August 27, 2025 • 40 mins
How do you know if you’re tracking the right project metrics—or if you’ve been chasing the wrong numbers all along? In this episode, Galen sits down with Lior Gerson, Co-founder & CEO of TargetBoard.ai, to unpack how AI is reshaping KPI management and why aligning metrics with business strategy is the real game-changer.
Together, they explore how project leaders can move beyond legacy metrics like velocity and utilization to focus on measures that truly drive impact. From tackling cultural mismatches around KPIs, to building real-time forecasting models, to leveraging AI for smarter, faster reporting—this is a conversation about cutting through the noise and making metrics meaningful.
Resources from this episode:
- Join DPM Membership
- Subscribe to the newsletter to get our latest articles and podcasts
- Connect with Lior on LinkedIn
- Check out TargetBoard.ai
- When the Old, and the New Metrics Fail – Jim Highsmith
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Galen Low (00:00):
How can an organization or a team
determine whether they'retracking the right metrics
and what can they do if theyrealize they've been measuring
the wrong thing all along?
Lior Gerson (00:09):
You need to track whatever is going to help you
get to where you wanna go.
Everything else is noise.
Galen Low (00:14):
Walk us through an example, either of a metric
that was maybe the wrongmetric, and that process
of how they changed towardsmeasuring the right thing.
Lior Gerson (00:23):
One of our customers, he wanted to
improve like how theyforecast what they're gonna
deliver every quarter.
He was able to take ourplatform and automate it
and it took him like a day.
But now whenever he wants,he has like that in front
of him, like, what am Igonna deliver this month?
Do you think there is a stagewhere like an AI agent becomes
a project manager or replacethe project manager completely?
Galen Low (00:45):
I think for some people's definition of what
a project manager does, yes.
Welcome to The Digital ProjectManager podcast — the show that
helps delivery leaders worksmarter, deliver faster, and
lead better in the age of AI.
I'm Galen, and every week wedive into real-world strategies,
(01:08):
new tools, proven frameworks,and the occasional war story
from the project front lines.
Whether you're steeringmassive transformation
projects, wrangling AIworkflows, or just trying to
keep the chaos under control,you're in the right place.
Let's get into it.
Today we are talking about howtraditional views of measuring
project success are beingchallenged, and why AI-powered
(01:29):
KPI management tools might beyour only fighting chance to
keep pace with the growing listof variables that could make or
break your project's outcomes.
With me today is LiorGerson, Co-founder and
CEO of TargetBoard.ai.
Lior started his career atmySupermarket, which was the
largest price comparison websitein the UK servicing tens of
millions of users every month.
(01:50):
There he was responsible formulti-million dollar projects
collaborating with giants suchas Walmart, WPP, and Dentzu.
And then he moved on tofound Vroom.com as CTO,
an online car dealershipthat went public for $2.5B.
After that, he led productfor Placer.ai, a location
analytics unicorn processingpetabytes of data.
And of course today he leadsTargetBoard, an AI-powered
(02:12):
KPI management tool.
Lior, thank you so muchfor joining us today.
Lior Gerson (02:16):
Thanks, Galen, pleasure of being here.
Galen Low (02:18):
I'm really excited to talk to you because you are
someone who's like incrediblydeep in the world of KPIs
and making data informeddecisions towards success.
Honestly, I hope thisconversation goes places
that I hadn't planned, buthere's the roadmap that I've
sketched out for us today.
So, to start us off, Iwanted to get one big burning
question outta the way,like the hot question that
everyone wants to know.
(02:38):
But after that, I'd liketo zoom out from that and
talk about three things.
First, I wanted to talkabout culture mismatch around
KPIs and how to translatemetrics across different
levels of an organization.
Then I'd like to talk abouthow project KPIs are changing
and where you see them going.
Lastly, I thought maybe wecould zero in on some practical
ways that businesses andproject teams can get started
(02:59):
on leveraging AI enhancedKPI tracking and how that
might impact their workflowsfor better or for worse.
How does that sound to you?
Lior Gerson (03:07):
Sounds good.
I think it's gonna afascinating discussion.
Galen Low (03:10):
Awesome.
Me too.
Me too.
All right.
Here's the hot question.
Lately, there's been a lotof talk about the fact that a
lot of projects and actuallyentire organizations might
be actually measuring andtracking the wrong thing.
So for projects, I'veseen a lot of discourse
challenging everything fromthe formerly sacred Iron
Triangle of scope, schedule,and budget, all the way to
Agile metrics like velocity.
(03:31):
And then organizationally,I've seen folks take aim at
things like revenue per FDE,gross margin and utilization.
The big thing that's beingtalked about in my world, at
least the project managementworld, is how project leaders
shouldn't just measure projecthealth, but also project impact.
So against that backdrop,my hot question is how can
(03:51):
an organization or a teamdetermine whether they're
tracking the right metricsand what can they do if they
realize they've been measuringthe wrong thing all along?
Lior Gerson (03:59):
Yeah, that's a great question.
The project initiativethat you're using are
set to be aligned with abusiness strategy, right,
with a product strategy.
Wherever thebusiness wants to go.
But when you put it in thebox of project management, all
you really wanna achieve issaying, okay, I wanna reach my
milestones on time, on budget,with, you know, as little
(04:19):
friction and as much, you know,accuracy and predictability
as possible, right?
So all the magic that youneed to track are the metrics
that are gonna help youget to that state, right?
It doesn't be anything.
It can be, you know, howfast tests are moving, where
things are blocked, vacationdays, how people are using AI.
You can track anything aslong as it keeps you in
(04:39):
that box of what you'reactually trying to achieve.
But if you're not trackingthe things that are gonna
help you reach that you'retracking the wrong things.
Galen Low (04:47):
I like that perspective because I think
we tend to gravitate towardsthe right things to measure.
I even said it in thequestion like, what is the
right thing to measure?
You're saying actually.
Whatever the thing is thathelps move you forward is
the right thing to measure.
Even if it's way different thanwhat that other team is doing.
Even if it's way differentthan what you did at
that other organization.
Like it should bealigned to the mission.
Lior Gerson (05:07):
You need to track whatever is going to help you
get to where you wanna go.
Everything else is,you know, is noise.
Galen Low (05:13):
That's fair.
That's fair.
So I mean, for folks whoare like, oh wow, Lior,
that's, you're speaking mylanguage, we are definitely
not tracking the right things.
We need to start.
How do you kind of map out.
What the right thingsare to push you forward?
Is there a sort of bestpractice or a process?
Lior Gerson (05:30):
You need to understand your business, right?
You need to understandyour project.
You understand what'smoving the needle?
What are the movers and shakers?
What's gonna make an impact?
Is it people?
Is it resources?
Is it complexity?
How do you sort ofunpack everything?
Say, okay, theseare the components.
This is what needs to happennow, how to attract that.
It's actually happeningand not sort of falling
(05:51):
between the cracks.
Is public.
It like you're gonnahave like lots of tools.
Like data is gonna beall over the place.
It's not gonna be updated.
And you're gonna say, oh wellpeople are not updating their
system so it's garbage andgarbage out and I can't do it.
You know, and I'm spendingall this time instead
showing any returns.
There's like a million waysand a million reasons why
it's not gonna work, butwe need it to work, so,
(06:11):
so you know, it's on you.
Galen Low (06:13):
Okay.
That makes sense to me.
I'm wondering, would you be ableto just maybe walk us through
an example, either of a metricthat was maybe the wrong metric
and that process of how theychanged towards measuring the
right thing, or even if therewas just some unconventional
metric that somebody thatyou worked with was tracking
that actually pushed forwardtowards the goals, but actually
(06:35):
is a pretty non-traditionalthing to measure?
Lior Gerson (06:38):
Let me talk about like one of our customers,
they're called Versa.
P. There are a PTF deliveringproduct operations.
It's called bu.
He is a brilliant guy.
We love him, we love workingwith him, and he wanted
to improve, like how theyforecast what they're gonna
deliver every quarter, right?
So plan was actual improvingthe planning accuracy,
improving forecasting.
(07:00):
They were running very fastand things are changing
all the time, right?
So priorities change,capacities change, people
move between teams.
So it becomesreally complicated.
And what he came up with wasa formula that helps take
the previous performance ofevery contributor and every
team and then add that on towhatever, like the current
capacity for that team orthat group is gonna be.
(07:22):
And instead of, you know,having to sort of crunch all
that data in Excel, you know,which takes a lot of time.
You have to bring in youknow, data about vacation
days and, you know, tasksand priorities and lots of
stuff and doing it in Excel.
So he was willing,you know, to take our
platform and automate it.
And it took himlike, like a day.
But now, whenever he wants, hehas like that in front of him.
(07:43):
Okay, what am I gonnadeliver with this month?
You know, so like the CEO or theCPO comes in and say, Hey, you
know, I need to shift priority.
Something else comes, youknow, is most t he has that
what's actually going tochange and how it's actually
impacting all the initiativesthat they're trying to push.
I thought it was brillianttaking data, you know.
Putting it in the rightcontext to tell the story
that he needs to tell.
Galen Low (08:04):
I was just at an Agile conference.
And I mentionedvelocity earlier, right?
And everyone's yeah, it'slike a poor measure of impact.
And sometimes it's just a poormeasure because you know, in
a lot of agile teams you'remeant to have a dedicated team.
You know, they'reresource full-time.
And people I talk to, they'relike, that never happens.
People are, you know, toyour point, they're going
on vacation, they're gettingpulled away to other projects.
So you look at this velocitymetric and it's going you
(08:25):
know, up and down and you'retrying to plan ahead and
you're like, what is going on?
It's it's not avery good metric.
And I would say mostpeople probably wouldn't.
They either wouldn't think tohave an aggregate metric that
is based on dynamic data or theywouldn't know how to build it.
Right.
But I think thatmakes a lot of sense.
It's almost like.
This notion of like real timeresourcing where it's okay, well
this person's gonna be away.
(08:46):
We should expect thisdip in this metric about
our output, about ourperformance, about our impact.
You know, whatever it is.
It's actually grabbing data fromother places, not just trying
to have the one measure, theone metric to rule them all.
I mean, it does functionas that, but it's
an aggregate metric.
Actually, I thinkthat's really cool.
Lior Gerson (09:03):
It's interesting you bring this up, right?
But think about like how datapropagates across the business.
That person goes away.
It takes time until peopleknow their way, right?
If manager's gonnaknow their way.
But until that propagates to theright person who has like the
understanding of how it's gonnaimpact the project timelines,
and you know, what, you know,secondary or tertiary effect
(09:23):
is gonna have, it's gonna takedays or weeks until that's time
you're not getting back, right?
You add more resources, you'rejust gonna extend the timeline.
It's not like it'sgonna make it shorter.
You have that learning curve.
So the faster you're ableto get that information
across your organization,getting it to the right
people, it's massive impact.
It's compounding interests.
Galen Low (09:40):
That lag is like so real.
In our community, there'salways a lot of conversation
about well, I wish someonehad told me that my lead
dev was gonna be going onholiday for three weeks.
And then even that lag, likeI see it in my role all the
time where it's like you getthe like a DP, like vacation
request and you go in there andthere's a process and a flow
(10:00):
and bottlenecking that happensbefore anyone even knows that
person's vacation is approved.
A little less whatlike date that is.
I like that kindof streams it in.
Lior Gerson (10:09):
Vacation is easy.
You know, think about, you know,you're working remote, you're
on Zoom, and somebody's gone.
Who's gone, you know, let go.
Nobody told you to be let go.
Galen Low (10:17):
Yeah.
Okay.
I see.
Lior Gerson (10:18):
You know, two weeks in, he is
not answering my calls.
I'm like, what's up?
Oh, he's not here anymore.
Galen Low (10:22):
He's not here.
Yeah.
We're hearing a lot aboutthat too, in the community.
Right?
It's like production'sin force without the
communication around it.
Yeah.
And then what.
Can you this is a bit of a sidequest, but can you at a high
level, walk me through whatthat integration looks like?
Because I'm picturing myselfopening Excel and being like,
okay, I need to like Zapieror make, or do some kind of
transformation of data andhave it like pasted into a
(10:43):
sheet and I have to writea formula and all that.
Would I still have to do thatin TargetBoard or is integration
a little bit more hands free?
Hands free, might notbe the right word, but
less labor intensive.
Lior Gerson (10:55):
So TargetBoard.ai works in a very different
way from any othersystem that exists there.
We connect to your sourcedata source system and we
automatically build allthe metrics and all the
KPIs that are relevant forwhatever you need to do.
Right.
And then we use AIto generate them.
But it doesn't matter if you'relooking, you know, at metrics
for how your capacity changes,how your velocity changes,
(11:15):
how the quality changes.
If you're looking at oneproject, multiple projects,
doesn't matter how youwork, don't have to change
anything about how you work.
We automatically learnhow you work your systems.
It doesn't matter if you'reusing Monday or if you're using
Jira or anything else, right?
And then all your metrics, allyour reports, all your insights
are automatically generatedand you can then customize.
(11:37):
It's like DIY customization,or you can do it with a team.
You'll get the exact metricsand the exact way of tracking
everything that you want.
It's zero effort, like it'slike day one, you're up and
running fully confident thatthe data you need is there and
it's accurate and you don't needto worry about excels anymore.
By the way, if you do useExcels for like manual
tracking and that's like yoursource of truth, that's fine.
(11:58):
We'll connect with Excel.
We'll pull it in also.
But you don't need toupdate anymore, right?
You don't need toexplain how things are.
How did you measure this?
What is this based on?
Like all these questions,they're answered
in the platform.
Like you share it like with yourexecutive with your C level.
They'll have the answers ofhow things are measured, where
they're coming from, where it'sgoing, what's moving the needle.
(12:19):
It's not you know, you don'thave a situation of, you
know, you're sitting in thatmeeting and you're getting
asked a hard question andyou don't have the answer.
You have to say letme get back to you and
let me get back to you.
It is like the worstthing to ever say.
It's I don't know.
I'm sorry I didn'tcome prepared.
I don't know.
I didn't study for this test,so it's, although it was
like one click and now youlike the follow up, is there.
Galen Low (12:39):
What I like as well is like the
human error component.
Like I do track a bunchof stuff in a spreadsheet.
There was an incident whereI was tracking page views
and Google Analytics for, andsomeone was putting in users,
so we had this discrepancy ofwe thought we were measuring
the same thing, but actually wehad two different data sources.
I was messing it up.
But anyways, that notion ofsomebody might have a different
(13:00):
interpretation of where thatdata comes from to measure that
metric versus, I like the ideathat like, okay, it's pulling
directly from a source of truth.
Oh, and by the way, like itgenerated some of the metrics
using AI based on, you know,what the requirements are.
To be like, you shouldprobably measure this.
And that gets sort of definedand integrated because I've been
in a lot of conversations wherewe can't quite decide on what
(13:22):
the right metric of success is,partly because we don't know
what we can measure, right?
We're pulling from our deckof cards that only has four
options and we're like, okay,which one of these four?
Whereas it could be alot of different things
To your earlier point.
Lior Gerson (13:35):
It's so common.
Listen I work at a companyand when I came in doing list
was tracking like a hundredthousand visits per month.
Be the big company.
I said, well, where'sthis coming from?
It's, it looks weird.
And I was leading productthere and turns out that the
website was actually getting10,000 visits, like 10%.
That other 90% were likekey automation bots.
(13:58):
And you know, thebusiness doesn't know.
The business doesn't know,also means that when you find
this, you have to explain it.
So the CMO has to go and say,Hey, by the way, you know how I
reported my CAC and everything,you know, for the last year,
by the way, I like 90% off.
Galen Low (14:12):
Not a fun conversation.
Yeah.
I wonder if we could use thatactually to zoom out a little
bit, because I find thatmetrics are just a thing, right?
Like the fact of the matteris that like even if a team
or department is measuring theright thing, like there's often
like this translation that needsto happen between different
levels of an organizationor different departments.
For example, a scrum teammight be tracking customer
(14:33):
satisfaction like CSAT.
Maybe they're tracking itrelease over release, but
like maybe the leadershipis tracking I don't know,
like recurring revenue andlike retention and churn.
Can a tool like TargetBoardhelp translate KPIs for one
audience in a way that makesthem meaningful for other
levels of an organization?
Or is this just one of thosethings that like a tool
on its own just can't fix?
Lior Gerson (14:54):
I wanna take a step back on this question.
If you think about metricsin general, the traditional
way of doing BI in trackingmetrics means that you
have to build every metric.
You have to do like asession and plan every
one of your metrics.
So, so there's an actual coston anything you wanna track
if you have to develop it.
Right.
And then when you think aboutprograms like OKRs, qbr,
(15:14):
they're sort of very top down.
They tell you exactly what youwant, need to track, and it
gives you like a tunnel visionof this is what I'm gonna track.
I might have a lot of otherthings that are gonna be really
important for me because theyhave to keep the border flow.
But I'm not gonna report 'emthem, and maybe I'm not getting
even track them because I don'thave the resources to do that.
So with TargetBoard, we trackeverything and we get it very
easy for you to pick and choose.
(15:35):
We even recommend what topick and choose to focus on.
Right?
And then you can sendalerts and get insights and
notifications, all that tomake sure that thing you care
about on track are there.
We also make it really easyto explain, like we actually
have features saying,how do I explain this up?
How do I explain this down?
It's like AI generated, okay,take this master, help me
explain this to my manager.
Help me explain it to mydeveloper or to my sales
(15:56):
rep to understand why it'simportant for me and how I
wanted to think about that.
So absolutely like gettingthe whole semantic layer
protecting you to make surethat everybody like, like we get
this feature request every day.
Can I rename my metric?
No, you can't because somebody'salready has this metric.
They always have a name for it.
You can't just go aheadand give it another name.
(16:17):
And by the way, if thatname is not representative,
what you're ex tracking,you can't give it that name.
So we have all these featuresto help you sort of protect.
Make sure that you're actuallytracking what you're saying.
You're tracking, and you'reactually able to explain
what it is that you aretracking, why that is.
Galen Low (16:32):
I think that's such a cool feature because
I don't know, I've workedwith a lot of dashboarding
software over the years.
Not like deep, but youknow, I have exposure to it.
And that's usuallythe gap, right?
To your point, it's okay,well that's all fine and good.
What is thisdashboard telling me?
Can you explain this?
And then the data storytellingbecomes the gap because not
everyone is great at it.
(16:53):
Some people are greatat it, and maybe that's
the superhero skill.
But I like the idea that AI canhelp you educate someone about
why this metric is important,where it comes from, what it
means if it's wavering, andthat it's not necessarily I'm
sure it could deliver thatanswer directly from the tool.
But I like the, your framingof arming a human to be able
to explain their metric wellto someone who is not them, to
(17:15):
someone who is in a differentdepartment, has different
specializations, has differentknowledge, and still can frame
that narrative of what thisall means so that we're not
just looking at a bunch ofnumbers and going, wow, that
changed 45% week over week.
That's interesting.
All right, let's move on.
Right.
Lior Gerson (17:31):
That's what happens.
Galen Low (17:32):
Everyone looks at it, they're like shrugging.
They're like, wow,that's a big number.
And then we like move on.
But no one actuallyunderstood what.
Lior Gerson (17:39):
Never read the reports.
You'll have scheduling reportsto have a dashboard held.
People don't really readthe reports unless they have
a very specific, they wantthe reports to be there.
Right.
They want to know thatsomebody is tracking that,
that is there in case theyneed it, but they very rarely
read the reports like, youknow, it's the outlier.
By the way, and when wesend reports, we also have
an AI agent that sort ofanalyzes them and summarizes
(17:59):
them for the business user.
So they get okay, so youjust gave me this big report
on what I to care about.
Galen Low (18:05):
Yeah.
You act pleasesummarize this for me.
I like that it's inbuilt.
I'm jumping ahead a littlebit, but I remember you
telling me in the green room.
That there is like thisconversational aspect to it
as well, like not just thesummary of here's what's
important about, you know, thisreport, but I was complaining
about, well Google Analyticsfor my favorite villain,
which looks like it's set upto have a conversation to be
(18:25):
like, you know, especiallyin the world of web, I'd be
like, how many unique visitorsdid I get to this page last
week compared to this sametime, you know, last year?
Maybe there's a way to do it.
It's never really worked for me.
Or even please explainthis metric to me.
Is set a feature that you havedeveloped are developing just a
conversational sort of insights.
Lior Gerson (18:45):
So I think, you know, a lot of people
are trying to crack it.
I think what you're experiencingin the, in GA4, you know,
you'll see the same thing inmixed panel table below and
so, and the problem is thatLLMs are really bad with data.
They seem to be getting better,but you know, if you ask them,
you know, how many you uploada CSV and you'll tell 'em,
Hey, can you tell me how,what's my email open rate?
They won't be able to give youthe answer unless you really
(19:05):
specify like each column,the CSV, what they did, so,
and we think we solved that.
So we haven't addedthat to the platform.
You are able to ask questions.
Get reliable, trustworthyinformation that the AI
also checks to verify thatit's not hallucinating.
And we think that's sort of,you know, it's gonna be a killer
feature because people aregoing to want to process and
(19:26):
consume data in multiple ways.
It's not gonna be onlyconversational or only the
dashboard, but you havedifferent ways and different
needs to interact with that.
And I think that theycompliment each other very well.
It's a little bit like,you know, retail 10 years
ago where I said, am I youinvesting mobile or not?
So, so everybody went into, didomnichannel and that's sort of,
I think where data's going also.
And it's working pretty cool,but not all of you are using it.
(19:48):
I think like when youthink about people being
able to actually phrase intext the question that you
want answered in data isnot that straightforward.
And because you have thewaiting cycle you have you
know, you're gonna have ask10 questions by, you know, to
get like the one answer you'regonna, you could have gone on a
dashboard like with two clicks.
Galen Low (20:07):
Yeah.
It's actually mightnot be shorter.
Lior Gerson (20:09):
How many deals did I close last month?
Oh, by the way, I actuallycare about one region.
So can you down to, and youknow, can you show it to me
by it's a lot of writing, onlywaiting, responsive where, you
know on web, it just clicks.
Galen Low (20:21):
And I like that idea that data is different, right?
And I have experienced thatwith quite simple data, like
time sheet data into ChatGPT,and it's just it just starts
hallucinating or, you know,like grabbing the wrong thing.
And I'm like, oh, you know,like our brains are so wired
to think in tabular data.
Not necessarily these LLMs.
I like this idea that A,yes, please don't just take
that report necessarilyand shove it into ChatGPT
(20:43):
and expect it to kind of begood at the data analysis.
That's why I like that you'rebuilding your own feature.
BI like that itchecks itself, right?
Maybe that's sort ofstandard fair for a data
scientist, but you know, Ilike the idea that it's like
there is another process.
It's gonna be like beforeI deliver this very
important informationabout, you know, performance
and metrics and impact.
Is it right before I justsort of blurt it out?
Lior Gerson (21:05):
Trust, trust is everything.
That's the risk, youknow, with MCP servers.
So like you're asking for datafrom like whatever system, you
don't actually know what the AIis gonna do with it and like how
accurate like the response isgonna, what we do with our MCP
server by the way, and that itverifies that the data and by,
that's across all your systems.
So pull data from all yoursystems, combine it together and
(21:25):
verify if that demonstrates andthe response that you're getting
is a hundred percent accurate.
Galen Low (21:29):
Yeah, it actually your MCP server.
Model context, protocol,that server is the backbone.
It's like the thing that'svetting all the data coming
into it, not just theone player on the field
coming in and coming up.
That's very cool.
I wanted to dig intothe future of metrics.
Just get your POV because thesense I get from the circles
(21:50):
I travel in is that thingsare changing around metrics.
You can't just have astatic scoreboard anymore
with the usual KPIs.
But I was hoping if I couldget your BOV and look a bit
into the future, what are someunconventional metrics that
you see emerging that might besetting the tone for what KPIs
might look like in the future?
Or what are your predictionsabout how organizations
(22:13):
will track KPIs like 3,5, 10 years from now?
Lior Gerson (22:16):
So I think at a micro level you'll see much more
like AI KPIs and we're alreadystarting to see that, right?
You know, how manyagents am I running?
How effective they are,what are they doing, you
know, to the business KPIs.
But at the business level, atthe high level, I think you care
less about how it's done, right?
So I think not alot's gonna change.
(22:36):
You know, if you're trackingARR, if you're tracking churn,
you're tracking deliverytimes, the top metrics
are gonna stay the same.
So that's the languageof your business.
What happens underneaththe hood is gonna change.
And they're also,I think you know.
So, so if you take it one stepdown saying, okay, like we need
to deliver A, B, and C, butnow we're tracking velocity or
we're tracking quality, then thevelocity and the quality are the
(22:59):
second level of those metrics.
If you're taking one, one levelbelow, then you're starting
to say, okay, so my velocity,what is it made up out of?
Right.
Okay.
So it's in development work.
It's you know, things that areblocked here and there, things
that are stuck in review.
Things that, you know, hadtheir scope changed in and
they had to circle back and,you know, I had to open the
context and, oh, by the way,I have all these AIs that are
(23:20):
doing stuff and some of them areworking, so they're not working.
And now I have to spend timeand resources and figuring out
like, why, you know, GPT-5 isnot giving me the same results
that I was expecting to before.
So you know thatit's that level of.
Like the nitty gritty detailsof, you know, what's moving
the needle behind the scenes.
Galen Low (23:39):
I love that sort of layering because
I think you're right.
I mean, you know, you're if, aslong as your business model's
the same, some of those top, Ithink you described earlier as
like a top down metric, right?
The OKR tunnel vision,sort of top of the chain
might not change that much.
I do love that ideathat like beneath it
could be changing a lot.
And that's where, you know, whenwe started this conversation,
you were like, you couldjust measure what's gonna
(24:00):
help you get to your goal.
If your goal is to have morepeople using AI to make better
decisions and that's gonnamove you towards your goal,
then you know, track that atthat lower level cycle time.
And actually the thing youwere mentioning earlier about,
people don't always know howto ask for what they want.
You had it in a data context.
But I'd say, you know, ingeneral for prompting and
(24:22):
I was tagged into a post onLinkedIn by Jim Highsmith who
was a co-author of the AgileManifesto, and he had written
this story you know, aboutthese folks, kind of like
thinking through new metrics.
And I don't know if I'm comingfrom a place of ignorance on
this if people are measuringthis, but one of the things
was prompt quality index.
Right.
Measuring the quality ofpeople's prompts, because
to your point, just becausepeople are using AI doesn't
(24:43):
mean it's actually makingthem more efficient.
They could be in circularconversations with a robot and
you know, they would still showup as using AI in their job,
but maybe not necessarily havinglike high quality prompts.
I dunno how you'd go aboutmeasuring that, by the way.
But I like that notion that,yeah, what matters to measure
at the lower levels will changeand can change quite a bit.
(25:05):
At the top levels mightnot change that much in
terms of, you know, likebusiness success and like
growth and revenue metrics.
But I guess maybe for me thequestion is isn't it a lot
like you, you had mentioned it.
I think I know what you can, Ithink I know what your answer
might be, but there's a lot ofoverhead every time you like
change a metric 'cause thenyou need to figure out a way.
(25:26):
To connect it to a sourceand educate people on what
it means and then get peoplein the cadence of reporting
on it and understanding howit's pushing things forward.
Is that going to be in thefuture or even now, a sort
of like heavy operationaloverhead that businesses and
teams should plan for to belike, yeah, we need like a
data person on everythingbecause when we change our
(25:46):
metrics because of ChatGPT 5releasing or whatever, like we
need someone who's gonna be.
They're ready to wire itup, you know, please wire
up this new metric sothat we can keep going.
Lior Gerson (25:56):
That's the stake today.
That's what's happening now.
It's been happening for years.
Right.
You have an analyst for,or analyst team for every
department, and that'swhat they do, right?
They just make sure that themetrics are working and it's.
It's not even custommetrics, right?
They're justreinventing the wheel.
Everybody's using prettymuch the same metrics right
there, but, you know, andyou know, big companies,
they love replatforming.
They love replatforming.
(26:17):
It's all they do all day isreplatforming, like migrating
from one system to anothersystem does the same thing.
So, but each time they'rereplatform it creates this
whole big data project ofoh, so now we have to update
all our reports, right?
So there's this huge cycleof job security there.
And part of what we dois saying, oh yeah, you
actually don't need all that.
Like you can replatform.
(26:37):
All your reporting, all yourmetrics are gonna stay the same.
If you have to update it, youknow, you, you can automatically
set it up to notify everybodywho needs to know that the
metric has been updated.
And you don't have to doanything to update it.
Does it on its own.
I think the world today isbroken because people have too
many systems or too many datathat they can't really use.
And I think where it's goingis that tools like TargetBoard
(26:59):
are going to make it mucheasier to use that data
and to get to that data.
And you don't need allthose men in the middle.
A lot of time don't actuallyunderstand your business.
Like we, we had a conversationwith the chief strategy officer
of you know, a ma major company.
I don't wanna say the thing.
And he said, well, I have ahundred BI and LS people on
my team, and every time hehas a question, he goes, every
(27:20):
time I have a new question, ittakes me at least six months
to get an answer that is there.
The infrastructure isthere, but that it has to
go through so many peoplefor them to understand
what I'm actually asking.
It.
That's all off.
We can give it to youtoday, by the way.
He told me.
Yeah, but I can't condemnthe data I guys to take it.
That's a different problem.
It's my problem, but butif you, where I'm going.
Galen Low (27:42):
Yeah.
No, absolutely.
And I've seen that happen.
I've seen it happen, especiallyin financial services.
I've seen it in like CX and thecustomer experience craft, like
they're trying to use data tocreate, you know, an intelligent
customer experience based onstuff that's being gathered
across many departments.
And yet to your point, they'regathering tons of data.
No one knows what it is or whatto do with it or how to turn
(28:02):
it into something that couldimprove customer experience.
It's just there.
And like sometimes peopleare just scoreboard
watching and then eventuallythey get disappointed
and they replatform.
Right?
It's so.
Lior Gerson (28:13):
It takes me back to what you said before you
know, what are people tracking?
So a lot people trackinglike costs, right?
And cloud costs.
Now people are tracking, youknow, problems cost, like
how much was they spendingand how to optimize prompts.
Do they spend lessmoney on their l lms?
And you have all this datathat is just sitting there in
companies and it has a cost,it has a storage cost and it
(28:34):
tracking cost and amazing.
And nobody knows why.
And you have all thesedata, nobody's using that.
So it's you know, theselike the people who collect
stuff and you know, in thegarage and don't use them.
So company becomehoarders endless systems.
They don't usethem for anything.
Galen Low (28:51):
Because you know what we were taught to
think it was cheap, right?
That storage is cheap.
This is just a few numbers.
Yeah, exactly.
It was cheap and nowit's not anymore.
And now we're thinking aboutYeah, like cost per prompt
and you know, these sortof transactional calls.
And also the storage,because it's more complex
now, and also we've beenaccruing it for decades.
It's not doing anything with it.
(29:11):
I can relate to that.
I'm a pack rat as well.
I try not to delete prettymuch anything, so thank
you cloud for making that areality that I could do that.
But also to your point,you know, even with some
of that stuff, like I'mnot doing anything with it.
I just, it comfortsme that it's there.
Lior Gerson (29:26):
Maybe a little bit.
Maybe in 10 yearsyou'll need it.
Galen Low (29:28):
Yep.
Yeah.
Exactly.
Yeah, that's exactly right.
I I had it here to ask yousort of about TargetBoard
and your positioning asan AI enhanced KPI tool.
You've already kindawalked us through some of
the AI driven features.
There's the sort of AI drivensuggestions of metrics.
There is the sort ofinsights summaries, I guess,
(29:49):
from a data perspective.
Yeah.
I mean, is there any otherlike AI feature that you wanna
talk through, or maybe eventhe other side of it is like.
What made you decideto double down on AI?
Like it's in the name,well, it's at least in
the domain name, right?
AI, like clearly was core tothe DNA of the solution that
you've built, what are youmost proud of about it and
(30:11):
where do you see it going?
Lior Gerson (30:13):
So I think it's a complicated question, right?
I'm proud about a lot of things.
I think we've been able toleverage AI from very early on
to really augment what we'retrying to build an experience.
We're trying to create.
There's pros and cons ofbeing an AI first company,
and I think we're AI secondcompany, but AI is very
prominent to everything we do.
And every feature webuild, we say, okay, how
(30:34):
does AI make it better?
Not how do we do this with AI,but why are we trying to build
and how does AI make this betterthan you could have before?
So lemme give you an example.
So we have all yourmetrics, right?
And for any metric, doesn'tmatter how you slice and
dice it, you can createnotifications, automations, and
alerts and so on and on, right?
So you have a deal that'ssecond, a pipeline.
You have a button in production,you have a support ticket
(30:54):
that's, you know, for a veryspecific QF customer and
you're not meeting your SLA.
Create an alert, send itto wherever it needs to go.
But having people think aboutthe alerts that they want to
create is a hassle, right?
People need to think aboutneed, having to think about
what they want to create.
How, so, for example, what wereleased this week is that the
AI, when you're looking at ametric, it will suggest the
(31:17):
automation that you can create.
To improve that metric.
We already have likerecommendations, like strategies
for how to improve metric.
Like I actually recommendthe automation that you can
create, like one quick clickof a button, create it for you.
And this was relativelyway harder to do pre AI
and now it's much easier.
Right?
We're moving fastertoday than I've ever
moved any other company.
Galen Low (31:39):
That's fair and also insane.
Based on what I knowabout your background,
it is a good use of it.
I mean, I think like the, whatI find is that there's a natural
sort of cultural inclusion ofAI in the world of data because,
you know, coming from like thebenefits of machine learning.
And frankly, just dealing withswaths and swaths of data.
Right?
You know, like even in yourprevious roles where like
(32:01):
your tools are like, they'reprocessing petabytes of data.
We don't even have alike conceptual, tangible
model for what that evenlooks like in our brains.
So it makes sense thatokay, we're gonna need
like compute power.
But what I really like is thatsort of and I like it actually
in the LLMs too right now.
Right?
Where it's suggest tome what to do next.
Whether it's right or wrong, andthen I will learn what you think
(32:21):
is the right or wrong next step.
And then I will continue tosuggest it because, you know,
to this day I, you know, I seea lot of folks and especially
when I'm outta my element,like I do a thing and I'm
like, what should I do next?
I'm not sure.
But if something, youknow, helpful clippy right?
Is going, Hey, you writinga letter, do you wanna
put a letterhead on?
It actually is a greatway to I dunno, just kind
of cut through discomfortor doubt or hesitation or
(32:44):
just like knowledge gaps ofactually I wasn't sure what
to do next, but actually alittle alert would be great.
Thank you very much.
A little notification andthen to your point, at
least I'm assuming it alsosets up that notification.
It's great, I'm on it,I've done it, it's done.
Every Tuesday you're gonnaget this notification about
this metric or when it's outof tolerance or, you know.
Lior Gerson (33:00):
It's a very different approach than, you
know, what data science waslike, you know, three years ago.
So taking an example, you know,from Gettacar, you know, we
had a team of data scientiststhat, you know, did autofinance
optimization which every timeyou get a credit application
which lender did you send it to?
That was, you know, a team thatworked on it nonstop trying to
maximize, you know, the singledigit percentages of how do you
(33:22):
get more applications approved.
And that was like adata science project.
Now it's every featureyou build saying, okay,
how do I make this better?
And it's not that it'snot simple, but if you
do it right, you're ableto get like just amazing
results and very quickly.
Galen Low (33:37):
I love how fast these things are changing.
Right.
Maybe just for fun,do you have a question
that you want to ask me?
Lior Gerson (33:43):
What are the most impactful trends
that you're seeing asproject management today?
Galen Low (33:48):
That's a good one.
I think the biggest one isactually the one I started
out with, which is thisnotion that project managers
are not just, you know, ironTriangle people trying to
deliver projects on time, onbudget, you know, within scope.
This sort of like thisrevelation that actually
we can be more strategic,but we need to know more.
We need to know more,we need to do more.
And we were alreadydoing a lot and.
(34:09):
Now we feel like we have tobe sort of strategists or
like delivery strategists.
There's the onus is on us tounderstand the business more.
And then there's thismysterious question of cool
I'm accountable in some wayfor impact, but you know,
I'm not necessarily involvedin measuring that impact.
In a lot of cases, we leada project and then we move
on, it launches and we, youknow, we go away onto the next
(34:31):
thing, off into the sunset.
And now there's this big mindsetshift of okay, well actually we
are value delivery specialists.
We actually are meant to createthe value, even if that means
the plan was wrong and we hadto change it, even if it means
the scope was wrong, we hadto change it along the way.
Even if it means battlingpersonalities in the boardrooms
and you know, or you know,in the, you know, in the
(34:51):
dev pit, there's a much morestrategic component to it.
And I think we're still tryingto figure it out, but then I
think it dovetails very nicelywith like our adoption of AI.
Where, you know, right nowit's helping us be more
efficient, shorten ourworkflows, create a bit more
speed, but fundamentally helpout with some of the stuff
that we got bogged down with.
(35:12):
You know, a lot of projectmanagers are like, I'm too busy
to then also be a strategicpartner for my stakeholders.
And now there's this sort ofrise that where it's okay.
What are you doing?
Well, okay.
Yeah, like all my statusreports oh, I've gotta
update the dashboard.
I've gotta go into Exceland update that spreadsheet.
It's cool.
You don't have to do thatanymore because we can
set up agents for that.
(35:32):
We can, you know, look to AIfor solutions, and then the
next thing is okay, but nowwhat do I do with that time?
How can I then learn theskills to be a bit more of
a leader, a strategic leaderin the delivery of value?
I think that's an interestingtrend in my world that.
I think intersects with a lot ofthis because in a perfect world,
we spend more of our time askingourselves the question, are we
(35:54):
doing the right thing to achievethe mission and have the impact?
And that's almost exactlywhere you started about
how do we know that we'remeasuring the right thing?
You're measuring theright thing if it's
helping you move forward.
And I think that's kind oflike an interesting aspect
of project management andproject leadership today.
Lior Gerson (36:10):
Very cool.
I'm gonna ask youanother question.
So, I know what I think, butI wanna hear what you think.
We've been talking about AI andyou know, talking about, you
know, how AI can help projectmanagers automate and become
more efficient and so on.
Do you think there is a stagewhere like an AI agent becomes a
project manager or replaces theproject manager like completely?
Galen Low (36:28):
I think for some people's definition of what
a project manager does, yes.
Because there is the sort oflevel of, oh, project managers
are just administrators.
They're taking notes, doingmeeting minutes, they're
following up with action items.
They are measuring, you know,an estimate against, you know,
an actual, how long it actuallytook to do all that stuff.
Yeah, definitely.
(36:49):
I think is almosteasily replaced by AI.
I think the parts thatare not are like the human
to human decision making.
Like I think for a whileat least some of the
big decisions, right?
Replatforming decisions,risks around, you know,
implementation, I think will beconversations between humans.
That's why I really likeyour data storytelling
(37:09):
feature because it's not oh,I, it'll be like a little
circle with a question markin it, and like the executive
can just click it and itgoes, this metric is this.
Instead, you're arming theperson who owns that metric
to tell the story about it.
And I think that is thelike decision making layer
in projects and in businessthat I think will continue
to stay the same, like humansmaking their case you know,
(37:29):
battling things out, convincingone another, persuading one
another to make good decisionsand maybe think a little bit
outside the box and thinkabout, you know, things that
maybe haven't been done yet.
And those sort ofinnovative thoughts.
And I think that comesfrom humans interacting
with one another.
Not to say that AI won'tor isn't innovative, like
I think it can create ideasthat, you know, most of its
(37:50):
users haven't thought of yet.
But fundamentally I thinkit's still remixing and
I think fundamentally westill treat it as a helper
and not someone who's gonnamake a decision for us.
That's changing.
But I think for now, I think.
Lior Gerson (38:01):
I agree with that.
It also what makes a differencebetween a good project manager
and a bad one and you know,was telling people, oh,
you know, we can't afforda project manager, you.
Do that on the side, likeyou manage this project and
then like it doesn't work.
It doesn't worry becauseyou know it's a profession.
Can't do it on the side.
Galen Low (38:17):
Yeah, I like that.
I like the idea that it'slike, it might not replace all
project managers, but it willprobably replace the bad ones.
Lior Gerson (38:23):
It's true for everything.
Galen Low (38:24):
Lior, thanks so much for spending
the time with me today.
This has been so much fun.
I love nerding out with you.
Before you go though, where canpeople learn more about you?
Lior Gerson (38:31):
LinkedIn, I get back to almost
anybody who reaches out.
Happy to speak to anybody.
Galen Low (38:37):
That's awesome.
I commend you.
My LinkedIn inboxis such a mess.
Thanks again.
Lior Gerson (38:44):
Bye Galen, it's been a pleasure.
Galen Low (38:47):
That's it for today's episode of The Digital
Project Manager Podcast.
If you enjoyed thisconversation, make sure
to subscribe whereveryou're listening.
And if you want even moretactical insights, case studies
and playbooks, head over tothedigitalprojectmanager.com.
Until next time,thanks for listening.
How can an organization or a team
determine whether they'retracking the right metrics
and what can they do if theyrealize they've been measuring
the wrong thing all along?
Lior Gerson (00:09):
You need to track whatever is going to help you
get to where you wanna go.
Everything else is noise.
Galen Low (00:14):
Walk us through an example, either of a metric
that was maybe the wrongmetric, and that process
of how they changed towardsmeasuring the right thing.
Lior Gerson (00:23):
One of our customers, he wanted to
improve like how theyforecast what they're gonna
deliver every quarter.
He was able to take ourplatform and automate it
and it took him like a day.
But now whenever he wants,he has like that in front
of him, like, what am Igonna deliver this month?
Do you think there is a stagewhere like an AI agent becomes
a project manager or replacethe project manager completely?
Galen Low (00:45):
I think for some people's definition of what
a project manager does, yes.
Welcome to The Digital ProjectManager podcast — the show that
helps delivery leaders worksmarter, deliver faster, and
lead better in the age of AI.
I'm Galen, and every week wedive into real-world strategies,
(01:08):
new tools, proven frameworks,and the occasional war story
from the project front lines.
Whether you're steeringmassive transformation
projects, wrangling AIworkflows, or just trying to
keep the chaos under control,you're in the right place.
Let's get into it.
Today we are talking about howtraditional views of measuring
project success are beingchallenged, and why AI-powered
(01:29):
KPI management tools might beyour only fighting chance to
keep pace with the growing listof variables that could make or
break your project's outcomes.
With me today is LiorGerson, Co-founder and
CEO of TargetBoard.ai.
Lior started his career atmySupermarket, which was the
largest price comparison websitein the UK servicing tens of
millions of users every month.
(01:50):
There he was responsible formulti-million dollar projects
collaborating with giants suchas Walmart, WPP, and Dentzu.
And then he moved on tofound Vroom.com as CTO,
an online car dealershipthat went public for $2.5B.
After that, he led productfor Placer.ai, a location
analytics unicorn processingpetabytes of data.
And of course today he leadsTargetBoard, an AI-powered
(02:12):
KPI management tool.
Lior, thank you so muchfor joining us today.
Lior Gerson (02:16):
Thanks, Galen, pleasure of being here.
Galen Low (02:18):
I'm really excited to talk to you because you are
someone who's like incrediblydeep in the world of KPIs
and making data informeddecisions towards success.
Honestly, I hope thisconversation goes places
that I hadn't planned, buthere's the roadmap that I've
sketched out for us today.
So, to start us off, Iwanted to get one big burning
question outta the way,like the hot question that
everyone wants to know.
(02:38):
But after that, I'd liketo zoom out from that and
talk about three things.
First, I wanted to talkabout culture mismatch around
KPIs and how to translatemetrics across different
levels of an organization.
Then I'd like to talk abouthow project KPIs are changing
and where you see them going.
Lastly, I thought maybe wecould zero in on some practical
ways that businesses andproject teams can get started
(02:59):
on leveraging AI enhancedKPI tracking and how that
might impact their workflowsfor better or for worse.
How does that sound to you?
Lior Gerson (03:07):
Sounds good.
I think it's gonna afascinating discussion.
Galen Low (03:10):
Awesome.
Me too.
Me too.
All right.
Here's the hot question.
Lately, there's been a lotof talk about the fact that a
lot of projects and actuallyentire organizations might
be actually measuring andtracking the wrong thing.
So for projects, I'veseen a lot of discourse
challenging everything fromthe formerly sacred Iron
Triangle of scope, schedule,and budget, all the way to
Agile metrics like velocity.
(03:31):
And then organizationally,I've seen folks take aim at
things like revenue per FDE,gross margin and utilization.
The big thing that's beingtalked about in my world, at
least the project managementworld, is how project leaders
shouldn't just measure projecthealth, but also project impact.
So against that backdrop,my hot question is how can
(03:51):
an organization or a teamdetermine whether they're
tracking the right metricsand what can they do if they
realize they've been measuringthe wrong thing all along?
Lior Gerson (03:59):
Yeah, that's a great question.
The project initiativethat you're using are
set to be aligned with abusiness strategy, right,
with a product strategy.
Wherever thebusiness wants to go.
But when you put it in thebox of project management, all
you really wanna achieve issaying, okay, I wanna reach my
milestones on time, on budget,with, you know, as little
(04:19):
friction and as much, you know,accuracy and predictability
as possible, right?
So all the magic that youneed to track are the metrics
that are gonna help youget to that state, right?
It doesn't be anything.
It can be, you know, howfast tests are moving, where
things are blocked, vacationdays, how people are using AI.
You can track anything aslong as it keeps you in
(04:39):
that box of what you'reactually trying to achieve.
But if you're not trackingthe things that are gonna
help you reach that you'retracking the wrong things.
Galen Low (04:47):
I like that perspective because I think
we tend to gravitate towardsthe right things to measure.
I even said it in thequestion like, what is the
right thing to measure?
You're saying actually.
Whatever the thing is thathelps move you forward is
the right thing to measure.
Even if it's way different thanwhat that other team is doing.
Even if it's way differentthan what you did at
that other organization.
Like it should bealigned to the mission.
Lior Gerson (05:07):
You need to track whatever is going to help you
get to where you wanna go.
Everything else is,you know, is noise.
Galen Low (05:13):
That's fair.
That's fair.
So I mean, for folks whoare like, oh wow, Lior,
that's, you're speaking mylanguage, we are definitely
not tracking the right things.
We need to start.
How do you kind of map out.
What the right thingsare to push you forward?
Is there a sort of bestpractice or a process?
Lior Gerson (05:30):
You need to understand your business, right?
You need to understandyour project.
You understand what'smoving the needle?
What are the movers and shakers?
What's gonna make an impact?
Is it people?
Is it resources?
Is it complexity?
How do you sort ofunpack everything?
Say, okay, theseare the components.
This is what needs to happennow, how to attract that.
It's actually happeningand not sort of falling
(05:51):
between the cracks.
Is public.
It like you're gonnahave like lots of tools.
Like data is gonna beall over the place.
It's not gonna be updated.
And you're gonna say, oh wellpeople are not updating their
system so it's garbage andgarbage out and I can't do it.
You know, and I'm spendingall this time instead
showing any returns.
There's like a million waysand a million reasons why
it's not gonna work, butwe need it to work, so,
(06:11):
so you know, it's on you.
Galen Low (06:13):
Okay.
That makes sense to me.
I'm wondering, would you be ableto just maybe walk us through
an example, either of a metricthat was maybe the wrong metric
and that process of how theychanged towards measuring the
right thing, or even if therewas just some unconventional
metric that somebody thatyou worked with was tracking
that actually pushed forwardtowards the goals, but actually
(06:35):
is a pretty non-traditionalthing to measure?
Lior Gerson (06:38):
Let me talk about like one of our customers,
they're called Versa.
P. There are a PTF deliveringproduct operations.
It's called bu.
He is a brilliant guy.
We love him, we love workingwith him, and he wanted
to improve, like how theyforecast what they're gonna
deliver every quarter, right?
So plan was actual improvingthe planning accuracy,
improving forecasting.
(07:00):
They were running very fastand things are changing
all the time, right?
So priorities change,capacities change, people
move between teams.
So it becomesreally complicated.
And what he came up with wasa formula that helps take
the previous performance ofevery contributor and every
team and then add that on towhatever, like the current
capacity for that team orthat group is gonna be.
(07:22):
And instead of, you know,having to sort of crunch all
that data in Excel, you know,which takes a lot of time.
You have to bring in youknow, data about vacation
days and, you know, tasksand priorities and lots of
stuff and doing it in Excel.
So he was willing,you know, to take our
platform and automate it.
And it took himlike, like a day.
But now, whenever he wants, hehas like that in front of him.
(07:43):
Okay, what am I gonnadeliver with this month?
You know, so like the CEO or theCPO comes in and say, Hey, you
know, I need to shift priority.
Something else comes, youknow, is most t he has that
what's actually going tochange and how it's actually
impacting all the initiativesthat they're trying to push.
I thought it was brillianttaking data, you know.
Putting it in the rightcontext to tell the story
that he needs to tell.
Galen Low (08:04):
I was just at an Agile conference.
And I mentionedvelocity earlier, right?
And everyone's yeah, it'slike a poor measure of impact.
And sometimes it's just a poormeasure because you know, in
a lot of agile teams you'remeant to have a dedicated team.
You know, they'reresource full-time.
And people I talk to, they'relike, that never happens.
People are, you know, toyour point, they're going
on vacation, they're gettingpulled away to other projects.
So you look at this velocitymetric and it's going you
(08:25):
know, up and down and you'retrying to plan ahead and
you're like, what is going on?
It's it's not avery good metric.
And I would say mostpeople probably wouldn't.
They either wouldn't think tohave an aggregate metric that
is based on dynamic data or theywouldn't know how to build it.
Right.
But I think thatmakes a lot of sense.
It's almost like.
This notion of like real timeresourcing where it's okay, well
this person's gonna be away.
(08:46):
We should expect thisdip in this metric about
our output, about ourperformance, about our impact.
You know, whatever it is.
It's actually grabbing data fromother places, not just trying
to have the one measure, theone metric to rule them all.
I mean, it does functionas that, but it's
an aggregate metric.
Actually, I thinkthat's really cool.
Lior Gerson (09:03):
It's interesting you bring this up, right?
But think about like how datapropagates across the business.
That person goes away.
It takes time until peopleknow their way, right?
If manager's gonnaknow their way.
But until that propagates to theright person who has like the
understanding of how it's gonnaimpact the project timelines,
and you know, what, you know,secondary or tertiary effect
(09:23):
is gonna have, it's gonna takedays or weeks until that's time
you're not getting back, right?
You add more resources, you'rejust gonna extend the timeline.
It's not like it'sgonna make it shorter.
You have that learning curve.
So the faster you're ableto get that information
across your organization,getting it to the right
people, it's massive impact.
It's compounding interests.
Galen Low (09:40):
That lag is like so real.
In our community, there'salways a lot of conversation
about well, I wish someonehad told me that my lead
dev was gonna be going onholiday for three weeks.
And then even that lag, likeI see it in my role all the
time where it's like you getthe like a DP, like vacation
request and you go in there andthere's a process and a flow
(10:00):
and bottlenecking that happensbefore anyone even knows that
person's vacation is approved.
A little less whatlike date that is.
I like that kindof streams it in.
Lior Gerson (10:09):
Vacation is easy.
You know, think about, you know,you're working remote, you're
on Zoom, and somebody's gone.
Who's gone, you know, let go.
Nobody told you to be let go.
Galen Low (10:17):
Yeah.
Okay.
I see.
Lior Gerson (10:18):
You know, two weeks in, he is
not answering my calls.
I'm like, what's up?
Oh, he's not here anymore.
Galen Low (10:22):
He's not here.
Yeah.
We're hearing a lot aboutthat too, in the community.
Right?
It's like production'sin force without the
communication around it.
Yeah.
And then what.
Can you this is a bit of a sidequest, but can you at a high
level, walk me through whatthat integration looks like?
Because I'm picturing myselfopening Excel and being like,
okay, I need to like Zapieror make, or do some kind of
transformation of data andhave it like pasted into a
(10:43):
sheet and I have to writea formula and all that.
Would I still have to do thatin TargetBoard or is integration
a little bit more hands free?
Hands free, might notbe the right word, but
less labor intensive.
Lior Gerson (10:55):
So TargetBoard.ai works in a very different
way from any othersystem that exists there.
We connect to your sourcedata source system and we
automatically build allthe metrics and all the
KPIs that are relevant forwhatever you need to do.
Right.
And then we use AIto generate them.
But it doesn't matter if you'relooking, you know, at metrics
for how your capacity changes,how your velocity changes,
(11:15):
how the quality changes.
If you're looking at oneproject, multiple projects,
doesn't matter how youwork, don't have to change
anything about how you work.
We automatically learnhow you work your systems.
It doesn't matter if you'reusing Monday or if you're using
Jira or anything else, right?
And then all your metrics, allyour reports, all your insights
are automatically generatedand you can then customize.
(11:37):
It's like DIY customization,or you can do it with a team.
You'll get the exact metricsand the exact way of tracking
everything that you want.
It's zero effort, like it'slike day one, you're up and
running fully confident thatthe data you need is there and
it's accurate and you don't needto worry about excels anymore.
By the way, if you do useExcels for like manual
tracking and that's like yoursource of truth, that's fine.
(11:58):
We'll connect with Excel.
We'll pull it in also.
But you don't need toupdate anymore, right?
You don't need toexplain how things are.
How did you measure this?
What is this based on?
Like all these questions,they're answered
in the platform.
Like you share it like with yourexecutive with your C level.
They'll have the answers ofhow things are measured, where
they're coming from, where it'sgoing, what's moving the needle.
(12:19):
It's not you know, you don'thave a situation of, you
know, you're sitting in thatmeeting and you're getting
asked a hard question andyou don't have the answer.
You have to say letme get back to you and
let me get back to you.
It is like the worstthing to ever say.
It's I don't know.
I'm sorry I didn'tcome prepared.
I don't know.
I didn't study for this test,so it's, although it was
like one click and now youlike the follow up, is there.
Galen Low (12:39):
What I like as well is like the
human error component.
Like I do track a bunchof stuff in a spreadsheet.
There was an incident whereI was tracking page views
and Google Analytics for, andsomeone was putting in users,
so we had this discrepancy ofwe thought we were measuring
the same thing, but actually wehad two different data sources.
I was messing it up.
But anyways, that notion ofsomebody might have a different
(13:00):
interpretation of where thatdata comes from to measure that
metric versus, I like the ideathat like, okay, it's pulling
directly from a source of truth.
Oh, and by the way, like itgenerated some of the metrics
using AI based on, you know,what the requirements are.
To be like, you shouldprobably measure this.
And that gets sort of definedand integrated because I've been
in a lot of conversations wherewe can't quite decide on what
(13:22):
the right metric of success is,partly because we don't know
what we can measure, right?
We're pulling from our deckof cards that only has four
options and we're like, okay,which one of these four?
Whereas it could be alot of different things
To your earlier point.
Lior Gerson (13:35):
It's so common.
Listen I work at a companyand when I came in doing list
was tracking like a hundredthousand visits per month.
Be the big company.
I said, well, where'sthis coming from?
It's, it looks weird.
And I was leading productthere and turns out that the
website was actually getting10,000 visits, like 10%.
That other 90% were likekey automation bots.
(13:58):
And you know, thebusiness doesn't know.
The business doesn't know,also means that when you find
this, you have to explain it.
So the CMO has to go and say,Hey, by the way, you know how I
reported my CAC and everything,you know, for the last year,
by the way, I like 90% off.
Galen Low (14:12):
Not a fun conversation.
Yeah.
I wonder if we could use thatactually to zoom out a little
bit, because I find thatmetrics are just a thing, right?
Like the fact of the matteris that like even if a team
or department is measuring theright thing, like there's often
like this translation that needsto happen between different
levels of an organizationor different departments.
For example, a scrum teammight be tracking customer
(14:33):
satisfaction like CSAT.
Maybe they're tracking itrelease over release, but
like maybe the leadershipis tracking I don't know,
like recurring revenue andlike retention and churn.
Can a tool like TargetBoardhelp translate KPIs for one
audience in a way that makesthem meaningful for other
levels of an organization?
Or is this just one of thosethings that like a tool
on its own just can't fix?
Lior Gerson (14:54):
I wanna take a step back on this question.
If you think about metricsin general, the traditional
way of doing BI in trackingmetrics means that you
have to build every metric.
You have to do like asession and plan every
one of your metrics.
So, so there's an actual coston anything you wanna track
if you have to develop it.
Right.
And then when you think aboutprograms like OKRs, qbr,
(15:14):
they're sort of very top down.
They tell you exactly what youwant, need to track, and it
gives you like a tunnel visionof this is what I'm gonna track.
I might have a lot of otherthings that are gonna be really
important for me because theyhave to keep the border flow.
But I'm not gonna report 'emthem, and maybe I'm not getting
even track them because I don'thave the resources to do that.
So with TargetBoard, we trackeverything and we get it very
easy for you to pick and choose.
(15:35):
We even recommend what topick and choose to focus on.
Right?
And then you can sendalerts and get insights and
notifications, all that tomake sure that thing you care
about on track are there.
We also make it really easyto explain, like we actually
have features saying,how do I explain this up?
How do I explain this down?
It's like AI generated, okay,take this master, help me
explain this to my manager.
Help me explain it to mydeveloper or to my sales
(15:56):
rep to understand why it'simportant for me and how I
wanted to think about that.
So absolutely like gettingthe whole semantic layer
protecting you to make surethat everybody like, like we get
this feature request every day.
Can I rename my metric?
No, you can't because somebody'salready has this metric.
They always have a name for it.
You can't just go aheadand give it another name.
(16:17):
And by the way, if thatname is not representative,
what you're ex tracking,you can't give it that name.
So we have all these featuresto help you sort of protect.
Make sure that you're actuallytracking what you're saying.
You're tracking, and you'reactually able to explain
what it is that you aretracking, why that is.
Galen Low (16:32):
I think that's such a cool feature because
I don't know, I've workedwith a lot of dashboarding
software over the years.
Not like deep, but youknow, I have exposure to it.
And that's usuallythe gap, right?
To your point, it's okay,well that's all fine and good.
What is thisdashboard telling me?
Can you explain this?
And then the data storytellingbecomes the gap because not
everyone is great at it.
(16:53):
Some people are greatat it, and maybe that's
the superhero skill.
But I like the idea that AI canhelp you educate someone about
why this metric is important,where it comes from, what it
means if it's wavering, andthat it's not necessarily I'm
sure it could deliver thatanswer directly from the tool.
But I like the, your framingof arming a human to be able
to explain their metric wellto someone who is not them, to
(17:15):
someone who is in a differentdepartment, has different
specializations, has differentknowledge, and still can frame
that narrative of what thisall means so that we're not
just looking at a bunch ofnumbers and going, wow, that
changed 45% week over week.
That's interesting.
All right, let's move on.
Right.
Lior Gerson (17:31):
That's what happens.
Galen Low (17:32):
Everyone looks at it, they're like shrugging.
They're like, wow,that's a big number.
And then we like move on.
But no one actuallyunderstood what.
Lior Gerson (17:39):
Never read the reports.
You'll have scheduling reportsto have a dashboard held.
People don't really readthe reports unless they have
a very specific, they wantthe reports to be there.
Right.
They want to know thatsomebody is tracking that,
that is there in case theyneed it, but they very rarely
read the reports like, youknow, it's the outlier.
By the way, and when wesend reports, we also have
an AI agent that sort ofanalyzes them and summarizes
(17:59):
them for the business user.
So they get okay, so youjust gave me this big report
on what I to care about.
Galen Low (18:05):
Yeah.
You act pleasesummarize this for me.
I like that it's inbuilt.
I'm jumping ahead a littlebit, but I remember you
telling me in the green room.
That there is like thisconversational aspect to it
as well, like not just thesummary of here's what's
important about, you know, thisreport, but I was complaining
about, well Google Analyticsfor my favorite villain,
which looks like it's set upto have a conversation to be
(18:25):
like, you know, especiallyin the world of web, I'd be
like, how many unique visitorsdid I get to this page last
week compared to this sametime, you know, last year?
Maybe there's a way to do it.
It's never really worked for me.
Or even please explainthis metric to me.
Is set a feature that you havedeveloped are developing just a
conversational sort of insights.
Lior Gerson (18:45):
So I think, you know, a lot of people
are trying to crack it.
I think what you're experiencingin the, in GA4, you know,
you'll see the same thing inmixed panel table below and
so, and the problem is thatLLMs are really bad with data.
They seem to be getting better,but you know, if you ask them,
you know, how many you uploada CSV and you'll tell 'em,
Hey, can you tell me how,what's my email open rate?
They won't be able to give youthe answer unless you really
(19:05):
specify like each column,the CSV, what they did, so,
and we think we solved that.
So we haven't addedthat to the platform.
You are able to ask questions.
Get reliable, trustworthyinformation that the AI
also checks to verify thatit's not hallucinating.
And we think that's sort of,you know, it's gonna be a killer
feature because people aregoing to want to process and
(19:26):
consume data in multiple ways.
It's not gonna be onlyconversational or only the
dashboard, but you havedifferent ways and different
needs to interact with that.
And I think that theycompliment each other very well.
It's a little bit like,you know, retail 10 years
ago where I said, am I youinvesting mobile or not?
So, so everybody went into, didomnichannel and that's sort of,
I think where data's going also.
And it's working pretty cool,but not all of you are using it.
(19:48):
I think like when youthink about people being
able to actually phrase intext the question that you
want answered in data isnot that straightforward.
And because you have thewaiting cycle you have you
know, you're gonna have ask10 questions by, you know, to
get like the one answer you'regonna, you could have gone on a
dashboard like with two clicks.
Galen Low (20:07):
Yeah.
It's actually mightnot be shorter.
Lior Gerson (20:09):
How many deals did I close last month?
Oh, by the way, I actuallycare about one region.
So can you down to, and youknow, can you show it to me
by it's a lot of writing, onlywaiting, responsive where, you
know on web, it just clicks.
Galen Low (20:21):
And I like that idea that data is different, right?
And I have experienced thatwith quite simple data, like
time sheet data into ChatGPT,and it's just it just starts
hallucinating or, you know,like grabbing the wrong thing.
And I'm like, oh, you know,like our brains are so wired
to think in tabular data.
Not necessarily these LLMs.
I like this idea that A,yes, please don't just take
that report necessarilyand shove it into ChatGPT
(20:43):
and expect it to kind of begood at the data analysis.
That's why I like that you'rebuilding your own feature.
BI like that itchecks itself, right?
Maybe that's sort ofstandard fair for a data
scientist, but you know, Ilike the idea that it's like
there is another process.
It's gonna be like beforeI deliver this very
important informationabout, you know, performance
and metrics and impact.
Is it right before I justsort of blurt it out?
Lior Gerson (21:05):
Trust, trust is everything.
That's the risk, youknow, with MCP servers.
So like you're asking for datafrom like whatever system, you
don't actually know what the AIis gonna do with it and like how
accurate like the response isgonna, what we do with our MCP
server by the way, and that itverifies that the data and by,
that's across all your systems.
So pull data from all yoursystems, combine it together and
(21:25):
verify if that demonstrates andthe response that you're getting
is a hundred percent accurate.
Galen Low (21:29):
Yeah, it actually your MCP server.
Model context, protocol,that server is the backbone.
It's like the thing that'svetting all the data coming
into it, not just theone player on the field
coming in and coming up.
That's very cool.
I wanted to dig intothe future of metrics.
Just get your POV because thesense I get from the circles
(21:50):
I travel in is that thingsare changing around metrics.
You can't just have astatic scoreboard anymore
with the usual KPIs.
But I was hoping if I couldget your BOV and look a bit
into the future, what are someunconventional metrics that
you see emerging that might besetting the tone for what KPIs
might look like in the future?
Or what are your predictionsabout how organizations
(22:13):
will track KPIs like 3,5, 10 years from now?
Lior Gerson (22:16):
So I think at a micro level you'll see much more
like AI KPIs and we're alreadystarting to see that, right?
You know, how manyagents am I running?
How effective they are,what are they doing, you
know, to the business KPIs.
But at the business level, atthe high level, I think you care
less about how it's done, right?
So I think not alot's gonna change.
(22:36):
You know, if you're trackingARR, if you're tracking churn,
you're tracking deliverytimes, the top metrics
are gonna stay the same.
So that's the languageof your business.
What happens underneaththe hood is gonna change.
And they're also,I think you know.
So, so if you take it one stepdown saying, okay, like we need
to deliver A, B, and C, butnow we're tracking velocity or
we're tracking quality, then thevelocity and the quality are the
(22:59):
second level of those metrics.
If you're taking one, one levelbelow, then you're starting
to say, okay, so my velocity,what is it made up out of?
Right.
Okay.
So it's in development work.
It's you know, things that areblocked here and there, things
that are stuck in review.
Things that, you know, hadtheir scope changed in and
they had to circle back and,you know, I had to open the
context and, oh, by the way,I have all these AIs that are
(23:20):
doing stuff and some of them areworking, so they're not working.
And now I have to spend timeand resources and figuring out
like, why, you know, GPT-5 isnot giving me the same results
that I was expecting to before.
So you know thatit's that level of.
Like the nitty gritty detailsof, you know, what's moving
the needle behind the scenes.
Galen Low (23:39):
I love that sort of layering because
I think you're right.
I mean, you know, you're if, aslong as your business model's
the same, some of those top, Ithink you described earlier as
like a top down metric, right?
The OKR tunnel vision,sort of top of the chain
might not change that much.
I do love that ideathat like beneath it
could be changing a lot.
And that's where, you know, whenwe started this conversation,
you were like, you couldjust measure what's gonna
(24:00):
help you get to your goal.
If your goal is to have morepeople using AI to make better
decisions and that's gonnamove you towards your goal,
then you know, track that atthat lower level cycle time.
And actually the thing youwere mentioning earlier about,
people don't always know howto ask for what they want.
You had it in a data context.
But I'd say, you know, ingeneral for prompting and
(24:22):
I was tagged into a post onLinkedIn by Jim Highsmith who
was a co-author of the AgileManifesto, and he had written
this story you know, aboutthese folks, kind of like
thinking through new metrics.
And I don't know if I'm comingfrom a place of ignorance on
this if people are measuringthis, but one of the things
was prompt quality index.
Right.
Measuring the quality ofpeople's prompts, because
to your point, just becausepeople are using AI doesn't
(24:43):
mean it's actually makingthem more efficient.
They could be in circularconversations with a robot and
you know, they would still showup as using AI in their job,
but maybe not necessarily havinglike high quality prompts.
I dunno how you'd go aboutmeasuring that, by the way.
But I like that notion that,yeah, what matters to measure
at the lower levels will changeand can change quite a bit.
(25:05):
At the top levels mightnot change that much in
terms of, you know, likebusiness success and like
growth and revenue metrics.
But I guess maybe for me thequestion is isn't it a lot
like you, you had mentioned it.
I think I know what you can, Ithink I know what your answer
might be, but there's a lot ofoverhead every time you like
change a metric 'cause thenyou need to figure out a way.
(25:26):
To connect it to a sourceand educate people on what
it means and then get peoplein the cadence of reporting
on it and understanding howit's pushing things forward.
Is that going to be in thefuture or even now, a sort
of like heavy operationaloverhead that businesses and
teams should plan for to belike, yeah, we need like a
data person on everythingbecause when we change our
(25:46):
metrics because of ChatGPT 5releasing or whatever, like we
need someone who's gonna be.
They're ready to wire itup, you know, please wire
up this new metric sothat we can keep going.
Lior Gerson (25:56):
That's the stake today.
That's what's happening now.
It's been happening for years.
Right.
You have an analyst for,or analyst team for every
department, and that'swhat they do, right?
They just make sure that themetrics are working and it's.
It's not even custommetrics, right?
They're justreinventing the wheel.
Everybody's using prettymuch the same metrics right
there, but, you know, andyou know, big companies,
they love replatforming.
They love replatforming.
(26:17):
It's all they do all day isreplatforming, like migrating
from one system to anothersystem does the same thing.
So, but each time they'rereplatform it creates this
whole big data project ofoh, so now we have to update
all our reports, right?
So there's this huge cycleof job security there.
And part of what we dois saying, oh yeah, you
actually don't need all that.
Like you can replatform.
(26:37):
All your reporting, all yourmetrics are gonna stay the same.
If you have to update it, youknow, you, you can automatically
set it up to notify everybodywho needs to know that the
metric has been updated.
And you don't have to doanything to update it.
Does it on its own.
I think the world today isbroken because people have too
many systems or too many datathat they can't really use.
And I think where it's goingis that tools like TargetBoard
(26:59):
are going to make it mucheasier to use that data
and to get to that data.
And you don't need allthose men in the middle.
A lot of time don't actuallyunderstand your business.
Like we, we had a conversationwith the chief strategy officer
of you know, a ma major company.
I don't wanna say the thing.
And he said, well, I have ahundred BI and LS people on
my team, and every time hehas a question, he goes, every
(27:20):
time I have a new question, ittakes me at least six months
to get an answer that is there.
The infrastructure isthere, but that it has to
go through so many peoplefor them to understand
what I'm actually asking.
It.
That's all off.
We can give it to youtoday, by the way.
He told me.
Yeah, but I can't condemnthe data I guys to take it.
That's a different problem.
It's my problem, but butif you, where I'm going.
Galen Low (27:42):
Yeah.
No, absolutely.
And I've seen that happen.
I've seen it happen, especiallyin financial services.
I've seen it in like CX and thecustomer experience craft, like
they're trying to use data tocreate, you know, an intelligent
customer experience based onstuff that's being gathered
across many departments.
And yet to your point, they'regathering tons of data.
No one knows what it is or whatto do with it or how to turn
(28:02):
it into something that couldimprove customer experience.
It's just there.
And like sometimes peopleare just scoreboard
watching and then eventuallythey get disappointed
and they replatform.
Right?
It's so.
Lior Gerson (28:13):
It takes me back to what you said before you
know, what are people tracking?
So a lot people trackinglike costs, right?
And cloud costs.
Now people are tracking, youknow, problems cost, like
how much was they spendingand how to optimize prompts.
Do they spend lessmoney on their l lms?
And you have all this datathat is just sitting there in
companies and it has a cost,it has a storage cost and it
(28:34):
tracking cost and amazing.
And nobody knows why.
And you have all thesedata, nobody's using that.
So it's you know, theselike the people who collect
stuff and you know, in thegarage and don't use them.
So company becomehoarders endless systems.
They don't usethem for anything.
Galen Low (28:51):
Because you know what we were taught to
think it was cheap, right?
That storage is cheap.
This is just a few numbers.
Yeah, exactly.
It was cheap and nowit's not anymore.
And now we're thinking aboutYeah, like cost per prompt
and you know, these sortof transactional calls.
And also the storage,because it's more complex
now, and also we've beenaccruing it for decades.
It's not doing anything with it.
(29:11):
I can relate to that.
I'm a pack rat as well.
I try not to delete prettymuch anything, so thank
you cloud for making that areality that I could do that.
But also to your point,you know, even with some
of that stuff, like I'mnot doing anything with it.
I just, it comfortsme that it's there.
Lior Gerson (29:26):
Maybe a little bit.
Maybe in 10 yearsyou'll need it.
Galen Low (29:28):
Yep.
Yeah.
Exactly.
Yeah, that's exactly right.
I I had it here to ask yousort of about TargetBoard
and your positioning asan AI enhanced KPI tool.
You've already kindawalked us through some of
the AI driven features.
There's the sort of AI drivensuggestions of metrics.
There is the sort ofinsights summaries, I guess,
(29:49):
from a data perspective.
Yeah.
I mean, is there any otherlike AI feature that you wanna
talk through, or maybe eventhe other side of it is like.
What made you decideto double down on AI?
Like it's in the name,well, it's at least in
the domain name, right?
AI, like clearly was core tothe DNA of the solution that
you've built, what are youmost proud of about it and
(30:11):
where do you see it going?
Lior Gerson (30:13):
So I think it's a complicated question, right?
I'm proud about a lot of things.
I think we've been able toleverage AI from very early on
to really augment what we'retrying to build an experience.
We're trying to create.
There's pros and cons ofbeing an AI first company,
and I think we're AI secondcompany, but AI is very
prominent to everything we do.
And every feature webuild, we say, okay, how
(30:34):
does AI make it better?
Not how do we do this with AI,but why are we trying to build
and how does AI make this betterthan you could have before?
So lemme give you an example.
So we have all yourmetrics, right?
And for any metric, doesn'tmatter how you slice and
dice it, you can createnotifications, automations, and
alerts and so on and on, right?
So you have a deal that'ssecond, a pipeline.
You have a button in production,you have a support ticket
(30:54):
that's, you know, for a veryspecific QF customer and
you're not meeting your SLA.
Create an alert, send itto wherever it needs to go.
But having people think aboutthe alerts that they want to
create is a hassle, right?
People need to think aboutneed, having to think about
what they want to create.
How, so, for example, what wereleased this week is that the
AI, when you're looking at ametric, it will suggest the
(31:17):
automation that you can create.
To improve that metric.
We already have likerecommendations, like strategies
for how to improve metric.
Like I actually recommendthe automation that you can
create, like one quick clickof a button, create it for you.
And this was relativelyway harder to do pre AI
and now it's much easier.
Right?
We're moving fastertoday than I've ever
moved any other company.
Galen Low (31:39):
That's fair and also insane.
Based on what I knowabout your background,
it is a good use of it.
I mean, I think like the, whatI find is that there's a natural
sort of cultural inclusion ofAI in the world of data because,
you know, coming from like thebenefits of machine learning.
And frankly, just dealing withswaths and swaths of data.
Right?
You know, like even in yourprevious roles where like
(32:01):
your tools are like, they'reprocessing petabytes of data.
We don't even have alike conceptual, tangible
model for what that evenlooks like in our brains.
So it makes sense thatokay, we're gonna need
like compute power.
But what I really like is thatsort of and I like it actually
in the LLMs too right now.
Right?
Where it's suggest tome what to do next.
Whether it's right or wrong, andthen I will learn what you think
(32:21):
is the right or wrong next step.
And then I will continue tosuggest it because, you know,
to this day I, you know, I seea lot of folks and especially
when I'm outta my element,like I do a thing and I'm
like, what should I do next?
I'm not sure.
But if something, youknow, helpful clippy right?
Is going, Hey, you writinga letter, do you wanna
put a letterhead on?
It actually is a greatway to I dunno, just kind
of cut through discomfortor doubt or hesitation or
(32:44):
just like knowledge gaps ofactually I wasn't sure what
to do next, but actually alittle alert would be great.
Thank you very much.
A little notification andthen to your point, at
least I'm assuming it alsosets up that notification.
It's great, I'm on it,I've done it, it's done.
Every Tuesday you're gonnaget this notification about
this metric or when it's outof tolerance or, you know.
Lior Gerson (33:00):
It's a very different approach than, you
know, what data science waslike, you know, three years ago.
So taking an example, you know,from Gettacar, you know, we
had a team of data scientiststhat, you know, did autofinance
optimization which every timeyou get a credit application
which lender did you send it to?
That was, you know, a team thatworked on it nonstop trying to
maximize, you know, the singledigit percentages of how do you
(33:22):
get more applications approved.
And that was like adata science project.
Now it's every featureyou build saying, okay,
how do I make this better?
And it's not that it'snot simple, but if you
do it right, you're ableto get like just amazing
results and very quickly.
Galen Low (33:37):
I love how fast these things are changing.
Right.
Maybe just for fun,do you have a question
that you want to ask me?
Lior Gerson (33:43):
What are the most impactful trends
that you're seeing asproject management today?
Galen Low (33:48):
That's a good one.
I think the biggest one isactually the one I started
out with, which is thisnotion that project managers
are not just, you know, ironTriangle people trying to
deliver projects on time, onbudget, you know, within scope.
This sort of like thisrevelation that actually
we can be more strategic,but we need to know more.
We need to know more,we need to do more.
And we were alreadydoing a lot and.
(34:09):
Now we feel like we have tobe sort of strategists or
like delivery strategists.
There's the onus is on us tounderstand the business more.
And then there's thismysterious question of cool
I'm accountable in some wayfor impact, but you know,
I'm not necessarily involvedin measuring that impact.
In a lot of cases, we leada project and then we move
on, it launches and we, youknow, we go away onto the next
(34:31):
thing, off into the sunset.
And now there's this big mindsetshift of okay, well actually we
are value delivery specialists.
We actually are meant to createthe value, even if that means
the plan was wrong and we hadto change it, even if it means
the scope was wrong, we hadto change it along the way.
Even if it means battlingpersonalities in the boardrooms
and you know, or you know,in the, you know, in the
(34:51):
dev pit, there's a much morestrategic component to it.
And I think we're still tryingto figure it out, but then I
think it dovetails very nicelywith like our adoption of AI.
Where, you know, right nowit's helping us be more
efficient, shorten ourworkflows, create a bit more
speed, but fundamentally helpout with some of the stuff
that we got bogged down with.
(35:12):
You know, a lot of projectmanagers are like, I'm too busy
to then also be a strategicpartner for my stakeholders.
And now there's this sort ofrise that where it's okay.
What are you doing?
Well, okay.
Yeah, like all my statusreports oh, I've gotta
update the dashboard.
I've gotta go into Exceland update that spreadsheet.
It's cool.
You don't have to do thatanymore because we can
set up agents for that.
(35:32):
We can, you know, look to AIfor solutions, and then the
next thing is okay, but nowwhat do I do with that time?
How can I then learn theskills to be a bit more of
a leader, a strategic leaderin the delivery of value?
I think that's an interestingtrend in my world that.
I think intersects with a lot ofthis because in a perfect world,
we spend more of our time askingourselves the question, are we
(35:54):
doing the right thing to achievethe mission and have the impact?
And that's almost exactlywhere you started about
how do we know that we'remeasuring the right thing?
You're measuring theright thing if it's
helping you move forward.
And I think that's kind oflike an interesting aspect
of project management andproject leadership today.
Lior Gerson (36:10):
Very cool.
I'm gonna ask youanother question.
So, I know what I think, butI wanna hear what you think.
We've been talking about AI andyou know, talking about, you
know, how AI can help projectmanagers automate and become
more efficient and so on.
Do you think there is a stagewhere like an AI agent becomes a
project manager or replaces theproject manager like completely?
Galen Low (36:28):
I think for some people's definition of what
a project manager does, yes.
Because there is the sort oflevel of, oh, project managers
are just administrators.
They're taking notes, doingmeeting minutes, they're
following up with action items.
They are measuring, you know,an estimate against, you know,
an actual, how long it actuallytook to do all that stuff.
Yeah, definitely.
(36:49):
I think is almosteasily replaced by AI.
I think the parts thatare not are like the human
to human decision making.
Like I think for a whileat least some of the
big decisions, right?
Replatforming decisions,risks around, you know,
implementation, I think will beconversations between humans.
That's why I really likeyour data storytelling
(37:09):
feature because it's not oh,I, it'll be like a little
circle with a question markin it, and like the executive
can just click it and itgoes, this metric is this.
Instead, you're arming theperson who owns that metric
to tell the story about it.
And I think that is thelike decision making layer
in projects and in businessthat I think will continue
to stay the same, like humansmaking their case you know,
(37:29):
battling things out, convincingone another, persuading one
another to make good decisionsand maybe think a little bit
outside the box and thinkabout, you know, things that
maybe haven't been done yet.
And those sort ofinnovative thoughts.
And I think that comesfrom humans interacting
with one another.
Not to say that AI won'tor isn't innovative, like
I think it can create ideasthat, you know, most of its
(37:50):
users haven't thought of yet.
But fundamentally I thinkit's still remixing and
I think fundamentally westill treat it as a helper
and not someone who's gonnamake a decision for us.
That's changing.
But I think for now, I think.
Lior Gerson (38:01):
I agree with that.
It also what makes a differencebetween a good project manager
and a bad one and you know,was telling people, oh,
you know, we can't afforda project manager, you.
Do that on the side, likeyou manage this project and
then like it doesn't work.
It doesn't worry becauseyou know it's a profession.
Can't do it on the side.
Galen Low (38:17):
Yeah, I like that.
I like the idea that it'slike, it might not replace all
project managers, but it willprobably replace the bad ones.
Lior Gerson (38:23):
It's true for everything.
Galen Low (38:24):
Lior, thanks so much for spending
the time with me today.
This has been so much fun.
I love nerding out with you.
Before you go though, where canpeople learn more about you?
Lior Gerson (38:31):
LinkedIn, I get back to almost
anybody who reaches out.
Happy to speak to anybody.
Galen Low (38:37):
That's awesome.
I commend you.
My LinkedIn inboxis such a mess.
Thanks again.
Lior Gerson (38:44):
Bye Galen, it's been a pleasure.
Galen Low (38:47):
That's it for today's episode of The Digital
Project Manager Podcast.
If you enjoyed thisconversation, make sure
to subscribe whereveryou're listening.
And if you want even moretactical insights, case studies
and playbooks, head over tothedigitalprojectmanager.com.
Until next time,thanks for listening.
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