I don't know that you would look at 61 sources and create something as comprehensive as this, but now this just becomes a baseline.

Now everybody does.

This is the starting point.

Once you get the hang of it, I mean, it's like endless opportunity.

Maddie, welcome to the show.

Hey, Aiden, thank you so much for having me.

This is going to be fun.

I've been following a bunch of your posts on LinkedIn and there's this term that I'm using more and more, which is this, this idea of being AI red pilled.

I've had to clarify this.

It's it's like matrix reference, you know, the red pill, blue pill.

And I've just noticed like over the past few months, more and more and more you're going all in basically on AI and, and everything that you're doing there.

So very excited to have you on.

So before we begin, I thought maybe a good place to start just so everybody has a context a little bit about you and you know, the company you work for and and your role.

Maybe, maybe just walk us through a little bit about you.

Absolutely.

So it's, it's great to be here again.

Thank you so much for having me.

So I work at Motive and at Motive we're really bringing the power of AI to the physical world.

We provide an integrated AI powered technology platform that helps companies in the physical economy to run safer, more productive and more profitable fleet operations.

We support today at Motive nearly 100,000 customers and that's 1.3 million drivers across various industries that span transportation and logistics all the way to retail, in the public sector and many more.

Basically anyone who's managing people, vehicles and equipment in motion.

And in my role as an AI leader in business technology, my focus is on the employee experience and productivity.

It's a critical role.

It directly sports one of our company wide objectives to increase our own productivity by 50%.

And so my team and I work to identify and build the internal tools and systems that our employees need to be more successful.

We're essentially our own customers, right?

Our job is to make sure every mode of employee has a seamless and productive experience.

Got it.

Yeah.

So a million plus drivers, that's quite a bit.

And the thing that like we, you know, we, we talk about is this idea that more and more organizations are starting to build specialized roles to make sure that the entire employee base can, you know, become AI native.

We're all going through this transitional process in order to do that.

And this is one of the reasons I wanted to chat with you in that, that motive.

Obviously there's this very ambitious goal.

And I just want to repeat what you said because it's kind of crazy, which is to increase the productivity of every employee by 50%, right?

And so that's kind of like that's a real goal that a real company like Motive has out there.

And it's important for just for me to emphasize that because as people are listening in, this is the kind of benchmarks that, you know, companies are setting.

And so everybody should set things like this.

So there's a bunch of things that I want to talk to you about today and a bunch of demos that we're going to go through, but maybe let's talk about this 50% productivity increase goal.

How do you go about even, you know, determining how you can do that and how you even measure that?

Maybe speak more about this 50% productive eagle.

It's very exciting and it's very real for all of us at Motive too.

And I think that there's a few different things that we're doing to make this happen and make sure it's successful.

The 1st is that we have a comprehensive vision and strategy inclusive of our future state architecture as well as the cultural mindset shifts that we need to make in order to achieve that.

And we've broken it down into 3 tiers.

So Tier 1 is focused on like democratized Gen.

AI across the workplace.

So this means empowering every employee with the tooling and capabilities and resources focused on upscaling and adoption, right?

Giving people the power to use these tools to increase their own efficiency.

Tier 2 builds upon that, right?

This at this tier, it's really automating the manual and complex processes that we see via things like agentic workflows.

So this is accelerating the productivity that we achieve in Tier 1 and really building that internal muscle to solve more business centric problems.

And then finally, Tier 3 is focused on transformational change.

So we're doing a lot of transformational change already when we think about that mindset shift.

But this is really taking our technical architecture and beginning to transform how we deliver value both internally and to our customers.

And leadership has made this a huge priority for us, right?

Not only do we have this strategy and vision, but it's now a priority for every single person.

And that means that we have AI champions across the different business domains.

We have updated job descriptions and very clear expectations that everybody should be experimenting and learning with the AI tools that they have today.

When we think about measuring it too, it's, it's pretty complicated.

So I, I think something we're all trying to figure out a little bit, but this is the groundwork that we're laying to really achieve the success metrics.

So this is company level, OK R it's, you know, increase productivity by 50%.

You said you had AI champions across the company, across the departments.

Obviously, this, this be a 50% more productive means different things depending on what part of the business you're working in.

And I also let like you know, you mentioned this idea of democratizing AI for everyone, just making it accessible, determining the tools that can be used, making sure that it's secure and provisioned and, and everything else.

And all these things come together to make something like this possible.

Hey everyone, hope you're enjoying the episode.

1 of the common things that we hear is, Hey, you all talk about all these different tools.

You walk through these demos, Sometimes it's hard for me to follow along.

We decided to do recently was take all the things that we talk about, put them in a weekly newsletter.

We literally list all the tools, we link to them and any demo that we walk through, we break it down step by step, put it in this newsletter.

So it's super easy for you to follow along.

And the other nice thing is that if there's someone on your team that you think could benefit, they're working on that subject area, they could use a little bit of AI injection into their workflow.

Send them the newsletter, send them that particular episode.

We make it super easy to do that.

It's free to sign up.

All you have to do is go to thisnewway.com, enter your e-mail address, and you'll get this weekly e-mail from us.

Hope you enjoy it.

With that said, let's go back to the episode.

So in terms of the AI stack, I know we want to do a few demos today.

So one of the tools that you all use a bunch is called Glean.

Do do you want to maybe explain what Glean is and you know when you started using it and maybe just a little bit about Glean itself?

Definitely.

And we're pretty lucky at Motive because we do have a lot of AI tools that are very incredible and powerful right at our disposal and Glean is certainly one of them.

We've been working with Glean since early this year, and I think the company evolved from the enterprise search to now a complete agentic platform.

You can actually build agents in the tool.

The beauty of it is that it's connected to almost all of our internal data sources.

So Glean, Glean knows almost everything, right, That a new employee or anybody at Motive who's been here forever would need to know.

And the other beautiful thing about it is that it's very secure.

So I as an end user in Glean, I only can see and access things that I can do in the subsequent data sources.

So if I don't have access to a tool, I'm not going to be able to use it within Glean and same with a piece of content.

So it's it's powerful because every employee can safely and securely work in it without those security risks.

Amazing.

And So what are we going to see first in terms of a demo?

I think there's a few things I would like to share with you.

One of them is we built an agent so it motive.

We just wrapped up one of our performance cycles and I think everybody probably can feel the pain that comes with performance cycles.

It's a lot of time, right?

It's it's hard to recall all of the things that you're assessing either yourself or your team or your manager, whoever on, but it is valuable.

It's how we learn and grow and it's very important that we continue to do that.

So we actually, I partnered with our HR team and we built out a Glean agent that goes through all of your past activity for a certain period of time and recalls all of the things you worked on.

And then looks at your job, your role, your level, where you should be at with competencies and expectations, along with past feedback and gets you, this is a self-assessment agent that I'm going to show.

So it really gets you thinking about what did you contribute to and are you meeting those objectives and what should you continue to focus on?

So I'd love to share that with you.

And as you're pulling that up, just just for everyone to, you know, just to explain a little bit more, Glean is this tool that actually connects to all the data sources in your company.

So you reference a bunch of documents so it connects to all these different systems and you can not only search, but you can actually build these agents that do things for you.

So they're like these mini workflow process things that go out and accomplish tasks.

And we're all kind of annoyed by the work it takes to do a performance review.

They are valuable.

They they do take a lot of work.

And so it sounds like you found this shortcut to make it a lot easier to do that work.

So this is pretty exciting.

And I think what's really important though to stress with these, and we had a lot of conversations about this and when we built this, we were very intentional that what we don't want to do is take away the time that people do spend reflecting.

So this agent is very intentionally built to help with recall, but it is also intended to get you thinking about your own growth.

And it's going to ask you questions and things like that, which really emphasize that, yes, this isn't a tool that we can use to expedite the process, but it should really make us more effective in our focus and in our feedback and how we think about our accomplishments.

So what you're going to see on the screen, this is the Glean Agent builder.

And as you can see, this is our self-assessment agent and it's got a bunch of different steps that are taking place.

So in Glean, you always start with a trigger.

And in this one, what we're asking the user to do is actually fill out some data.

So we're asking for the role, the job architecture matrix that we use, which is pre populated for most, but we have some versions and then some very basic questions.

We also ask them to input their past feedback form as an example.

Then what it's going to do is run through a couple of steps that you'll see here.

It's going to clarify the start date.

It's going to just show that it's accepting of all this information that you're giving it.

And then it's going to jump into a sub agent.

And what the sub agent does, it's going to loop through a time chunked analysis of the past six months, all the activities that I've engaged in as an individual in a loop fashion.

So it's going to respond and it's going to show it on the screen.

And I'll show you the output in a second with everything that I because I'm going to run this has have worked on.

Hey Maddie, how does it I?

Was gonna say, how does it know all of the things that you have worked on?

Yeah, there's a couple things that I can highlight here.

So I'm gonna share the sub agent step with you exactly.

But what this is going to do is it's looking at certain data sources within a company search.

So Glean has different steps in the agent builder where you add what you want it to do.

Company search is basically saying look across all the content in the company for things that I've worked on.

And in this particular one, I'm giving it specific sources where I do most of my work right, Slack, Google Drive, Confluence, Gmail, etcetera.

And it's going to loop through those in a time series to understand.

And it has access to all these data sources.

So Glee knows what emails I sent over the past six months and what slacks I sent.

And it is able to figure out what's the most pertinent to my role and identify how those stack up to contributions toward my own goals.

Very interesting.

And so on.

This Slack data source does it.

So when you're sending DMS, does it have access to those too?

It has access to my personal DMS, but I couldn't see your DMS, Aiden.

OK.

There's some.

Pretty interesting stuff in there.

It is.

Yep.

Yep.

It's looking through everything and but that's important because we all work in so many different tools all of the time.

And that's the value.

I don't know, have to go back and dig for one message that I sent about a project that I worked on, right?

That's pretty cool.

I mean, this is one of the things.

I mean, sometimes at the end of the week, I don't remember what I did that week now, let alone looking for a whole year.

I mean, that is just way too difficult.

Exactly.

It's really hard.

It takes a lot of time, and we want people to take the time to do that, right?

So let's make it easier.

I'm going to breeze through some of these too.

So it's after it does that deep and heavy analysis of what I've worked on, then it's going to review the job architecture matrix for my role and my competencies.

And then it's going to begin to understand how the work that I've done aligns with those competencies.

It's also going to look at my past feedback.

So we have right now, every six months, we do a performance review cycle.

And I take my feedback seriously.

And so hopefully we all do right.

And the feedback that we're getting from our teams, we've incorporated into our work.

So it's going to look to understand that as well.

It's going to do a bunch of analysis, a bunch of thinking, and then ultimately it's going to create a table that shows how I took the feedback that was given to me and actioned against it, and then summarize an output.

And then ultimately create a document of all of my contributions with some thought provoking and probing questions.

And I can show you.

I can show you an example output if you'd like to see it.

I could also write it, but it takes a bit of time to run.

Yeah, no, let's see.

Let's see an example output.

So by the way, as you're pulling this up, you said there's some.

So it pulls in all the information and then gives you some thought provoking questions before it gives the output, right?

So what's an example of a thought provoking question it might ask?

Yeah, let's take a look.

So what it's doing is that the outputs going to have that thought provoking question because we want the agent to run and give the employee the response that shows everything.

But we don't want the employee to just copy and paste that into our performance tool.

So we structured it in such a way that it actually understands what the the different competencies are.

So as an example, domain expertise, in the process of rolling out Glean to Motive, how did you deepen your technical and operational expertise?

Did your analytical judgement shape any decision points and project decisions?

So it's really looking at what I did and putting together very structured and relevant probing questions that I will think about then when I go to write my own feedback.

And So what the output has done.

And you'll see there's so much data here because it, it ran through six months of work.

This is the 61 sources of data that I hit on just for the work that I did over a six month period.

And it's showing not only the initiatives that I worked on, but my contributions with evidence for each.

So that's the recall, right?

And then what it's doing is it's taking the feedback that I had received my last cycle and giving me this really comprehensive table and the competency level.

So what feedback did I get last time and how did I work toward achieving better outcomes based on that?

And then ultimately what it's doing is putting together a summary and those reflection questions that we just talked about.

So this gives me everything I need then to really understand how what I contributed to that past cycle and how I can continue to grow without doing it for me and just resulting in a copy paste of an AI output which nobody wants.

That's super interesting.

So these questions that are asked, it's not like you're answering these questions in the workflow and then it is helping you generate, you know, the actual thing that you're going to paste into the HR tool, right?

These are just more like, hey, think about these things.

Yep, exactly.

These are the probing questions to help me think about and reflect versus having the AI just generate it for me because.

Got it.

Yeah, we feel it's important still.

Like I still I, I find value in taking a step back and being a human and thinking about what I'm doing and focusing.

And so that's intentional here.

I think that makes sense.

And part of it is that even though there's all these data sources, it still doesn't know necessarily like the why and the thinking And so the these reflective questions.

And this extra work is obviously like piercing together these things and helping you connect the dots.

The annoying part, the recall part, I mean, all of that is taken away, which is amazing, I suppose.

Like what is the feeling that you get when you use this?

It's really empowering because I'm someone, I mean, I spent a lot of time trying to take up that information and seeing it just put in front of you, even if you know some things, maybe you're not 100% aligned with the AI.

Maybe missed a little bit of context here.

It's really eye opening because if you can do something like this for a twice a year event, how can you incorporate this into your day-to-day?

It really gets those creative juices flowing and it makes me personally open up my eyes and and my mind to new ways of thinking about how I get things done.

It's amazing.

It's crazy because nobody, you know, even if you were super duper diligent, I don't know that you would look at 61 sources and you know, create something as comprehensive as, as this.

But now this just becomes a baseline.

Now everybody does this.

This is where this is the starting point.

This is one of the things that AI allows you to do is it just lifts that baseline and all of a sudden everybody is at this extraordinarily high level across the company.

That's that's really cool.

Once you get the hang of it, I mean, it's like endless opportunity.

Super cool, very, very nice and and this is rolled out, people are using this.

Oh yeah, we, we used this a lot.

This particular agent, it had over 1000 unique runs this cycle.

We had a similar one for collecting stakeholder feedback.

And I mean, if you assume that this saves even 30 minutes, which I think that's a pretty conservative guess there.

I mean, the impact is really broad.

We're talking about thousands of hours of time saved across our company.

Oh, what does the the stakeholder one do?

The stakeholder one actually looks at, so we do stakeholder feedback at motive as well.

And one person might have to give feedback for multiple people, right?

And So what this does is it's it's similar in the sense that you're able to share which stakeholder you're giving feedback for.

And then Gleen will go and figure out how you worked with them on what types of projects and also understand your unique roles to help you think about how you should give them feedback.

That is super useful.

So, so you're using, I mean, everybody's used these like 360 type tools.

So they and and you know, surprise, you get chosen to get feedback on this person, but then now you have to remember all the ways that you work with that person.

And so using this Glean agent, you can now.

Like it'll just tell you here are all the ways you work with them.

Now you can think about what the feedback is and maybe some nice questions to help make it easier for you to do that thinking.

Yeah, it shifts the focus from like spending hours on recall to hours of actually thinking about how you can help that person be more effective.

What I love about all of this is that it for the same amount of effort, even if it didn't save you any time And, and obviously it saves you time, but the reason that someone might, you might say it doesn't save you time is because you would have never spent your response would have never been this comprehensive, right?

But even for the same amount of time, it allows you to give a 10X better answer.

Exactly.

If it's if it's not efficiency gain, it's effective, right effectiveness overall.

So the, the thing that we thought about too when we built this is we didn't want that copy paste scenario where then you look at your team's feedback and it all sounds like you come from an AI robot.

We don't, we don't want that.

That's not valuable.

What's really valuable is the reflection.

So that's what we we tried to emphasize here.

Love it.

This was amazing, thanks for showing it to us.

Absolutely.

Hey everyone.

Just a quick pause on today's episode to tell you about my day job.

In addition to this new way, I'm the CEO of a company called Fellow dot AI, and Fellow is an AI meeting assistant.

It joins all your meetings.

It summarizes them, tracks the actions and the decision and does that better than any other tool that you've seen.

We've spent a ton of time making sure that the meeting notes and summaries and action items that come out of Fellow are the most accurate, the most precise.

It beats any human you have to try it.

But in addition to that, what makes fellow different is that it is the first AI know take care built from the ground up with security and privacy in mind.

This means that you can use it for all your meetings, not just the customer facing ones, but also the sensitive ones.

Things like one on ones and executive team meetings and those QBRS and everything in between.

It's not really good judgement.

So for example, if you start a meeting and you're talking about some social stuff that you don't really want on the record that's going to get get emailed to everyone afterwards, Fellow just knows.

It just doesn't include those things.

Or say you have something that you talked about and later on you realize, oh man, that shouldn't have been there.

Makes it really easy.

You can go back to the meeting, select that part, delete it, and then it's gone from the record.

It connects to all of the other tools you use in the organization, whether that's Slack or Asana or a HubSpot or a Salesforce or Linear or Jira or Confluence, whatever it is, Fellow integrates with all those things.

I think the best part is that Fellow also acts as an AI chief of staff because it sits on all the meetings and the conversations that you have access to.

You can ask it really cool questions such as what are the biggest opportunities in my company?

What are the bottlenecks in engineering?

And Fellow just pieces together information, sees trends, and they can answer those sorts of questions.

You can even do things like, hey, based on all of the one on ones that I've had with this particular person, can you create them a performance review?

And it can do things like that too.

And the Sky's really the limit.

What I really wanted you to do is have the opportunity to try out fellow, Check it out.

And we're making a special offer available to all our listeners.

So just go to fellow dot AI slash, this new way to try fellow.

There's a discount code in there for you if you decide to continue with it.

Either way, I would love for you to try it and let me know what you think.

And with that said, let's go back to the episode.

So what's the next agent we're going to take a look at?

So the next one I'd love to share with you and let me get it pulled up is our executive account summary agent.

So the executive account summary agent is pretty significant for our company.

And what it does is it you give it any customer account and that's it.

And what this agent does is it runs through structured data like our CRM data as well as unstructured data.

And it outputs a very clear summary of how that accounts doing, what the challenges and open risks might be and things to think about for that specific account.

And we've seen this really useful not only for our executive team who want to go in and just get an update on where a specific customer is in their journey, but also for like our go to market teams who are developing weekly business reviews and things like that.

So this is a huge time saver for them.

The way that this guy works, it's pretty simple.

You just, you give it an account and then what it does is it actually runs through, it does a Salesforce so-called query and it looks for that specific account and understands what's going on on from an opportunity perspective, any open cases against that account.

And then it looks for additional information.

It looks for information on Slack and other sources, prioritizing certain messages from certain people.

It looks at Gmail for any interactions you might have had with that customer and then additional information and Drive, where we house a lot of this content related to our customers.

And then ultimately what it does is it summarizes for you a general account overview, the open cases and opportunities, and then puts together an kind of a recap of things to think about for that particular account.

I can run this one for you, but I just ran it for one of our accounts this morning and you can see that it thought through.

I gave it a specific account.

It tells me really quickly in a paragraph all about, you know, what the ARR is for that one, what the recent Slack threads related to that customer are surfacing.

And then it outlines all of the open opportunities and close dates out of the CRM.

So I don't have to go to Salesforce and pull up a report.

I can get it all right here.

And then finally, it looks at open cases and then potential risks and those might be derived from the Slack messages, it seeing certain conversations that have taken place or cases, you name it.

The AI helps figure that out for us.

Ultimately giving that's pretty short but effective executive summary if you just need a snapshot.

How does this work?

So basically people have an interface into this.

Before a call, they might type in the account name and then they just get this report.

Or will it also get generated automatically knowing that you have a meeting coming up with that person?

It's a great question.

So typically the way our teams are using it right now is as an as needed basis.

So if they're putting together, you know, weekly business review, maybe the AE is running this one for their specific accounts and then that's getting used in those reports.

Alternatively, if a leader in our sales team just wants to get a quick update, they might come here instead of slacking the AE or the Rep associated with that customer.

Can we think about scheduling it and doing that?

Yes, that's something that we're actively thinking about, but for now we're really just trying to use it as needed to see how it could provide the most value.

This is all pretty amazing.

These are real agents doing real work in the workplace.

And what I love about it is, again, this is stuff that you're using and you're using this at scale.

Thousands of employees are actually using these agents.

And just like a logistical question, how do you figure out what kind of agents to build?

Do employees themselves, can they build their own agents?

Is there a process where like people build their own and when something becomes popular, then you make it, you know, something that you promote internally.

So I imagine even today you probably have hundreds, maybe thousands of agents in your workspace there.

So how do you know which agents, any good, which ones you should use?

How do you do the traffic control?

It's a good question that you're asking and I think it's evolving, but there are a few forums and ideas in place that we're starting to build out and build upon to do just that.

So yes, you are right.

We want all of our employees to start experimenting and building these and we have upwards of 1000 in the system and we we see benefit in that because we want people to start thinking this way and really adopting this.

But in terms of giving people the space and the resources to do it more effectively, there's a couple of things.

So we rolled out at Motive and AI Lab.

So every week we spend 45 minutes, it's open to anybody at the company.

They can submit an idea, a draft agent or other AI build that they're working on and we review it.

We review the problem statement, what they're trying to do and optimize it in real time over a meeting.

It's incredibly awesome because we get different cross functional teams helping each other.

Just this week we had people from our HR team working together with engineering to optimize their agent, which completely different use cases and workflows, but a lot of the same core concepts underneath.

And so that's been highly effective and collaborative for the team here.

The other thing that we do is an AI Genius Bar.

So instead of optimizing agents or AI builds, we're giving people 1/2 hour every week to just come and ask their questions.

And this is hard because we're all learning together and we might not know the answer, but we're giving people the space to come and ask and we try and solve it together or at least get them a step further.

Beyond that, we have a very active Slack channel.

We have an AI at Motive channel that has everybody in it and everybody posts.

And the posts are everything from announcements to sharing out an agent that is really powerful that that team uses to asking questions.

And then I think you and I touched on like one of my ideas that I shared a while back.

But we're starting to just get one-on-one and partner over short meetings with people to break down the barriers in a no stress environment.

Set 15 minutes up and just get together and build for 15 minutes and see what we can do.

I think I coined them the let's see what AI can do sessions, but really establishing just an open and honest and low pressure environment and place for people to experiment and build.

This is really the playbook for people who are thinking about how do I make my company more AI native.

It is a lot of blocking and tackling.

You're doing a lot.

You're you're doing 3 different types of sessions.

I know you, you all do a bunch of different trainings.

There's a Slack channel, I assume it even in your old company meetings, there's probably cool use cases that are demonstrated.

So it's really like at every level of the organization.

Yeah, it is.

It's coming from top down and bottom up, right.

We're tackling it on both sides.

And what's really just eye opening is that it's it's fostering a community that is collaborative and creative foreseeing.

I know it's a hot take that a lot of people might say that AI is killing creativity.

I feel completely the opposite about that because I'm able to explore and do things that I otherwise felt like I couldn't in my own role.

And seeing other people have that same light light bulb moment is so cool.

It it's one of those things and there's almost like this special type of creativity that happens.

I find even when I start to use AI in some way after I finish using that, that gives me more ideas on what else I can do.

But if you just, you know, sit back and you know, you watch video here or you hear from someone else, you don't quite get the same.

You have to actually experiment.

You actually have to try and build your own work flows.

And then you'll get more ideas and you'll get deeper and deeper down this rabbit hole.

So Speaking of the the rabbit hole, you had another one which I thought was was really, really cool around how you use notebook LM.

Yeah.

So we're embracing no fuq element motive too.

We have we have lots of tools that we're able to play around with.

It's it's pretty fun.

And something that we're, we're thinking about as a, as a company too, is how do we continue to ensure that we're all working on the right things and unblocking those barriers for our employees.

And one concept that we're doing, and right now it's, it's very small phase, but this concept of writing down every week, you know, what you did that week, what you need to focus on the next week and any blockers.

And we, we coin it a snippet.

I think it's a pretty industry wide term.

But by doing that, you're increasing accountability and transparency and allowing your team to really help and reduce, you know, blockers and redundant work, right?

And So what we've done is we've actually built out, and I'll, I'll share with you, but some logic that every individual can have a single document that they manage and own, that they can log this stuff.

And it's a running, living, breathing document.

And what we've built to support that.

Yeah, there's different names for this, I guess, you know, snippet is 1, but but the idea is very basic, like what did you do this week?

What are the blockers?

I mean, some people do this in the form of stand ups and some people do it weekly.

And so, yeah, I'll let you you, you take it from there.

Exactly.

And So what we have is this agent that we can trigger and it reads your personal activity from the past seven days.

So taking what we did for that performance review cycle, first six month period and really looking at a seven day period, it's a little bit easier.

It thinks about everything that you've worked on.

So whether you're in GitHub developing code or you're doing your work in Slack or in Drive, it looks at it and thinks about your activities.

And it's, it's so simple.

It just responds with what you worked on, what those accomplishments were, and it articulates what it believes would be your goals for the next week and blockers.

And when you go ahead and run this, I'll go ahead and do so now.

It gives you that output.

And then what we do is we really interact with that and then put it into that document and in order to roll this out like this process is highly effective.

We've had really good feedback for the teams who are beginning to embrace it.

But what we're starting to do is build out a basic like enablement guide out of notebook LM, which is now what I'm showing on my screen.

So in Notebook LM you can simply upload a few sources and put together video overviews with the click of a button.

So long gone are the days where as APM or you know a product enablement team, you have to spend hours building out these how to guides.

We have a simple Word document that doesn't need to be tidy or clean and we can create a video in seconds in this tool.

We've talked about notebook LM on this show before, but I didn't even know that it created videos, so apparently this is a new thing.

This is a new thing.

I think I got access to it yesterday.

I know it was a roll out at the enterprise level that was phased, but yeah, this is super new and it's so cool.

It's it's like a game changer.

Just to repeat, you basically uploaded a document that described this process and it generated a video a lot, you know, with voice and with slides and everything and and now you have all your enablement material.

This is amazing.

Yeah, it's, it's absolutely incredible.

This is all been very interesting.

I, I love like everything that we we've talked about Maddie, from, you know, the various agents that are working at motive today.

I bet you before the end of the year, you're going to have the flip inning moment where you'll have more agents than employees and that'll be, that'll be interesting.

But it's coming, right.

Like you could probably project as to when when that moment will come, but it's probably not very far away.

No, it's, it's evolving so fast.

I mean, it's every day we see, yes, the metrics around usage and activity and some of these platforms is just steadily increasing.

And then when you have relevant moments like our performance cycle, you see spikes, right?

Because that's a key you have to make when, when we're rolling out AI, one of the things I found is so important is the use case that you use to enable people and, and help them understand and play with the tools.

It has to be relevant.

Otherwise people are going to, you know, shut down, stop listening.

And so moments like that, when you have an initiative or purpose or whatever that you can apply to a broad group of people, take advantage of it because that gets activity in your tool, that gets those creative juices that you spoke about earlier flowing.

And people start to say, oh, if I could do it for this use case, I could apply it here.

And those moments that we really have to capitalize on.

I think a lot of people are excited about this stuff and in any company there's going to be laggards.

But, but are you seeing like any one of these?

I, I think when you have these broad use cases and you make it easy for people, they'll like you, you can almost convince them that this is the way like this is how the future of work looks like.

And have you found that that does do the trick?

I think so.

I think what's hard about scaling AI to such a large group is that yes, everybody, every role is so unique and people are working on very different things and they're working in very different platforms and with very different tools.

So yes, I think that when you find a use case that meets a lot of people and the value is very easy to articulate and see, you have to take the biggest problems, right and show how this solution improves that particular area.

And we know, we know, everybody knows, right?

When you think about something like writing performance reviews and recalling everything you could have possibly worked on, it's hard.

It's hard.

And a lot of people, most people don't like it.

So the ability to take that and say that thing that you have to do that takes you hours, that you dread, you can now do it in minutes and then focus on what really matters, which is, you know, that strategic thinking and that reflection for that particular use case.

So it's shifting the mindset, but using big moments like that to do it.

It's incredible to hear how you all have been doing this stuff and the demos have been amazing.

Thanks for giving us a glimpse of what goes on at Motive.

The final question I always like to ask is what are you most excited about over the next 12 months?

I think that we are just scraping the surface.

When you see something like Notebook LM now can create a video in seconds.

It's mind boggling.

It's like nothing I've ever seen before, So easy and in the hands of so many people.

I'm most excited though about this level playing field and people sharing ideas.

I think it's really important that when we go into these sessions at Motive and we talk about what we're doing and what's working, we don't just talk about the successes and the shiny objects, but we also talk about the learnings along the way.

I'm most excited to see people become comfortable with not being an expert, but being alongside their leaders and, you know, subject matter experts in an area who are also not the expert in an AI tool and feeling empowered to really build and act because that's going to take us to the next level.

We're unlocking and breaking down barriers for siloed roles that no longer exist.

So let's see what everybody can do when they get those juices flowing.

Exciting times ahead, Maddie.

Thanks so much for doing this.

Thank you so much for having me Ada, and this has been great.

And that's it for today.

Thank you so much for tuning into this episode of this new way.

If you like the content, be sure to rate, review and subscribe so you can get notified when we post the next episode.

See you next time.