October 17, 2023

The AI-EQ Connection: How Emotionally Intelligent AI is Reshaping Management

As more and more companies bolt AI onto their products, the importance of being intentional with our use of it is growing fast. 

So what are the best ways to embed AI into management products in a purposeful way?  

In this episode of the Built Right podcast, we speak to Brennan McEachran, CEO and Co-Founder of Hypercontext, an AI-empowered tool that helps managers run more effective one-on-ones, leading to better performance reviews.  

Brennan shares how he and his co-founder created the tool to aid their own work and later found how it could enhance multiple areas of performance management for leadership teams, creating faster, more streamlined processes. 

Read on to find out how AI can improve EQ in the workplace or listen to the podcast episode below. 

The story behind Hypercontext 

Hypercontext has a unique, organic backstory. Brennan and his co-founder, Graham McCarthy, worked together at a previous company, gaining enough experience as builders and sellers of products to become managers. 

As they focused on being manager-operators, Brennan and Graham concluded that their strengths still lay in building great products. They began building small-scale desk tools to make their work easier and, as COVID struck and everyone became a remote manager overnight, they made this their main focus. 

Brennan shares that one of the products that steadily took off was what would later become Hypercontext, helping managers become the best bosses their team has ever seen. 

Initially guiding managers through one-to-one and internal meetings using the superpowers of AI, Hypercontext has branched out into providing useful tools for performance reviews too. 


How AI is quietly revolutionizing HR 

Brennan remembers first taking demos out to HR managers and receiving a mixed response. 

Despite loving the concept, these managers were sceptical because of its use of AI and feared that it was too forward-thinking.  

However, the boom of ChatGPT and other AI tools in late 2022 caused a change of heart. Many HR professionals also realized that their managers had been using AI tools for their performance reviews for a while and warmed to the idea that it could be used to enhance their meetings. 

Brennan notes that cultural reservations can stand in the way of progress. With the AI wave tending to hit tech first: “If we’re not ready for it, we’re in for a world of hurt.” 


How AI is transforming EQ  

One of the main concerns surrounding AI is its lack of human touch. But Brennan suggests that, used in the right way, it can actually enhance the things that make us human. 

All managers, in HR or otherwise, have those tasks that are regularly cast aside in favor of more ‘pressing’ jobs. If they had “infinite time”, maybe things would be different. Brennan suggests that AI can take these tasks on and streamline the working processes of managers. 

He also explains how Hypercontext can provide the information that makes us more human. From its conversation starters, to the data it gathers about team members, it can actually make reviews and meetings more empathetic. 

Brennan says: “I think a lot of people have fears about AI taking jobs or removing the humanity in certain things. Done right, AI has that potential to make you more human, in and of yourself.”   


The future of developers

Did you know that, of the users who use Copilot, around half of the code committed to GitHub is AI-generated? It’s no secret that AI will impact software development, but this fact begs the question – what does the future hold for developers? 

Brennan thinks this is the time for software developers to pivot to a new focus and suggests those doing “wildly different things” could be setting themselves up for success.  


Using AI to write performance reviews 

When Brennan realized that AI could be used to write performance reviews, he was hesitant to fight big-name industry players to find a solution. However, he was determined to be the person to do it the right way. 

He explains that he didn’t want to see a bolt-on tool created that “generates superfluous text around nothing” and was eager to make something that genuinely made managers better in their work. 

Brennan explains how Hypercontext allows managers to compile findings from multiple peer- and self-reviews, identify key themes and tee up the conversations to build upon these themes, all in a minute; something a human just couldn’t do! 

He adds: “80% of people feel like our process is both better and faster than their previous one. Who wouldn’t want that?” 

Fuelled by the desire to make this tool the right way and prove that AI can enrich HR management, Hypercontext built a one-of-a-kind tool and set the HR-AI standard in the process.  

For more insights into using AI intentionally to become a better manager, head to episode 15 of the Built Right podcast.

Step into the future of software development with HatchWorks – see how our Generative-Driven Development™ can transform your projects.

Matt (00:02.067)

Welcome, Built Right listeners. Today, we’re chatting with Brennan McEacher, and CEO and co-founder of Hypercontext, an AI-empowered tool that helps managers run more effective one-on-ones, which leads to better performance reviews. And it’s trusted by 100K managers, companies like Netflix, HubSpot, and Zendesk. Welcome to the show, Brennan.

Brennan (00:23.414)

Thank you for having me. So excited to be here.

Matt (00:25.547)

Yeah, excited to talk today. So the topic we have for our listeners is one, everyone really needs to stop what they’re doing and listen to. And today we’re going to get into how you should be strategically thinking about embedding AI into your products in an intentional way. And with the current, you know, AI hype, hype cycle we’re in, or everybody and their mom is bolting AI onto their products. And I don’t mean that in a good way necessarily. This is a conversation worth.

worth having, but, but Brendan, as a way to kind of set context, I love getting into our guests with, you know, what problem they saw in the market. They kind of triggered them to start their, their company, give some good context for the background.

Brennan (01:08.806)

Awesome, can do on the contact side.

I think the story of Hypercontext, the story of us founding it is sort of an organic one. Myself and my co-founder, Graham, had a previous company. We’ve been working together for a little over a decade and that previous company was successful enough or maybe we were successful enough at building and selling product that we ended up becoming managers. We had enough employees and staff around us to help us build a bigger and better business. And as we stopped…

building and selling, we realized that we sort of accidentally fell into a new career of managing and that this new task of being manager-operators is

Matt (01:51.201)


Brennan (01:54.358)

completely different and very hard. So we did what we knew best, which was build. We built some little side of desk tools to help us be better managers and be better bosses. And as a long story short on that business, as COVID kind of came around and wiped out industries temporarily, we were sort of caught up in that mess and that business disappeared almost overnight. But these little side of desk projects that we had built.

Matt (02:18.698)


Brennan (02:23.966)

Uh, exploded everyone in the world became a remote manager overnight in the middle of a crisis and, uh, felt the pain of being a manager, uh, and being a remote manager and all of the problems that come along with that. And these little tools that we put out on the internet went from, you know, a couple of signups here and there to, uh, in some cases, thousands of thousands a week. Um, and so we, you know, made some tough choices, but otherwise we’re able to

Matt (02:29.215)


Brennan (02:54.36)

pivot almost all of our energy towards what you see today. Hypercontext, which is as you mentioned, building tools to make managers the best bosses their team has ever seen. So we start with one-on-ones, we start with internal meetings, team meetings, add goals to it all the way through to now just recently launching performance reviews and I think that sort of leads into trying to build the performance review piece the right way.

Matt (03:18.616)


Matt (03:23.071)

Yeah, I love the, it’s kind of like the Slack story, right? Where you kind of built this thing on the side and powering it like, oh, this thing actually has legs. And I was just chatting with a friend yesterday, same kind of thing. They had like this side thing they had built and people were asking about it. And it’s like, well, maybe this is the thing. And it’s kind of an interesting story when, you know, something like a pandemic just changes your whole business model, right?

Brennan (03:47.626)

Yeah, I think the saying right of like scratch your own itch is sort of relevant here. We, um, we definitely started it as like, uh, something to scratch your own itch and, um, as early as we possibly could try to get, um, external people’s input on it. Um,

Matt (03:51.805)


Brennan (04:07.858)

One of the things that I think I learned in the first business is like what you want and you know, what helps you is not always the exact same thing as what helps other people. So we tried to look for the general solutions, um, to some of these problems instead of a specific ones that would work for me being, you know, uh, you know, a tech guy, a product guy, whatever, we wanted to look for something that had sort of that broader appeal. And that’s actually how we landed on one-on-ones. We, we initially thought, Hey, there’s maybe more meetings that we could.

Matt (04:17.732)


Brennan (04:37.992)

And when we went out and tried to talk to people and figure out a general solution, the amount of build we would have to do was just so big. We ended up looking for, like, what are some commonalities? One-on-ones ended up being really appealing.

Matt (04:47.817)


Brennan (04:53.934)

for a variety of reasons, but one of the main ones is that an engineer having a one-on-one with their manager is very similar to anyone else at any other company having a one-on-one with their manager, almost by definition, right? You’re not supposed to talk about the tactical day-to-day stuff, and so you talk about more of the meta conversations, which can be similar. So that sort of led us down some of these pathways.

Matt (05:04.087)


Matt (05:21.463)

Yeah, I think that’s an interesting point, like just to pause there for anybody in product, you know, we talk about building your solution, the right building, the right solution and building it the right way. Building the right solution, start with a smaller use case. That’s a critical piece. Like you could have boiled the ocean and tried to figure out every meeting under the sun and all this stuff. And then your head would just explode with everything under HR. But you started with the one-on-one because it was one that, you know, universal.

It needed help, right? So you identified this problem in the market. I just love that. And now it’s turning into more as you’ve built proof behind it.

Brennan (06:00.114)

Yeah, you know, we started with just exploring the, the exploration sort of led us to say, you know, and especially coming from the last business where it was a lot of change management to kind of sell the product. Um, we wanted to avoid some of the change management. So we’re like, what is already existing? Um, and the only thing that I could kind of point to as proof was like the calendar. So like

When we’re building some of these products, it was what it already exists on the calendar. Let’s not make people do something new. Let’s look at their calendar first and see if there’s anything we can do on that calendar to make it 10 times better. And so, um, you know, the one-on-one was there. Um, so was the team meetings was the board meeting. So was that, you know, the QBRs, all these other types of meetings. The interesting thing, there’s so many things that are interesting about, um, uh, one-on-ones for us as a business, um, you know, almost every manager has one. So there’s lots of entry points into the organization.

Matt (06:29.495)


Matt (06:54.881)


Brennan (06:55.212)

Which was a key piece of what we thought our strategy would be Very easy to Try because you can try it with one person. You don’t have like a town hall is tough to try You have to do it with your whole company

Matt (07:09.615)


Brennan (07:10.938)

Um, so you, so with a one-on-one, you can pick the most open-minded person on your team or try out the product. If it works well, you sort of get into some of the other things. It’s, it’s, um, very replicable, right? If you have something that works with one person, it should work for other people. Um,

So many other things it can spread, right? Like you have a one-on-one with direct reports. You also have one with your boss, right? You’re all the way up to the CEO and the CEO all the way down to a different department so you can spread it exists on the calendar. So many things that led us to it. And just because you have seven direct reports, seven one-on-ones, um, plus your boss, plus maybe a peer one-on-one just by frequency of meeting it’s, uh, it’s a very high frequency meet meeting. Um, there’s way more one-on-ones than there are team meetings at, at businesses. So.

Matt (07:32.803)

Mm-hmm. Yeah.

Matt (07:56.629)


Brennan (07:57.424)

All of these things as we sort of bumped into it, we said, you know, hey, maybe there’s something here. What would it look like to do a 10x better job? And sort of honed in on that use case. What are people already using for it? What are they, you know, what are the good, the bad? Who are some of the competitors? And for a long time, the only people building tools for this space were the big boring HR companies, right? And like, no one wants to open up.

Matt (08:22.689)


Brennan (08:24.53)

SAP, you know, or ADP and go into like this tiny little module to fill in a text box when you can have Apple Notes or something like that. So, zoomed in on that for sure.

Matt (08:36.241)

I still have nightmares from using one of those, but the time entry system, I’m like what button do I click? Who designed this thing? But they can’t get out of their own way because they have so much legacy, just what’s the word, technical debt that exists there, right?

Brennan (08:53.422)

And like they have to cover so many things or they have to do payroll for globe for, for every culture and company type and all that stuff. And you’re just one tiny module on there. So.

Matt (08:56.895)

Mm-hmm. Yeah. Oh my god. Yeah

Matt (09:05.059)

Yeah, but a lot of great, you know, PLG type of motions there, like you mentioned, the high frequency of using the product, building the habit. I think we talked about the book Hooked, which if anybody has not read that, check that out. It’s great. And there it is, the yellow blur and the background that stands out like a sore thumb, which is another great way of standing out. But I want to, let’s get into now AI, right? So your company was started post pandemic. This was pre.

Gen AI, you know, large language model craziness, even though they’ve been around for a long time, the crazy hype there. And you had AI integrated into the tool, but I’d love to get into this evolution. Cause one thing that struck me, uh, when we talked earlier, it’s like somebody’s going to do this talking about competitors, embedding AI.

but they’re just not going to do it the right way, right? And we want to do it the right way. But talk about that evolution, because so many folks, they just bolt on AI. It’s from a marketing perspective. They just want to key into the hype. But it’s such a bad way to do it from a strategic standpoint.

Brennan (10:04.118)


Brennan (10:10.57)

Yeah, it’s funny the place where we have AI the least right now is actually on the marketing side of something that we’re trying to fix. It’s definitely pretty heavy on the product. No, you’re exactly right. We wanted to build, um, the best, you know, for, for lack of a better term, we wanted to build the world’s best one-on-one tool for managers. Right. Um, and.

Matt (10:18.391)


Matt (10:34.648)


Brennan (10:39.586)

that mission will never truly be accomplished because the market moves so quickly and we always have to serve the managers in that use case. But like largely, you know, quote unquote, mission accomplished. We sort of have the best hyper-connected workspace for one-on-ones for managers, whether it’s, you know, just one-on-ones or you wanna bring that team in once we have it for team meetings.

Matt (11:01.281)


Brennan (11:03.978)

We added goals to it. So if you’re working on professional development goals, you’re working on team goals or OKRs. We have the largest library of goal and OKR examples on the internet built into the product. Like, you know, largely anything a manager, a team lead would need out of a platform for leading their team, sort of built it out of the box, PLG, go try it for free. And…

Matt (11:28.062)


Brennan (11:31.182)

I mentioned some of the benefits of one-on-ones and some of these team meetings and that we get this organizational spread. Well, that started to happen, right? We would start to spread across these organizations through calendar invites. As people sort of discovered our tool and shared our tool, if COVID taught us nothing else, it’s that, you know, look for the super spreaders, right? Like we were sort of looking for the people who would spread our tool internally. And they did. And then

Matt (11:45.067)


Brennan (12:01.09)

Because we say the word manager so often, because we say the word one-on-one so often, when it came time for the organization to look at this tool and say, well, what is this tool used for? It’s often used for one-on-ones and for gold. Managers love it. It sort of fell on the desk of HR. It fell into the budget of HR. And HR looked at it and said, this is great.

this actually might be a sign that our organization is maturing. Maybe we need some more of these HR.

big HR tools, right? Maybe we need a platform for performance management, which it can do all of these goals and can do all these one-on-ones, but can also do surveys and can also do performance reviews and can also do all these other things. And the managers are like, no, don’t get in our way. Don’t ruin our thing. And often they would use us almost as an excuse to buy their tool, to buy the big, boring HR tool, consolidate the money that the company is spending on us and, you know, double it, triple it,

Matt (12:34.18)


Brennan (13:02.192)

something else and the management revolts and stuff like that. We would try to fight back as best we could, but ultimately when we started talking to the folks in HR, they were like, well, I need performance reviews or something like that. We didn’t want to build it, but we started looking at building it.

and sort of taking that fight on. You know, what would it look like if we did round out our platform to incorporate some of the more traditional aspects of performance management? Hate that word, by the way, performance management. That’s like a micromanage-y word, like HR is gonna perform. I think that performance enablement, I think that the goal of HR getting involved in…

Matt (13:27.191)


Matt (13:40.64)

What do you prefer? Is there another word you prefer?

Matt (13:48.707)


Brennan (13:52.546)

performance management is to help people be performed. They’re not really there to micromanage performance. They’re not getting fired if marketing misses their KPIs or sales misses their KPIs. So why are they in charge of performance management? Doesn’t even make sense. But enabling performance at the company, that makes sense for HR to centralize. So we looked at.

Matt (13:55.192)


Matt (14:08.163)


Brennan (14:18.454)

you know, what were the other people doing? Maybe there’s integration plays that we can do, et cetera. And one of the first things that popped into our mind was just the quote that HR kept bringing to us, which is, well, if people are doing their one-on-ones properly, if they’re doing their one-on-ones right, then come performance review season, there should be no surprises. No employee should be surprised. So we sort of thought, well, not, you know,

Matt (14:42.327)

But how many managers do that though, is the thing, right? Like I’ve been through that experience. It’s like, I have great intentions come January. I’m gonna document everything that happens. I’m gonna have this great thing at the end of the year. And I’m okay at it sometimes, but I’m not great at it, right?

Brennan (15:01.13)

Well, and that’s like a huge piece of what our core product tries to solve, right? Like, how do we make a one-on-one tool so good that you prefer to use it over something else and can we build in some of these workflows where you can follow through on those great intentions? Um, I think most managers with those great intentions try to, uh, implement them with like a Moleskine notebook, right? They get like a new Moleskine notebook and they’re like this year it’s going to be better and that Moleskine notebook has like four pages and then it’s, it’s tossed to the side. And HR said,

Matt (15:11.461)


Matt (15:22.66)


Matt (15:26.877)

I like that analogy. Yeah. Yep.

Brennan (15:31.164)

well, we’re gonna make it better by giving you like a Commodore 64 and you’re like I’m not gonna use a Commodore 64 for my like You know

notes, that’s insane. I’ve got modern tech over here. So we wanted to build, what would the Apple version of this look like? And you’re exactly right. If we did the daily habits right, things would be much better. We’ve spent so much time on the daily habits that we legitimately help managers to the point where they spread the word internally. So when we went to HR, it was like, well, if they’re doing everything right,

Matt (15:39.808)


Matt (15:47.268)


Brennan (16:07.094)

then there should be no surprises in performance reviews. Can we actually make it so that it’s not just that there’s no surprises, that it’s effortless? What would that look like? And we started exploring around with AI just sort of making like proof of concept demos of, can we take the notes from your one-on-ones? Can we take the goal updates on your OKRs?

Can we take some of the stats our platform can generate and integrate that with your HRIS system? And maybe you calibrate the AI with a couple of quick questions. Maybe the AI can actually write the review for you. Could that actually work?

Tech demo sort of proved that it could. And to the point where, you know, I’m sitting there looking at it being like, I don’t know if I wanna build this. I don’t know if I wanna enter this battle and fight some of these big name players.

Matt (16:45.518)


Brennan (16:58.838)

but someone is gonna do it, and those people are not gonna do it with the right intentions. They’re gonna do it as a marketing play, as a bolt-on thing, they have performance reviews where no one uses the one-on-one functionality, and they are just gonna have an AI blindly, dumbly generate some, you know, supple first text around nothing. And people are gonna, you know, feel wowed temporarily until the gimmick sort of wears off.

Matt (17:03.998)


Matt (17:16.94)


Brennan (17:26.798)

And in order to accomplish, I think, using AI the right way and implementing this sort of AI and HR the correct way, you need the daily use. You need the use from the manager every single day, documented, properly categorized, in order to build on the everyday, to write that end of quarter, end of biannual or annual review.

And we had just so happened to have spent, you know, an extreme amount of energy over years working on those daily habits that we sort of felt uniquely able to build this the right way in a way that it seemed like no one else was even able to attempt. So we sort of threw our hands in the air and said, like, you know, we got to get this out first so that people know, you know, the right way to do this. And then that’s what we launched so far. It’s been amazing.

Matt (18:20.743)

Now, take me back to when that time happened, because if I recall, y’all were trying to do some of this pre having, you know, open AI and others kind of opening their APIs. And then they have that and it kind of just democratize things in a lot of ways where you get access to these large language models that you could then apply to your data, correct? And then it becomes differentiating because it’s unique to you, even though you’re leveraging something that’s, you know,

Brennan (18:33.423)


Matt (18:51.395)

I guess you get into a whole debate of open AI, not technically open source, but it’s all another discussion.

Brennan (18:57.206)

Yeah, that’s right. No, you’re exactly right. We’ve been using machine learning AI for quite a while on the how do we make the meetings better, right? So from categorizing what you’re talking about in a one-on-one, using those with AI into an engagement framework. So if you’re not talking about certain things in an engagement framework, the system’s aware of that. It’s able to use that information to suggest.

Matt (19:05.836)


Brennan (19:23.95)

Content to cover your blind spots So if you haven’t checked on someone’s motivation in a while, we’ll sort of recommend here’s a conversation starter that checks on motivation Because you haven’t checked on motivation with this person a while things that like busy managers Have all the right intentions, but they’re just busy, right? They’re not gonna be able to keep track of like when was the last time I checked on this person’s motivation It’s more like, you know, if things are silent I’m gonna assume things are good and until I get sort of punched in the gut a couple weeks down the line

Matt (19:26.761)


Matt (19:34.723)


Matt (19:51.997)


Brennan (19:53.47)

So we’ve been using some of those things, same with our next steps. You type a next step out, we would automatically figure out the date with machine learning, we automatically figure out who to assign it with machine learning, all that stuff. When it came time to sort of think about using that to a greater extent,

Uh, in, in writing the written formal, um, feedback for the managers. Um, obviously there’s way more data we wanted to pull. It wasn’t just, you know, what have you typed recently? It was like six months of meeting notes. It was six months of goal updates. Um, on top of, you know, data from a reporting on top of a whole bunch of other stuff. So it could generate a lot more, um, high quality feedback, but there’s also little things about like, you know, coaching and training this model.

and when we first took these sort of tech demos out to Folks in HR the reaction was like wow. I feel like I saw the future I just don’t believe it will work like I just don’t believe the techs there yet. I’m like, what do you mean? I just showed this to you. They’re like, yeah. No, I see it. I’m looking at the future, but I Just don’t think the world is there yet Like and I think they were more reacting like culturally like this wouldn’t be

Matt (20:50.456)

Don’t believe it, yeah.

Matt (21:00.792)


Brennan (21:05.962)

You know, they feel like they’ve seen it but like they’re not sure if they’ve just you know, if they’re being tricked or what’s going on and Until the reaction was like overwhelmingly positive Yet very reserved and then when chat GPT came out was like November December and You know obviously took off exploded never went on their uncle was using chat GPT for a bit

Matt (21:12.776)


Brennan (21:34.154)

come January when everyone did their annual reviews, most people in HR found out that like half of their managers had, you know, chat GPT writing reviews for them. And so there’s a few times or some of the HR folks I, you know, talked to in maybe November or October, the prior year, were like, you’re completely right. I got it completely wrong. The world is ready for this. We’re already doing it. The issue is like, obviously the chat GPT is, you know, sending private data to chat GPT, it’s, you know, obviously biased in its own way.

Matt (21:43.147)


Brennan (22:04.088)

have information about it, doesn’t have all this knowledge, sort of came back to say, all right, we should check this out and earn it. So a lot of the stuff I think around AI is sort of like a cultural reservation around are we ready for it. And I think what’s interesting for tech companies to sort of catch up on is like,

Matt (22:17.593)


Brennan (22:25.494)

we sort of have to be ready for it, right? Like the wave hits tech first. And if we’re not ready for it, then we’re in for a world of hurt. So I think playing with some of these things internally feels a lot more palatable than playing with some of these AI tools with like your prospects or your customers, right? That’s a little bit more scary, so.

Matt (22:30.295)


Matt (22:42.678)


Matt (22:46.319)

Yeah, that’s true. I mean, we’re doing this right now at Hatchworks, right? The generative AI, one of the big areas it will impact is software development. So we’re leaning into it, almost trying to disrupt ourselves before competition or somebody else does. So we’re taking a similar approach where, OK, we have this new tool and functionality. How can we leverage it and empower our teams with it, ultimately our clients, at the end of the day?

Brennan (23:13.078)

Yeah, I heard the stat the other day. I think it was the CTO of GitHub was saying of the GitHub copilot users, which is sort of auto-complete within your development editor, about half of all code committed into GitHub is written by an AI. So of the users who use copilot or OpenAI’s code AI.

Matt (23:24.303)


Matt (23:33.771)

Wow, I have not heard that yet.

Brennan (23:41.33)

about half of the code checked in is written by AI. So, I don’t know, if you chart that curve a few more years into the future, some of this stuff is like a year old. Will we have developers in the way we’ve always known them as or we’ve known them recently as, or will developers be, I think they’ll still be around, but will they be doing just wildly different things, right? And obviously the people who are…

Matt (23:53.646)


Matt (24:04.876)


Brennan (24:09.398)

The developers who are doing wildly different things first will have a leg up quite a bit on those who aren’t, or the companies who have armies of developers like that. But for us, it’s even more nuanced in that we’re building an AI tool now, in that we want to use the AI tools to understand what are the interfaces that work for AI right now.

Matt (24:27.997)


Matt (24:35.32)


Brennan (24:35.53)

So a big part of like us building it right is like, we actually have to artificially inflate how much AI tools we use so that we get a sense of like, oh, this pattern, this UI pattern really, really works. This UI pattern.

Matt (24:47.715)


Brennan (24:49.334)

um, doesn’t right where we had years of understanding the UI patterns of search. We’ve had years of understanding the UI patterns of like top bar sidebar navigation, how do you interact with an AI? No one knows, right? Like, um, we’re in early, early days of just understanding how you interact with it and obviously the first breakout interface has been chat.

Matt (25:02.626)


Brennan (25:11.606)

like surprise, but there’s a lot more. And so, you know, just rolling these out, you know, even some basic things and getting not only customer feedback, which has been really helpful, but us using tools like GitHub Copilot to understand the auto-complete UI using AI is like a really powerful interface, right? Like it can sort of predict a paragraph of text at a time, which is an incredible time.

Matt (25:39.74)


Brennan (25:41.74)

I mean, half of code checked in and sort of accepted AI code. So if it can autocomplete code in your code base, like imagine what it could do on some of the more monotonous tasks at your workforce.

Matt (25:54.619)

Yeah. Yeah, the QA aspect of it becomes ultra important. But then again, you can leverage AI for that as well. And I think the UI element you mentioned was interesting. One of the best explanations I’ve heard is CEO of HubSpot. He talked about we’ve lived in this kind of imperative approach of like point and click, and that’s how we interact with technology. But it’s potentially move into this more of a declarative approach, which can really change how we interact with technology at a fundamental

level, so it’s really interesting there. I want to get your take here to kind of round out the episode. Your product’s in HR. It’s innately kind of this intimate human thing, right? You’re talking about people’s careers, their goals, what do they want to do? It’s this human thing. Does AI degrade that experience in any way? What’s your view on how…

Brennan (26:46.946)


Matt (26:52.711)

AI impacts that either for the positive or the negative.

Brennan (26:57.598)

Yeah, such a good question. When we, you know, pre AI, when we were first starting out, people used to ask like, you know, using an app for one-on-ones, that seems silly. Management is sort of like looking at people, you know, face to face, eye to eye. And obviously with remote, that becomes a little bit more challenging.

Matt (27:16.024)


Brennan (27:18.95)

Um, and I used to always say like, this was, this sort of feels like the same thing that like the older generation would say to the younger generation about almost every new technological advance, right? Like people used to like to read newspapers, you know, feel books and read newspapers and, um, uh, you know, have, have journalistic integrity and these bloggers, what do they know? And, um, uh, or dating, right? Like, shouldn’t you, you meet people in real life versus like an app. And obviously we know the apps.

Matt (27:35.17)


Brennan (27:49.524)

taking care of the majority of marriages, I think, in North America for a few years now. Why not the workplace? Why not some of these management practices as well? But AI is a whole new angle to that.

Matt (27:53.433)


Brennan (28:04.394)

because if the AI is doing it, then what are we doing, right? Especially when it comes to the things that we think of as innately human. If the AI is writing performance feedback, then what the heck am I doing as CEO, right? And I think that’s where people can get weirded out or scared, et cetera. But I think that the first thing is that the goal, at least the way we’re trying to build it, is to allow

Matt (28:17.043)


Brennan (28:34.082)

the humans to be more human, to have more EQ, to have more time to spend with each other face to face. And so you look at, well, what can AI do? And I think the current state of AI, and this is obviously gonna be out of date, even if you publish it tomorrow, but the current state of AI is if you can sort of train an intern to do it, you know, in their first couple of weeks on the job.

Matt (28:37.017)


Matt (29:02.518)


Brennan (29:03.082)

you can get an AI to do it right now. So the first task is sort of breaking down these little things into small enough tasks that an intern could do it in the first couple of weeks on the job. And most tasks we do at work can sort of be broken down in that way into these repeatable steps. But the difference is when you have AI, you can kind of scale that to the almost like infinite dimension. So…

Matt (29:06.467)


Brennan (29:30.802)

most managers could look through six months of one-on-one notes for all seven people they have to do a performance review on. They could do that, but they don’t zero percent well because they don’t have the willpower to do it. They don’t have the discipline to do it. They don’t have all of these little things that are needed and they don’t have time. Truthfully, they don’t have time. They’re dealing with a fire and that fire is happening in their functional department and HR is like, by the way, you have to get your reviews done.

Matt (29:38.871)

0% will.

Matt (29:44.121)


Matt (29:58.168)


Brennan (30:00.676)

So they’re pretty busy. They could look through six months of gold data. They probably won’t. So biases creep in and some of those biases are okay to have. Some of those biases are less so, and people often talk about biases in AI. But the AI can actually reduce other bias, like can severely reduce recency bias because it can read all of this data. It can severely reduce other sets of biases because you can withhold information

about is this person a male or a female? Is this person named John or some other name, right? That otherwise would lead to bias. You can sort of take some of those things out and the AI doesn’t know about it, so it’s just going to treat everyone the same. And you can inject bias of, you know, making this be harsher, universally harsher or universally softer, and put everyone on a unique playground. But what’s…

Matt (30:36.333)


Brennan (30:58.794)

Further to that is in many of these companies, you’re doing a 360 review. So you have a person you’re reviewing, the manager’s got to do that review, but they’re doing a self-evaluation, peer evaluation, et cetera. So again, the manager for all of those seven people they’re doing these reviews on, they could look at all three peer reviews that they, you know, received on this person and the self-review. And they could analyze the different scores and notes of feedback between these various different peers. And they could, you know, group those,

into themes and psychoanalyze that and understand if there’s a confidence issue happening with this direct report. They’re just not going to do that. They just don’t have time. But all of the things I sort of mentioned there, the AI does in under a minute. So it will analyze what everyone else submitted about a person. It will try to understand if there’s a theme in any of these peer responses that are different from the themes in the self-eval, that are different from the themes in the self-eval.

in your avowl, it will highlight those differences, what are some of the common causes of it, help you frame some of your responses to better tee up a productive conversation instead of like a frustrating conversation, give you prompting conversation starters for what to talk about in your next one-on-one that could help resolve some of these issues. All of these things the world’s best manager would do.

Matt (32:19.311)


Brennan (32:27.014)

Um, if they had infinite time, um, and they don’t, what’s neat about AI is you can give those, those people, all of the people sort of infinite time in certain directions and all of the directions that AI wants to go are the ones humans don’t want to go. And so in a way, bringing the AI into some of these tasks allows you to do the things that are innately more human. Do that way more. Like because you have all this knowledge.

Matt (32:54.326)


Brennan (32:56.688)

can go and be more empathetic with this person, right? Because you now have the notes needed and some of the questions needed to be more empathetic. So yeah, I think a lot of people have fears about maybe AI taking over jobs or AI, you know, removing some of the humanity in certain things. And I think often the stuff that AI might end up doing is the things we knew we should always do, but we got too lazy, right? And now that we’re,

have this, you know, most infinite willpower source to pull from with AI, what are we now able to do knowing that we’re doing the best job ever in some of those places we were previously lazy and often I think that’s being more human, being a better person in many ways.

Matt (33:45.555)

Yeah. And AI has that potential to, if we do it the right way, to actually make you more human in and of yourself. And I love the EQ tie. It’s like AI done the right way enhances EQ for the individuals using it. It’s kind of like this co-pilot, good name for GitHub, right? But it’s like a co-pilot mindset of how AI is used.

Brennan (34:03.73)

Yeah. I’ll give you like such a good example of that. Cause this is something that’s universally come back from our customers is sort of that, right? Like, um, we take your notes, we take your goal updates, et cetera. But we also ask before we sort of do the written feedback, we, we asked for some calibration questions. Um, and those calibration questions might be the same questions from yourself, a valve from your peers, evaluation from your managers, evaluation in there, you might get different scores. You might get different jot notes from your peers and your manager or whatever.

and AI will just go in there. We show these steps.

um, to our users, the AI can be a black box, right? So what we’ve tried to do is like what we were taught in math class, instead of just spitting out the answer, we sort of show the long division step-by-step show your work. So, um, you know, one of the areas we show our work is, is in analyzing that the peer responses or sorry, analyzing sort of those, those calibration assessment questions. We give them to the manager. So the manager can do all of the analysis themselves if they want to.

Matt (34:43.666)


Matt (34:49.931)

Show your work, yeah.

Brennan (35:08.464)

for them and just sort of summarize the insights out of it. And almost universally, everyone who’s seen that has been like, that’s the most valuable thing. I’ve…

Matt (35:17.123)

Mm-hmm. Yeah.

Brennan (35:17.166)

had in my management career, right? Someone, something to read this, analyze it, talk about the surprising, the interesting, the confusing. You know, and like some of the stuff that it gave me and others is like, you know, the person rated themselves low here, their peers rated them high, you rated them, you know, mid to high. And the commentary was sort of overwhelmingly positive. The fact that they’re rating themselves low either suggests that they might have

there’s a misunderstanding of expectations or something else. Like maybe you want to, you know, bring up, introduce this or ask about these, these types of things in this type of way. And every manager is like, holy shit, right? Like that’s incredible. And, and the truth is like, you know, obviously I’m biased in saying, saying that, you know, our tool is incredible and it can kind of present incredible, but like legitimately is this is like what other people have been saying. So

Matt (35:49.767)


Matt (36:01.449)


Brennan (36:16.394)

You know, if you do these things right, if you kind of show your work, you break the steps out, you kind of break things into these tiny steps that AI can do a great job of on, you can sort of build into some pretty incredible stuff. And that’s where we’ve been getting some of the latest stats. I think I shared with you earlier, right? 80% of people feel like our process is faster than their previous one, if not significantly faster. And 80% of the people receiving feedback say it’s better

Matt (36:45.348)


Brennan (36:46.388)

prior, right? So 80% better, 80% faster. Like who doesn’t want this in their work life? And I think, you know, we’re doing that for HR, we’re doing that for performance reviews, but you can sort of tackle any functional area, any pain point and say, all right, how do we make this 80% faster and 80% higher quality as well? And what do you do now as a functional person in that role with that much free time back? And I think the answer is do more human things.

Matt (36:47.82)


Matt (37:16.511)

Yeah. And quick plug for a Bill Wright episode in the past, episode 8. The intern comment you mentioned triggered me. So we had Jason Schlechter, founder of AI Empowerment Group, on. We’re talking about how to identify and vet winning use cases with generative AI. And the struggle, a lot of the times, is framing and how folks think about how they can use AI. And one thought exercise he likes is pretend you literally have an army of interns that you can put to work. Now what could you do?

Brennan (37:38.527)


Matt (37:46.239)

It’s that reframing of how you can empower things with AI, just to start thinking through use cases. So I wanted to do that quick plug, but Brendan, this has been an awesome episode, great chat. Where can folks find you?

Brennan (37:56.258)


Brennan (38:02.178)

So you can find us hypercontext.com or we’re hypercontext app on Twitter, you know the LinkedIn, similar words. And then myself personally, I’m on LinkedIn, find me Brennan McCackren or Twitter, I underscore am underscore Brennan. Should be pretty universal across there.

Matt (38:20.897)


Well, Brendan, thanks for joining us on Built Right Today.

Brennan (38:27.21)

All right, Matt, thanks for having me. Thanks everyone.

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