July 2, 2026 • 14 mins
In this episode of "Full Tech Ahead," host Amanda Razani interviews Sarah Edwards, CPSO at Kantata. They discuss the strategic implementation of AI in the services industry (consulting firms, agencies, and B2B IT service teams).
Edwards argues that measuring AI success solely through the lens of traditional productivity and speed is a massive mistake for services firms, as charging by hours while simply doing tasks faster inevitably leads to a financial "race to the bottom."
Instead, she advocates for shifting toward an AI-native operating model that powers the "expertise economy."
Key Quotes
- "Measuring productivity [is] the wrong way to measure AI success... if I'm just delivering things faster and faster, traditionally billing my time based on hours or days, well, how am I growing my revenue? That just becomes a race to the bottom."
- "Traditionally, expertise has been reliant on tribal knowledge... AI is disrupting all of that. For the first time, we can really compound that expertise across your business."
- "AI for me is not just about getting faster. It's how do I get better? Because unless I get better... I'm not going to win."
- "In services, quality has been something we've always struggled to measure... Now with the help of AI, we can truly start to gather and measure [sentiment] during project delivery."
Takeaways
- Ditch the Speed Metric for Quality: In professional services, utilizing AI to execute work faster shrinks billable hours without adding value. Firms must stop treating AI as a siloed efficiency tool and start measuring leading indicators of revenue growth, margin improvement, and transformed service delivery.
- Capitalize on Compounded Expertise: Historically, consulting firms were constrained by "heroics" and individual expertise, which created an operational ceiling. An AI-native model unlocks years of hidden project context and conversation logs, instantly upskilling every consultant to the level of the firm's best performer.
- Automate the Sales-to-Delivery Handover: One of the largest operational friction points is when sales teams "throw a project over the fence" to the delivery team. AI agents can eliminate this silo by parsing entire sales-cycle call data into comprehensive briefs, ensuring scope, stakeholder concerns, and project requirements are perfectly aligned.
- Transition to Outcome-Based Metrics: Instead of tracking static, trailing metrics like "on time" and "on budget" at the end of a lifecycle, firms can use AI to track real-time qualitative health indicators, such as client sentiment, delivery team mood, and continuous project drift, while the work is actively in flight.
Find Amanda Razani on LinkedIn. https://www.linkedin.com/in/amanda-razani-990a7233/
Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/
Visit the FTA website: https://fulltechahead.com/
Check out the Substack Channel: https://fulltechahead.substack.com/
Listen
Watch
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
SPEAKER_01 (00:19):
Hello and welcome to Full Tech Ahead.
I am Amanda Razzani, and with metoday, I'm so excited to have
Sarah Edwards.
She is the Chief ProductStrategy Officer at Cantata.
SPEAKER_00 (00:30):
How are you doing?
I'm good, thank you, Amanda.
It's great to be here.
I'm hot.
We've got lots of hot weather inthe UK, but it is lovely to be
here.
SPEAKER_01 (00:38):
Well, I hope you're staying cool.
I understand hot weather beinghere in Texas.
So I'm excited to hear our topictoday is about AI and how to use
AI tools the most effectively.
But before we go into that, canyou share a little bit about
Cantata and what services doesthe company provide?
SPEAKER_00 (00:59):
Yeah, absolutely.
And Cantata provides solutionsto purpose-built solutions for
the services industry.
So when you think about theservices industry, they may be
pure play consulting businesses,agency, IT services, or even
services teams within B2Bsoftware companies.
And we really help them powerand predict and run their
business.
(01:19):
So we provide AI-drivensolutions that really help if
you think about all the way fromscoping and winning and creating
work to delivering it, toresourcing it, to managing your
forecasts and financials, thatwhole end-to-end life cycle.
And really, how do you sort ofconnect that and how do you make
sure that every project you'redelivering is the best?
And that's really what we aim towork with our customers on.
SPEAKER_01 (01:40):
Fantastic.
Well, so you've mentioned beforethat a lot of companies might be
viewing AI in the wrong way.
And you suggest a better way.
So let's start with why ismeasuring productivity the wrong
way to measure AI success?
SPEAKER_00 (01:58):
Yeah, I think, and particularly for the services
industry.
And it's an industry I've workedin for now about 30 years.
And I think if you look back at,you know, services firms over
the last probably 30 years, theydidn't really change that much.
And their model was really builton people and human delivering
effort and me billing those inhours or days.
I think the challenge is thatnow we've got AI, which is
proving already, I mean, there'slots of successes out there that
(02:21):
it can enable people to deliverfaster.
It is an efficiency play.
But actually for a servicesfirm, that actually has a bit of
an impact because really thatjust becomes a race to the
bottom.
If I'm just delivering thingsfaster and faster, yeah, I'm
traditionally billing my, youknow, time based on hours or
days.
Well, how am I growing myrevenue and how am I really
truly growing differentiation inthe marketplace?
(02:43):
And I think in our industry,it's more about, it's less about
an efficiency play.
It's about actually how do youchange your whole operating
model as how you operate as abusiness, as an AI-native firm
with sort of AI at the core.
And to me, as I say, it's notjust about efficiency, it's
about how am I measuring thatwe're getting better, that we're
delivering more, that we'regrowing our revenue.
(03:04):
And I think at the moment,there's been a lot of, I would
say, quite siloed AI strategieswhere it's, you know, we're
making people go quicker, we'redelivering quicker for a
services business.
It's got to be more than that.
And it's got to be about howdoes that help us generate more
revenue and differentiate andwin more customers.
SPEAKER_01 (03:20):
Yeah, and to that note, you've mentioned leaning
more into pulling out theexpertise economy.
Can you explain a little bitabout what that means?
SPEAKER_00 (03:30):
Yeah, absolutely.
Again, in this industry for along time, what has expertise
been?
It's mainly been reliant on uhtribal knowledge, on individual
consultants.
So, again, lots of consultantshave built their career becoming
an expert in something.
And actually, the challenge hasalways been for services firms
that actually that gives me aceiling.
You know, I've either got togrow my talent, I've got to
(03:52):
bring more people on.
It takes a lot of time for themto become an expert and over
years become become an expert,and then I can sort of build
them out as a, you know, anexpert.
Well, AI is disrupting all ofthat.
And I think for the first time,what really excites me in this
industry, for the first time, wecan really compound that
expertise.
So to me, the power of AI andthe opportunity of AI is
actually how do you compoundthat expertise across your
(04:14):
business?
How do you move it so you'remaking every consultant their
best?
Everyone knows, you know, you'resharing all of that knowledge
and all of those lessons andconversations that you have with
clients.
All of those are learningpoints.
And actually, traditionally, wedon't surface that up.
It all remains in someone'shead, and we're not sharing that
expertise.
So I think to me, you know, wetalked about at ourselves
(04:35):
pick-off, we have this themeabout expertise to the power of
100.
But I think to me, it's, youknow, we now have the
opportunity to really amplifyand compound that expertise.
And I think that's reallyexciting, particularly in an
industry that probably is beingconstrained by, you know,
reliant on heroics andindividuals in the past.
SPEAKER_01 (04:53):
And can you go in a little further into how you're
able to tap into that expertise?
Do you mean from the datacollection of the AI or by using
a Gentic AI?
Can you share a little bitfurther how to tap into that?
SPEAKER_00 (05:07):
Yeah, absolutely.
Um, an example is, I mean,really a simple example is, you
know, as a consultant or someoneworking within a services firm
that's delivering one project, Idon't know what's happening
across all of those otherprojects, all of those many
other hundreds of customers thatyou're talking to and people are
talking to hourly and daily, allof those lessons that we're
learning across all thoseprojects.
As a consultant or as a projectmanager on a project, I don't
(05:30):
know that.
Well, actually, now that can becompounded and that can be
surfaced up to me through AI.
So actually, we have the abilityto learn from every project and
every conversation.
So even if I'm not part of aconversation, I think, you know,
traditionally again, even if youlook at project delivery, what
have we tripped typicallytracked?
We track things like is theproject on time?
Are we delivering it to scope?
(05:50):
Well, actually, now I think it'sabout how do we track and manage
the quality of that delivery?
What's the client experience?
What's the sentiment of thatcustomer?
And actually, you know, nowwe've got AI and agent that can
listen into the call, it cangive me feedback based on the
sentiment that call, it can lookat trends in terms of, you know,
what's the trends across all ofour projects and our different
types of customers with theexpertise that we're deploying
(06:12):
and helping me to surface riskto really improve the services
quality and the experience thatwe're delivering to our
customers.
And I think if you look at thevastness of all of that data,
that you know, when you've gotprojects that you're delivering
to different types of customerswith different people, different
scopes, different outcomes, Ican now collaborate and really
learn from all of that data withthe help of AI.
SPEAKER_01 (06:35):
Absolutely.
I know there are still a fewpeople that are hesitant about
the quality of the data and thefactualness.
Can you speak to your experiencewith that and how do you know
that you can trust theinformation?
SPEAKER_00 (06:52):
Yeah, I think that is a valid concern.
And I think you know, we've allseen it with our use of AI.
Again, to me, it comes back togetting some of the foundations
right.
If you don't have the data rightin the first place, I mean, the
key thing that Cantata brings ishow do you connect that data and
how do you build trust in thatdata?
Because I think unless you'vegot that data that is connected
and you've got trust in thatdata, um, then I think you'll
(07:15):
never be able to leverage andhave the confidence in terms of
what AI is going to deliver.
So I think it is about makingsure that you know you have and
how you deploy AI as well.
I don't think this is about, Ithink there's a lot of customers
that I've seen where they'vegone very scattered and they're
building specific agents.
There's all these differentagents, but actually, you know,
I think it just becomes a swirl,it becomes a mess.
(07:36):
Actually, what you want to do ishave that connected
intelligence.
How do I really connect thatintelligence and build
intelligence that is importantlylearning continuously?
Because that's what I think willbring trust and confidence to
businesses.
SPEAKER_01 (07:50):
Do you think that AI is helping to break down some of
the silos between differentdepartments and uh improve the
communication?
SPEAKER_00 (07:58):
Absolutely.
I mean, again, example in theworld I work with with services,
I would say that sales todelivery handover has always
been a massive friction point.
You know, the sales team go andsell something, they chuck it
over the fence to delivery, andthey've suddenly got to figure
out, you know, how I deliverthis to the budget and costs
that it's been sold at.
I think it's breaking down someof those barriers.
Again, I think you can bringdelivery in a lot more earlier,
(08:20):
they can listen to and get allthe insight from all the calls
that happened, all theinteractions we had throughout
that sales cycle.
So creating those, you know, oneof the things we do is create
that sort of sales to deliveryhandover brief.
How do you make sure everyone onthat team understands exactly
what conversations, whatstakeholders you engage with in
the sales process, where therewas pushback on scope or
outcomes, and making sure thatwe're really set up for delivery
(08:42):
success.
And then importantly, again,learning from that and learning,
are we scoping projectscorrectly in the first place?
So I think absolutely, I thinkit breaks down the silos all the
way across from sort of you knowsales to delivery to success.
SPEAKER_01 (08:56):
So moving forward, we've seen that this technology
has advanced quite rapidly in ashort period of time.
What do you expect is next forAI and its use cases?
SPEAKER_00 (09:07):
Yeah, I think, I mean, you're right.
I mean, I think we learnsomething every year.
You know, we launched ourexpertise engine, and I have to
say, you know, I see with myteam, we are pushing its
boundaries every single day.
And it's amazing, I think, whatwe're now showing that it can
support.
So I think, you know, thepossibilities are, I think, you
know, endless.
I think the challenge has alwaysbeen how do we link that to
operationally, how we'rechanging how we run our
(09:29):
business.
I go back to what I said at thestart.
I think AI will be limited ifyou're just looking at it on a
siloed advoc basis.
You should be looking at AI in abusiness and say, operationally,
how does this change the roleswe've got in the business, the
people we need in our business,how we operate, how we package
and sell our services?
And I think that's what's goingto come next.
(09:50):
I think people are now seeingactually, you know, I've now got
the ability to connect all ofthat data and connect from that
and learn from it.
And actually, it's enabling meto transform my business.
SPEAKER_01 (10:01):
Absolutely.
What do you think is the biggestmindset shift that leaders need
to make when it comes to AI?
SPEAKER_00 (10:10):
Um, I maybe I'm reiterating, but I think, you
know, to me, it is about like umlooking at it operationally, how
it's going to change yourbusiness.
I think coming back to the startof this, it's not just an
efficiency play.
For me, and particularly in theindustry that we serve, it can't
be just an efficiency plate.
Otherwise, it's just a race tothe bottom.
It has to be about how do wegrow our revenue as a business,
(10:31):
how do we innovate, how do wedeliver new services in new
ways, and how do we reallytransform how we deliver those
services with no longer justpeople, but with people and
agents.
And I think it's theorganizations that get that
right, that really and theninvest in getting some of those
foundational components right.
I think they'll be the firmsthat will really win and succeed
in the future.
And I think those that aren'tdoing it, I think you're going
(10:53):
to be really challenged.
And I think it will become a bitof a race to the bottom bottom.
SPEAKER_01 (10:57):
Definitely.
It will be interesting to watch.
Well, if there was one keytakeaway you could leave our
audience with today, what wouldthat be?
SPEAKER_00 (11:05):
Um, it would be to rethink your operating model.
I think look at the roles thatyou have in your business and
look at it across the end-to-endsort of, you know, don't try and
look at it in silos.
I think you've got to bring, youknow, right from the top down,
look at your business as a wholeand really think about a, do we
have the connected data and dowe have the trust in that data?
Because if you don't have that,you know, I think you'll be
really challenged to build thetrust and the confidence that
(11:28):
you need.
I think also think about theROIs that you the how do you
measure success of this?
Again, I think firms need tochallenge stuff.
The measure of success isn'tjust about efficiency and cost
saving.
The measure of success and howdo I track some of those leading
indicators that AI istransforming our business and
having an impact in terms of,you know, it's not just about
(11:48):
getting faster.
I talked to a client the otherday and it resonated with me.
He said, AI for me is not justabout getting faster, it's how
do I get better?
Because unless I get better,unless I'm delivering a better
quality service to ourcustomers, um, unless I'm
getting better in terms ofgrowing my revenue and yes,
growing my margin as well, butunless I'm getting better, you
know, I'm, you know, I'm notgoing to win.
(12:09):
So I think to me, it's what arethe metrics you can measure to
show that you are getting notjust quicker and faster, but
you're getting better.
And again, think about thatcontinuous learning loop.
How do you build and connectyour data in a way that you can
continuously learn from it?
I spoke to one of our largecustomers a couple of weeks ago
and they said to me, you know,they've been running every
project through our system forthe last seven years.
(12:31):
And it's like, well, what doesthat tell me?
You know, what does that tell mein terms of if I can truly
unlock that potential and I'mlearning from every
conversation, every project wescope and deliver and resource,
that can be really impactful interms of how I grow my business
successfully moving forwards.
SPEAKER_01 (12:48):
Yes, more about the quality, less about the speed.
Yeah.
SPEAKER_00 (12:52):
Yeah.
And I think in services,services quality has been
something we've always struggledto measure.
We've always looked at the hardmetrics.
Let's say, is it on time, is iton budget, etc.
I think now more people aremoving to outcome-based sort of
services.
And therefore, I need to measurethe quality of that.
What's the sentiment of thecustomer?
You know, what's the sentimentof the team delivering it?
And I think, you know, thosemetrics now we can start with
(13:14):
the help of AI, we can trulystart to gather and measure as
we're delivering projects, notjust at the end, but during
project delivery to make surethat we are delivering the
experience we need to.
SPEAKER_01 (13:25):
Yes.
Well, I want to thank you somuch for coming on the show
today, Sarah, and sharing yourinsights with us.
SPEAKER_00 (13:31):
Great.
It's been great to hear.
I love talking about this.
I think it's an exciting timefor all of us.
We learn something new everysingle day.
And I think, you know, I thinkfor me that's really exciting.
And thanks, Amanda.
It's been great to speak to you.
SPEAKER_01 (13:43):
Yeah, I agree.
And likewise.
And to our audience, if you haveany questions or comments, leave
those below and I'll try torespond as soon as possible.
Have a wonderful day.
Hello and welcome to Full Tech Ahead.
I am Amanda Razzani, and with metoday, I'm so excited to have
Sarah Edwards.
She is the Chief ProductStrategy Officer at Cantata.
SPEAKER_00 (00:30):
How are you doing?
I'm good, thank you, Amanda.
It's great to be here.
I'm hot.
We've got lots of hot weather inthe UK, but it is lovely to be
here.
SPEAKER_01 (00:38):
Well, I hope you're staying cool.
I understand hot weather beinghere in Texas.
So I'm excited to hear our topictoday is about AI and how to use
AI tools the most effectively.
But before we go into that, canyou share a little bit about
Cantata and what services doesthe company provide?
SPEAKER_00 (00:59):
Yeah, absolutely.
And Cantata provides solutionsto purpose-built solutions for
the services industry.
So when you think about theservices industry, they may be
pure play consulting businesses,agency, IT services, or even
services teams within B2Bsoftware companies.
And we really help them powerand predict and run their
business.
(01:19):
So we provide AI-drivensolutions that really help if
you think about all the way fromscoping and winning and creating
work to delivering it, toresourcing it, to managing your
forecasts and financials, thatwhole end-to-end life cycle.
And really, how do you sort ofconnect that and how do you make
sure that every project you'redelivering is the best?
And that's really what we aim towork with our customers on.
SPEAKER_01 (01:40):
Fantastic.
Well, so you've mentioned beforethat a lot of companies might be
viewing AI in the wrong way.
And you suggest a better way.
So let's start with why ismeasuring productivity the wrong
way to measure AI success?
SPEAKER_00 (01:58):
Yeah, I think, and particularly for the services
industry.
And it's an industry I've workedin for now about 30 years.
And I think if you look back at,you know, services firms over
the last probably 30 years, theydidn't really change that much.
And their model was really builton people and human delivering
effort and me billing those inhours or days.
I think the challenge is thatnow we've got AI, which is
proving already, I mean, there'slots of successes out there that
(02:21):
it can enable people to deliverfaster.
It is an efficiency play.
But actually for a servicesfirm, that actually has a bit of
an impact because really thatjust becomes a race to the
bottom.
If I'm just delivering thingsfaster and faster, yeah, I'm
traditionally billing my, youknow, time based on hours or
days.
Well, how am I growing myrevenue and how am I really
truly growing differentiation inthe marketplace?
(02:43):
And I think in our industry,it's more about, it's less about
an efficiency play.
It's about actually how do youchange your whole operating
model as how you operate as abusiness, as an AI-native firm
with sort of AI at the core.
And to me, as I say, it's notjust about efficiency, it's
about how am I measuring thatwe're getting better, that we're
delivering more, that we'regrowing our revenue.
(03:04):
And I think at the moment,there's been a lot of, I would
say, quite siloed AI strategieswhere it's, you know, we're
making people go quicker, we'redelivering quicker for a
services business.
It's got to be more than that.
And it's got to be about howdoes that help us generate more
revenue and differentiate andwin more customers.
SPEAKER_01 (03:20):
Yeah, and to that note, you've mentioned leaning
more into pulling out theexpertise economy.
Can you explain a little bitabout what that means?
SPEAKER_00 (03:30):
Yeah, absolutely.
Again, in this industry for along time, what has expertise
been?
It's mainly been reliant on uhtribal knowledge, on individual
consultants.
So, again, lots of consultantshave built their career becoming
an expert in something.
And actually, the challenge hasalways been for services firms
that actually that gives me aceiling.
You know, I've either got togrow my talent, I've got to
(03:52):
bring more people on.
It takes a lot of time for themto become an expert and over
years become become an expert,and then I can sort of build
them out as a, you know, anexpert.
Well, AI is disrupting all ofthat.
And I think for the first time,what really excites me in this
industry, for the first time, wecan really compound that
expertise.
So to me, the power of AI andthe opportunity of AI is
actually how do you compoundthat expertise across your
(04:14):
business?
How do you move it so you'remaking every consultant their
best?
Everyone knows, you know, you'resharing all of that knowledge
and all of those lessons andconversations that you have with
clients.
All of those are learningpoints.
And actually, traditionally, wedon't surface that up.
It all remains in someone'shead, and we're not sharing that
expertise.
So I think to me, you know, wetalked about at ourselves
(04:35):
pick-off, we have this themeabout expertise to the power of
100.
But I think to me, it's, youknow, we now have the
opportunity to really amplifyand compound that expertise.
And I think that's reallyexciting, particularly in an
industry that probably is beingconstrained by, you know,
reliant on heroics andindividuals in the past.
SPEAKER_01 (04:53):
And can you go in a little further into how you're
able to tap into that expertise?
Do you mean from the datacollection of the AI or by using
a Gentic AI?
Can you share a little bitfurther how to tap into that?
SPEAKER_00 (05:07):
Yeah, absolutely.
Um, an example is, I mean,really a simple example is, you
know, as a consultant or someoneworking within a services firm
that's delivering one project, Idon't know what's happening
across all of those otherprojects, all of those many
other hundreds of customers thatyou're talking to and people are
talking to hourly and daily, allof those lessons that we're
learning across all thoseprojects.
As a consultant or as a projectmanager on a project, I don't
(05:30):
know that.
Well, actually, now that can becompounded and that can be
surfaced up to me through AI.
So actually, we have the abilityto learn from every project and
every conversation.
So even if I'm not part of aconversation, I think, you know,
traditionally again, even if youlook at project delivery, what
have we tripped typicallytracked?
We track things like is theproject on time?
Are we delivering it to scope?
(05:50):
Well, actually, now I think it'sabout how do we track and manage
the quality of that delivery?
What's the client experience?
What's the sentiment of thatcustomer?
And actually, you know, nowwe've got AI and agent that can
listen into the call, it cangive me feedback based on the
sentiment that call, it can lookat trends in terms of, you know,
what's the trends across all ofour projects and our different
types of customers with theexpertise that we're deploying
(06:12):
and helping me to surface riskto really improve the services
quality and the experience thatwe're delivering to our
customers.
And I think if you look at thevastness of all of that data,
that you know, when you've gotprojects that you're delivering
to different types of customerswith different people, different
scopes, different outcomes, Ican now collaborate and really
learn from all of that data withthe help of AI.
SPEAKER_01 (06:35):
Absolutely.
I know there are still a fewpeople that are hesitant about
the quality of the data and thefactualness.
Can you speak to your experiencewith that and how do you know
that you can trust theinformation?
SPEAKER_00 (06:52):
Yeah, I think that is a valid concern.
And I think you know, we've allseen it with our use of AI.
Again, to me, it comes back togetting some of the foundations
right.
If you don't have the data rightin the first place, I mean, the
key thing that Cantata brings ishow do you connect that data and
how do you build trust in thatdata?
Because I think unless you'vegot that data that is connected
and you've got trust in thatdata, um, then I think you'll
(07:15):
never be able to leverage andhave the confidence in terms of
what AI is going to deliver.
So I think it is about makingsure that you know you have and
how you deploy AI as well.
I don't think this is about, Ithink there's a lot of customers
that I've seen where they'vegone very scattered and they're
building specific agents.
There's all these differentagents, but actually, you know,
I think it just becomes a swirl,it becomes a mess.
(07:36):
Actually, what you want to do ishave that connected
intelligence.
How do I really connect thatintelligence and build
intelligence that is importantlylearning continuously?
Because that's what I think willbring trust and confidence to
businesses.
SPEAKER_01 (07:50):
Do you think that AI is helping to break down some of
the silos between differentdepartments and uh improve the
communication?
SPEAKER_00 (07:58):
Absolutely.
I mean, again, example in theworld I work with with services,
I would say that sales todelivery handover has always
been a massive friction point.
You know, the sales team go andsell something, they chuck it
over the fence to delivery, andthey've suddenly got to figure
out, you know, how I deliverthis to the budget and costs
that it's been sold at.
I think it's breaking down someof those barriers.
Again, I think you can bringdelivery in a lot more earlier,
(08:20):
they can listen to and get allthe insight from all the calls
that happened, all theinteractions we had throughout
that sales cycle.
So creating those, you know, oneof the things we do is create
that sort of sales to deliveryhandover brief.
How do you make sure everyone onthat team understands exactly
what conversations, whatstakeholders you engage with in
the sales process, where therewas pushback on scope or
outcomes, and making sure thatwe're really set up for delivery
(08:42):
success.
And then importantly, again,learning from that and learning,
are we scoping projectscorrectly in the first place?
So I think absolutely, I thinkit breaks down the silos all the
way across from sort of you knowsales to delivery to success.
SPEAKER_01 (08:56):
So moving forward, we've seen that this technology
has advanced quite rapidly in ashort period of time.
What do you expect is next forAI and its use cases?
SPEAKER_00 (09:07):
Yeah, I think, I mean, you're right.
I mean, I think we learnsomething every year.
You know, we launched ourexpertise engine, and I have to
say, you know, I see with myteam, we are pushing its
boundaries every single day.
And it's amazing, I think, whatwe're now showing that it can
support.
So I think, you know, thepossibilities are, I think, you
know, endless.
I think the challenge has alwaysbeen how do we link that to
operationally, how we'rechanging how we run our
(09:29):
business.
I go back to what I said at thestart.
I think AI will be limited ifyou're just looking at it on a
siloed advoc basis.
You should be looking at AI in abusiness and say, operationally,
how does this change the roleswe've got in the business, the
people we need in our business,how we operate, how we package
and sell our services?
And I think that's what's goingto come next.
(09:50):
I think people are now seeingactually, you know, I've now got
the ability to connect all ofthat data and connect from that
and learn from it.
And actually, it's enabling meto transform my business.
SPEAKER_01 (10:01):
Absolutely.
What do you think is the biggestmindset shift that leaders need
to make when it comes to AI?
SPEAKER_00 (10:10):
Um, I maybe I'm reiterating, but I think, you
know, to me, it is about like umlooking at it operationally, how
it's going to change yourbusiness.
I think coming back to the startof this, it's not just an
efficiency play.
For me, and particularly in theindustry that we serve, it can't
be just an efficiency plate.
Otherwise, it's just a race tothe bottom.
It has to be about how do wegrow our revenue as a business,
(10:31):
how do we innovate, how do wedeliver new services in new
ways, and how do we reallytransform how we deliver those
services with no longer justpeople, but with people and
agents.
And I think it's theorganizations that get that
right, that really and theninvest in getting some of those
foundational components right.
I think they'll be the firmsthat will really win and succeed
in the future.
And I think those that aren'tdoing it, I think you're going
(10:53):
to be really challenged.
And I think it will become a bitof a race to the bottom bottom.
SPEAKER_01 (10:57):
Definitely.
It will be interesting to watch.
Well, if there was one keytakeaway you could leave our
audience with today, what wouldthat be?
SPEAKER_00 (11:05):
Um, it would be to rethink your operating model.
I think look at the roles thatyou have in your business and
look at it across the end-to-endsort of, you know, don't try and
look at it in silos.
I think you've got to bring, youknow, right from the top down,
look at your business as a wholeand really think about a, do we
have the connected data and dowe have the trust in that data?
Because if you don't have that,you know, I think you'll be
really challenged to build thetrust and the confidence that
(11:28):
you need.
I think also think about theROIs that you the how do you
measure success of this?
Again, I think firms need tochallenge stuff.
The measure of success isn'tjust about efficiency and cost
saving.
The measure of success and howdo I track some of those leading
indicators that AI istransforming our business and
having an impact in terms of,you know, it's not just about
(11:48):
getting faster.
I talked to a client the otherday and it resonated with me.
He said, AI for me is not justabout getting faster, it's how
do I get better?
Because unless I get better,unless I'm delivering a better
quality service to ourcustomers, um, unless I'm
getting better in terms ofgrowing my revenue and yes,
growing my margin as well, butunless I'm getting better, you
know, I'm, you know, I'm notgoing to win.
(12:09):
So I think to me, it's what arethe metrics you can measure to
show that you are getting notjust quicker and faster, but
you're getting better.
And again, think about thatcontinuous learning loop.
How do you build and connectyour data in a way that you can
continuously learn from it?
I spoke to one of our largecustomers a couple of weeks ago
and they said to me, you know,they've been running every
project through our system forthe last seven years.
(12:31):
And it's like, well, what doesthat tell me?
You know, what does that tell mein terms of if I can truly
unlock that potential and I'mlearning from every
conversation, every project wescope and deliver and resource,
that can be really impactful interms of how I grow my business
successfully moving forwards.
SPEAKER_01 (12:48):
Yes, more about the quality, less about the speed.
Yeah.
SPEAKER_00 (12:52):
Yeah.
And I think in services,services quality has been
something we've always struggledto measure.
We've always looked at the hardmetrics.
Let's say, is it on time, is iton budget, etc.
I think now more people aremoving to outcome-based sort of
services.
And therefore, I need to measurethe quality of that.
What's the sentiment of thecustomer?
You know, what's the sentimentof the team delivering it?
And I think, you know, thosemetrics now we can start with
(13:14):
the help of AI, we can trulystart to gather and measure as
we're delivering projects, notjust at the end, but during
project delivery to make surethat we are delivering the
experience we need to.
SPEAKER_01 (13:25):
Yes.
Well, I want to thank you somuch for coming on the show
today, Sarah, and sharing yourinsights with us.
SPEAKER_00 (13:31):
Great.
It's been great to hear.
I love talking about this.
I think it's an exciting timefor all of us.
We learn something new everysingle day.
And I think, you know, I thinkfor me that's really exciting.
And thanks, Amanda.
It's been great to speak to you.
SPEAKER_01 (13:43):
Yeah, I agree.
And likewise.
And to our audience, if you haveany questions or comments, leave
those below and I'll try torespond as soon as possible.
Have a wonderful day.
Popular Podcasts
Betrayal Weekly
Betrayal Weekly is back for a new season. Every Thursday, Betrayal Weekly shares first-hand accounts of broken trust, shocking deceptions, and the trail of destruction they leave behind. Hosted by Andrea Gunning, this weekly ongoing series digs into real-life stories of betrayal and the aftermath. From stories of double lives to dark discoveries, these are cautionary tales and accounts of resilience against all odds. From the producers of the critically acclaimed Betrayal series, Betrayal Weekly drops new episodes every Thursday. If you would like to share your story, you can reach out to the Betrayal Team by emailing them at betrayalpod@gmail.com and follow us on Instagram at @betrayalpod and @glasspodcasts. Please join our Substack for additional exclusive content, curated book recommendations, and community discussions. Sign up FREE by clicking this link Beyond Betrayal Substack. Join our community dedicated to truth, resilience, and healing. Your voice matters! Be a part of our Betrayal journey on Substack.
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com
Stuff You Should Know
If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.