October 21, 2025 • 30 mins
AI isn’t just generating insights anymore—it’s acting on them. In this episode, I talk with Francisco Marin, CEO of Cognitive Talent Solutions, and Dan George, the company’s Chief Experience Officer, about how agentic AI is changing the game for HR. These aren’t your standard dashboards—they’re autonomous systems that detect workforce patterns in real time and proactively intervene, from mentoring and onboarding to retention and burnout prevention.
We get into what makes AI “agentic” in the first place, why consent and trust must sit at the core of any autonomous HR system, and how early pilots are already cutting onboarding time by 40%. If you’ve been wondering what comes after analytics and automation in HR—this is it.
Related Links:
- Join the People Managing People community forum
- Subscribe to the newsletter to get our latest articles and podcasts
- Connect with Dan on LinkedIn
- Check out Cognitive Talent Solutions
- Network-First Manifesto
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
David Rice (00:00):
What makes these agents truly agentic
as opposed to like say, adashboard or a smart assistant?
Francisco Marin (00:06):
An agentic AI is not really waiting for
a human to ask a question.
It makes real time decisionsproactively, or identifying
interventions based onthe universal metrics that
they AI has access to.
David Rice (00:17):
How do you ensure trust when AI agents initiate
something as sensitive asretention or mentoring?
Dan George (00:23):
We've gotta make anything agentic, have the right
level of consent in ethical use.
So click the button inconsent and at any point they
can rescind their consent.
David Rice (00:32):
So what does success look like?
Francisco Marin (00:34):
What a mentorship market intervention,
you could be looking atsavings between 20k to 30k by
shortening the 10th productivityof a new hire by up to 40%.
David Rice (00:49):
Welcome to the People Managing People
Podcast, the show wherewe dig into the human side
of work, the technologyinfluencing it, and the bold
ideas that are gonna shape thefuture of people practices.
I'm your host, David Rice.
I have two guests today.
Francisco Marin isthe founder and CEO of
Cognitive Talent Solutions.
He's also the author of theNetwork-First Manifesto, and
(01:10):
a pioneer in organizationalnetwork analysis.
Francisco leads a growingglobal community focused on
how informal networks and AIcollaboration can accelerate
onboarding, mentoring,and informal leadership
across large enterprises.
Dan George is the ChiefExperience Officer at CTS
and the founder and CEOof Piper Key Analytics.
(01:31):
He's an award-winningexpert in people analytics,
workforce planning, anddata-driven HR transformation.
He's got experience acrossFortune 100 firms and mid-size
innovators, and he's helpedreimagine HR as a strategic
analytics powered function.
He's also a member of oureditorial advisory board.
In this conversation, we'regonna try to dive deep into
(01:52):
how AI agents and networkanalysis are transforming
HR from static reporting toactionable autonomous workflows.
Francisco's gonna sharesome insights from early
pilots where AI agentsmatch mentors and mentees.
It maps informal leadershipand accelerates onboarding
across critical networks.
Dan's gonna break down howthese agents can scale the
(02:12):
people analytics functionitself, automating insights,
and enabling HR professionalsto focus on strategic work.
Whether you lead HR and peopleops or talent strategy, or you
just wanna understand wherethe next frontier of workforce
transformation is heading,this conversation is for you.
Let's jump right into it.
(02:32):
Alright!Francisco, Dan — welcome!
Dan George (02:34):
Great to be here.
Thank you.
David Rice (02:36):
So I want to start by talking about sort of the
vision that you all have withCognitive Talent Solutions.
So why these eight agents?
What were the sort of key areasthat you were looking to address
when you look at HR as a whole?
Dan George (02:49):
So, you know, from our perspective, there are
plenty of ways that AI agentscould be effective within
HR, but these eight were someof the most kind of obvious
that we could not only easilycollect consent and ensure
that all parties involved inthe process were willing and
(03:10):
able, but additionally, theywere some of the ones that we
could track an ROI specificallyto how they impact the overall
operations of HR and even kindof the necessary aspects of
being a first generation output.
So we didn't want to go toocrazy with, but we wanted to
obviously try to make an impact,but also knowing that all of
(03:34):
this is gonna evolve eventually.
So these eight were byfar kinda the ones that
made the most sense.
Francisco Marin (03:39):
I completely agree with what Dan shared.
And to complement it alittle bit, we observed that
there were some agentic AIcapabilities deployed for
core HR processes like payrollor regulatory compliance.
But we were missingthat generation of use
cases that affect peopleanalytics, and especially
(04:00):
those that were alignedwith the network framework
that we use at CTS, right?
So how they could helpus rethink some of those
key processes as changemanagement, leadership
development, or onboarding interms of social activation.
So we pick up thisgroup of eight.
We get a lot of traction.
We can discuss later on thatwith the onboarding one, with
(04:20):
the mentorship match, butwe felt that it was a group
of use cases that reflectedthe message that, hey, this
is the new way of agent,again, capabilities for the
people analytics industry asa whole, beyond the field of
organizational network analysis.
David Rice (04:37):
Dan, you mentioned their consent.
How do you ensure consent,trust, transparency when AI
agents initiate somethingas sensitive as like a
retention intervention or apeer mentoring connection?
Dan George (04:50):
Exactly.
And so, you know, that waskind of one of our preemptive
thoughts on all of this is thatwe've gotta make, you know,
anything that we do agentic,have the right level of consent
and ethical use of kind ofthese automatic processes.
So one of the best ways thatwe look to do it is, you know,
throughout our platform we'lltake in, you know, obviously
(05:10):
the service and emails aspectsof both the takers and the
other people within the org.
And so when the agent seesa specific criteria, it can
launch an automated emailthat can be generated by
one of the administrators.
And so they can start itor they can just start the
whole process of all thesignatures that were identified.
(05:33):
But both parties would thenreceive an email that they
click the button and consent to.
So with that, we've notifiedboth they've, you know,
given their consent to startthis process, and at any
point they can rescind theirconsent so that we stay not
only compliant with GDPR,but just compliant with
overall kind of ethical use.
Francisco Marin (05:54):
Yeah, and also I, we had ongoing
discussions with the teamabout all the nuances of each
use case separately, right?
Because in the case of talentretention, for example, we had
the discussion of, well, doesit make sense to provide these
insights at aggregate level?
Say, Hey, you have a 13 teambased on these signals that
(06:16):
we are seeing that has ahigh risk of attrition, and
these are some of the actionsthat you can implement.
Or does make sense to do itat individual level and notify
the immediate supervisor.
In some cases, we are testingthis at aggregate level with
others we're doing at individuallevel, and we are kinda like
seeing what is the reaction thatwe get from the first companies
that are piloting this andfrom the employees themselves.
(06:38):
The principle here is toreally, whatever possible
bill, these opting mechanisms.
So if you identify as a mentorfor a new hire, for example,
you have to provide yourconsent to participate in
the mentorship opportunity.
Then like the othercomponent there is like
what's the role that theimmediate supervisor plays?
Should the immediate supervisorbe a gatekeeper that has to
(06:59):
authorize the intervention orshould he or she be informed
about this opportunity havingbeen identified, but being the
mentor, for example, ultimatelythe person that has to provide
the consent to participate in.
It's something that we areevolving, but the idea here
is to be fully compliant withGDPR and other regulations
and also to build this upmechanisms whenever possible.
David Rice (07:19):
Let's talk functionality for a second,
'cause I'm curious what makesthese agents truly agentic, so
to speak, as opposed to likesay a dashboard or a smart
assistant, or what are thethings that sort of gen AI
is normally associated with?
Francisco Marin (07:33):
The idea here is the name implies
in agentic AI is that theAI has more agency, right?
So this AI is not reallywaiting for a human to
analyze a dashboard or aska question in order to.
Basically come up with thisquestion proactively or
identify these interventions.
The human component isstill needed to authorize
(07:53):
the intervention from theplatform, but the AI is
constantly monitoring thissignal and identifying these
interventions in real time.
And then it also can makecontext aware the decisions.
So in some cases, you'll.
Number of metrics available.
In other cases, there are othermetrics available that based
on the universal metrics thatdata has access to, and within
(08:15):
the re augmented generation,basically in the framework,
the overall agent orchestratorwith the different use cases,
it makes real time decisionsabout how to compose this,
email notifications, howto make this interventions.
So the level of agency thatthey has is way higher than,
say, for example, in traditionalgenerative AI interface,
like the ones that we builtpreviously in the platform.
David Rice (08:36):
You mentioned there that there's some pilots that
you're doing in those pilotstudies, you know, what are some
of the agents that have shownthe most immediate traction
or really sparked the interestof the folks using them?
And why do you think that is?
Francisco Marin (08:49):
It's the mentorship match
with onboarding.
You wanna take this one, Dan?
Dan George (08:52):
Yeah, for the most part, yeah.
The midterm ship matchis by far probably one of
the lowest bars in termsof risk and or accident.
So with this and again, I'ma huge believer in having
not just one mentor, butmultiple mentors on that.
And I think, you know, again,for any organization, you know,
looking for engagement withnew hires or new managers,
(09:14):
new people, managers, having amentor and being able to match
with someone that is maybe notin direct line or congenitally,
you know, associated with you.
The mentorship manager reallykind of looks to understand
a little bit more about thatindividual, their level where
kind of what some of theiraspirations are, depending
on, you know, how in depththe active ONA survey is.
(09:35):
And so with this one, both thepotential mentee and mentor
get these consent emails.
They can both say yes, andthen that introduction kind
of begins and at scale.
That just makes a lot ofthese things really easy
because again, I've run thisprocess manually in the past.
When I was a former CHRO,I've been in charge of, you
(09:56):
know, head of people analyticsat different organizations.
I've had to come up withlists and send them to LNOD
or other talent engagementteams, and so having it as
kind of an automated authorizedspot where an admin can
go and just be like click.
It just makes the processthat much easier and gets
us out of just selecting thetypical mentors and mentees
(10:18):
that we just always kindof go to right off the bat.
So overall it's just that, It'sprobably one of the easiest,
lowest risks and most impactfulthings that an organization can
do for to get for the price.
It's incredible.
Francisco Marin (10:33):
And people can grasp it conceptually
very easily because all ofus who have worked at a large
multinational, we know thatit makes a huge difference if
the first person you interactwith and that shows you the
robes in the organization.
It's a person that is inspiringand supportive and engaged.
Or if it's a person that isdisengaged and maybe wants to
leave the organization, maybeit's easier as a competitor.
(10:54):
And this is especiallyrelevant nowadays because
many new hire are joiningin hybrid setups, right.
Without face-to-faceinteraction with their team.
They're joining organizationsthat are going through a re
organization in real time.
Right?
And it's a very uncertainenvironment where you know,
it's critical to optimizethe onboarding process,
(11:15):
to shorten the dental toproductivity of the new hire.
And then what we're doinghere is to rethink onboarding
a social activation.
David Rice (11:22):
Very interesting.
And I can imagine on theenterprise level, like if you're
a multinational, like you saidyou know, and a lot of companies
are hiring across bordersnow, this would be excellent
for making those connections.
You mentioned when we weretalking before this, that
these agents can start toidentify burnout risks.
They can automatically, likeyou said, connect somebody
with a mentor or manager.
(11:44):
So in terms of the workflowitself of how it kind of
does that, what does thatlook like in practice?
Francisco Marin (11:52):
Maybe we can explain the workflow of
the mentorship match, becausethat's the one that is right
now operationally implementedacross multiple companies.
The one that we start usingas a starting point, right.
So the way it worksbasically is a new hire
joins the organization.
The AI identifies who'sthe person that is best
positioned to be the mentoror body of this person.
(12:13):
This identified based onmetrics like, you know,
informal leadership that hasbeen met previously through
organizational network analysisis technical consideration.
The role, the department,the level of performance,
the years of experience.
The more data you feedthe system, the more
sophisticated and actionablethe recommendations are.
But once this matching has beenidentified, the intervention
(12:36):
is showcased to the HR useron the network analyzer
platform, and then the HRuser has to authorize the
beginning of this intervention.
Then the AI reaches out tothe mentor to gather his or
her consent to participatein this initiative, informing
the first liner, which isnot the same, them asking
(12:56):
for permission to the firstliner for this to happen.
If the mentor provides theconsent, then the AI makes
an email introduction betweenthe mentor and the mentee.
Schedules a meeting so theyget to know each other, and
then documents whether themeeting has taken place or
not, and assesses the impact.
Of this intervention withspecific savings in terms
(13:17):
of shortening the time,productivity of the new hire.
So that would be an exampleof a full agent AI workflow
for onboarding withthe mentorship matcher.
David Rice (13:25):
Let's talk a little bit about outcomes.
So what does success for oneof these agents look like?
What signals or results areyou tracking to determine that?
Dan George (13:33):
You know, overall on the impact we have, the research
that's been out there for along time and collected what we
believe are some pretty averageROIs as it pertains to kind
of these different scenarios.
And as the agents completetheir tasks, and the people,
again, for me, that matchare like they have their
(13:54):
meetings and initial setup,it begins to kind of select.
Those counts and then appliesa dollar amount to it.
All that is aggregated kind ofat the top of the dashboard.
So for any HR, admin or otheradmin of the system, they
can see kind of what that is.
I mean, obviously with thecounts, you know, they can
apply their own ROI or changethat ROI if they want to kind
of either add dollar amountor subtract dollar amount.
(14:16):
But overall, we're lookingat the aspect of an automated
flow that takes intoaccounts, understands that
all this thing is scheduled,coordinated, and executed upon.
And that in of itself can savetens in, you know, depending
on the size of the, you know,the scope of the work group.
Like it could be, youknow, tens or hundreds
of hours if, depending onhow long it's being run.
(14:39):
And so all of that kind ofROI is kinda stationed there.
All those metrics can be putinto different presentation
styles on the dashboardso that they can then show
impact with their teammeetings or leadership stuff.
So all of that is aggregatedso that everyone kinda
understands the volume that'sexisted and executed upon.
Francisco Marin (15:00):
I just had maybe just go add a
bit more specific to your,for a mentorship, maximum
intervention, you could belooking at savings between
20k to 30k by shorteningthe 10th productivity of
a new hire by up to 40%.
Again, this can be implementedat scale across every new
hire in the organization.
And then, like right now,these AI agents are available
in our cell service platformConnect network analyzer, but
(15:22):
we are going in the directionof integrating these AI agents
natively in platforms likeServiceNow and Google, so
that our clients can consumethis within their existing
IT infrastructure as well.
And that's where theseagents can interact with
all the universe of, forexample, case management
information or HRSD in theemployee workflows ecosystem
(15:44):
within ServiceNow, right?
So it's a very interesting andexciting period right now where
we're going to be basicallydiscovering these new use cases
and getting a lot of feedback.
Getting this ready so thatwe can deploy ultimately
the all the aid agents atscale and go beyond this
dismissal pilot stage.
Dan George (16:00):
Yeah, and we've also got additional interest
from other CRMs as well.
So the idea behind kind ofadding this as a native app
in their marketplaces, youknow, helps to just streamline
the overall process for usingthis, you know, leveraging
this technology withintheir current ecosystems.
David Rice (16:19):
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(16:42):
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I imagine obviously there'sa lot of interest from HR,
but you mentioned there kindof the ability to talk to the
rest of the C-suite and presentto them, and I'm curious, are
you seeing a lot of interestcoming out of like operations
and CEOs, things like that?
Dan George (17:04):
More and more conversations we have,
you know, every week,every month is actually
coming from outside of HR.
Typically we have enteredin through either people
analytics, professionals, otherHR transformation, you know,
talent management professionals.
But more and more, you know,we started talking with leaders
(17:24):
outside of the HR functionsimply because they have an
interest in understandingall the skill sets and
networks of their employees.
So that they can speed upinnovation, figure out how to
collaborate better, understandkind of the cultural dynamics
of their teams, and even at thekind of most simple cases, you
know, for any change initiative,leaders can then understand
(17:47):
who are our top influencers.
That we know or maybe don'tknow and be able to tailor
specific communications tothose individuals and their
networks, whether it's in themiddle of the organization,
the top or the loweraspects of the organization.
Doesn't matter where thereare, there's influencers
everywhere within the org.
And being able to tap intothose people to accelerate
(18:10):
change adoption is justa huge use case for us.
David Rice (18:14):
From a technical perspective, 'cause every
organization, right, like thedata and sort of infrastructure
you're gonna encounter mightbe a little bit different.
So what kinds of data andinfrastructure do companies,
where do they need tobe essentially before
they can realisticallyadopt these agents?
Francisco Marin (18:30):
That's a great question because the
inter barriers are muchlower than people think.
The beautiful thing herereally is that we combine
active ONA with passive ONA.
Active ONA relying ononline surveys and passive
ONA, looking at aggregatedmetadata of collaborative
tools like Microsoft, Google,slack, and others, right?
So in terms of specific metricsyou need to have really, like,
(18:52):
the most important would bethe employee name, the email.
The manager's name, themanager email, and then
that's really about it.
I mean like in terms ofbeing able to launch a pilot.
Then from there, any additionaldata that you add, department,
division, performance,engagement, attrition, data
(19:12):
history, attrition data.
Right?
The more, the better, butthat's the beauty of the
agentic AI, that it's able tomake context aware decisions
and work with the basicinformation that it has.
Obviously, if the AI doesn'tknow who your manager is or
what's the email, it's not gonnabe able to inform or write or
provide these notifications.
But many of these informationis already by default in the
(19:34):
API of Microsoft or Google.
Then the data thatyou have available to
complement it work better.
Dan George (19:40):
Yeah.
So we've had teams upload rathersimplistic hierarchy structures,
and then we've had otherclients that have uploaded,
gosh, two or three separatehierarchies depending on Yes.
How they wanna view theirdifferent organizations and how
they wanna kind of understandand track kind of trends.
And so with that, we're able topretty much ingest any structure
(20:01):
that makes sense for them.
And I think that's gonna, thebeauty of how our specific LLM
in the backend, and then againif we're within one of those
other kind of vendor systems.
We can also kind of join inwith those specific LLMs so
that holistically we have acouple different LLMs that are
helping our specific ONA engineto analyze these networks.
Francisco Marin (20:26):
And a good example would be,
we recently worked withfortune five of the company.
They had like seven differentmetrics to define their
teams with this, you know,supervisory org 5, 6, 7,
functional reports, directreports, indirect reports.
Right.
And AI was able to make senseof all that universal metrics
that we were fitting in.
Right.
And include this into therecommendations and replace
(20:47):
to the prompt also with thegenerative AI component, which
is another feature also thatis making a big difference
in the way that clientsconsume this type of insights.
David Rice (20:54):
I'm curious because like, you know.
I've seen some research thatsuggests, like HR folks in
particular, they feel likemaybe they're not qualified
to use a lot of AI tools.
Right?
Like, so, you know, maybethey're not the data folks.
And I'm wondering, haveyou faced any skepticism or
resistance in bringing agenticAI into some of these, you
know, traditionally humancentered processes, and how
(21:16):
have you dealt with that?
Francisco Marin (21:18):
I would say so far, at least my experience is
like, it is very well understoodthat AI is gonna play a critical
role in the future of work.
And now it doesn't matter ifyou work in HR, it or finance,
you need to deal with this andyou need to be exposed to this
because this is going to impactyour job in the short term.
So it's that sense of urgencymaybe that is kinda like.
(21:40):
Lowering the resistance toadopt and be exposed to these
type of technologies, at leastcompared to previous waves.
For example, when we're, well,not probably the third company
in the people analytics industrythat integrated generative
AI into our solutions.
And there I saw somecompanies that were saying,
Hey, we love the technology.
We want to use the technology,but we are not ready to add the
(22:01):
generative AI component yet.
But then those same companies,two months later, launch an
agentic AI across the wholeorganization and think,
oh wow, this different.
Now they're getting itlike they have to be
part of alien adopters orthey're out of the market.
And I'm seeing likethe mentality shifting
(22:21):
in that regard.
Also, I think it's because AI isjust becoming a more important
part of our daily lives.
I think mostly genetic AI versusagentic AI, but yeah, that has
been my experience, much lowerresistance than I was expecting.
Dan George (22:37):
Yeah, so I think there's two aspects to this.
One is we've designed ourplatform for any professional,
whether you have an extensiveunderstanding of the
background and mathematicsbehind ONA and graph
databases, in theory is great.
All the metrics that you wouldexpect to find are there.
However, what we display frontand center on the platform
(23:00):
in our reports, you know, andwhat we can generate are all.
For individuals that don'thave a ton of background
on ONA, but understand kindof networks and just the
connections and collaborationthat needs to happen within
any typical enterprise.
So what we do is we kind oflayer this in two separate ways.
(23:21):
One is that our general reportsand impact metrics are very easy
to understand, but if you wannadive in a little bit deeper, all
the information is there too.
You can download into differentspreadsheets so you can.
Augment your current databaseor HR data warehouse with our
information as easily as justuploading a new spreadsheet.
(23:41):
And again, it doesn'tmatter if it's, you know,
you're doing 300 peopleor 30,000, all this can be
integrated within your system.
So there is the aspect ofboth focusing on non-technical
reports and insights, butalso knowing that all the
mathematics are there as well.
And then kind of to pointout, for Francisco's, I would
just recently was chattingwith the customer, that
(24:04):
kind of that same thing.
They wanted to start withoutthe generative and agenda ai,
just simply because that'smore comfortable to them.
But then as soon as they startedto kinda get more comfortable
with the system, what wasavailable, how their view of
the survey and the results went,they were like, you know what?
It really would be greatto add this because of,
(24:25):
you know, two main aspects.
One is that it's in anenvironment that is kind
of separate from theirother major HR systems.
So they know that since it'snot a system wide AI tool,
it's only confined to the spacewithin that platform, and that
just makes it a lot easierto kind of feel a bit safer
about it not accessing stuffthat it shouldn't, because
(24:45):
that clients only sent us thedemographic information that
they feel comfortable sharingoutside of their system.
Francisco (24:50):
That message is key.
Like we really reinforce themessage when speaking with
HR and other stakeholdersthat, hey, the users that
have access to the platformare in complete control on
how those insights are sharedacross the organization.
You can authorizeintervention, not authorize it.
You can share an executivesummary in PDF format.
(25:11):
You can completely customizeby yourself, what metrics,
what insights, and what chartsare included in the report.
You can customize thescope of the report, so
you are the gatekeeper andyou decide basically how
this is shared, and thisresonates with my HR teams.
David Rice (25:26):
I imagine that sort of customizable experience
and being able to shape itaround like the level of
the person using it would bea big factor in buying in.
And that kind of leads me tomy next question, which is,
'cause I mean, I imagine that'staken a while to create all
that and to develop that levelof functionality, but where
do you see this going next?
(25:47):
Are there agentic capabilitiesthat you're already dreaming
about for the next wave or?
Francisco Marin (25:52):
Yeah.
Dan George (25:52):
Yeah.
So I'll go first and I knowFrancisco's very passionate.
I mean, he's had a brilliantvision on this for a long time.
Like I said, we'veprobably known each other
five or six years now.
We've met before the pandemicand have been noodling
on this for a long time.
But I would say.
There's plenty of areas thatI see where we can not only
advance the second generationof the eight that we have
(26:15):
right now, but then alsodive into more as people
kind of get more comfortable.
But you know, we're also tryingto match the pace of, you know,
what we're seeing and alwaystrying to understand what trends
are kind of the most popularor the most impactful for the
moment, kind of, I think froma competitive standpoint,
I wanna keep a couple ofthose closer to my chest.
Overall, though, we're reallyexcited about not only kind
(26:37):
of this first generation,but the future second
generation and any of thekind of following ones that
we can come to fruition here.
Francisco Marin (26:44):
Yeah.
I would say really for me,the future organization,
it's network that ispowered by agents, right?
Where AI agents areto deploy these macro
interventions at scale, right?
And this component of largemultinationals acting as
incubators of this new wayof work, what we call a
network, first feature of work.
And there is a component ofnew organizations being born
(27:08):
and scaling, integratingthese practices at scale
the same way that we aredoing it at cs, right?
We're a team of 50 people,very decentralized community,
including consultants,ambassadors, a core
team here in California.
That ultimately we want tocreate a case study with the way
that we manage CTS itself of howthis new way of work looks like.
And we've even launched,not from CTS, but
(27:30):
even a personal level.
We launched a month agothis initiative called the
Network-First Manifesto, wherewe're embodying basically
anybody that resonateswith this concept of the
Network-First feature awardto draft this manifesto and,
you know, create a series ofprinciples we're collaborating
with, thought leaders likeMike Lorena Andras seven, and
other people in the industry.
(27:51):
We have right now a groupof 200 people, 200 funding
members with about 80 innursing organizations.
Right.
And this was just in a month.
And we're gonna add theratification events on
almost 13, and then we'regoing to announce a series
of next steps that aregoing to be very exciting.
That's really why we're doingthis right, is how can we,
within our limited capacityand influence, how can we
(28:12):
spearhead the transition froma hierarchy first model to a
network, first future to work?
Because at the end of theday when a new hire joins an
organization in the currentenvironment, and you know,
there is a, like the restrictiveinsights or where you have to
work, who you have to work with.
Where you have to work, whenyou have to work, right?
It's a model that is growingin collaborative control.
(28:32):
It's not the model that isachieving a healthy balance
between centralization anddecentralization between human
capital and social capital.
So what we're trying to dokinda like, is to embed in
this elements structurallevel so that the person that
joins the larger organizationcan have similar incentives
and similar experiencethan if you were joining a
startup in Silicon Valley.
Right?
(28:52):
That's what this is about.
About creating a feature towork that we are all excited
about and not scared about.
David Rice (28:58):
That's great.
That's amazing.
I love Michael'swork on networks.
It's, he's done somereally cool stuff, so.
Well, Dan, Francisco, thankyou for joining us today.
Before we go, I just wantto give you a chance to
tell people where they canfind out more about what you
all have going on and learnmore about these agents.
Francisco Marin (29:13):
First, I mean, like you can visit
cognitivetalentsolutions.comanytime.
We have also the newsletteron LinkedIn, CTS running sites
where you can get articles toyour email on a weekly basis.
And then separatelyfrom CTS as well, we
launched the Network-FirstManifesto initiative,
networkfirstmanifesto.com, ornetworkfirstmanifesto.com/join.
(29:36):
And there you can learnabout the initiative as well.
And this open to anybodythat wants to be part of it.
David Rice (29:41):
Alright, well thank you both for joining us today.
It's been a great talk.
Dan Georg (29:44):
Appreciate it, David.
Francisco Mar (29:45):
Thank you, David.
What makes these agents truly agentic
as opposed to like say, adashboard or a smart assistant?
Francisco Marin (00:06):
An agentic AI is not really waiting for
a human to ask a question.
It makes real time decisionsproactively, or identifying
interventions based onthe universal metrics that
they AI has access to.
David Rice (00:17):
How do you ensure trust when AI agents initiate
something as sensitive asretention or mentoring?
Dan George (00:23):
We've gotta make anything agentic, have the right
level of consent in ethical use.
So click the button inconsent and at any point they
can rescind their consent.
David Rice (00:32):
So what does success look like?
Francisco Marin (00:34):
What a mentorship market intervention,
you could be looking atsavings between 20k to 30k by
shortening the 10th productivityof a new hire by up to 40%.
David Rice (00:49):
Welcome to the People Managing People
Podcast, the show wherewe dig into the human side
of work, the technologyinfluencing it, and the bold
ideas that are gonna shape thefuture of people practices.
I'm your host, David Rice.
I have two guests today.
Francisco Marin isthe founder and CEO of
Cognitive Talent Solutions.
He's also the author of theNetwork-First Manifesto, and
(01:10):
a pioneer in organizationalnetwork analysis.
Francisco leads a growingglobal community focused on
how informal networks and AIcollaboration can accelerate
onboarding, mentoring,and informal leadership
across large enterprises.
Dan George is the ChiefExperience Officer at CTS
and the founder and CEOof Piper Key Analytics.
(01:31):
He's an award-winningexpert in people analytics,
workforce planning, anddata-driven HR transformation.
He's got experience acrossFortune 100 firms and mid-size
innovators, and he's helpedreimagine HR as a strategic
analytics powered function.
He's also a member of oureditorial advisory board.
In this conversation, we'regonna try to dive deep into
(01:52):
how AI agents and networkanalysis are transforming
HR from static reporting toactionable autonomous workflows.
Francisco's gonna sharesome insights from early
pilots where AI agentsmatch mentors and mentees.
It maps informal leadershipand accelerates onboarding
across critical networks.
Dan's gonna break down howthese agents can scale the
(02:12):
people analytics functionitself, automating insights,
and enabling HR professionalsto focus on strategic work.
Whether you lead HR and peopleops or talent strategy, or you
just wanna understand wherethe next frontier of workforce
transformation is heading,this conversation is for you.
Let's jump right into it.
(02:32):
Alright!Francisco, Dan — welcome!
Dan George (02:34):
Great to be here.
Thank you.
David Rice (02:36):
So I want to start by talking about sort of the
vision that you all have withCognitive Talent Solutions.
So why these eight agents?
What were the sort of key areasthat you were looking to address
when you look at HR as a whole?
Dan George (02:49):
So, you know, from our perspective, there are
plenty of ways that AI agentscould be effective within
HR, but these eight were someof the most kind of obvious
that we could not only easilycollect consent and ensure
that all parties involved inthe process were willing and
(03:10):
able, but additionally, theywere some of the ones that we
could track an ROI specificallyto how they impact the overall
operations of HR and even kindof the necessary aspects of
being a first generation output.
So we didn't want to go toocrazy with, but we wanted to
obviously try to make an impact,but also knowing that all of
(03:34):
this is gonna evolve eventually.
So these eight were byfar kinda the ones that
made the most sense.
Francisco Marin (03:39):
I completely agree with what Dan shared.
And to complement it alittle bit, we observed that
there were some agentic AIcapabilities deployed for
core HR processes like payrollor regulatory compliance.
But we were missingthat generation of use
cases that affect peopleanalytics, and especially
(04:00):
those that were alignedwith the network framework
that we use at CTS, right?
So how they could helpus rethink some of those
key processes as changemanagement, leadership
development, or onboarding interms of social activation.
So we pick up thisgroup of eight.
We get a lot of traction.
We can discuss later on thatwith the onboarding one, with
(04:20):
the mentorship match, butwe felt that it was a group
of use cases that reflectedthe message that, hey, this
is the new way of agent,again, capabilities for the
people analytics industry asa whole, beyond the field of
organizational network analysis.
David Rice (04:37):
Dan, you mentioned their consent.
How do you ensure consent,trust, transparency when AI
agents initiate somethingas sensitive as like a
retention intervention or apeer mentoring connection?
Dan George (04:50):
Exactly.
And so, you know, that waskind of one of our preemptive
thoughts on all of this is thatwe've gotta make, you know,
anything that we do agentic,have the right level of consent
and ethical use of kind ofthese automatic processes.
So one of the best ways thatwe look to do it is, you know,
throughout our platform we'lltake in, you know, obviously
(05:10):
the service and emails aspectsof both the takers and the
other people within the org.
And so when the agent seesa specific criteria, it can
launch an automated emailthat can be generated by
one of the administrators.
And so they can start itor they can just start the
whole process of all thesignatures that were identified.
(05:33):
But both parties would thenreceive an email that they
click the button and consent to.
So with that, we've notifiedboth they've, you know,
given their consent to startthis process, and at any
point they can rescind theirconsent so that we stay not
only compliant with GDPR,but just compliant with
overall kind of ethical use.
Francisco Marin (05:54):
Yeah, and also I, we had ongoing
discussions with the teamabout all the nuances of each
use case separately, right?
Because in the case of talentretention, for example, we had
the discussion of, well, doesit make sense to provide these
insights at aggregate level?
Say, Hey, you have a 13 teambased on these signals that
(06:16):
we are seeing that has ahigh risk of attrition, and
these are some of the actionsthat you can implement.
Or does make sense to do itat individual level and notify
the immediate supervisor.
In some cases, we are testingthis at aggregate level with
others we're doing at individuallevel, and we are kinda like
seeing what is the reaction thatwe get from the first companies
that are piloting this andfrom the employees themselves.
(06:38):
The principle here is toreally, whatever possible
bill, these opting mechanisms.
So if you identify as a mentorfor a new hire, for example,
you have to provide yourconsent to participate in
the mentorship opportunity.
Then like the othercomponent there is like
what's the role that theimmediate supervisor plays?
Should the immediate supervisorbe a gatekeeper that has to
(06:59):
authorize the intervention orshould he or she be informed
about this opportunity havingbeen identified, but being the
mentor, for example, ultimatelythe person that has to provide
the consent to participate in.
It's something that we areevolving, but the idea here
is to be fully compliant withGDPR and other regulations
and also to build this upmechanisms whenever possible.
David Rice (07:19):
Let's talk functionality for a second,
'cause I'm curious what makesthese agents truly agentic, so
to speak, as opposed to likesay a dashboard or a smart
assistant, or what are thethings that sort of gen AI
is normally associated with?
Francisco Marin (07:33):
The idea here is the name implies
in agentic AI is that theAI has more agency, right?
So this AI is not reallywaiting for a human to
analyze a dashboard or aska question in order to.
Basically come up with thisquestion proactively or
identify these interventions.
The human component isstill needed to authorize
(07:53):
the intervention from theplatform, but the AI is
constantly monitoring thissignal and identifying these
interventions in real time.
And then it also can makecontext aware the decisions.
So in some cases, you'll.
Number of metrics available.
In other cases, there are othermetrics available that based
on the universal metrics thatdata has access to, and within
(08:15):
the re augmented generation,basically in the framework,
the overall agent orchestratorwith the different use cases,
it makes real time decisionsabout how to compose this,
email notifications, howto make this interventions.
So the level of agency thatthey has is way higher than,
say, for example, in traditionalgenerative AI interface,
like the ones that we builtpreviously in the platform.
David Rice (08:36):
You mentioned there that there's some pilots that
you're doing in those pilotstudies, you know, what are some
of the agents that have shownthe most immediate traction
or really sparked the interestof the folks using them?
And why do you think that is?
Francisco Marin (08:49):
It's the mentorship match
with onboarding.
You wanna take this one, Dan?
Dan George (08:52):
Yeah, for the most part, yeah.
The midterm ship matchis by far probably one of
the lowest bars in termsof risk and or accident.
So with this and again, I'ma huge believer in having
not just one mentor, butmultiple mentors on that.
And I think, you know, again,for any organization, you know,
looking for engagement withnew hires or new managers,
(09:14):
new people, managers, having amentor and being able to match
with someone that is maybe notin direct line or congenitally,
you know, associated with you.
The mentorship manager reallykind of looks to understand
a little bit more about thatindividual, their level where
kind of what some of theiraspirations are, depending
on, you know, how in depththe active ONA survey is.
(09:35):
And so with this one, both thepotential mentee and mentor
get these consent emails.
They can both say yes, andthen that introduction kind
of begins and at scale.
That just makes a lot ofthese things really easy
because again, I've run thisprocess manually in the past.
When I was a former CHRO,I've been in charge of, you
(09:56):
know, head of people analyticsat different organizations.
I've had to come up withlists and send them to LNOD
or other talent engagementteams, and so having it as
kind of an automated authorizedspot where an admin can
go and just be like click.
It just makes the processthat much easier and gets
us out of just selecting thetypical mentors and mentees
(10:18):
that we just always kindof go to right off the bat.
So overall it's just that, It'sprobably one of the easiest,
lowest risks and most impactfulthings that an organization can
do for to get for the price.
It's incredible.
Francisco Marin (10:33):
And people can grasp it conceptually
very easily because all ofus who have worked at a large
multinational, we know thatit makes a huge difference if
the first person you interactwith and that shows you the
robes in the organization.
It's a person that is inspiringand supportive and engaged.
Or if it's a person that isdisengaged and maybe wants to
leave the organization, maybeit's easier as a competitor.
(10:54):
And this is especiallyrelevant nowadays because
many new hire are joiningin hybrid setups, right.
Without face-to-faceinteraction with their team.
They're joining organizationsthat are going through a re
organization in real time.
Right?
And it's a very uncertainenvironment where you know,
it's critical to optimizethe onboarding process,
(11:15):
to shorten the dental toproductivity of the new hire.
And then what we're doinghere is to rethink onboarding
a social activation.
David Rice (11:22):
Very interesting.
And I can imagine on theenterprise level, like if you're
a multinational, like you saidyou know, and a lot of companies
are hiring across bordersnow, this would be excellent
for making those connections.
You mentioned when we weretalking before this, that
these agents can start toidentify burnout risks.
They can automatically, likeyou said, connect somebody
with a mentor or manager.
(11:44):
So in terms of the workflowitself of how it kind of
does that, what does thatlook like in practice?
Francisco Marin (11:52):
Maybe we can explain the workflow of
the mentorship match, becausethat's the one that is right
now operationally implementedacross multiple companies.
The one that we start usingas a starting point, right.
So the way it worksbasically is a new hire
joins the organization.
The AI identifies who'sthe person that is best
positioned to be the mentoror body of this person.
(12:13):
This identified based onmetrics like, you know,
informal leadership that hasbeen met previously through
organizational network analysisis technical consideration.
The role, the department,the level of performance,
the years of experience.
The more data you feedthe system, the more
sophisticated and actionablethe recommendations are.
But once this matching has beenidentified, the intervention
(12:36):
is showcased to the HR useron the network analyzer
platform, and then the HRuser has to authorize the
beginning of this intervention.
Then the AI reaches out tothe mentor to gather his or
her consent to participatein this initiative, informing
the first liner, which isnot the same, them asking
(12:56):
for permission to the firstliner for this to happen.
If the mentor provides theconsent, then the AI makes
an email introduction betweenthe mentor and the mentee.
Schedules a meeting so theyget to know each other, and
then documents whether themeeting has taken place or
not, and assesses the impact.
Of this intervention withspecific savings in terms
(13:17):
of shortening the time,productivity of the new hire.
So that would be an exampleof a full agent AI workflow
for onboarding withthe mentorship matcher.
David Rice (13:25):
Let's talk a little bit about outcomes.
So what does success for oneof these agents look like?
What signals or results areyou tracking to determine that?
Dan George (13:33):
You know, overall on the impact we have, the research
that's been out there for along time and collected what we
believe are some pretty averageROIs as it pertains to kind
of these different scenarios.
And as the agents completetheir tasks, and the people,
again, for me, that matchare like they have their
(13:54):
meetings and initial setup,it begins to kind of select.
Those counts and then appliesa dollar amount to it.
All that is aggregated kind ofat the top of the dashboard.
So for any HR, admin or otheradmin of the system, they
can see kind of what that is.
I mean, obviously with thecounts, you know, they can
apply their own ROI or changethat ROI if they want to kind
of either add dollar amountor subtract dollar amount.
(14:16):
But overall, we're lookingat the aspect of an automated
flow that takes intoaccounts, understands that
all this thing is scheduled,coordinated, and executed upon.
And that in of itself can savetens in, you know, depending
on the size of the, you know,the scope of the work group.
Like it could be, youknow, tens or hundreds
of hours if, depending onhow long it's being run.
(14:39):
And so all of that kind ofROI is kinda stationed there.
All those metrics can be putinto different presentation
styles on the dashboardso that they can then show
impact with their teammeetings or leadership stuff.
So all of that is aggregatedso that everyone kinda
understands the volume that'sexisted and executed upon.
Francisco Marin (15:00):
I just had maybe just go add a
bit more specific to your,for a mentorship, maximum
intervention, you could belooking at savings between
20k to 30k by shorteningthe 10th productivity of
a new hire by up to 40%.
Again, this can be implementedat scale across every new
hire in the organization.
And then, like right now,these AI agents are available
in our cell service platformConnect network analyzer, but
(15:22):
we are going in the directionof integrating these AI agents
natively in platforms likeServiceNow and Google, so
that our clients can consumethis within their existing
IT infrastructure as well.
And that's where theseagents can interact with
all the universe of, forexample, case management
information or HRSD in theemployee workflows ecosystem
(15:44):
within ServiceNow, right?
So it's a very interesting andexciting period right now where
we're going to be basicallydiscovering these new use cases
and getting a lot of feedback.
Getting this ready so thatwe can deploy ultimately
the all the aid agents atscale and go beyond this
dismissal pilot stage.
Dan George (16:00):
Yeah, and we've also got additional interest
from other CRMs as well.
So the idea behind kind ofadding this as a native app
in their marketplaces, youknow, helps to just streamline
the overall process for usingthis, you know, leveraging
this technology withintheir current ecosystems.
David Rice (16:19):
Imagine having access to the
world's best talent.
Whether it's that engineerin São Paulo, that head of
sales in Dublin, or thatincredible designer in Cape
Town — your next great hirecould be anywhere in the world.
With Oyster, they don't haveto be the one that got away.
Oyster helps companies hiretalent globally, run accurate
and on time payroll, and staycompliant every step of the way.
(16:42):
Build your dream team and growwith confidence because the
world truly is your oyster.
I imagine obviously there'sa lot of interest from HR,
but you mentioned there kindof the ability to talk to the
rest of the C-suite and presentto them, and I'm curious, are
you seeing a lot of interestcoming out of like operations
and CEOs, things like that?
Dan George (17:04):
More and more conversations we have,
you know, every week,every month is actually
coming from outside of HR.
Typically we have enteredin through either people
analytics, professionals, otherHR transformation, you know,
talent management professionals.
But more and more, you know,we started talking with leaders
(17:24):
outside of the HR functionsimply because they have an
interest in understandingall the skill sets and
networks of their employees.
So that they can speed upinnovation, figure out how to
collaborate better, understandkind of the cultural dynamics
of their teams, and even at thekind of most simple cases, you
know, for any change initiative,leaders can then understand
(17:47):
who are our top influencers.
That we know or maybe don'tknow and be able to tailor
specific communications tothose individuals and their
networks, whether it's in themiddle of the organization,
the top or the loweraspects of the organization.
Doesn't matter where thereare, there's influencers
everywhere within the org.
And being able to tap intothose people to accelerate
(18:10):
change adoption is justa huge use case for us.
David Rice (18:14):
From a technical perspective, 'cause every
organization, right, like thedata and sort of infrastructure
you're gonna encounter mightbe a little bit different.
So what kinds of data andinfrastructure do companies,
where do they need tobe essentially before
they can realisticallyadopt these agents?
Francisco Marin (18:30):
That's a great question because the
inter barriers are muchlower than people think.
The beautiful thing herereally is that we combine
active ONA with passive ONA.
Active ONA relying ononline surveys and passive
ONA, looking at aggregatedmetadata of collaborative
tools like Microsoft, Google,slack, and others, right?
So in terms of specific metricsyou need to have really, like,
(18:52):
the most important would bethe employee name, the email.
The manager's name, themanager email, and then
that's really about it.
I mean like in terms ofbeing able to launch a pilot.
Then from there, any additionaldata that you add, department,
division, performance,engagement, attrition, data
(19:12):
history, attrition data.
Right?
The more, the better, butthat's the beauty of the
agentic AI, that it's able tomake context aware decisions
and work with the basicinformation that it has.
Obviously, if the AI doesn'tknow who your manager is or
what's the email, it's not gonnabe able to inform or write or
provide these notifications.
But many of these informationis already by default in the
(19:34):
API of Microsoft or Google.
Then the data thatyou have available to
complement it work better.
Dan George (19:40):
Yeah.
So we've had teams upload rathersimplistic hierarchy structures,
and then we've had otherclients that have uploaded,
gosh, two or three separatehierarchies depending on Yes.
How they wanna view theirdifferent organizations and how
they wanna kind of understandand track kind of trends.
And so with that, we're able topretty much ingest any structure
(20:01):
that makes sense for them.
And I think that's gonna, thebeauty of how our specific LLM
in the backend, and then againif we're within one of those
other kind of vendor systems.
We can also kind of join inwith those specific LLMs so
that holistically we have acouple different LLMs that are
helping our specific ONA engineto analyze these networks.
Francisco Marin (20:26):
And a good example would be,
we recently worked withfortune five of the company.
They had like seven differentmetrics to define their
teams with this, you know,supervisory org 5, 6, 7,
functional reports, directreports, indirect reports.
Right.
And AI was able to make senseof all that universal metrics
that we were fitting in.
Right.
And include this into therecommendations and replace
(20:47):
to the prompt also with thegenerative AI component, which
is another feature also thatis making a big difference
in the way that clientsconsume this type of insights.
David Rice (20:54):
I'm curious because like, you know.
I've seen some research thatsuggests, like HR folks in
particular, they feel likemaybe they're not qualified
to use a lot of AI tools.
Right?
Like, so, you know, maybethey're not the data folks.
And I'm wondering, haveyou faced any skepticism or
resistance in bringing agenticAI into some of these, you
know, traditionally humancentered processes, and how
(21:16):
have you dealt with that?
Francisco Marin (21:18):
I would say so far, at least my experience is
like, it is very well understoodthat AI is gonna play a critical
role in the future of work.
And now it doesn't matter ifyou work in HR, it or finance,
you need to deal with this andyou need to be exposed to this
because this is going to impactyour job in the short term.
So it's that sense of urgencymaybe that is kinda like.
(21:40):
Lowering the resistance toadopt and be exposed to these
type of technologies, at leastcompared to previous waves.
For example, when we're, well,not probably the third company
in the people analytics industrythat integrated generative
AI into our solutions.
And there I saw somecompanies that were saying,
Hey, we love the technology.
We want to use the technology,but we are not ready to add the
(22:01):
generative AI component yet.
But then those same companies,two months later, launch an
agentic AI across the wholeorganization and think,
oh wow, this different.
Now they're getting itlike they have to be
part of alien adopters orthey're out of the market.
And I'm seeing likethe mentality shifting
(22:21):
in that regard.
Also, I think it's because AI isjust becoming a more important
part of our daily lives.
I think mostly genetic AI versusagentic AI, but yeah, that has
been my experience, much lowerresistance than I was expecting.
Dan George (22:37):
Yeah, so I think there's two aspects to this.
One is we've designed ourplatform for any professional,
whether you have an extensiveunderstanding of the
background and mathematicsbehind ONA and graph
databases, in theory is great.
All the metrics that you wouldexpect to find are there.
However, what we display frontand center on the platform
(23:00):
in our reports, you know, andwhat we can generate are all.
For individuals that don'thave a ton of background
on ONA, but understand kindof networks and just the
connections and collaborationthat needs to happen within
any typical enterprise.
So what we do is we kind oflayer this in two separate ways.
(23:21):
One is that our general reportsand impact metrics are very easy
to understand, but if you wannadive in a little bit deeper, all
the information is there too.
You can download into differentspreadsheets so you can.
Augment your current databaseor HR data warehouse with our
information as easily as justuploading a new spreadsheet.
(23:41):
And again, it doesn'tmatter if it's, you know,
you're doing 300 peopleor 30,000, all this can be
integrated within your system.
So there is the aspect ofboth focusing on non-technical
reports and insights, butalso knowing that all the
mathematics are there as well.
And then kind of to pointout, for Francisco's, I would
just recently was chattingwith the customer, that
(24:04):
kind of that same thing.
They wanted to start withoutthe generative and agenda ai,
just simply because that'smore comfortable to them.
But then as soon as they startedto kinda get more comfortable
with the system, what wasavailable, how their view of
the survey and the results went,they were like, you know what?
It really would be greatto add this because of,
(24:25):
you know, two main aspects.
One is that it's in anenvironment that is kind
of separate from theirother major HR systems.
So they know that since it'snot a system wide AI tool,
it's only confined to the spacewithin that platform, and that
just makes it a lot easierto kind of feel a bit safer
about it not accessing stuffthat it shouldn't, because
(24:45):
that clients only sent us thedemographic information that
they feel comfortable sharingoutside of their system.
Francisco (24:50):
That message is key.
Like we really reinforce themessage when speaking with
HR and other stakeholdersthat, hey, the users that
have access to the platformare in complete control on
how those insights are sharedacross the organization.
You can authorizeintervention, not authorize it.
You can share an executivesummary in PDF format.
(25:11):
You can completely customizeby yourself, what metrics,
what insights, and what chartsare included in the report.
You can customize thescope of the report, so
you are the gatekeeper andyou decide basically how
this is shared, and thisresonates with my HR teams.
David Rice (25:26):
I imagine that sort of customizable experience
and being able to shape itaround like the level of
the person using it would bea big factor in buying in.
And that kind of leads me tomy next question, which is,
'cause I mean, I imagine that'staken a while to create all
that and to develop that levelof functionality, but where
do you see this going next?
(25:47):
Are there agentic capabilitiesthat you're already dreaming
about for the next wave or?
Francisco Marin (25:52):
Yeah.
Dan George (25:52):
Yeah.
So I'll go first and I knowFrancisco's very passionate.
I mean, he's had a brilliantvision on this for a long time.
Like I said, we'veprobably known each other
five or six years now.
We've met before the pandemicand have been noodling
on this for a long time.
But I would say.
There's plenty of areas thatI see where we can not only
advance the second generationof the eight that we have
(26:15):
right now, but then alsodive into more as people
kind of get more comfortable.
But you know, we're also tryingto match the pace of, you know,
what we're seeing and alwaystrying to understand what trends
are kind of the most popularor the most impactful for the
moment, kind of, I think froma competitive standpoint,
I wanna keep a couple ofthose closer to my chest.
Overall, though, we're reallyexcited about not only kind
(26:37):
of this first generation,but the future second
generation and any of thekind of following ones that
we can come to fruition here.
Francisco Marin (26:44):
Yeah.
I would say really for me,the future organization,
it's network that ispowered by agents, right?
Where AI agents areto deploy these macro
interventions at scale, right?
And this component of largemultinationals acting as
incubators of this new wayof work, what we call a
network, first feature of work.
And there is a component ofnew organizations being born
(27:08):
and scaling, integratingthese practices at scale
the same way that we aredoing it at cs, right?
We're a team of 50 people,very decentralized community,
including consultants,ambassadors, a core
team here in California.
That ultimately we want tocreate a case study with the way
that we manage CTS itself of howthis new way of work looks like.
And we've even launched,not from CTS, but
(27:30):
even a personal level.
We launched a month agothis initiative called the
Network-First Manifesto, wherewe're embodying basically
anybody that resonateswith this concept of the
Network-First feature awardto draft this manifesto and,
you know, create a series ofprinciples we're collaborating
with, thought leaders likeMike Lorena Andras seven, and
other people in the industry.
(27:51):
We have right now a groupof 200 people, 200 funding
members with about 80 innursing organizations.
Right.
And this was just in a month.
And we're gonna add theratification events on
almost 13, and then we'regoing to announce a series
of next steps that aregoing to be very exciting.
That's really why we're doingthis right, is how can we,
within our limited capacityand influence, how can we
(28:12):
spearhead the transition froma hierarchy first model to a
network, first future to work?
Because at the end of theday when a new hire joins an
organization in the currentenvironment, and you know,
there is a, like the restrictiveinsights or where you have to
work, who you have to work with.
Where you have to work, whenyou have to work, right?
It's a model that is growingin collaborative control.
(28:32):
It's not the model that isachieving a healthy balance
between centralization anddecentralization between human
capital and social capital.
So what we're trying to dokinda like, is to embed in
this elements structurallevel so that the person that
joins the larger organizationcan have similar incentives
and similar experiencethan if you were joining a
startup in Silicon Valley.
Right?
(28:52):
That's what this is about.
About creating a feature towork that we are all excited
about and not scared about.
David Rice (28:58):
That's great.
That's amazing.
I love Michael'swork on networks.
It's, he's done somereally cool stuff, so.
Well, Dan, Francisco, thankyou for joining us today.
Before we go, I just wantto give you a chance to
tell people where they canfind out more about what you
all have going on and learnmore about these agents.
Francisco Marin (29:13):
First, I mean, like you can visit
cognitivetalentsolutions.comanytime.
We have also the newsletteron LinkedIn, CTS running sites
where you can get articles toyour email on a weekly basis.
And then separatelyfrom CTS as well, we
launched the Network-FirstManifesto initiative,
networkfirstmanifesto.com, ornetworkfirstmanifesto.com/join.
(29:36):
And there you can learnabout the initiative as well.
And this open to anybodythat wants to be part of it.
David Rice (29:41):
Alright, well thank you both for joining us today.
It's been a great talk.
Dan Georg (29:44):
Appreciate it, David.
Francisco Mar (29:45):
Thank you, David.
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