May 26, 2025 • 41 mins
On this episode of the Sell Me This Podcast, Keith Daser talks with Matthew Nielsen, founder and CEO of Ethicrithm and former CEO of Fishbone Analytics. With decades of experience in IT outsourcing, enterprise analytics, and digital transformation, Matthew shares how turning customer frustrations into solutions helped him build and scale a thriving business.
They explore the key decisions that fueled Fishbone’s growth and eventual sale, from early-stage pivots to leadership lessons that shaped the company’s trajectory. Matthew also explains why measuring service performance goes beyond checking off SLA boxes and how real value comes from aligning with business outcomes.
The conversation wraps with a look at his latest venture, Ethicrithm, and how it's helping organizations harness AI in practical and responsible ways. Whether you're scaling a tech company, managing client expectations, or exploring how AI fits into your strategy, this episode is packed with insight and real-world experience.
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If you believe you deserve more from your technology partnerships – connect with the team at:
https://www.deliverdigital.ca/?utm_source=videodescription&utm_id=youtube
Sell Me This Podcast is brought to you by the team at Deliver Digital, a Calgary-based consulting organization that guides progressive companies through the selection, implementation, and governance of key technology partnerships. Their work is transforming the technology solution and software provider landscape by helping organizations reduce costs and duplication, enhance vendor alignment, and establish sustainable operating models that empower digital progress.
This episode of the Sell Me This Podcast was expertly edited, filmed, and produced by Laila Hobbs and Bretten Roissl of Social Launch Labs, who deliver top-tier storytelling and technical excellence. A special thanks to the entire team for their dedication to crafting compelling content that engages, connects, and inspires.
Find the team at Social Launch Labs at:
www.sociallaunchlabs.com
Sell Me This Podcast is brought to you by the team at Deliver Digital, a Calgary-based consulting organization that guides progressive companies through the selection, implementation, and governance of key technology partnerships. Their work is transforming the technology solution and software provider landscape by helping organizations reduce costs and duplication, enhance vendor alignment, and establish sustainable operating models that empower digital progress.
If you believe you deserve more from your technology partnerships – connect with the team at:
www.deliverdigital.ca
This episode of Sell Me This Podcast was expertly edited, filmed, and produced by Laila Hobbs and Bretten Roissl of Social Launch Labs, who deliver top-tier storytelling and technical excellence. A special thanks to the entire team for their dedication to crafting compelling content that engages, connects, and inspires.
Find the team at Social Launch Labs at:
www.sociallaunchlabs.com
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
There's so much hype and there's so much investment
that is going in and they'rewildly overvalued companies and,
again, I'm not sure that I havethe wherewithal to pick the
winners and the losers out ofthis.
Speaker 2 (00:19):
Welcome to Sell Me this Podcast.
Today.
We're joined by Matthew Nielsen, the founder of EthicRhythm and
the former CEO of FishboneAnalytics.
He has decades of experience inIT outsourcing, analytics and
enterprise transformation, andMatthew shares how he turned
customer frustrations into athriving business and why
measuring service performance ismore than just hitting SLAs.
(00:41):
We dive into pivotal decisionsdefining Fishbone's success and
sale and how his new venture,ethic Rhythm, is helping
organizations harness AI througha partnership with Palantir.
If you've ever wondered abouthow to scale AI in a way that's
both ethical and impactful, thisone's for you.
Let's dive in, matthew.
We are so excited to have youon the show here today.
(01:02):
Thanks so much for taking thetime.
I'm going to dive right intothings.
I know you have an incrediblestory and an incredible journey
around how you arrived where youare today.
You have a startup background.
You have a passion for buildingbusinesses.
I would love to start offdiving right into your journey
in building Fishbone and howthat brought you to where you
are today.
Speaker 1 (01:22):
Thanks, keith, it's great to be here.
My story started a long timeago in IT outsourcing and I
worked for some very largeoutsourcing companies and worked
with a lot of organizationsacross Canada and I saw systemic
failures in outsourcing and Iwas part of some outsourcing
engagements that worked reallywell, but there were more and
(01:43):
more that just did not and Ispent a lot of time
contemplating why was that?
And if I could do somethingdifferent, what would it be?
And I felt like if I couldmeasure the performance of
outsourcing better anddifferently than what was being
done, then I think I could helpclients extract more value from
those relationships andbasically create an
(02:04):
accountability engine over thoserelationships.
It really crystallized for mewhen there was a large energy
company in town with tens ofthousands of employees and the
CIO, who was new, surveyed theirentire community and asked them
how IT was performing for them.
They had four major outsourcersthat were delivering services
and in any given month they wereall green on their SLAs.
(02:27):
But 60% of the population ofthat company were not very happy
to very unhappy with theservice that was being provided.
And it led me to this ahamoment that there's this
customer experience chasmbetween what a service level
agreement says and what acustomer actually experiences
through service delivery.
(02:47):
And so I gathered someexecutives that I could trust
with a secret, that I wasthinking about doing something
different, and I gave them beerand pizza over a series of
sessions.
I called them my customeradvisory board and I walked them
through what I thought theproblem was and when we agreed
on the problem statement, Iwalked them through what I
thought the problem was and whenwe agreed on the problem
statement, I walked them throughwhat I thought the solution was
(03:09):
.
And it was very iterative andthey had some really valuable
feedback for me through thatprocess and so when we landed on
the solution statement, Iturned to them and said that's
fantastic.
Thank you for your help.
Now I need your money and withthat I launched Fishbone
Analytics as a customer-fundedcompany.
We walked into a couple ofcontracts right away and our
(03:30):
mission was really to build ananalytics engine that would take
all the data that was createdthrough service management and
service delivery and apply ITILand Six Sigma kind of deep
analytical views so that wecould measure performance
differently and better than whatwas being done previously.
And that worked very well untiloil went to $20 a barrel and the
(03:52):
analytics that we wereproviding was really a
discretionary spend and I waslooking at an oil recession in
Calgary and realized that A Ineeded to diversify my client
base and geography and I neededto really listen to what our
customers were asking us to do,and I had dinner with one of my
clients and he was talking abouthis cost of managing a
(04:14):
ServiceNow application and Imore or less said tongue in
cheek why don't you let us dothat?
We're taking all the data fromit?
And he said okay, and so then Ihustled, like entrepreneurs do,
and I hired a very intelligentguy that I used to work with,
Mark Raychuck, and asked him totake a course, and he did, and
then we took on managing thisapplication and that was the
(04:36):
beginning of a pivot into whateventually became a full
ServiceNow practice and FishboneAnalytics, and that's really
where we grew and scaled ourbusiness and led to a successful
exit to a private equitycompany in California.
Speaker 2 (04:49):
I love that story and I think that problem that you
described at the very beginning.
I come from the serviceindustry and there's so many
times when you're pointing atthis board of all green and
you're saying all of the checkmarks are saying the right
things, even NPS could be strong, your SLAs could be strong, but
there's something that's kindof hiding behind the curtain.
That just doesn't feel right,and so the ability to measure
that is, I can see, incrediblypowerful.
(05:12):
How did you find that pivotfrom the data analytics company
around customer success toreally honing in on service now?
Was that a hard pivot for you?
Was it easy because yourcustomers took you there?
What did that feel like?
Speaker 1 (05:23):
Yeah, I think the advantage of being a customer
funded company is, every month,your customers are voting with
their dollars, and so you reallyhave to follow the thing that
is most important to them.
So I would say when we werepivoting I wouldn't even
characterize it as a pivot, Iwas just following the revenue
and following our customers,with a focus on making our
customers really happy At thebeginning I didn't wake up and
(05:45):
go we're going to become aServiceNow partner.
We just grew into that andrealized, just based on good
fortune and good timing, thatwas a market that probably had
more legs than what we weretrying to do previously, and so
it was really only in hindsightthat I look back on it when, oh,
we actually pivoted the company, like we started doing A and
ended doing B, and thankgoodness we did that.
Speaker 2 (06:07):
Yeah, and I feel like that ability to take those
leaps and take those steps isreally important.
I was on a hike last summerwith someone just as we were
starting our business, and heradvice to me was don't fall in
love with your idea, fall inlove with the problem.
And if you chase the problems,your business is going to change
and evolve a hundred differenttimes in a little iterative
steps.
But if you get stuck on reallyproving out your initial idea,
(06:29):
sometimes you can fall into alittle bit of a trap.
Speaker 1 (06:31):
Absolutely, yeah.
Yeah, you have idea bias?
Yeah, my idea is fantastic.
Why don't you understand that?
Speaker 2 (06:37):
Yeah, so then I'm assuming you had a tremendous
amount of experience learningfrom some of these organizations
through the analytics work,through the ServiceNow work.
What are some of those insightsthat you learn from those
initial engagements that arereally shaping what you're
building with EthicRhythm?
Speaker 1 (06:55):
Yeah.
So Fishbone was, I guess, a10-year journey for me,
including the time I spent withthe company that acquired us,
and it was really.
I couldn't have asked for abetter education through the
whole process, everything fromstarting a company and
contemplating cash flow and theimplications that it has on
scale or vice versa, to bringingstructure to how you operate a
(07:20):
company and also how you deliverservices.
I've always been verypassionate about creating what I
call raving fans and I alwaysjust subscribe to this notion
that if you keep your clientsreally happy, a it's good for
long-term revenue Like it's justgood business fundamentals, but
also they become the bestmarketing engine that you could
have.
And I would say, going throughthis process I maintained my
(07:41):
passion for creating raving fansand I really got schooled on
the fundamentals of business, offinance, of contracting and
just managing an operation, andso I've taken a lot of those
lessons forward and I can giveyou like a few examples of
things that I learned that I amlike absolutely taking forward
(08:02):
in my new company Ethic Rhythm.
One is that Fishbone greworganically.
I had a plan, but it wasn'tvery structured and I didn't
think about things like equityfor employees, so we never had a
corporate structure set up thatwe could easily allocate equity
to our team and when it cametime for an exit, we ended up
(08:23):
bonusing people that worked forus and I was very happy to do it
.
But it was a terriblyinefficient use of our capital
because everyone takes that asemployment income and half of
the money goes to the government.
In Ethic Rhythm we'restructuring it so that all
employees are equityparticipants, because A we want
to be able to deliver cash inthe most efficient, tax
efficient manner possible, butalso I think it changes the
discussions that you can havewith your team.
(08:45):
If everyone feels like they'vegot the owner's mindset, then
it's permission to havedifficult conversations about
the things that you have to doas an organization at times to
react to situations like whatwe're finding ourselves now with
tariffs.
So that was a big lesson justallocating equity because it's
the right thing to do andbecause it helps you drive your
(09:06):
business forward with everyonein the same boat, rowing in the
same direction.
Something else I learnedFishbone, I feel like, for the
60-ish people that we were whenwe exited the business.
We were quite a well-runorganization.
We had plans, we had structure,we had KPIs and benchmarks.
We knew what we were doing butwe were not built for scale.
(09:28):
We were not capable of goingfrom 60 people to 500 people and
that had implications for usafter the acquisition, where all
of our processes changed toanother operating standard and
tools changed.
It created, candidly, a lot offrustration for our team and I
would not like to be in thatposition again.
(09:50):
So, as I build Ethic Rhythm,before we hired anyone external
into the organization, before welanded on our value proposition
, before we started looking forcustomers, we spent four months
just building the infrastructureof the organization, putting in
our financial system and ourprofessional services automation
platform and our HR managementsystem.
(10:12):
So when we were ready to start,we weren't having to think about
these things, we weren't havingto build on the fly.
It was already established andit sounds ridiculous, but it was
a big achievement that ourfirst employee that we hired,
jamie he had a laptop before hestarted.
Oh wow, it was secure and itwas in our security model and
(10:33):
his email accounts wereprovisioned and he had a system
to log into to enter his payrolldetails, like it was just done.
And so, just like onboardingthe first employee and not
really screwing it up, was likea big accomplishment.
And now we're at the pointwhere we're starting to look at
scaling.
We're hiring more people, we'rein customer acquisition mode
and I just don't need to thinkabout how are they going to
(10:54):
request their T4 slip, or howare they going to request time
off, or how are we going toaccount for that in our
scheduling software.
Speaker 2 (11:01):
Yeah, so you can really focus on growing the
business and building a business, rather than putting in that
infrastructure, growing thebusiness, building the business,
but also becoming astandards-based delivery
organization.
Speaker 1 (11:12):
I talk a lot about raving fans, which I've
mentioned, and I feel like oneof the secrets to creating
raving fans is to be veryconsistent in what you deliver.
A hypothesis years ago that youcan deliver consistently bad
service and your customers mightbe okay with you.
You can deliver consistentlygood service and your clients
will be really happy with you.
If you deliver inconsistentservice and they don't know what
(11:33):
to expect from you, then thatis a recipe for disaster.
We're really focused onbecoming a standards-based
delivery organization, so thatwe have scheduled checkpoints
with our clients, that we areannotating code in a particular
way, that everything isstructured and we have scheduled
checkpoints with our clients,that we are annotating code in a
particular way, that everythingis structured and we have
accountability down to theindividual that's delivering a
service for our client, but alsoso we can share accountability
(11:54):
with our clients on the thingsthat they need to do to make us
successful in their environment.
Speaker 2 (11:59):
Of course, and I think that's a really
interesting jumping off point,because if you look and I've had
interesting discussions withfolks like even like CTOs out of
Silicon Valley and they're a200 person organization and
their deployment strategy forthat first day is, well, go grab
a MacBook out of the closet andthe ability to have that
infrastructure in place, I think, is a huge advantage,
especially in a space like AI,which is so in flux right now.
(12:22):
There's so much opportunity butalso a lot of uncertainty about
what it really means.
So, in that AI space and I dowant to spend some time diving
into your thoughts andperspective around AI but let's
first start with ethic rhythmand tell me about the
organization.
I know we've talked about thestructure.
I know we've talked about someof the foundations you put in
place, but what is it thatyou're delivering to customers?
(12:43):
What is it you're looking tobuild and what's the journey
that you're on?
Speaker 1 (12:46):
Yeah, so our journey is to become the most trusted
Palantir partner in theecosystem.
Speaker 2 (12:53):
Sorry for our listeners.
Speaker 1 (12:54):
So who's Palantir?
Yeah, palantir is a US-basedenterprise intelligent workflow
and AI organization.
They are the antithesis of SaaS.
So where you've got workflowsthat need intelligence that you
can apply AI to that you don'twant to manipulate your business
(13:14):
process to fit into, palantiris a great option because it's a
very flexible platform whereyou can consume enormous amounts
of data from as many sources asyou can contemplate, have that
data in a central location andthen apply workflow to it, make
that workflow intelligent andthen bring AI to bear.
We talk internally about thisconcept of a self-optimizing
(13:37):
enterprise, and for me, that'san enterprise that will leverage
technology to the best of itsability and at some point in the
future, that technology isgoing to start to identify
inefficiencies and processes andstart to self-correct it, at
first with humans in the loopand then potentially
autonomously.
And when an organization getsto this vision of the
self-optimizing enterprise, Ifeel like they're going to have
(13:59):
an unfair competitive advantageand they're going to return more
shareholder value.
Speaker 2 (14:03):
Amazing.
So what role does yourorganization play in helping
customers get there?
Because obviously that is areally interesting vision and I
think that there's lots ofdiscussion around the role that
AI is going to play in this newworld of business.
But in taking those first steps, what's the role that you play
with companies and organizations?
Speaker 1 (14:21):
Yeah, I think step number one is helping them
identify use cases where there'sa return on the investment.
Like all good platforms, thereis a cost to the platform and
there's a cost to implement theplatform.
Compared to the typical SI route, where you will subscribe to
many platforms and then stitchthem together, there's an
(14:41):
opportunity for considerabletechnical debt and also reliance
on SIs who have high consultingrates and are there for years,
not months.
Notwithstanding that, there isa cost to implement and if
there's no real return, thenit's not a project that should
be contemplated.
So I think step one is sayingwhat are the biggest problems
you've got in your business and,if you could solve them, what
(15:03):
does that mean for you as anorganization?
Are you improving your employeeretention so you're reducing
your HR costs?
Are you creating new revenueopportunities?
Are you becoming more efficient?
Are you satisfying a compliancerequirement that could stop you
from being fined, however manymillions of dollars?
So once we land on those usecases, then it's really about
proving out the model consumingdata, working with the clients
(15:25):
as subject matter experts oftheir own business and being
able to apply the workflows andAI on top of that so that they
can learn and drive the outcomethat they're looking for.
Speaker 2 (15:35):
Interesting.
So is most of the work thencoaching leaders on how to
identify those things.
Do you go in and help them findthem?
Because a lot of the differentbusiness leaders that I talk to
they are very curious about AI.
There's a degree of healthyoptimism, there's an expectation
from their board, from theirshareholders, from their
executive leadership teams to dosomething with it.
(15:56):
But I feel like where peopleare getting stuck is those first
steps of what do I practicallydo?
How could you advise a leaderto even identify some of those
workflows and kind of what makesa really good candidate for
initial AI?
Speaker 1 (16:09):
jobs.
Yeah, so I think it comes downto what are the problems that
you face as a business.
So we're talking to anorganization in Western Canada
that is involved in naturalresources and they have a
business unit that isunderperforming by about 60%
compared to industry, and sothat becomes the basis of the
conversation.
(16:29):
Well, why is that happening?
What is unique about whatyou're doing that is leading to
these inefficiencies?
And really for them, we'vediscovered that it has to do
with resource and assetmanagement and utilization.
They are distributed over for acouple of provinces and they
have these big heavy assets thatwill get stranded out of sight
and not used or someone thinksis not available.
(16:52):
So then they go rent a heavyasset to work on a project.
Meanwhile they've got this idleasset sitting over here and
then just being able to schedulepeople and stitch projects
together in a way that is mostefficient.
Our thesis with that clientright now is that we can help
them cut their inefficiency inhalf by the end of this year.
They'll still be trailingindustry, but by the end of next
(17:14):
year they expect to be at theindustry standards for gross
margin on that particularbusiness unit.
Ai comes into it by helpingschedule and plan out asset
utilization and asset location,as well as the individuals that
are going to be operating thosemachines and completing the work
.
Just to give you an example, wecan consume data from things
(17:36):
like Caterpillar and we can seewhen assets are being used and
when they're sitting idle.
We can see where they're, atwhat locations they're at and
how long they've been sittingthere.
We can see where they're, atwhat locations they're at and
how long they've been sittingthere.
We can pull all that data inwith other metrics from the
business and say what is theutilization of this asset?
How much time is it sat runningidle?
Because someone is, they'retaking their lunch break in the
(17:58):
air-conditioned cabin, whichdestroys warranty, which
consumes fuel, which erodes theresale value on that device, and
there's lots of implications tosomeone just having lunch in a
cab in an air-conditionedenvironment.
Right, we can take all thesedata points and then start to
map out.
This is the order in which youshould be completing projects.
These assets should beallocated to those projects and
(18:19):
these are the teams that shouldbe involved in that, and for a
human to do all of that, itwould be a lot of analysis and
I'm not sure that they couldactually get to end of task on
that without the use of AI.
Speaker 2 (18:29):
Well, that's the question I was going to ask.
Is it something that they justdon't have, the bandwidth for,
the capacity, for the capability?
Because it sounds like a hugefinancial problem for them.
It sounds like it's costingthem a ton of money.
Have they just not been able toapply people to these
challenges?
Or where does AI come in tosolve those things?
Speaker 1 (18:47):
So in this particular organization, they have
multiple business units, so this, like we got one business unit
that's underperforming, but whatyou create can be applied
equally to the other businessunits, and I think it's just not
enough capacity within a humanor a team to be very efficient
at this, because there's so muchdata that gets consumed and so
(19:09):
many different ways that you cancorrelate and so many trends
that it's just difficult for ourbrains to be able to comprehend
.
Right, like, how do you takedata that's over here and data
that's over here that you maynot have total visibility to or
understand the context of, andthen bring those together to
learn something new that youdidn't know yesterday?
Speaker 2 (19:26):
I think that's very difficult to do and AI is a
perfect use case for that, andso I'm going to open up a tiny
bit of a can of worms here witha conversation I know that we've
had previously around SaaSapplications, and so you talked
about Palantir being a littlebit of the SaaS killer, and
there's been some big statementsfrom saying, hey, you know what
?
Saas is dead.
What is the problem thatorganizations have with their
(19:47):
SaaS platforms right now, andhow does AI help them solve?
Speaker 1 (19:50):
that I think.
So I'll talk about SaaS andI'll talk about AI.
Yeah, Generally, saas requiresa business to conform to the
standards that have beenestablished in the SaaS.
If you choose to go outside ofthat, then you will often create
technical debt and a lot ofcomplexity when it comes to
(20:13):
managing the currency of thatapplication, but also all the
interdependencies that surroundthat application and the
integrations.
I think we're at a point nowwhere platforms are so capable,
like Palantir, where you canjust develop within many
applications within one platformthat work well together, that
(20:33):
eliminate the interdependenciesand the technical debt that gets
created, and you build them toalign to the business process
that you've been creating overdecades in your organization,
whereas SaaS, you're having tochange some of those business
processes to avoid technicaldebt.
So I think, as a business user,you don't want to conform to
(20:54):
what someone else has prescribedin industry.
Sometimes those industrystandards are better for sure,
but sometimes they're not.
Speaker 2 (21:01):
That makes sense because a lot of the
conversations and I'm sureyou've had these before even in
your previous life where it'scheaper, more efficient, more
practical to adjust yourbusiness process rather than try
to customize a software orplatform to do the thing that
you want to do, and maybe youhave 30 people that are doing it
a certain way and instead ofautomating it, just add X, step
(21:22):
over here and it actually startsto make way more sense versus
trying to write a whole bunch ofcode or customizations around
that ecosystem.
What you're saying is that inthis new world, that challenge
goes away and you canessentially start to map exactly
how people work and solve itwith technical amplification,
rather than having to fragmentit to fit certain molds that are
(21:43):
kind of precast.
Speaker 1 (21:45):
Yes, I think one of the advantages of Palantir.
I'll just take a step back.
Before we landed on Palantir,we evaluated a lot of different
platforms and applications.
We contemplated being AIconsulting generalists, but I
honestly just couldn't get myhead around that and I had a
fear about working with a clientand recommending a platform
that may not exist in two years,because there's so much hype
(22:07):
and there's so much investmentinto AI today that 90% of those
companies could well not existin the next 24 to 36 months just
because they run out of fundingor they get acquired by one of
the big players.
And it was really important tome that we landed on a platform
that we felt very comfortablewith.
So we looked for something aplatform that was enduring, that
would be around for the longhaul, that provided deep
(22:32):
capabilities around enterprise,intelligent workflow, that could
help our clients realize avision of their self-optimizing
enterprise and that was very AI.
Forward.
Through our process ofelimination, we got down to
Palantir, which is a 19-year-oldcompany.
They are very high growth, theyare profitable and they're
debt-free and they're investingsignificant amounts of their
(22:53):
capital in developing thePalantir Foundry and AIP
platforms.
Speaker 2 (22:57):
A lot of work that people do is in terms of how
evaluating these platforms, andtheir mindset is going to go
from evaluating SaaS platformthat does this specific thing to
now making a much biggerdecision around evaluating a
singular platform that candeliver an incredible amount of
enterprise value.
But also, what I've seen isthat as the value of those
decisions goes up, so does thedecision paralysis.
(23:18):
If you were to lend some of theexperience that you had in
evaluating over the last fourmonths, what are some of the
considerations you had?
And you talked about thebusiness.
You talked about the stability.
Was technology part of that oris it more of a kind of a
business conversation at thatpoint?
Speaker 1 (23:36):
So I think it's both and thank you, you triggered
where I was going with my lastpoint which was that it doesn't
have to be a fundamental shiftto start.
All companies have platformsthat they have invested hundreds
of thousands to millions tohundreds of millions of dollars
in, and one of the things I likeabout Palantir is they don't
(23:58):
necessarily have to walk awayfrom that investment.
Palantir has a flexibility topull data from many sources.
There's a use case for AT&Twhere they have 995 integrations
pulling data into Palantir'sFoundry platform and then they
operate within that data andworkflows inside of Palantir.
They're not requiring people towork in these 995 applications.
(24:21):
The work moves into Palantirand it writes back to these
applications.
If you're not ready to rip andreplace and rebuild your ERP or
your data warehouse, palantircan sit over top of that.
It can consume the data it canwrite back.
So, regardless of where acustomer is in their journey in
trying to create a moreintelligent operating
(24:43):
infrastructure, palantir fitsreally well on top of that and
over time you can deprecatethose platforms and rebuild
those workflows in a moreintelligent way inside of
Palantir.
Speaker 2 (24:53):
Interesting.
So one of the things that youbrought up which has popped into
my head is this idea of SaaS isgoing away.
You're really saying there's aplatform that people can build
on.
Is there entry gates to that?
Is there a size of organizationthat works for it?
If I'm a large oil and gascompany, obviously I think that
makes a lot of sense.
If I'm a $10 millionconstruction company that has a
(25:16):
couple of folks and doesn't havea ton of technical depth, is
this something that's in mywheelhouse or is this something
that is going to solely servefor the big guys out there?
Speaker 1 (25:30):
It used to be for the big guys.
I think Palantir has recognizedthat there is a downstream
market opportunity for them, sothey created what they call
Palantir for Builders or Foundryfor Builders.
So it scales down to startups.
So we are a startup, we're 10or 12 employees and we own
Foundry and we're building someof our core workflows inside of.
Speaker 2 (25:46):
Foundry.
So this is, I think, can leansome really practical examples
for people that are buildingtheir businesses.
What are some of the and, ifyou're okay sharing this, some
of the practical examples of howyou're using AI and that
Foundry workflow within.
Speaker 1 (26:00):
EthicRhythm.
Yeah, every week is a newadventure for me, because the
team is doing more things.
The use case that we're workingon right now is around helping
us identify candidates to gothrough our hiring process.
So I more or less click baitedinto creating a job opening on
LinkedIn, which was free andended up costing me $500.
Speaker 2 (26:25):
I have the same story .
We can share that oneafterwards.
Speaker 1 (26:28):
Yeah, and by the end of the first day we had
parameters so minimum of threeyears of Python and TypeScript
skills must be in Canada, and afew other variables, and I
really didn't expect that wewould get very many resumes from
this.
In the first day we had 120resumes.
By the end of the first week wehad 400 resumes and when you're
(26:50):
a small organization, to takethe time to review, like manual
review, all these resumes isvery difficult.
We created a workflow where wecan ingest all the applications
and then ai will parse throughthe resumes and it will provide
a summary of the experience andthen a summary paragraph and
then also make a recommendationwhether this is someone that we
should be talking to or not.
And there's a couple of thingsI like about that.
(27:12):
One is it's highly efficientand b it takes away the bias
that someone would have inreading a resume.
So they're not looking at aname and introducing bias.
They're not looking at thecompany that they used to work
for and introducing bias.
It's just based on the skillsand experience that they've
expressed in their resume andthen providing a summary of that
and making a recommendation.
Speaker 2 (27:35):
Is there any other examples you can think of?
Speaker 1 (27:36):
By the end of the year.
I would venture that most ofour business will be run on
platform.
Speaker 2 (27:41):
So I'm going to pivot a tiny bit into more general AI
conversation.
So you talked about the usecase of your HR hiring and
there's a ton of productized AIelements that are out there, so
not dissimilar to SaaSapplications, but AI for very
specific use cases.
How are you viewing that in theecosystem, and is that
something that companies shouldbe exploring for niche elements,
(28:04):
or should they be looking atthe broader AI conversation
before investing in some ofthose individual elements?
Speaker 1 (28:12):
I think they should do both.
I think there's some reallyinnovative companies out there
and you can learn from them.
My fear about the innovationthat's happening in the AI
market in general right now isthere's so much hype and there's
so much investment that isgoing in and they're wildly
overvalued companies and, again,I'm not sure that I have the
(28:35):
wherewithal to pick the winnersand the losers out of this.
As an operator, when I'mlooking at platforms, that's
going to be part of my decisioncriteria Is this niche platform,
this niche capability, going toexist in two years, or is it
going to lose funding or getacquired by a competitive
(28:55):
organization who sunsets it?
There's many things that canhappen, and this happened during
the dot-com boom, right, so I Iwould be fearful of picking
niche players yeah, it's almostlike picking, it's almost
gambling at this point.
Speaker 2 (29:07):
right and you're, and you find these individual
organizations and we saw itduring the dot-com boom and
we're seeing it even with theSaaS boom.
The barriers to entry become solow, the valuations become
crazy, which increases themarketing budget, and
essentially all of theseorganizations contribute to an
enormous amount of vendor sprawl.
And I think we're starting tosee some of that same thing
(29:28):
happen on the AI front, whereall of these individual business
units are picking these nicheplayers without an overall
strategy.
Speaker 1 (29:36):
I also think it exposes your risk factor goes up
quite a bit when you've gotsprawl and you don't understand
how your data is being used orhow it's feeding the large
language, model training and soon.
I've got fear about that.
I do think there's a lot ofconsumerization around AI which
is infiltrating boards andexecutives and they're looking
(29:57):
at the chat GPTs and the Geminisof the world, which do amazing
things, and they're trying tofigure out how to incorporate
that into their business.
So I think looking at theoverall strategy and finding
enduring companies is reallyimportant.
I had a conversation with a CEOof a fintech related company in
the US earlier this week and wetalked about the promise of AI
(30:22):
is not being realized inside ofthe enterprise yet, and that's
the opportunity and thechallenge, I think for a large
enterprise today is to figureout how do they actually
leverage AI so that they candrive intelligent workflow, so
that they can bring AI to bear,so that they can build trust
with the AI platforms, so thatthey feel like they can make
(30:42):
autonomous decisions at somepoint in the future.
There's some great use casesthat have human in the loop
today, with Palantirspecifically, where they're
really renovating supply chainmanagement systems and
transportation systems, butit'll be a while before
decisions are made autonomously.
They have to have humans in theloop.
Speaker 2 (31:04):
That makes a lot of sense.
There's no shortage of some ofthese bold claims in the AI
space.
What are some of the red flagsthat you're seeing when you see
salespeople, marketing et ceterain the industry that are making
these big, bold claims aroundwhat AI can do for organizations
?
Are there any red flags thatjust stand out to you that
buyers should be looking out for?
Speaker 1 (31:26):
I don't know if I'm not sure I can comment on how
sellers are selling theirplatforms.
I do think that buyers andenterprises need to really
contemplate the implication ofbringing in AI A.
It's not easy, it requiresintegration and it requires data
to drive the decisions.
It also affects how people workand there's a big
(31:48):
organizational change managementcomponent that is required when
you're introducing theseintelligent workflows into your
environment.
I don't think that should beunderestimated and I think
executives need to contemplatethe fortitude that they have to
embrace change, because it isn'teasy.
It's a very disruptive and ifit's not well thought out and
(32:08):
it's not well executed andpeople don't understand why it's
important to do what you'redoing, like many technology
initiatives, it's destined forfailure.
Speaker 2 (32:17):
Yeah, so does that go back to the original comments
you made around really findingthose practical workflows, those
practical use cases that theirteams and people can connect
with, they're consulted on first?
Is that why that step is so?
Speaker 1 (32:28):
important?
Yeah, absolutely.
I think A.
Is there an ROI?
I think that should be thefirst question.
Can we actually do something tochange our earnings per share
and our shareholder value?
And if there is, then how do weapproach this in a thoughtful
way that's actually going tolead to the outcome that we're
intending for?
Speaker 2 (32:45):
Yeah, so the concept of ROI and AI is a really
interesting one, and when youshare the example earlier of the
utility of different worksiteequipment, that becomes really
clear to me, from an ROIperspective, how you can measure
that.
I think a lot of leaders havehad this exposure to ChatGPT or
to Copilot or to Gemini or tosome of these platforms where
(33:06):
they are big investments butthey're having a really hard
time quantifying that return andoutput when it comes down to
those individual employees.
Is there a way or a secretsauce that you can think of in
terms of best measuring ROI forsome of these AI initiatives and
how to tell whether or notthey're actually working?
Speaker 1 (33:24):
Yeah.
So I think that is acomplicated question.
So, even in the example I gaveyou with a resource company that
we've been talking with theirexecutives, how are we going to
definitively measure the benefitof the cost of this project,
because there's factors outsideof our control?
Will people take the cue that'sgiven to them by the platform
that says there is someone thatis having lunch in their big
(33:46):
heavy excavator and send them amessage saying, hey, why don't
you go into the work shack andstop idling the machine?
If they don't do that, thenthere's not going to be a
benefit for that little tinyslice, right?
So there's lots of variablesthere.
It's out of control.
I think when you are looking atoverall programs, there's a use
(34:06):
case for the supply chainorganization, the Support 20s.
That's not our client, it's aPalantir initiative, but they've
been able to quantify the valueof improving their supply chain
using data and AI on thePalantir platforms and they have
generated huge savings and alsodramatically improved the time
(34:30):
to provide supplies to theirrestaurants.
I think they've got about 4,200restaurants across North
America.
So when they run an example,when they run a sale event so if
they put up like a Frosty onsale, I think it's Frosty's.
If there's a particular flavor,they can start to understand
what are the sales metricsaround that particular sale
(34:51):
event and are there inventoryshortages in particular regions?
And if so, then how do theyload balance that inventory?
How do they short ship storesthat have a surplus of inventory
to feed stores that have ashortage of inventory?
And how do they engage theirsuppliers to create more syrup
or whatever the specialingredients are that they need
to be able to support that andthey've been able to get dialed
(35:12):
in on their ROI in terms of timeto deliver and the impact on
sales.
And I can't talk to allindustries, but it's a needle
that has to be threaded in orderto substantiate the value of
the investment to the boards.
Speaker 2 (35:25):
Totally.
Do you think that executivesare prepared right now to be
able to have those conversations, or what steps in education do
you think needs to take place atsome of those boards and
executive level to evenunderstand the implications of
some of these technologies?
Speaker 1 (35:39):
Yeah, I think boards need to understand the
difference between consumer AIand enterprise AI.
Chat GPT in and of itself itmay bring some user productivity
, but it's not going to changethe scale of your supply chain
organization.
It can support that.
It can integrate with someplatforms like volunteer that
can do that, but it's not chatGPT in and of itself that's
(36:01):
going to accomplish that.
Speaker 2 (36:01):
for you yeah, is that one of the biggest
misconceptions that you see withAI Cause I feel like a lot of
people do feel like AI is how doI apply chat GPT to my business
, or how do I add a chat bot, orI feel like it's very
interactive based still versus.
I think some of those truemeaningful chunks forward are
when you start to apply AI todecision-making and to
(36:22):
enterprise data there.
Speaker 1 (36:24):
Yeah, yes, I think there's a consumerization effect
.
I think it's like when theiPhone was introduced in 2007,.
It took years for applicationsto catch up what iPhone did, but
there was an expectation.
They call it ServiceNow used tocall it the Sunday to Monday
effect.
You were on your phone onSunday and you've got all these
beautiful applications andvisual displays, and then you
(36:45):
show up to work on Monday andyou've got green screens or
whatever.
So I think we're going through abit of that with AI today and
the challenge is to figure outhow do we incorporate that into
the enterprise.
So, instead of asking aquestion and getting a contract
reviewed by ChatGPT as anexample which I've done and it
was remarkably fast, like itprobably saved me two hours of
effort, which is fantasticthere's a logistics organization
(37:08):
in town that uses Palantir tounderstand the implications of
their distribution network.
So if there is an event in aparticular part of the continent
that is going to affect theirdistribution network, ai,
through Foundry, is coming backand making recommendations on
alternate routing based onservice level agreements that
(37:30):
they've got, based on theinventory that they're shipping,
and it's coming back and sayinghere are the three best options
for you to reroute theseproducts.
Which would you like to choose?
They choose and then itexecutes some of those orders
Like that's enterprise AI.
That's not chat GPT saying hey,you should be doing this, or
yes, I looked at this and itlooks fantastic.
Speaker 2 (37:50):
So that level of data , that level of integration,
brings up some reallyinteresting questions around
ethics.
And I know that Ethic Rhythmthe name itself, it's a node to
ethics.
What does ethical AI mean toyou very practically, when
you're embarking on some ofthese changes with your
customers?
Speaker 1 (38:13):
I think it's using the opportunity to remove bias
from the information that'spresented and ensuring that the
outcomes aren't going to haveunintended consequences.
This is such a large, difficultarea.
Ai is going to lead to ethicalquandaries, no matter what, just
by virtue of what it is capableof doing or the promise of what
it's going to be capable ofdoing over the next few years.
I'm not totally sure how wegrapple with that as a society
(38:35):
Inside of an organization.
I think it comes back toorganizational change management
and understanding what is theimplication of this AI.
How does it affect the waypeople work, or how many people
do the work, and then supportingyour population through that
change?
Speaker 2 (38:49):
Yeah, is there a way and you talked about the ability
to manage that change, or Ithink there's always going to be
in this trade-off of move fastand break things or move slow
and meticulously and figure outsteps along the way?
There's someone that I talkedto that had alluded to the idea
that some of these enterpriseorganizations are like tanker
ships sometimes and you do needthe little speedboats to kind of
orbit them to be able to taketheir cargo off and bring it to
(39:12):
shore.
Is there a way that it's topractically move fast in AI
while not breaking some of theseethical considerations?
Speaker 1 (39:24):
I would like to think so.
I think move fast and breakthings shouldn't apply to ethics
.
Personally, I think when itcomes to ethical standards,
everyone in an organization hasto stand up for ethics, whether
that's the bias that thingscreate or that you can eliminate
.
So I don't feel like thatshould be part of the move fast
(39:46):
and break things mantra.
There's other opportunities tomove quickly and hopefully I
don't know, I don't reallysubscribe to the move fast and
break things to be candid I alsodon't subscribe to moving
really slowly and getting intoanalysis paralysis.
I think there is a happy mediumthere, but I've never really
contemplated that with thecontext of ethics.
But I personally wouldn't bendon my ethical perspectives in
(40:12):
favor of speed.
That's not my character.
Speaker 2 (40:15):
I love it.
For leaders that are evaluatingAI this year, what are a couple
of things that they can focuson to avoid falling into rabbit
holes or wasting time or money.
What's the things that theyshould be looking at as they
build out their strategies?
Speaker 1 (40:31):
I think it comes down to what are the biggest
problems they've got in theirenvironment, in their operations
.
Is their data available or thatcan be created to lead to a
different outcome, and how willthat affect their business?
I don't think they should belooking at the platform.
I don't think they should belooking at capabilities, which
(40:52):
is is how solutions have beensold in the past.
Right, I have this capability.
It will solve this problem foryou and it puts you in a bit of
a box.
I think the opportunity now isfor executives to eliminate the
box and just think about whatare their core business problems
.
What is it that they're tellingtheir shareholders they need to
solve to be more competitive,to reduce costs, to respond to
(41:15):
existential challenges liketariffs?
What are those challenges thatthey need to solve for?
And then thinking about howthey can approach them
differently.
Speaker 2 (41:23):
Matthew, thank you so much for this amazing
conversation.
You're clearly veryknowledgeable in this space.
If someone wanted to connectwith you and learn more, what's
the best way for them to reachout?
Speaker 1 (41:33):
Yeah, I think going to our website ethicrhythmcom,
which is like ethical algorithms, and also on LinkedIn, matt.
Speaker 2 (41:40):
Nielsen.
Perfect, and I'll make sure tolink you in the show notes here.
I really appreciate your timetoday and thank you so much for
coming on the show.
Thanks for having me.
There's so much hype and there's so much investment
that is going in and they'rewildly overvalued companies and,
again, I'm not sure that I havethe wherewithal to pick the
winners and the losers out ofthis.
Speaker 2 (00:19):
Welcome to Sell Me this Podcast.
Today.
We're joined by Matthew Nielsen, the founder of EthicRhythm and
the former CEO of FishboneAnalytics.
He has decades of experience inIT outsourcing, analytics and
enterprise transformation, andMatthew shares how he turned
customer frustrations into athriving business and why
measuring service performance ismore than just hitting SLAs.
(00:41):
We dive into pivotal decisionsdefining Fishbone's success and
sale and how his new venture,ethic Rhythm, is helping
organizations harness AI througha partnership with Palantir.
If you've ever wondered abouthow to scale AI in a way that's
both ethical and impactful, thisone's for you.
Let's dive in, matthew.
We are so excited to have youon the show here today.
(01:02):
Thanks so much for taking thetime.
I'm going to dive right intothings.
I know you have an incrediblestory and an incredible journey
around how you arrived where youare today.
You have a startup background.
You have a passion for buildingbusinesses.
I would love to start offdiving right into your journey
in building Fishbone and howthat brought you to where you
are today.
Speaker 1 (01:22):
Thanks, keith, it's great to be here.
My story started a long timeago in IT outsourcing and I
worked for some very largeoutsourcing companies and worked
with a lot of organizationsacross Canada and I saw systemic
failures in outsourcing and Iwas part of some outsourcing
engagements that worked reallywell, but there were more and
(01:43):
more that just did not and Ispent a lot of time
contemplating why was that?
And if I could do somethingdifferent, what would it be?
And I felt like if I couldmeasure the performance of
outsourcing better anddifferently than what was being
done, then I think I could helpclients extract more value from
those relationships andbasically create an
(02:04):
accountability engine over thoserelationships.
It really crystallized for mewhen there was a large energy
company in town with tens ofthousands of employees and the
CIO, who was new, surveyed theirentire community and asked them
how IT was performing for them.
They had four major outsourcersthat were delivering services
and in any given month they wereall green on their SLAs.
(02:27):
But 60% of the population ofthat company were not very happy
to very unhappy with theservice that was being provided.
And it led me to this ahamoment that there's this
customer experience chasmbetween what a service level
agreement says and what acustomer actually experiences
through service delivery.
(02:47):
And so I gathered someexecutives that I could trust
with a secret, that I wasthinking about doing something
different, and I gave them beerand pizza over a series of
sessions.
I called them my customeradvisory board and I walked them
through what I thought theproblem was and when we agreed
on the problem statement, Iwalked them through what I
thought the problem was and whenwe agreed on the problem
statement, I walked them throughwhat I thought the solution was
(03:09):
.
And it was very iterative andthey had some really valuable
feedback for me through thatprocess and so when we landed on
the solution statement, Iturned to them and said that's
fantastic.
Thank you for your help.
Now I need your money and withthat I launched Fishbone
Analytics as a customer-fundedcompany.
We walked into a couple ofcontracts right away and our
(03:30):
mission was really to build ananalytics engine that would take
all the data that was createdthrough service management and
service delivery and apply ITILand Six Sigma kind of deep
analytical views so that wecould measure performance
differently and better than whatwas being done previously.
And that worked very well untiloil went to $20 a barrel and the
(03:52):
analytics that we wereproviding was really a
discretionary spend and I waslooking at an oil recession in
Calgary and realized that A Ineeded to diversify my client
base and geography and I neededto really listen to what our
customers were asking us to do,and I had dinner with one of my
clients and he was talking abouthis cost of managing a
(04:14):
ServiceNow application and Imore or less said tongue in
cheek why don't you let us dothat?
We're taking all the data fromit?
And he said okay, and so then Ihustled, like entrepreneurs do,
and I hired a very intelligentguy that I used to work with,
Mark Raychuck, and asked him totake a course, and he did, and
then we took on managing thisapplication and that was the
(04:36):
beginning of a pivot into whateventually became a full
ServiceNow practice and FishboneAnalytics, and that's really
where we grew and scaled ourbusiness and led to a successful
exit to a private equitycompany in California.
Speaker 2 (04:49):
I love that story and I think that problem that you
described at the very beginning.
I come from the serviceindustry and there's so many
times when you're pointing atthis board of all green and
you're saying all of the checkmarks are saying the right
things, even NPS could be strong, your SLAs could be strong, but
there's something that's kindof hiding behind the curtain.
That just doesn't feel right,and so the ability to measure
that is, I can see, incrediblypowerful.
(05:12):
How did you find that pivotfrom the data analytics company
around customer success toreally honing in on service now?
Was that a hard pivot for you?
Was it easy because yourcustomers took you there?
What did that feel like?
Speaker 1 (05:23):
Yeah, I think the advantage of being a customer
funded company is, every month,your customers are voting with
their dollars, and so you reallyhave to follow the thing that
is most important to them.
So I would say when we werepivoting I wouldn't even
characterize it as a pivot, Iwas just following the revenue
and following our customers,with a focus on making our
customers really happy At thebeginning I didn't wake up and
(05:45):
go we're going to become aServiceNow partner.
We just grew into that andrealized, just based on good
fortune and good timing, thatwas a market that probably had
more legs than what we weretrying to do previously, and so
it was really only in hindsightthat I look back on it when, oh,
we actually pivoted the company, like we started doing A and
ended doing B, and thankgoodness we did that.
Speaker 2 (06:07):
Yeah, and I feel like that ability to take those
leaps and take those steps isreally important.
I was on a hike last summerwith someone just as we were
starting our business, and heradvice to me was don't fall in
love with your idea, fall inlove with the problem.
And if you chase the problems,your business is going to change
and evolve a hundred differenttimes in a little iterative
steps.
But if you get stuck on reallyproving out your initial idea,
(06:29):
sometimes you can fall into alittle bit of a trap.
Speaker 1 (06:31):
Absolutely, yeah.
Yeah, you have idea bias?
Yeah, my idea is fantastic.
Why don't you understand that?
Speaker 2 (06:37):
Yeah, so then I'm assuming you had a tremendous
amount of experience learningfrom some of these organizations
through the analytics work,through the ServiceNow work.
What are some of those insightsthat you learn from those
initial engagements that arereally shaping what you're
building with EthicRhythm?
Speaker 1 (06:55):
Yeah.
So Fishbone was, I guess, a10-year journey for me,
including the time I spent withthe company that acquired us,
and it was really.
I couldn't have asked for abetter education through the
whole process, everything fromstarting a company and
contemplating cash flow and theimplications that it has on
scale or vice versa, to bringingstructure to how you operate a
(07:20):
company and also how you deliverservices.
I've always been verypassionate about creating what I
call raving fans and I alwaysjust subscribe to this notion
that if you keep your clientsreally happy, a it's good for
long-term revenue Like it's justgood business fundamentals, but
also they become the bestmarketing engine that you could
have.
And I would say, going throughthis process I maintained my
(07:41):
passion for creating raving fansand I really got schooled on
the fundamentals of business, offinance, of contracting and
just managing an operation, andso I've taken a lot of those
lessons forward and I can giveyou like a few examples of
things that I learned that I amlike absolutely taking forward
(08:02):
in my new company Ethic Rhythm.
One is that Fishbone greworganically.
I had a plan, but it wasn'tvery structured and I didn't
think about things like equityfor employees, so we never had a
corporate structure set up thatwe could easily allocate equity
to our team and when it cametime for an exit, we ended up
(08:23):
bonusing people that worked forus and I was very happy to do it
.
But it was a terriblyinefficient use of our capital
because everyone takes that asemployment income and half of
the money goes to the government.
In Ethic Rhythm we'restructuring it so that all
employees are equityparticipants, because A we want
to be able to deliver cash inthe most efficient, tax
efficient manner possible, butalso I think it changes the
discussions that you can havewith your team.
(08:45):
If everyone feels like they'vegot the owner's mindset, then
it's permission to havedifficult conversations about
the things that you have to doas an organization at times to
react to situations like whatwe're finding ourselves now with
tariffs.
So that was a big lesson justallocating equity because it's
the right thing to do andbecause it helps you drive your
(09:06):
business forward with everyonein the same boat, rowing in the
same direction.
Something else I learnedFishbone, I feel like, for the
60-ish people that we were whenwe exited the business.
We were quite a well-runorganization.
We had plans, we had structure,we had KPIs and benchmarks.
We knew what we were doing butwe were not built for scale.
(09:28):
We were not capable of goingfrom 60 people to 500 people and
that had implications for usafter the acquisition, where all
of our processes changed toanother operating standard and
tools changed.
It created, candidly, a lot offrustration for our team and I
would not like to be in thatposition again.
(09:50):
So, as I build Ethic Rhythm,before we hired anyone external
into the organization, before welanded on our value proposition
, before we started looking forcustomers, we spent four months
just building the infrastructureof the organization, putting in
our financial system and ourprofessional services automation
platform and our HR managementsystem.
(10:12):
So when we were ready to start,we weren't having to think about
these things, we weren't havingto build on the fly.
It was already established andit sounds ridiculous, but it was
a big achievement that ourfirst employee that we hired,
jamie he had a laptop before hestarted.
Oh wow, it was secure and itwas in our security model and
(10:33):
his email accounts wereprovisioned and he had a system
to log into to enter his payrolldetails, like it was just done.
And so, just like onboardingthe first employee and not
really screwing it up, was likea big accomplishment.
And now we're at the pointwhere we're starting to look at
scaling.
We're hiring more people, we'rein customer acquisition mode
and I just don't need to thinkabout how are they going to
(10:54):
request their T4 slip, or howare they going to request time
off, or how are we going toaccount for that in our
scheduling software.
Speaker 2 (11:01):
Yeah, so you can really focus on growing the
business and building a business, rather than putting in that
infrastructure, growing thebusiness, building the business,
but also becoming astandards-based delivery
organization.
Speaker 1 (11:12):
I talk a lot about raving fans, which I've
mentioned, and I feel like oneof the secrets to creating
raving fans is to be veryconsistent in what you deliver.
A hypothesis years ago that youcan deliver consistently bad
service and your customers mightbe okay with you.
You can deliver consistentlygood service and your clients
will be really happy with you.
If you deliver inconsistentservice and they don't know what
(11:33):
to expect from you, then thatis a recipe for disaster.
We're really focused onbecoming a standards-based
delivery organization, so thatwe have scheduled checkpoints
with our clients, that we areannotating code in a particular
way, that everything isstructured and we have scheduled
checkpoints with our clients,that we are annotating code in a
particular way, that everythingis structured and we have
accountability down to theindividual that's delivering a
service for our client, but alsoso we can share accountability
(11:54):
with our clients on the thingsthat they need to do to make us
successful in their environment.
Speaker 2 (11:59):
Of course, and I think that's a really
interesting jumping off point,because if you look and I've had
interesting discussions withfolks like even like CTOs out of
Silicon Valley and they're a200 person organization and
their deployment strategy forthat first day is, well, go grab
a MacBook out of the closet andthe ability to have that
infrastructure in place, I think, is a huge advantage,
especially in a space like AI,which is so in flux right now.
(12:22):
There's so much opportunity butalso a lot of uncertainty about
what it really means.
So, in that AI space and I dowant to spend some time diving
into your thoughts andperspective around AI but let's
first start with ethic rhythmand tell me about the
organization.
I know we've talked about thestructure.
I know we've talked about someof the foundations you put in
place, but what is it thatyou're delivering to customers?
(12:43):
What is it you're looking tobuild and what's the journey
that you're on?
Speaker 1 (12:46):
Yeah, so our journey is to become the most trusted
Palantir partner in theecosystem.
Speaker 2 (12:53):
Sorry for our listeners.
Speaker 1 (12:54):
So who's Palantir?
Yeah, palantir is a US-basedenterprise intelligent workflow
and AI organization.
They are the antithesis of SaaS.
So where you've got workflowsthat need intelligence that you
can apply AI to that you don'twant to manipulate your business
(13:14):
process to fit into, palantiris a great option because it's a
very flexible platform whereyou can consume enormous amounts
of data from as many sources asyou can contemplate, have that
data in a central location andthen apply workflow to it, make
that workflow intelligent andthen bring AI to bear.
We talk internally about thisconcept of a self-optimizing
(13:37):
enterprise, and for me, that'san enterprise that will leverage
technology to the best of itsability and at some point in the
future, that technology isgoing to start to identify
inefficiencies and processes andstart to self-correct it, at
first with humans in the loopand then potentially
autonomously.
And when an organization getsto this vision of the
self-optimizing enterprise, Ifeel like they're going to have
(13:59):
an unfair competitive advantageand they're going to return more
shareholder value.
Speaker 2 (14:03):
Amazing.
So what role does yourorganization play in helping
customers get there?
Because obviously that is areally interesting vision and I
think that there's lots ofdiscussion around the role that
AI is going to play in this newworld of business.
But in taking those first steps, what's the role that you play
with companies and organizations?
Speaker 1 (14:21):
Yeah, I think step number one is helping them
identify use cases where there'sa return on the investment.
Like all good platforms, thereis a cost to the platform and
there's a cost to implement theplatform.
Compared to the typical SI route, where you will subscribe to
many platforms and then stitchthem together, there's an
(14:41):
opportunity for considerabletechnical debt and also reliance
on SIs who have high consultingrates and are there for years,
not months.
Notwithstanding that, there isa cost to implement and if
there's no real return, thenit's not a project that should
be contemplated.
So I think step one is sayingwhat are the biggest problems
you've got in your business and,if you could solve them, what
(15:03):
does that mean for you as anorganization?
Are you improving your employeeretention so you're reducing
your HR costs?
Are you creating new revenueopportunities?
Are you becoming more efficient?
Are you satisfying a compliancerequirement that could stop you
from being fined, however manymillions of dollars?
So once we land on those usecases, then it's really about
proving out the model consumingdata, working with the clients
(15:25):
as subject matter experts oftheir own business and being
able to apply the workflows andAI on top of that so that they
can learn and drive the outcomethat they're looking for.
Speaker 2 (15:35):
Interesting.
So is most of the work thencoaching leaders on how to
identify those things.
Do you go in and help them findthem?
Because a lot of the differentbusiness leaders that I talk to
they are very curious about AI.
There's a degree of healthyoptimism, there's an expectation
from their board, from theirshareholders, from their
executive leadership teams to dosomething with it.
(15:56):
But I feel like where peopleare getting stuck is those first
steps of what do I practicallydo?
How could you advise a leaderto even identify some of those
workflows and kind of what makesa really good candidate for
initial AI?
Speaker 1 (16:09):
jobs.
Yeah, so I think it comes downto what are the problems that
you face as a business.
So we're talking to anorganization in Western Canada
that is involved in naturalresources and they have a
business unit that isunderperforming by about 60%
compared to industry, and sothat becomes the basis of the
conversation.
(16:29):
Well, why is that happening?
What is unique about whatyou're doing that is leading to
these inefficiencies?
And really for them, we'vediscovered that it has to do
with resource and assetmanagement and utilization.
They are distributed over for acouple of provinces and they
have these big heavy assets thatwill get stranded out of sight
and not used or someone thinksis not available.
(16:52):
So then they go rent a heavyasset to work on a project.
Meanwhile they've got this idleasset sitting over here and
then just being able to schedulepeople and stitch projects
together in a way that is mostefficient.
Our thesis with that clientright now is that we can help
them cut their inefficiency inhalf by the end of this year.
They'll still be trailingindustry, but by the end of next
(17:14):
year they expect to be at theindustry standards for gross
margin on that particularbusiness unit.
Ai comes into it by helpingschedule and plan out asset
utilization and asset location,as well as the individuals that
are going to be operating thosemachines and completing the work
.
Just to give you an example, wecan consume data from things
(17:36):
like Caterpillar and we can seewhen assets are being used and
when they're sitting idle.
We can see where they're, atwhat locations they're at and
how long they've been sittingthere.
We can see where they're, atwhat locations they're at and
how long they've been sittingthere.
We can pull all that data inwith other metrics from the
business and say what is theutilization of this asset?
How much time is it sat runningidle?
Because someone is, they'retaking their lunch break in the
(17:58):
air-conditioned cabin, whichdestroys warranty, which
consumes fuel, which erodes theresale value on that device, and
there's lots of implications tosomeone just having lunch in a
cab in an air-conditionedenvironment.
Right, we can take all thesedata points and then start to
map out.
This is the order in which youshould be completing projects.
These assets should beallocated to those projects and
(18:19):
these are the teams that shouldbe involved in that, and for a
human to do all of that, itwould be a lot of analysis and
I'm not sure that they couldactually get to end of task on
that without the use of AI.
Speaker 2 (18:29):
Well, that's the question I was going to ask.
Is it something that they justdon't have, the bandwidth for,
the capacity, for the capability?
Because it sounds like a hugefinancial problem for them.
It sounds like it's costingthem a ton of money.
Have they just not been able toapply people to these
challenges?
Or where does AI come in tosolve those things?
Speaker 1 (18:47):
So in this particular organization, they have
multiple business units, so this, like we got one business unit
that's underperforming, but whatyou create can be applied
equally to the other businessunits, and I think it's just not
enough capacity within a humanor a team to be very efficient
at this, because there's so muchdata that gets consumed and so
(19:09):
many different ways that you cancorrelate and so many trends
that it's just difficult for ourbrains to be able to comprehend
.
Right, like, how do you takedata that's over here and data
that's over here that you maynot have total visibility to or
understand the context of, andthen bring those together to
learn something new that youdidn't know yesterday?
Speaker 2 (19:26):
I think that's very difficult to do and AI is a
perfect use case for that, andso I'm going to open up a tiny
bit of a can of worms here witha conversation I know that we've
had previously around SaaSapplications, and so you talked
about Palantir being a littlebit of the SaaS killer, and
there's been some big statementsfrom saying, hey, you know what
?
Saas is dead.
What is the problem thatorganizations have with their
(19:47):
SaaS platforms right now, andhow does AI help them solve?
Speaker 1 (19:50):
that I think.
So I'll talk about SaaS andI'll talk about AI.
Yeah, Generally, saas requiresa business to conform to the
standards that have beenestablished in the SaaS.
If you choose to go outside ofthat, then you will often create
technical debt and a lot ofcomplexity when it comes to
(20:13):
managing the currency of thatapplication, but also all the
interdependencies that surroundthat application and the
integrations.
I think we're at a point nowwhere platforms are so capable,
like Palantir, where you canjust develop within many
applications within one platformthat work well together, that
(20:33):
eliminate the interdependenciesand the technical debt that gets
created, and you build them toalign to the business process
that you've been creating overdecades in your organization,
whereas SaaS, you're having tochange some of those business
processes to avoid technicaldebt.
So I think, as a business user,you don't want to conform to
(20:54):
what someone else has prescribedin industry.
Sometimes those industrystandards are better for sure,
but sometimes they're not.
Speaker 2 (21:01):
That makes sense because a lot of the
conversations and I'm sureyou've had these before even in
your previous life where it'scheaper, more efficient, more
practical to adjust yourbusiness process rather than try
to customize a software orplatform to do the thing that
you want to do, and maybe youhave 30 people that are doing it
a certain way and instead ofautomating it, just add X, step
(21:22):
over here and it actually startsto make way more sense versus
trying to write a whole bunch ofcode or customizations around
that ecosystem.
What you're saying is that inthis new world, that challenge
goes away and you canessentially start to map exactly
how people work and solve itwith technical amplification,
rather than having to fragmentit to fit certain molds that are
(21:43):
kind of precast.
Speaker 1 (21:45):
Yes, I think one of the advantages of Palantir.
I'll just take a step back.
Before we landed on Palantir,we evaluated a lot of different
platforms and applications.
We contemplated being AIconsulting generalists, but I
honestly just couldn't get myhead around that and I had a
fear about working with a clientand recommending a platform
that may not exist in two years,because there's so much hype
(22:07):
and there's so much investmentinto AI today that 90% of those
companies could well not existin the next 24 to 36 months just
because they run out of fundingor they get acquired by one of
the big players.
And it was really important tome that we landed on a platform
that we felt very comfortablewith.
So we looked for something aplatform that was enduring, that
would be around for the longhaul, that provided deep
(22:32):
capabilities around enterprise,intelligent workflow, that could
help our clients realize avision of their self-optimizing
enterprise and that was very AI.
Forward.
Through our process ofelimination, we got down to
Palantir, which is a 19-year-oldcompany.
They are very high growth, theyare profitable and they're
debt-free and they're investingsignificant amounts of their
(22:53):
capital in developing thePalantir Foundry and AIP
platforms.
Speaker 2 (22:57):
A lot of work that people do is in terms of how
evaluating these platforms, andtheir mindset is going to go
from evaluating SaaS platformthat does this specific thing to
now making a much biggerdecision around evaluating a
singular platform that candeliver an incredible amount of
enterprise value.
But also, what I've seen isthat as the value of those
decisions goes up, so does thedecision paralysis.
(23:18):
If you were to lend some of theexperience that you had in
evaluating over the last fourmonths, what are some of the
considerations you had?
And you talked about thebusiness.
You talked about the stability.
Was technology part of that oris it more of a kind of a
business conversation at thatpoint?
Speaker 1 (23:36):
So I think it's both and thank you, you triggered
where I was going with my lastpoint which was that it doesn't
have to be a fundamental shiftto start.
All companies have platformsthat they have invested hundreds
of thousands to millions tohundreds of millions of dollars
in, and one of the things I likeabout Palantir is they don't
(23:58):
necessarily have to walk awayfrom that investment.
Palantir has a flexibility topull data from many sources.
There's a use case for AT&Twhere they have 995 integrations
pulling data into Palantir'sFoundry platform and then they
operate within that data andworkflows inside of Palantir.
They're not requiring people towork in these 995 applications.
(24:21):
The work moves into Palantirand it writes back to these
applications.
If you're not ready to rip andreplace and rebuild your ERP or
your data warehouse, palantircan sit over top of that.
It can consume the data it canwrite back.
So, regardless of where acustomer is in their journey in
trying to create a moreintelligent operating
(24:43):
infrastructure, palantir fitsreally well on top of that and
over time you can deprecatethose platforms and rebuild
those workflows in a moreintelligent way inside of
Palantir.
Speaker 2 (24:53):
Interesting.
So one of the things that youbrought up which has popped into
my head is this idea of SaaS isgoing away.
You're really saying there's aplatform that people can build
on.
Is there entry gates to that?
Is there a size of organizationthat works for it?
If I'm a large oil and gascompany, obviously I think that
makes a lot of sense.
If I'm a $10 millionconstruction company that has a
(25:16):
couple of folks and doesn't havea ton of technical depth, is
this something that's in mywheelhouse or is this something
that is going to solely servefor the big guys out there?
Speaker 1 (25:30):
It used to be for the big guys.
I think Palantir has recognizedthat there is a downstream
market opportunity for them, sothey created what they call
Palantir for Builders or Foundryfor Builders.
So it scales down to startups.
So we are a startup, we're 10or 12 employees and we own
Foundry and we're building someof our core workflows inside of.
Speaker 2 (25:46):
Foundry.
So this is, I think, can leansome really practical examples
for people that are buildingtheir businesses.
What are some of the and, ifyou're okay sharing this, some
of the practical examples of howyou're using AI and that
Foundry workflow within.
Speaker 1 (26:00):
EthicRhythm.
Yeah, every week is a newadventure for me, because the
team is doing more things.
The use case that we're workingon right now is around helping
us identify candidates to gothrough our hiring process.
So I more or less click baitedinto creating a job opening on
LinkedIn, which was free andended up costing me $500.
Speaker 2 (26:25):
I have the same story .
We can share that oneafterwards.
Speaker 1 (26:28):
Yeah, and by the end of the first day we had
parameters so minimum of threeyears of Python and TypeScript
skills must be in Canada, and afew other variables, and I
really didn't expect that wewould get very many resumes from
this.
In the first day we had 120resumes.
By the end of the first week wehad 400 resumes and when you're
(26:50):
a small organization, to takethe time to review, like manual
review, all these resumes isvery difficult.
We created a workflow where wecan ingest all the applications
and then ai will parse throughthe resumes and it will provide
a summary of the experience andthen a summary paragraph and
then also make a recommendationwhether this is someone that we
should be talking to or not.
And there's a couple of thingsI like about that.
(27:12):
One is it's highly efficientand b it takes away the bias
that someone would have inreading a resume.
So they're not looking at aname and introducing bias.
They're not looking at thecompany that they used to work
for and introducing bias.
It's just based on the skillsand experience that they've
expressed in their resume andthen providing a summary of that
and making a recommendation.
Speaker 2 (27:35):
Is there any other examples you can think of?
Speaker 1 (27:36):
By the end of the year.
I would venture that most ofour business will be run on
platform.
Speaker 2 (27:41):
So I'm going to pivot a tiny bit into more general AI
conversation.
So you talked about the usecase of your HR hiring and
there's a ton of productized AIelements that are out there, so
not dissimilar to SaaSapplications, but AI for very
specific use cases.
How are you viewing that in theecosystem, and is that
something that companies shouldbe exploring for niche elements,
(28:04):
or should they be looking atthe broader AI conversation
before investing in some ofthose individual elements?
Speaker 1 (28:12):
I think they should do both.
I think there's some reallyinnovative companies out there
and you can learn from them.
My fear about the innovationthat's happening in the AI
market in general right now isthere's so much hype and there's
so much investment that isgoing in and they're wildly
overvalued companies and, again,I'm not sure that I have the
(28:35):
wherewithal to pick the winnersand the losers out of this.
As an operator, when I'mlooking at platforms, that's
going to be part of my decisioncriteria Is this niche platform,
this niche capability, going toexist in two years, or is it
going to lose funding or getacquired by a competitive
(28:55):
organization who sunsets it?
There's many things that canhappen, and this happened during
the dot-com boom, right, so I Iwould be fearful of picking
niche players yeah, it's almostlike picking, it's almost
gambling at this point.
Speaker 2 (29:07):
right and you're, and you find these individual
organizations and we saw itduring the dot-com boom and
we're seeing it even with theSaaS boom.
The barriers to entry become solow, the valuations become
crazy, which increases themarketing budget, and
essentially all of theseorganizations contribute to an
enormous amount of vendor sprawl.
And I think we're starting tosee some of that same thing
(29:28):
happen on the AI front, whereall of these individual business
units are picking these nicheplayers without an overall
strategy.
Speaker 1 (29:36):
I also think it exposes your risk factor goes up
quite a bit when you've gotsprawl and you don't understand
how your data is being used orhow it's feeding the large
language, model training and soon.
I've got fear about that.
I do think there's a lot ofconsumerization around AI which
is infiltrating boards andexecutives and they're looking
(29:57):
at the chat GPTs and the Geminisof the world, which do amazing
things, and they're trying tofigure out how to incorporate
that into their business.
So I think looking at theoverall strategy and finding
enduring companies is reallyimportant.
I had a conversation with a CEOof a fintech related company in
the US earlier this week and wetalked about the promise of AI
(30:22):
is not being realized inside ofthe enterprise yet, and that's
the opportunity and thechallenge, I think for a large
enterprise today is to figureout how do they actually
leverage AI so that they candrive intelligent workflow, so
that they can bring AI to bear,so that they can build trust
with the AI platforms, so thatthey feel like they can make
(30:42):
autonomous decisions at somepoint in the future.
There's some great use casesthat have human in the loop
today, with Palantirspecifically, where they're
really renovating supply chainmanagement systems and
transportation systems, butit'll be a while before
decisions are made autonomously.
They have to have humans in theloop.
Speaker 2 (31:04):
That makes a lot of sense.
There's no shortage of some ofthese bold claims in the AI
space.
What are some of the red flagsthat you're seeing when you see
salespeople, marketing et ceterain the industry that are making
these big, bold claims aroundwhat AI can do for organizations
?
Are there any red flags thatjust stand out to you that
buyers should be looking out for?
Speaker 1 (31:26):
I don't know if I'm not sure I can comment on how
sellers are selling theirplatforms.
I do think that buyers andenterprises need to really
contemplate the implication ofbringing in AI A.
It's not easy, it requiresintegration and it requires data
to drive the decisions.
It also affects how people workand there's a big
(31:48):
organizational change managementcomponent that is required when
you're introducing theseintelligent workflows into your
environment.
I don't think that should beunderestimated and I think
executives need to contemplatethe fortitude that they have to
embrace change, because it isn'teasy.
It's a very disruptive and ifit's not well thought out and
(32:08):
it's not well executed andpeople don't understand why it's
important to do what you'redoing, like many technology
initiatives, it's destined forfailure.
Speaker 2 (32:17):
Yeah, so does that go back to the original comments
you made around really findingthose practical workflows, those
practical use cases that theirteams and people can connect
with, they're consulted on first?
Is that why that step is so?
Speaker 1 (32:28):
important?
Yeah, absolutely.
I think A.
Is there an ROI?
I think that should be thefirst question.
Can we actually do something tochange our earnings per share
and our shareholder value?
And if there is, then how do weapproach this in a thoughtful
way that's actually going tolead to the outcome that we're
intending for?
Speaker 2 (32:45):
Yeah, so the concept of ROI and AI is a really
interesting one, and when youshare the example earlier of the
utility of different worksiteequipment, that becomes really
clear to me, from an ROIperspective, how you can measure
that.
I think a lot of leaders havehad this exposure to ChatGPT or
to Copilot or to Gemini or tosome of these platforms where
(33:06):
they are big investments butthey're having a really hard
time quantifying that return andoutput when it comes down to
those individual employees.
Is there a way or a secretsauce that you can think of in
terms of best measuring ROI forsome of these AI initiatives and
how to tell whether or notthey're actually working?
Speaker 1 (33:24):
Yeah.
So I think that is acomplicated question.
So, even in the example I gaveyou with a resource company that
we've been talking with theirexecutives, how are we going to
definitively measure the benefitof the cost of this project,
because there's factors outsideof our control?
Will people take the cue that'sgiven to them by the platform
that says there is someone thatis having lunch in their big
(33:46):
heavy excavator and send them amessage saying, hey, why don't
you go into the work shack andstop idling the machine?
If they don't do that, thenthere's not going to be a
benefit for that little tinyslice, right?
So there's lots of variablesthere.
It's out of control.
I think when you are looking atoverall programs, there's a use
(34:06):
case for the supply chainorganization, the Support 20s.
That's not our client, it's aPalantir initiative, but they've
been able to quantify the valueof improving their supply chain
using data and AI on thePalantir platforms and they have
generated huge savings and alsodramatically improved the time
(34:30):
to provide supplies to theirrestaurants.
I think they've got about 4,200restaurants across North
America.
So when they run an example,when they run a sale event so if
they put up like a Frosty onsale, I think it's Frosty's.
If there's a particular flavor,they can start to understand
what are the sales metricsaround that particular sale
(34:51):
event and are there inventoryshortages in particular regions?
And if so, then how do theyload balance that inventory?
How do they short ship storesthat have a surplus of inventory
to feed stores that have ashortage of inventory?
And how do they engage theirsuppliers to create more syrup
or whatever the specialingredients are that they need
to be able to support that andthey've been able to get dialed
(35:12):
in on their ROI in terms of timeto deliver and the impact on
sales.
And I can't talk to allindustries, but it's a needle
that has to be threaded in orderto substantiate the value of
the investment to the boards.
Speaker 2 (35:25):
Totally.
Do you think that executivesare prepared right now to be
able to have those conversations, or what steps in education do
you think needs to take place atsome of those boards and
executive level to evenunderstand the implications of
some of these technologies?
Speaker 1 (35:39):
Yeah, I think boards need to understand the
difference between consumer AIand enterprise AI.
Chat GPT in and of itself itmay bring some user productivity
, but it's not going to changethe scale of your supply chain
organization.
It can support that.
It can integrate with someplatforms like volunteer that
can do that, but it's not chatGPT in and of itself that's
(36:01):
going to accomplish that.
Speaker 2 (36:01):
for you yeah, is that one of the biggest
misconceptions that you see withAI Cause I feel like a lot of
people do feel like AI is how doI apply chat GPT to my business
, or how do I add a chat bot, orI feel like it's very
interactive based still versus.
I think some of those truemeaningful chunks forward are
when you start to apply AI todecision-making and to
(36:22):
enterprise data there.
Speaker 1 (36:24):
Yeah, yes, I think there's a consumerization effect
.
I think it's like when theiPhone was introduced in 2007,.
It took years for applicationsto catch up what iPhone did, but
there was an expectation.
They call it ServiceNow used tocall it the Sunday to Monday
effect.
You were on your phone onSunday and you've got all these
beautiful applications andvisual displays, and then you
(36:45):
show up to work on Monday andyou've got green screens or
whatever.
So I think we're going through abit of that with AI today and
the challenge is to figure outhow do we incorporate that into
the enterprise.
So, instead of asking aquestion and getting a contract
reviewed by ChatGPT as anexample which I've done and it
was remarkably fast, like itprobably saved me two hours of
effort, which is fantasticthere's a logistics organization
(37:08):
in town that uses Palantir tounderstand the implications of
their distribution network.
So if there is an event in aparticular part of the continent
that is going to affect theirdistribution network, ai,
through Foundry, is coming backand making recommendations on
alternate routing based onservice level agreements that
(37:30):
they've got, based on theinventory that they're shipping,
and it's coming back and sayinghere are the three best options
for you to reroute theseproducts.
Which would you like to choose?
They choose and then itexecutes some of those orders
Like that's enterprise AI.
That's not chat GPT saying hey,you should be doing this, or
yes, I looked at this and itlooks fantastic.
Speaker 2 (37:50):
So that level of data , that level of integration,
brings up some reallyinteresting questions around
ethics.
And I know that Ethic Rhythmthe name itself, it's a node to
ethics.
What does ethical AI mean toyou very practically, when
you're embarking on some ofthese changes with your
customers?
Speaker 1 (38:13):
I think it's using the opportunity to remove bias
from the information that'spresented and ensuring that the
outcomes aren't going to haveunintended consequences.
This is such a large, difficultarea.
Ai is going to lead to ethicalquandaries, no matter what, just
by virtue of what it is capableof doing or the promise of what
it's going to be capable ofdoing over the next few years.
I'm not totally sure how wegrapple with that as a society
(38:35):
Inside of an organization.
I think it comes back toorganizational change management
and understanding what is theimplication of this AI.
How does it affect the waypeople work, or how many people
do the work, and then supportingyour population through that
change?
Speaker 2 (38:49):
Yeah, is there a way and you talked about the ability
to manage that change, or Ithink there's always going to be
in this trade-off of move fastand break things or move slow
and meticulously and figure outsteps along the way?
There's someone that I talkedto that had alluded to the idea
that some of these enterpriseorganizations are like tanker
ships sometimes and you do needthe little speedboats to kind of
orbit them to be able to taketheir cargo off and bring it to
(39:12):
shore.
Is there a way that it's topractically move fast in AI
while not breaking some of theseethical considerations?
Speaker 1 (39:24):
I would like to think so.
I think move fast and breakthings shouldn't apply to ethics
.
Personally, I think when itcomes to ethical standards,
everyone in an organization hasto stand up for ethics, whether
that's the bias that thingscreate or that you can eliminate
.
So I don't feel like thatshould be part of the move fast
(39:46):
and break things mantra.
There's other opportunities tomove quickly and hopefully I
don't know, I don't reallysubscribe to the move fast and
break things to be candid I alsodon't subscribe to moving
really slowly and getting intoanalysis paralysis.
I think there is a happy mediumthere, but I've never really
contemplated that with thecontext of ethics.
But I personally wouldn't bendon my ethical perspectives in
(40:12):
favor of speed.
That's not my character.
Speaker 2 (40:15):
I love it.
For leaders that are evaluatingAI this year, what are a couple
of things that they can focuson to avoid falling into rabbit
holes or wasting time or money.
What's the things that theyshould be looking at as they
build out their strategies?
Speaker 1 (40:31):
I think it comes down to what are the biggest
problems they've got in theirenvironment, in their operations
.
Is their data available or thatcan be created to lead to a
different outcome, and how willthat affect their business?
I don't think they should belooking at the platform.
I don't think they should belooking at capabilities, which
(40:52):
is is how solutions have beensold in the past.
Right, I have this capability.
It will solve this problem foryou and it puts you in a bit of
a box.
I think the opportunity now isfor executives to eliminate the
box and just think about whatare their core business problems
.
What is it that they're tellingtheir shareholders they need to
solve to be more competitive,to reduce costs, to respond to
(41:15):
existential challenges liketariffs?
What are those challenges thatthey need to solve for?
And then thinking about howthey can approach them
differently.
Speaker 2 (41:23):
Matthew, thank you so much for this amazing
conversation.
You're clearly veryknowledgeable in this space.
If someone wanted to connectwith you and learn more, what's
the best way for them to reachout?
Speaker 1 (41:33):
Yeah, I think going to our website ethicrhythmcom,
which is like ethical algorithms, and also on LinkedIn, matt.
Speaker 2 (41:40):
Nielsen.
Perfect, and I'll make sure tolink you in the show notes here.
I really appreciate your timetoday and thank you so much for
coming on the show.
Thanks for having me.
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