I think if they start with people who face the steepest barriers to communication and then seeing how those solutions benefit more and more people, it can really help alleviate a lot of this communication friction. One of the most common communication barriers comes from hearing loss.

[00:04:54] It's invisible a lot of times in the workplace and widespread. About one in six working age adults has some degree of hearing loss. And so if we're talking about a hundred person company, that's 15 or 16 people. And many may never actually mention it at work. And calls and meetings, these are places that decisions are made and where work gets done. And when something actually needs to get resolved, people still hop on calls just like we are here today.

[00:05:21] And friction on calls and meetings is friction to the core of the business at the end of the day. And so I think the appetite for fixing this is actually broader than you might expect. While we were promoting our intercaption service for people with hearing loss, we actually had a lot of people that didn't have hearing difficulty actually come to us and say that they would also love captions for their phone calls,

[00:05:44] for notes and transcripts afterwards, and also to be able to understand better while they were live in that conversation too. And so what you'll see is that communication friction often gets overlooked, but it's something that when you start solving for some of the most challenging situations, you'll actually be able to see the impact more broadly to a bigger workforce as well.

[00:06:10] And to bring to life what we're talking about here, before you came on today, I was reading research that suggests that employees with hearing loss can quite literally lose hours every week through missed context, repetition and misunderstanding. And that list goes on. So how can leaders begin to measure the wider business cost of poor communication that often remains invisible on some of those reports that we see? Yeah, that's a great point.

[00:06:38] You can measure it to some extent, but I think what you have to do is look for proxies to understand the full and broader impact. So in terms of some of the direct data that I've seen, more than a third of employees with hearing difficulty lose five or more hours a week to communication gaps. And that's real payroll time that's quietly being lost or disappearing. But I think the harder and the bigger cost is decision quality.

[00:07:02] And it's like the idea that never got shared, someone mishearing an instruction, work getting redone. And those don't necessarily show up as lost hours as cleanly, but it's an oftentimes larger hit to overall productivity for the team. So I think it's good for business leaders to really look for signals and to even use pulse surveys and things like that to ask anonymously, because a lot of times people won't raise this in the open.

[00:07:27] So it's about asking questions like how confident do you feel communicating during meetings? Do you feel like you're getting everything that you need? Would tools like live captioning potentially help you? Are there areas where you're not provided the right tools to be able to be most effective? And are you having a good amount of meetings or maybe too many meetings because you feel like things are getting repeated or misdirected? And I think those types of pulse surveys that don't just talk about are you happy at work,

[00:07:55] but really get into some of these communication issues without necessarily directly asking are you getting everything, could be good indicators of these types of frictions that are manifesting themselves. And real-time captioning was traditionally viewed primarily as an accessibility tool, but research suggests that it can actually improve comprehension, attention, and recall for everyone. I know a lot of people will watch Netflix and things and always have subtitles on for that very reason,

[00:08:24] but what does that teach us about designing for accessibility and how it can ultimately create a better workplace experience for all employees? Yeah, it's such a great point. There's a great concept called the curb cut effect. Yeah. Curb cuts are those little ramps on the sidewalk that were originally built for wheelchair users and kind of done out of compliance initially, but it turns out that a lot of people use them and benefit from them. So, you know, I'm a father of young children.

[00:08:54] When, you know, I was going around with strollers or when I'm traveling and pulling luggage around or, you know, riding bikes with my kids, all of those times if you didn't have those curb cuts, it would be very, very difficult and challenging. And so when you start designing for the hardest challenges and use cases and solving the hardest problems, it actually ends up improving and providing a benefit to a lot of other people that you might not have expected or intended initially.

[00:09:23] And captions, I think, are a very similar story. They were built for people with hearing loss, but plenty of people now who hear pretty well use them consistently and they've gotten used to them watching a video on their phone, catching dialogue in a noisy background or catching a name or a word that they didn't quite recognize. Once people feel the benefit, they're going to leave them on more and more. And I think there's real hard numbers to back this up.

[00:09:50] Well, I was just reading a survey that around 80% of adults aged 18 to 24, so young adults, now watch TV with subtitles on, even though in that demographic only a small percentage actually has a hearing difficulty. And so I think captioning has really gone mainstream very quickly. And I think a lot of studies really do show that captions improve comprehension, attention and recall, whether you have a hearing loss or not. And so we're seeing this playing out in real time. We've actually been in conversations on our Bridge product

[00:10:18] with a large multinational hospitality company. And they initially found us when they were looking for accommodations for people with hearing loss. And what surprised their own team was that a lot of people were actually requesting captions when they actually started having conversations and surveying folks that didn't have hearing loss. And they found that especially younger people who are so used to having captions and subtitles everywhere they go, really felt like it would help them in meeting conversations as well.

[00:10:46] And this is also the case for people for whom English is a second language. They also found that for those folks as well, the captions really, really helped them. And so I think demand and captioning for work is growing well beyond the group it was originally built for. Bridge combines real-time captioning, transcription, translation, meeting summaries, and meeting intelligence. And it's incredibly exciting to see where this could head.

[00:11:12] So from your personal perspective, if you were to look into the future, how do you eventually see AI further changing a meeting from something that just disappears when everyone leaves the call or the meeting room into a more searchable organizational knowledge that can continue creating value days, weeks, or even months after that meeting? Yeah, this is where we're heading. And it's part of what I think is really exciting about the work that we're doing because we're already starting to live it a bit ourselves.

[00:11:40] So Bridge today provides real-time captioning for workplace phone systems. And we're about to launch a companion app that brings that captioning experience to in-person conversations and meetings. And although our in-person transcription app was built with our deaf and hard of hearing users in mind, we actually found something that surprised us while we were testing it out for the last year plus. It turned out to be so accurate, much more so than generic free note-taker apps out there,

[00:12:09] that our own team members started using it to transcribe their meetings, even if they didn't have a hearing loss, because they were getting detailed, reliable notes that they could actually summarize and act on later. And so that's really when it clicked for us, that when you capture the conversation at the most accurate level possible, it's then easier to institutionalize that knowledge and make it retrievable later and turn it into summaries and really distill a discussion down to what actually matters. And to give you an example,

[00:12:38] we had a meeting to do a deep dive and brainstorm with a lot of different team members together on one of our product ideas. And in the past, the painful part wasn't the discussion itself. That was actually the fun part where everyone's super engaged and having a great time, spitballing ideas with each other. But it was after the meeting was done, someone would then have to go and collect everyone's notes, all the whiteboard scribbles, all the sticky notes on the wall, and distill all of that information into a short list that we could actually then review

[00:13:08] and then decide on what to keep, what to do, and what to drop. And now AI could do that consolidation for us. It doesn't make the decision. That's still really on the humans in the room. But it brings 100 ideas down to a digestible set of choices that we can actually act on and does so in such an incredibly efficient way. And I think that's really what AI is best at here. It takes that mechanical work, the capturing, the consolidating,

[00:13:35] it takes that off people's plates so that the people can focus on the judgment, which we need the human in the loop for. It doesn't replace human decisions. It clears the path to a better one. And it makes those onerous tasks far less onerous and complicated. And that means we can actually have more of these highly productive and fun and engaging brainstorming sessions, because nobody is stuck with a day's worth of work consolidating that information after the fact.

[00:14:02] And I think that intelligence layer being built on top of that bridge product is exactly where we aspire to go. And it doesn't work unless we get all that underlying record keeping as most accurate and dependable as possible. And that's why we're starting with that layer and building upon that foundation. Incredibly cool. And if we look even bigger than that, and everything we're talking about here, for many of the conversations I'm having with companies all around the world,

[00:14:30] we're now seeing more distributed and multilingual teams, all working across different locations and time zones, using those different time zones as an advantage. So the business is always up and running. So from this side of things, how do you see AI removing communication barriers without losing the nuance, the context, and human connection that effective collaboration has always depended on? Yeah, the interesting thing about language, I think, is that it's not just words.

[00:14:59] It's really that nuance that makes it human. And anyone that speaks more than one language really knows this easily. You can't, there's certain jokes that only land in one language and some expressions that just don't carry over. And a direct word for word translation often misses what's most important. And when you're not fully fluent in a language, you're also kind of boxed in. I've heard people say, you know, I sound a lot smarter in my own language. And I think it's true that when you speak in your native language,

[00:15:28] you can articulate better, you can express yourself with more confidence. And so I think the real opportunity and where AI is heading is actually letting each person speak in the language that they're most confident and expressive in, and then let the AI help carry it across. And so modern language models are actually really, really good at this now in ways that older tools were never able to do. And so instead of trying to translate in your head while you're trying to explain a hard concept at work,

[00:15:57] you can just speak naturally and help and let the AI help bridge that. And I think there's a real beauty still in people sharing common language and using it to communicate directly. And I don't want to lose that necessarily. But I think from a workplace productivity perspective, when you allow people to fully express themselves and use AI to help them bridge the gap, it's really, really empowering for those individuals. And I see this in our own company too. We're a mostly US-based team.

[00:16:28] We have a small office in South Korea though. And a few years back, a colleague of ours moved from Korea to be on site with us here in California. And I'm also Korean by background. He understands English well enough, but he's far more confident communicating outbound in Korean. So in meetings, I let him speak in Korean and I just translate for him. But the rest of the meeting is conducted in English. And when I translate for him, I don't do it word for word. I try to carry the substance and the nuance of what he's actually trying to say,

[00:16:58] even if the exact words coming that I'm translating are slightly different. And that's exactly, I think, what good AI translation should do. It should capture the meaning and the human nuance, not just the words. And I think we're actively working now with that in mind on bringing this type of translation into our bridge workplace communication suite as we see the technology evolve and improve. Yeah, I completely agree with you there.

[00:17:24] And there's an old saying in IT that you can only improve what you measure. And there has been a tendency to measure AI productivity through time saved or how many tasks are automated. But I'm curious from your viewpoint, should businesses also be measuring factors such as fewer misunderstandings or better knowledge retention, improved inclusion or faster decision making just to fully understand the true ROI of workplace AI? Because I know it's a huge topic right now.

[00:17:54] And it feels like there's almost room for a different way of measuring. Yeah, it's an interesting point. And I think to some extent, you know, I think CFOs will always look for ROI. But there will be some things you can measure and some things you cannot. And I think at the end of the day, you know, the easy stuff to measure is time savings and, you know, where we can automate repetitive, simple work.

[00:18:18] But I think it's really what you do with the time you save with those automations that actually leads to real gains longer term. And I think at the end of the day, what really matters is what impact does that have for the business overall? And I think this needs to be done on a longer term time horizon. Because, you know, just like the example of the brainstorming meeting that I was referring to

[00:18:44] earlier, it'll actually manifest more deeply. And when it comes to people leveling up, creating more value, communicating with each other better. And I think if we have a longer term view, and we actually look at things like velocity of product development, how many brainstorm meetings are you able to have because people want to have them more now that they have AI to assist them with the consolidation of nodes afterwards? How much meaningful work is getting done?

[00:19:12] And what is the improvement in quality of the decisions coming out? I think these are going to have to play out in multi-year arcs. But I think those are the real signals. Unfortunately, that means that it's maybe harder to tie, you know, short term spend on ROI. But I think those are the real things that people should be looking out for, because that's what will build competitive advantage for companies in the longer run. And I think that it's especially true with communication barriers.

[00:19:39] When more people can fully participate and express themselves, the value that they generate will go up. And you're only going to see that really in long term time horizons. So I do hope that people, you know, try to find good short term ROI metrics that are helpful, but also don't get lost in the weeds there and really look at some of these more, what do I want for my team and my business longer term? And am I actually helping them get there? That's, I think, a better way to look at ROI in the long run.

[00:20:09] And on a personal note, you're now CEO of both Bridge and InnoCaption. So I've got to ask, what have you learned from working at the crossroads of accessibility, communication and AI about building technology that solves a specific human need, but can also ultimately benefit a much broader audience? It feels very inspirational what you're doing here, but I'm curious the biggest lessons learned there. Yes.

[00:20:37] So the biggest lesson for me is that going deep on a specific underserved need that is very challenging to solve for actually lifts the standard that you hold yourself to and then leads to building more broadly valuable things. And I think accessibility is a perfect example of this because for most companies, it's not really a focus. It's a compliance checkbox.

[00:21:03] It's a nice to have side feature in a main product. Whereas for us, accessibility has been at the core focus of what we do. And there's not that many technology companies that really center on this. And so that holding that focus has put a very high bar for what we consider acceptable. And real-time captions is obviously the main thing that we've been focusing a lot of our time on. But we want to make sure that it's not a good enough product because that's not what we want for our users.

[00:21:32] It's not what they expect from us. We want the highest, best quality level product for that challenging situation to break down that communication barrier. And I think constantly pushing towards that higher standard means that in our area of focus, we build a lot of hard-won know-how. And it's the kind that you can't get any other way. It's expertise that's built on years of working with our users, getting feedback, and diving

[00:22:00] deep into what their needs are and how we can solve them. And I think that has really built a firm foundation for us to then layer in other things like AI intelligence and all of the other things that we've talked about today. But I think that that business advantage really comes at the end of the day from firsthand real human interaction communication experiences. And that's something that you can just look up somewhere or have an AI come up with for you.

[00:22:29] It's something that actually has to be lived and experienced and something that you have to go deep in. And I think that's where working at accessibility, I've come to really appreciate the depth that we have to go into to really solve these difficult issues. And to give listeners an actionable takeaway, especially if we've got business leaders listening that are currently evaluating their next AI investment, any tips on what they should look for

[00:22:56] to identify the everyday points of friction that AI can genuinely remove and help employees do their best work rather than just adding another AI tool onto the already heaving technology stack? Any tips there? Yeah, I think at our company, I've kind of been the tip of the spear with regards to testing out the capabilities of AI and figuring out where we should adopt it and where we also should not.

[00:23:26] And I think that is really important. I think business leaders actually have to be the ones understanding the technology and then also understanding deeply what is it that your teams do? You can't just look at it at a whole company level. It has to be at a department level, a team level, an individual level, and really understand instead of just trying to push from the top down a metric of AI adoption,

[00:23:53] which doesn't work and creates bad incentives, but rather understand what tools are there and help be the connector that actually says, what do you do day to day? What are the things that you find most exciting? What are the things that you find to be most mundane and repetitive? And help employees with the repetitive side to the extent you can by leveraging AI tools and then enable them to spend more time on the highly complicated,

[00:24:21] interesting things that keep them going, that motivate them, that drive them. And I think that's how we'll see whole organizational change and uplift and people actually loving their jobs more because of AI adoption and not feeling threatened by it because they really shouldn't if this is done right, with a good understanding of the benefits and the limitations of what AI tools can do.

[00:24:44] So I think that's where I would start is by going deep into how the technology works and where its strengths and weaknesses are, and also going deep into, you know, one at a time into where in your organization you think that these tools can actually help benefit or where you should also be more cautious as well. Wow. And I think that is a powerful moment to end on. And for anybody listening that would like to carry on the conversation with you or just learn more about anything we talked about today,

[00:25:14] where would you like me to point everyone listening? Sure. So I'm going to try to be more active on LinkedIn now to share more of these thoughts and viewpoints. So folks have looked me up, Paul Lee, InnoCaption and Bridge on my LinkedIn. And then in terms of what we do from a company perspective, we're at bridgecaption.com for the workplace solutions and InnoCaption.com for our consumer products. Well, I love what you're doing here.

[00:25:43] And rather than viewing AI as a replacement for people, which is something we're seeing in our news feeds at the moment, I cannot thank you enough for providing a somewhat of a refreshing perspective. And that is the organization seeing the greatest return on AI investment are the ones that are helping employees communicate better, collaborate more effectively and recover thousands of hours otherwise lost to miscommunication. Incredible work you're doing here.

[00:26:08] I will add links to everything that you've mentioned today and encourage people listening to go check that out. But thank you for sharing your story today. Really appreciate you, Tom. Thank you so much for your time and for having me on your podcast, Neil. Really great discussion. I think Paul's curb cut example really hit home today because that was a design created for people facing the steepest barrier.

[00:26:31] But it can also improve the experience of parents with strollers, travelers with luggage and eventually almost everybody. And workplace captioning can follow the exact same path. And the figure shared by bridge deserve attention today. Nearly 20% of employees with hearing loss reportedly lose over 10 hours each week through missed context, repetition and misunderstanding.

[00:26:58] Meanwhile, over 100 peer-reviewed studies have found benefits from real-time captioning for comprehension, attention and recall. So all these things give leaders a human and commercial reason to treat communication as part of their AI plans. But over to you before buying another AI tool. Maybe it's time to ask where communication friction is quietly stealing time from your team.

[00:27:26] And what could become possible if everybody could participate and everybody could be understood? TechTalksNetwork.com. You'll find a blog post associated with this episode along with all the links that Paul shared there at the end. And you can also learn more about how to work with me, meet me at an event, record an interview or just send me an audio message. But that's it for today. I'll be back again real soon with another episode. But thank you for listening as always. Bye for now.

[00:27:56] Bye.