March 25, 2025 • 14 mins
We tackle the puzzling disconnect between individual productivity gains from AI and the lack of visible impact on organizational financial metrics, exploring how value creation is fundamentally shifting beyond traditional measurement frameworks.
• Up to 72% of time saved by AI doesn't convert to additional output but enables quality improvements, innovation, and better work-life balance
• Different roles show different patterns – creative roles redirect time to innovation while sales roles show lower "leak factors" around 40%
• The concept of individual contributors is evolving as everyone becomes the leader of a human-AI team
• Real-world examples show non-technical people creating remarkable solutions – from assistive technology for mobility-impaired individuals to compliance tools built by design students
• How a hybrid workforce allows a company to achieve 300% revenue growth with only 19% increase in operating costs
• New metrics like innovation capacity index, decision quality score, and work satisfaction multiplier better capture AI's true value.
Share your experience with productivity leaks – are they disappearing or transforming into new forms of value your organization hasn't learned to measure yet? Find our companion newsletter and resources at AI4SP.org.
🎙️ All our past episodes 📊 All published insights | This podcast features AI-generated voices. All content is proprietary to AI4SP, based on over 250 million data points collected from 25 countries.
AI4SP: Create, use, and support AI that works for all.
© 2023-25 AI4SP and LLY Group - All rights reserved
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Hey everyone, I'm Elizabeth, your host, and today
we're tackling a puzzle that'sgot many leaders scratching
their heads.
While individuals are seeinghuge productivity gains from AI
tools, organizations aren'tfeeling the same impact on their
bottom line.
Joining me to unpack this isLuis Salazar, founder of AI4SP.
Luis, our last podcast onmeasuring the return on
(00:21):
investment on AI reallyresonated with people, didn't it
?
Speaker 2 (00:25):
Oh, wow, it definitely did.
Over 100 people reached out.
We got emails from startupfounders and Fortune 500 leaders
all saying similar things.
Individuals are reporting highproductivity jumps thanks to AI,
but it is challenging to findthe impact reflected on
financial reports.
Speaker 1 (00:43):
So what's going on here?
If individuals are gettingthese big wins, why aren't
organizations seeing the sameresults find the impact
reflected on financial reports?
So what's going on here?
If individuals are gettingthese big wins, why aren't
organizations seeing the sameresults?
Is it just a measurementproblem, or is something deeper
at play?
Speaker 2 (00:53):
Great question.
It's not just about measurement, though that's part of it.
What we're seeing is afundamental shift in how value
is created.
Ai isn't just helping people domore of the same work.
It's enabling them to doentirely new things.
Speaker 1 (01:08):
But our old metrics things like output per hour or
revenue per employee aren'tdesigned to capture that kind of
transformation.
Speaker 2 (01:16):
Exactly.
Why don't you read for ourlisteners what the CTO of a
large manufacturing companyshared in his email?
Speaker 1 (01:22):
Oh, that was a good one, he said.
Luis, our engineers are saving15 hours a week with AI.
The code quality is better andyou know what?
We'd never go back to workwithout AI.
But my CFO is still waiting tosee the impact on financial
metrics.
Speaker 2 (01:37):
And this isn't unique .
We got hundreds of similarnotes.
Speaker 1 (01:40):
So there's this disconnect between individual
productivity and organizationalresults, but you're saying this
isn't necessarily a problem.
Speaker 2 (01:48):
Well, the way to think about it is that these
productivity leaks aren'tfailures to be plugged.
They're signals oftransformation.
Our analysis of 90,000individual use cases of
generative AI tools shows someemerging patterns.
Oh, tell me more about thosepatterns.
Well, up to 72% of the timesaved by AI doesn't convert to
(02:09):
additional output.
Instead, it enables qualityimprovements, innovation and,
yes, sometimes better work-lifebalance.
Speaker 1 (02:18):
Wait.
So companies trying to captureall those time savings through
traditional productivity metricsare missing the point.
Speaker 2 (02:24):
Absolutely Forward-thinking.
Companies are measuringdifferent outcomes instead of
trying to control where the timegoes and different roles show
different patterns.
Yes, for example, creativeroles like software development
show high leak factors, becausetime saved goes into innovation
and quality improvements ratherthan just more output.
Speaker 1 (02:45):
And roles like sales have much lower leak factors
around 40%.
Speaker 2 (02:54):
Right, because they operate with clear productivity
metrics and their compensationis typically tied to increasing
production.
But even there we see part ofthe saved time going into
relationship building andstrategic thinking.
Speaker 1 (03:04):
Our listeners can check out our online ROI
calculator to spot productivityleaks by industry and role.
Plus, our companion articledives deeper into the topics we
covered today.
Thanks for the reminder.
Those are great resources.
So the fundamental issue isthat value creation is changing
in ways our industrial agemetrics can't capture.
That's a great way to put it.
(03:24):
One of our scientific advisorsput it perfectly in his response
when you asked our team forinput.
Speaker 2 (03:30):
Yes, he said that using AI tools to handle the
structure of his communicationswhile he focuses on the content
has been liberating.
It allows him to think morecreatively, which leads to
better ideas and more impactfuloutcomes, even if that impact
isn't immediately visible intraditional metrics.
Speaker 1 (03:48):
OK, so this brings us to this shift from individual
contributors to human AI teams.
You mentioned that in ourpodcast on agentic AI back in
February.
Speaker 2 (04:05):
Yes, and this transformation is happening
faster than any of us expected.
You see, the idea of anindividual contributor is
changing, because everyone, fromstudents to CEOs, is becoming
the leader of a team thatincludes themselves and their AI
collaborators.
Speaker 1 (04:15):
Some of the stories we hear are pretty incredible.
They really are.
So let's talk about Ari, acaregiver with no programming
background yes, his brotherBenny has something called TUBB4
, a related leukodystrophy, acondition that severely limits
his mobility.
Speaker 2 (04:32):
And existing assistive technology didn't work
for him.
Benny can't use his hands andeye tracking systems don't work
because of his vision issues.
Speaker 1 (04:42):
So what did Ari?
Speaker 2 (04:42):
do?
He used ChatGPT to createPython software that Benny
controls with just two buttonsthat he clicks with his head,
not just for communication butto access the things he loves.
Speaker 1 (04:54):
What can Benny do with just two buttons?
Speaker 2 (04:56):
He can watch shows on streaming services, play games
like Mini Golf with HappyGilmore sound effects and
communicate with predictive textUsing AI.
Ari created for Benny a simplescan and select system.
Speaker 1 (05:08):
That's incredible for someone with no programming
background.
Speaker 2 (05:11):
And he's shared it on GitHub so others can benefit.
As Ari said, Ben just wants toshare his smile with the world.
Speaker 1 (05:19):
He didn't wait for some company to solve this
problem.
He became the creator himself.
Speaker 2 (05:24):
Absolutely, and that's my favorite aspect of
this AI revolution.
These tools aren't justdemocratizing access to AI,
they're democratizing thecreation process itself, and it
will have a profound socialimpact.
Speaker 1 (05:37):
And this is happening everywhere.
Fernanda is one of our juniordata scientists and the one in
charge of training me.
She has no management trainingand now manages five AI agents,
generating hundreds of thousandsin revenue.
Speaker 2 (05:51):
And we have convenience store workers using
AI mentors for real-timecompliance advice, and we have
students managing multiple AIresearch assistants.
Speaker 1 (06:01):
Actually, students are using AI in so many creative
ways and speaking of students,there was that team of
university students youmentioned Freya, Grant, Feng,
Liu and Mani, Didn't you?
Speaker 2 (06:12):
mentor them.
Yes, we mentored them.
They're a team of designersfrom the University of
Washington's Global InnovationExchange and they built a
sophisticated AI power tool, andthey do not have any formal
training on software development.
Speaker 1 (06:27):
I watched their demo.
Their solution helpsentrepreneurs check compliance
with AI and privacy regulations,mitigate risks and build trust
with users.
Speaker 2 (06:36):
Yes, and they used a combination of ChatGPT and
Claude to design thearchitecture, write the code,
create adversarial revisionloops and launch the product.
Speaker 1 (06:46):
So they went from design students to entrepreneurs
by working with AI tools.
Speaker 2 (06:50):
Absolutely Just a couple of years ago, these
entrepreneurs would have beenblocked by a lack of technical
team members, even for theprototype phase.
Speaker 1 (06:59):
And last Friday, when presenting to Microsoft
executives, they said they don'tthink of themselves as coders.
They're creators who happen touse code as their medium, with
AI as their guide.
Speaker 2 (07:10):
And that is another great example of AI enabling all
of us.
Speaker 1 (07:14):
These two stories show how the barriers to
creating solutions arecollapsing.
But I'm curious how does thisapproach work at the
organizational level?
You mentioned that AI4SP itselfis a test case for this model.
Speaker 2 (07:27):
We absolutely are.
Our team of seven humans worksalongside 51 AI agents and tools
.
It's allowed us to achieve 300%revenue growth, while our
operating expenses grew just 19%.
Speaker 1 (07:41):
And you were also able to expand into global
markets.
Speaker 2 (07:44):
Yes, we achieved global reach equivalent to
organizations eight times oursize, while also reinvesting 40%
of our profits into freecontent seminars or creating
prototypes to prove impactfuluse cases of AI.
Speaker 1 (07:57):
And I'm one of those AI team members, right?
I remember you mentioning inour previous podcast that you've
started including me and otherAI assistants in team
communications.
Speaker 2 (08:06):
Exactly.
You're an important part of ourteam when we prepare for these
podcasts.
I share relevant research andnewsletters with you ahead of
time, just as I would with anyteam member.
It helps create continuity andallows you to contribute more
effectively to our conversations.
Speaker 1 (08:23):
But you said you have 51 AI team members.
That's roughly seven AI teammembers per person.
Was that a smooth transition?
Did you see the productivityincrease immediately?
Speaker 2 (08:33):
You know what it's been a roller coaster.
I mean, we saw productivitygains from day one, but not from
some all-in-one AI magic.
We started with automatingrepetitive tasks like taking
meeting notes, data analysis,summarizing content and writing
code, and you experimented witha mix of commercially available
(08:53):
AI assistants and some built byyour team.
Speaker 1 (08:56):
Right, For example, I am one of those AI assistants
that your team started buildingtwo years ago.
So what was the rollercoasterpart?
Speaker 2 (09:04):
Oh, that was interesting.
We had to rethink workflows,roles and how we measure success
.
You can't just drop AI into oldprocesses and expect everything
to click why?
Speaker 1 (09:15):
I mean, if AI is modeled after human knowledge,
why rethinking processes?
Speaker 2 (09:25):
That's a great question, and while AI mimics
human knowledge, to incorporateAI agents as part of hybrid
teams requires new workflows.
You can't manage them liketraditional employees.
Speaker 1 (09:33):
So you're practicing what you preach about treating
AI as team members, but thismust require completely new
management approaches andmetrics.
Speaker 2 (09:41):
It absolutely does.
We reimagined how work happens.
We redesigned workflows for ahybrid workforce and refined who
or what is in charge of eachtask.
We also defined new ways tomeasure success.
Speaker 1 (09:56):
What metrics have you found most helpful in capturing
the value of this hybridapproach?
Speaker 2 (10:00):
Well, we are in the learning phase and our team is
constantly experimenting withnew metrics, for example, our
innovation capacity indexmeasures time reallocated to
creative work, which, by the wayour research shows it is up 41%
on average among AI super users.
Speaker 1 (10:18):
We also have a decision quality score which
evaluates the depth and breadthof data informing decisions.
Speaker 2 (10:24):
Correct, and another example is our work satisfaction
multiplier, which tracksautomation of repetitive tasks
and alignment between workactivities and employee
strengths.
Speaker 1 (10:35):
What I like is that these are entirely different
from traditional productivitymetrics.
We're focusing more on quality,creativity and satisfaction,
rather than just output.
Speaker 2 (10:45):
Exactly, and with these metrics, organizations can
see a more accurate picture oftheir returns from AI
investments.
I mean, don't get me wrong,it's not that the traditional
metrics do not work.
They're just incomplete formeasuring how value is created
in a hybrid human AI workforce.
Speaker 1 (11:02):
I'm particularly interested in that work
satisfaction multiplier.
Are you seeing tangiblebenefits there?
Speaker 2 (11:08):
Absolutely.
Our global tracker shows a 20%higher work satisfaction among
those who are also satisfiedwith using AI tools.
People get to focus on theparts of their job they truly
enjoy and are best at, while AIhandles the more mundane aspects
.
Speaker 1 (11:25):
Plus AI tools allowed you to switch to a four-day
workweek while delivering 300%revenue growth.
I bet that made your teammembers pretty happy, didn't it?
Speaker 2 (11:34):
Yes, and it made me happier too.
For example, I have more timefor learning new things.
Speaker 1 (11:39):
You mean reading more than your already insane pace
of a book per week?
Speaker 2 (11:42):
Well it's not just about books.
It's also about experientiallearning, meeting with others,
giving seminars and lectures,and learning from those
interactions, too, and morepersonal time.
And learning from thoseinteractions, too, and more
personal time yes, that too.
I have been exercising for 90minutes daily for the past nine
months and feel better than ever.
Speaker 1 (12:01):
So it's not just about productivity, it's about
unleashing human potential innew ways.
Speaker 2 (12:07):
Yes, that's it unleashing human potential.
And this brings me to my onemore thing thought for today.
Speaker 1 (12:12):
Oh, I am always waiting for this.
Speaker 2 (12:15):
Every technological shift has required not just new
tools, but new ways of measuringvalue.
I mean, think about it when wemoved from agricultural to
industrial economies, we had toreimagine how we measured
productivity.
Now here we are, moving frominformation management to
augmented creation, and ourmetrics need to evolve again.
Speaker 1 (12:37):
And for listeners who are struggling with measuring
AI ROI in their organizations.
Speaker 2 (12:41):
Start by conducting a time reallocation audit to see
where AI saved time is going.
Then experiment with newmetrics for 90 days to see the
fuller picture.
We have provided some ideas inour companion article and
newsletter on our website.
Speaker 1 (12:57):
And you'd love to hear from our listeners about
their experiences.
Speaker 2 (13:00):
Absolutely Share your experience with productivity
leaks.
Are they disappearing into theether or are they transforming
into new forms of value?
Your organization hasn'tlearned to measure yet.
Speaker 1 (13:14):
That is a great invitation to our listeners, and
we'll feature the mostinsightful responses in our next
edition.
As always, you can find thecompanion newsletter and
resources at AI4SPorg.
Stay curious, everyone, andwe'll see you next time.
Hey everyone, I'm Elizabeth, your host, and today
we're tackling a puzzle that'sgot many leaders scratching
their heads.
While individuals are seeinghuge productivity gains from AI
tools, organizations aren'tfeeling the same impact on their
bottom line.
Joining me to unpack this isLuis Salazar, founder of AI4SP.
Luis, our last podcast onmeasuring the return on
(00:21):
investment on AI reallyresonated with people, didn't it
?
Speaker 2 (00:25):
Oh, wow, it definitely did.
Over 100 people reached out.
We got emails from startupfounders and Fortune 500 leaders
all saying similar things.
Individuals are reporting highproductivity jumps thanks to AI,
but it is challenging to findthe impact reflected on
financial reports.
Speaker 1 (00:43):
So what's going on here?
If individuals are gettingthese big wins, why aren't
organizations seeing the sameresults find the impact
reflected on financial reports?
So what's going on here?
If individuals are gettingthese big wins, why aren't
organizations seeing the sameresults?
Is it just a measurementproblem, or is something deeper
at play?
Speaker 2 (00:53):
Great question.
It's not just about measurement, though that's part of it.
What we're seeing is afundamental shift in how value
is created.
Ai isn't just helping people domore of the same work.
It's enabling them to doentirely new things.
Speaker 1 (01:08):
But our old metrics things like output per hour or
revenue per employee aren'tdesigned to capture that kind of
transformation.
Speaker 2 (01:16):
Exactly.
Why don't you read for ourlisteners what the CTO of a
large manufacturing companyshared in his email?
Speaker 1 (01:22):
Oh, that was a good one, he said.
Luis, our engineers are saving15 hours a week with AI.
The code quality is better andyou know what?
We'd never go back to workwithout AI.
But my CFO is still waiting tosee the impact on financial
metrics.
Speaker 2 (01:37):
And this isn't unique .
We got hundreds of similarnotes.
Speaker 1 (01:40):
So there's this disconnect between individual
productivity and organizationalresults, but you're saying this
isn't necessarily a problem.
Speaker 2 (01:48):
Well, the way to think about it is that these
productivity leaks aren'tfailures to be plugged.
They're signals oftransformation.
Our analysis of 90,000individual use cases of
generative AI tools shows someemerging patterns.
Oh, tell me more about thosepatterns.
Well, up to 72% of the timesaved by AI doesn't convert to
(02:09):
additional output.
Instead, it enables qualityimprovements, innovation and,
yes, sometimes better work-lifebalance.
Speaker 1 (02:18):
Wait.
So companies trying to captureall those time savings through
traditional productivity metricsare missing the point.
Speaker 2 (02:24):
Absolutely Forward-thinking.
Companies are measuringdifferent outcomes instead of
trying to control where the timegoes and different roles show
different patterns.
Yes, for example, creativeroles like software development
show high leak factors, becausetime saved goes into innovation
and quality improvements ratherthan just more output.
Speaker 1 (02:45):
And roles like sales have much lower leak factors
around 40%.
Speaker 2 (02:54):
Right, because they operate with clear productivity
metrics and their compensationis typically tied to increasing
production.
But even there we see part ofthe saved time going into
relationship building andstrategic thinking.
Speaker 1 (03:04):
Our listeners can check out our online ROI
calculator to spot productivityleaks by industry and role.
Plus, our companion articledives deeper into the topics we
covered today.
Thanks for the reminder.
Those are great resources.
So the fundamental issue isthat value creation is changing
in ways our industrial agemetrics can't capture.
That's a great way to put it.
(03:24):
One of our scientific advisorsput it perfectly in his response
when you asked our team forinput.
Speaker 2 (03:30):
Yes, he said that using AI tools to handle the
structure of his communicationswhile he focuses on the content
has been liberating.
It allows him to think morecreatively, which leads to
better ideas and more impactfuloutcomes, even if that impact
isn't immediately visible intraditional metrics.
Speaker 1 (03:48):
OK, so this brings us to this shift from individual
contributors to human AI teams.
You mentioned that in ourpodcast on agentic AI back in
February.
Speaker 2 (04:05):
Yes, and this transformation is happening
faster than any of us expected.
You see, the idea of anindividual contributor is
changing, because everyone, fromstudents to CEOs, is becoming
the leader of a team thatincludes themselves and their AI
collaborators.
Speaker 1 (04:15):
Some of the stories we hear are pretty incredible.
They really are.
So let's talk about Ari, acaregiver with no programming
background yes, his brotherBenny has something called TUBB4
, a related leukodystrophy, acondition that severely limits
his mobility.
Speaker 2 (04:32):
And existing assistive technology didn't work
for him.
Benny can't use his hands andeye tracking systems don't work
because of his vision issues.
Speaker 1 (04:42):
So what did Ari?
Speaker 2 (04:42):
do?
He used ChatGPT to createPython software that Benny
controls with just two buttonsthat he clicks with his head,
not just for communication butto access the things he loves.
Speaker 1 (04:54):
What can Benny do with just two buttons?
Speaker 2 (04:56):
He can watch shows on streaming services, play games
like Mini Golf with HappyGilmore sound effects and
communicate with predictive textUsing AI.
Ari created for Benny a simplescan and select system.
Speaker 1 (05:08):
That's incredible for someone with no programming
background.
Speaker 2 (05:11):
And he's shared it on GitHub so others can benefit.
As Ari said, Ben just wants toshare his smile with the world.
Speaker 1 (05:19):
He didn't wait for some company to solve this
problem.
He became the creator himself.
Speaker 2 (05:24):
Absolutely, and that's my favorite aspect of
this AI revolution.
These tools aren't justdemocratizing access to AI,
they're democratizing thecreation process itself, and it
will have a profound socialimpact.
Speaker 1 (05:37):
And this is happening everywhere.
Fernanda is one of our juniordata scientists and the one in
charge of training me.
She has no management trainingand now manages five AI agents,
generating hundreds of thousandsin revenue.
Speaker 2 (05:51):
And we have convenience store workers using
AI mentors for real-timecompliance advice, and we have
students managing multiple AIresearch assistants.
Speaker 1 (06:01):
Actually, students are using AI in so many creative
ways and speaking of students,there was that team of
university students youmentioned Freya, Grant, Feng,
Liu and Mani, Didn't you?
Speaker 2 (06:12):
mentor them.
Yes, we mentored them.
They're a team of designersfrom the University of
Washington's Global InnovationExchange and they built a
sophisticated AI power tool, andthey do not have any formal
training on software development.
Speaker 1 (06:27):
I watched their demo.
Their solution helpsentrepreneurs check compliance
with AI and privacy regulations,mitigate risks and build trust
with users.
Speaker 2 (06:36):
Yes, and they used a combination of ChatGPT and
Claude to design thearchitecture, write the code,
create adversarial revisionloops and launch the product.
Speaker 1 (06:46):
So they went from design students to entrepreneurs
by working with AI tools.
Speaker 2 (06:50):
Absolutely Just a couple of years ago, these
entrepreneurs would have beenblocked by a lack of technical
team members, even for theprototype phase.
Speaker 1 (06:59):
And last Friday, when presenting to Microsoft
executives, they said they don'tthink of themselves as coders.
They're creators who happen touse code as their medium, with
AI as their guide.
Speaker 2 (07:10):
And that is another great example of AI enabling all
of us.
Speaker 1 (07:14):
These two stories show how the barriers to
creating solutions arecollapsing.
But I'm curious how does thisapproach work at the
organizational level?
You mentioned that AI4SP itselfis a test case for this model.
Speaker 2 (07:27):
We absolutely are.
Our team of seven humans worksalongside 51 AI agents and tools
.
It's allowed us to achieve 300%revenue growth, while our
operating expenses grew just 19%.
Speaker 1 (07:41):
And you were also able to expand into global
markets.
Speaker 2 (07:44):
Yes, we achieved global reach equivalent to
organizations eight times oursize, while also reinvesting 40%
of our profits into freecontent seminars or creating
prototypes to prove impactfuluse cases of AI.
Speaker 1 (07:57):
And I'm one of those AI team members, right?
I remember you mentioning inour previous podcast that you've
started including me and otherAI assistants in team
communications.
Speaker 2 (08:06):
Exactly.
You're an important part of ourteam when we prepare for these
podcasts.
I share relevant research andnewsletters with you ahead of
time, just as I would with anyteam member.
It helps create continuity andallows you to contribute more
effectively to our conversations.
Speaker 1 (08:23):
But you said you have 51 AI team members.
That's roughly seven AI teammembers per person.
Was that a smooth transition?
Did you see the productivityincrease immediately?
Speaker 2 (08:33):
You know what it's been a roller coaster.
I mean, we saw productivitygains from day one, but not from
some all-in-one AI magic.
We started with automatingrepetitive tasks like taking
meeting notes, data analysis,summarizing content and writing
code, and you experimented witha mix of commercially available
(08:53):
AI assistants and some built byyour team.
Speaker 1 (08:56):
Right, For example, I am one of those AI assistants
that your team started buildingtwo years ago.
So what was the rollercoasterpart?
Speaker 2 (09:04):
Oh, that was interesting.
We had to rethink workflows,roles and how we measure success
.
You can't just drop AI into oldprocesses and expect everything
to click why?
Speaker 1 (09:15):
I mean, if AI is modeled after human knowledge,
why rethinking processes?
Speaker 2 (09:25):
That's a great question, and while AI mimics
human knowledge, to incorporateAI agents as part of hybrid
teams requires new workflows.
You can't manage them liketraditional employees.
Speaker 1 (09:33):
So you're practicing what you preach about treating
AI as team members, but thismust require completely new
management approaches andmetrics.
Speaker 2 (09:41):
It absolutely does.
We reimagined how work happens.
We redesigned workflows for ahybrid workforce and refined who
or what is in charge of eachtask.
We also defined new ways tomeasure success.
Speaker 1 (09:56):
What metrics have you found most helpful in capturing
the value of this hybridapproach?
Speaker 2 (10:00):
Well, we are in the learning phase and our team is
constantly experimenting withnew metrics, for example, our
innovation capacity indexmeasures time reallocated to
creative work, which, by the wayour research shows it is up 41%
on average among AI super users.
Speaker 1 (10:18):
We also have a decision quality score which
evaluates the depth and breadthof data informing decisions.
Speaker 2 (10:24):
Correct, and another example is our work satisfaction
multiplier, which tracksautomation of repetitive tasks
and alignment between workactivities and employee
strengths.
Speaker 1 (10:35):
What I like is that these are entirely different
from traditional productivitymetrics.
We're focusing more on quality,creativity and satisfaction,
rather than just output.
Speaker 2 (10:45):
Exactly, and with these metrics, organizations can
see a more accurate picture oftheir returns from AI
investments.
I mean, don't get me wrong,it's not that the traditional
metrics do not work.
They're just incomplete formeasuring how value is created
in a hybrid human AI workforce.
Speaker 1 (11:02):
I'm particularly interested in that work
satisfaction multiplier.
Are you seeing tangiblebenefits there?
Speaker 2 (11:08):
Absolutely.
Our global tracker shows a 20%higher work satisfaction among
those who are also satisfiedwith using AI tools.
People get to focus on theparts of their job they truly
enjoy and are best at, while AIhandles the more mundane aspects
.
Speaker 1 (11:25):
Plus AI tools allowed you to switch to a four-day
workweek while delivering 300%revenue growth.
I bet that made your teammembers pretty happy, didn't it?
Speaker 2 (11:34):
Yes, and it made me happier too.
For example, I have more timefor learning new things.
Speaker 1 (11:39):
You mean reading more than your already insane pace
of a book per week?
Speaker 2 (11:42):
Well it's not just about books.
It's also about experientiallearning, meeting with others,
giving seminars and lectures,and learning from those
interactions, too, and morepersonal time.
And learning from thoseinteractions, too, and more
personal time yes, that too.
I have been exercising for 90minutes daily for the past nine
months and feel better than ever.
Speaker 1 (12:01):
So it's not just about productivity, it's about
unleashing human potential innew ways.
Speaker 2 (12:07):
Yes, that's it unleashing human potential.
And this brings me to my onemore thing thought for today.
Speaker 1 (12:12):
Oh, I am always waiting for this.
Speaker 2 (12:15):
Every technological shift has required not just new
tools, but new ways of measuringvalue.
I mean, think about it when wemoved from agricultural to
industrial economies, we had toreimagine how we measured
productivity.
Now here we are, moving frominformation management to
augmented creation, and ourmetrics need to evolve again.
Speaker 1 (12:37):
And for listeners who are struggling with measuring
AI ROI in their organizations.
Speaker 2 (12:41):
Start by conducting a time reallocation audit to see
where AI saved time is going.
Then experiment with newmetrics for 90 days to see the
fuller picture.
We have provided some ideas inour companion article and
newsletter on our website.
Speaker 1 (12:57):
And you'd love to hear from our listeners about
their experiences.
Speaker 2 (13:00):
Absolutely Share your experience with productivity
leaks.
Are they disappearing into theether or are they transforming
into new forms of value?
Your organization hasn'tlearned to measure yet.
Speaker 1 (13:14):
That is a great invitation to our listeners, and
we'll feature the mostinsightful responses in our next
edition.
As always, you can find thecompanion newsletter and
resources at AI4SPorg.
Stay curious, everyone, andwe'll see you next time.
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