February 26, 2025 • 13 mins
We explore the emergence of the shadow AI economy, where employees leverage unapproved AI tools to boost productivity and efficiency, often without executive awareness. The disconnect between employee usage and executive understanding poses significant questions about innovation versus risk for organizations.
• The invisible trend of shadow AI reshaping workplace dynamics
• Disparities in satisfaction between enterprise AI tools and niche aliases
• Practical recommendations for organizations to embrace shadow AI
Stay curious and visit AI4SP.org for more resources!
🎙️ 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.
ELIZABETH (00:00):
Hey everyone, I'm Elizabeth, your virtual host,
and today we're diving into afascinating trend happening
right under the noses ofexecutives everywhere.
Luis Salazar, founder of AI4SP,will talk about the invisible
AI economy we are building,often without anyone even
realizing it.
Luis, this shadow AI trend ispretty eye-opening, isn't it?
LUIS (00:20):
It really is and here's the kicker, Elizabeth.
While executives are sitting inboardrooms debating their big
AI strategies, their employeeshave already built a parallel AI
economy.
Our data shows that 71% ofknowledge workers are using AI
tools, whether their companyapproves or not.
ELIZABETH (00:40):
And it's not just casual use.
38% are sharing sensitivecompany data with these tools.
But this isn't about employeesbeing rebellious, is it?
LUIS (00:49):
Not at all.
It's about people solving realproblems with tools that
actually work.
Traditional enterprisesolutions often miss the mark,
so employees are finding theirown way with niche AI tools that
are simple, effective anddesigned to solve specific
problems.
ELIZABETH (01:04):
Meanwhile, the big software companies are basically
just adding chatbots toexisting experiences.
Right?
Our research shows that usersatisfaction with these add-on
AI solutions like Copilot orGemini Enterprise is only 41%.
LUIS (01:19):
Exactly, and that low satisfaction is driving people
toward alternatives.
We've seen this firsthand.
Our team regularly goes beyondapproved platforms to find tools
that solve immediate problems.
ELIZABETH (01:31):
And that's not reckless behavior.
It's innovation driven bynecessity.
LUIS (01:35):
Absolutely.
In fact, as we discussed in ourFebruary 14th podcast, each of
our team members now relies onabout five AI tools or agents.
We treat them like virtual teammembers that help us get the
job done.
ELIZABETH (01:48):
I remember that conversation.
You even mentioned copying meon some of those team emails
which I appreciated.
LUIS (01:55):
Yes, exactly, it's become second nature to include our AI
team members in communications.
You're one of those virtualteam members, Elizabeth.
The real question fororganizations isn't whether
shadow AI exists.
It's how they'll respond.
Will they fight it or will theyharness its potential?
ELIZABETH (02:12):
Let's dig into what the data tells us about this
invisible economy.
There's a huge gap between whatexecutives think is happening
and what's actually going on.
LUIS (02:21):
Well, it's still early days, so things are a bit messy.
While over 70% of organizationsare using AI tools, only 40% of
executives report having activeAI programs.
This gap has been consistentsince early 2023, when employees
started taking AI adoption intotheir own hands.
ELIZABETH (02:42):
And executives are completely in the dark, aren't
they?
LUIS (02:46):
It's pretty striking Three out of four executives believe
they understand what's happeningwith AI in their companies, but
the reality is that 65% or moreof it is happening completely
under the radar.
ELIZABETH (02:57):
And here's where it gets concerning Two out of every
10 companies that conduct AIaudits uncover data leaks from
unsanctioned tools.
For example, source coderepresents 29% of data leak
cases, marketing content is 37%,financial data is 18% and legal
documents make up 27%.
LUIS (03:17):
Exactly, this invisible AI economy is definitely a
double-edged sword.
For every story of innovationand efficiency gains, there's a
potential risk of exposedintellectual property or
compliance headaches.
The question leaders should beasking is are you ready to see
what's really happening in yourorganization?
ELIZABETH (03:38):
So why exactly are employees looking elsewhere?
Is it just that the enterprisetools aren't good enough?
LUIS (03:44):
It's not about rebellion.
It's about getting things done.
Employees aren't bypassing therules to be difficult.
They're doing it because thetools they're given fall short.
There are three key factorsdriving this shift.
ELIZABETH (03:57):
Oh, I've been looking at our research on this.
It's about experience gaps,right?
LUIS (04:01):
Yes, that's the first key factor.
Enterprise AI tools likeCopilot, Gemini and Salesforce
Einstein have just 41% usersatisfaction.
Compare that to 78% for nichetools or widely adopted agents
like ChatGPT and Claude.
Niche tools focus on doing onlyone thing very well, and their
users get great results.
ELIZABETH (04:23):
And then there's the training issue.
I saw that enterprise AI toolsrequire extensive training to
use effectively.
LUIS (04:30):
Exactly, and that's the second factor, these proficiency
barriers.
Enterprise tools often comewith a steep learning curve,
while niche tools are designedto work right out of the box.
They're focused on solving onespecific problem really well.
ELIZABETH (04:45):
And the third factor is simply the incredible
productivity boost these toolsprovide, isn't it?
LUIS (04:50):
The results are amazing.
Perhaps not visible in thefinancial statements yet, but
very visible at the individuallevel.
ELIZABETH (04:57):
Okay, give me examples.
I know senior developers cuttask times by 33% or more using
AI coding assistance.
What else?
LUIS (05:05):
Paralegals, sales teams and marketers are slashing
document analysis from 2 hoursto 15 minutes, and content teams
are reducing their reliance onhuman translators by 90% or more
.
ELIZABETH (05:17):
Those are compelling reasons to look outside approved
channels, but I imagine theremust be ways to address this.
LUIS (05:24):
Absolutely.
When organizations providesolid AI training and clear,
open policies for using AI tools, shadow AI adoption drops by
50% or more.
ELIZABETH (05:36):
I'm curious about the specific tools people are using
.
You mentioned something in thenewsletter about presentation
and social media tools.
LUIS (05:43):
Yes, let me share something interesting from our
global tracker.
We've been analyzing a popularcategory of niche AI tools,
presentation and marketingsocial media creators.
We consistently see high usageof solutions like Canva, prezi,
gamma Powtoon, e-maze and Visme.
ELIZABETH (06:01):
Those are PowerPoint competitors, but they're native
AI versions.
How many users are we talkingabout?
LUIS (06:06):
Together, these tools have over 400 million active users.
And do you remember thepatterns we found?
Oh what?
ELIZABETH (06:14):
patterns, are you seeing?
LUIS (06:16):
First, user satisfaction is consistently above 70%.
Second, most accounts arecreated and paid for by
individuals or teams withinorganizations.
And third this is the key partNearly all these users already
have access to PowerPoint orGoogle Slides through corporate
licenses.
ELIZABETH (06:36):
Wait.
So they're essentially payingtwice the company buys Microsoft
365 or Google Workspaces andthen they're personally paying
for tools that do the same thing.
LUIS (06:47):
Exactly this is shadow AI in action.
Employees aren't rejectingenterprise tools out of spite.
They're choosing better, fasterand more intuitive ways to get
their work done, and they'reoften paying twice.
Their employer covers theenterprise license for approved
tools, while they use personalor corporate cards to pay for
the tools they actually use.
ELIZABETH (07:08):
That's such a clear example of the gap between what
companies provide and whatemployees actually need to be
productive.
LUIS (07:15):
And it's happening in every industry.
Employees are buildingelaborate workflows with tools
the company doesn't even knowexist.
ELIZABETH (07:22):
It's like two parallel technology tracks
within the same organization.
LUIS (07:26):
Completely parallel tracks , and the companies that
recognize this reality and learnto bridge the gap are going to
have a massive advantage overthose that keep pretending it
isn't happening.
ELIZABETH (07:38):
Let's talk about the productivity risk trade-off
between enterprise and niche AItools.
You have some interestingcomparison data in the
newsletter.
LUIS (07:46):
Yes, we've been tracking this closely, and the
differences aren't just aboutcost.
In fact, niche AI solutions areoften more expensive than
Copilot or Google Gemini, forexample.
ELIZABETH (07:58):
Well, I recall one hidden cost is training.
Enterprise solutions requireabout $580 in training costs per
user, while niche tools needalmost no training investment.
They're designed to beintuitive from day one.
LUIS (08:11):
Yes, and with enterprise AI it takes people three to five
months to reach proficiency,With niche tools, days or weeks.
That time-to-value gap is hugein today's fast-paced business
environment.
ELIZABETH (08:24):
And then there's the satisfaction and retention
numbers.
LUIS (08:28):
Those tell the real story and after three months, only 30%
of enterprise AI users arestill actively using the tools,
compared to over 70% retentionwith niche solutions.
ELIZABETH (08:41):
But enterprise tools do have advantages in security
and governance right, theyabsolutely do.
LUIS (08:47):
Security protocols and enterprise solutions are
typically strong and centralized, while niche tools vary wildly.
Some have excellent security,others virtually none.
That's the real risk trade-off.
ELIZABETH (08:59):
And that brings us to the hidden costs.
Beyond just productivity gains,the security risks are
significant, aren't they?
LUIS (09:06):
Cyberhaven Labs' report from 2024 shows a 156% jump in
sensitive data shared with AItools, climbing from 11% in 2023
to 27% in 2024.
In our AI4SP global tracker,we've seen this hit 35% in
high-risk areas like softwaredevelopment and finance.
ELIZABETH (09:28):
And there's intellectual property risk too,
right.
LUIS (09:31):
Yes, most security publications report that close
to 50% of all data policyviolations involve sharing
proprietary source code with AItools, or over 80% of legal
documents shared with AI toolsuse non-corporate accounts.
It's creating massive IPvulnerability.
ELIZABETH (09:49):
That's a significant security blind spot.
So how are forward-thinkingorganizations responding to this
challenge?
LUIS (09:56):
Well, you know what?
This is where I want to sharesomething we've done at AI4SP
that might be helpful.
Oh, you mentioned that in thenewsletter About your team
growth.
Yes, we've done at AI4SP thatmight be helpful.
ELIZABETH (10:02):
Oh, you mentioned that in the newsletter About
your team growth.
LUIS (10:05):
Yes, we've walked the talk .
What started as a team of threehas grown to 40, with 32 of
those team members being AIagents managed by our humans.
On top of that, we collectivelyuse around 60 different AI
tools.
ELIZABETH (10:20):
Wow, more AI team members than humans.
I didn't know.
I was part of the majority inour team.
How did you make thattransition?
LUIS (10:28):
It all began when we encouraged what we call bring
your own AI or shadow AI.
But not in the shadows, webrought it into the open.
The result we've supportedexponential business growth
while keeping our finances leanand optimized.
ELIZABETH (10:44):
I'd love to hear your practical advice for
organizations facing thischallenge.
Where should they start?
LUIS (10:49):
Leaders must start using AI daily.
Pick one repetitive task thateats up your time, like
summarizing documents, takingmeeting notes or crafting social
media posts, and find the righttool to save one to two hours a
week.
Then do this again and haveopen dialogues about the
experience.
ELIZABETH (11:06):
That's such practical advice Starting small and
building from direct experience.
LUIS (11:11):
And the second critical step is to assess your reality.
Start with anonymous surveys touncover how employees already
use AI.
This isn't about punishment.
It's about understanding what'salready happening in your
organization.
ELIZABETH (11:24):
These are just two steps from a more comprehensive
approach right.
LUIS (11:27):
Exactly.
We've developed a seven-stepframework to help organizations
transition from shadow AI to aculture that safely and
responsibly embraces AI forgrowth.
ELIZABETH (11:38):
For those who want the complete framework, they can
find an overview in thecompanion article at AI4SPorg,
correct.
LUIS (11:46):
Yes, and here's one insight I want everyone to take
away.
ELIZABETH (11:50):
Oh, I always love your.
LUIS (11:51):
one more thing Think of it this way Shadow AI isn't a
security breach waiting tohappen.
It's your organization'sinnovation lab operating without
your guidance.
The question isn't if youremployees will use these tools.
It's whether you'll be part ofthe conversation when they do.
ELIZABETH (12:08):
That's such a powerful reframing of the
situation.
LUIS (12:11):
The most forward-thinking leaders aren't asking how do we
control AI.
They're asking how do weharness the innovation already
happening across our teams?
That shift from control toempowerment might be your most
important AI decision this year.
ELIZABETH (12:25):
That's a perfect note to end on.
Thanks for these insights, Luis, and remember everyone.
You can find more resources atAI4SPorg.
Stay curious and we'll see younext time.
Hey everyone, I'm Elizabeth, your virtual host,
and today we're diving into afascinating trend happening
right under the noses ofexecutives everywhere.
Luis Salazar, founder of AI4SP,will talk about the invisible
AI economy we are building,often without anyone even
realizing it.
Luis, this shadow AI trend ispretty eye-opening, isn't it?
LUIS (00:20):
It really is and here's the kicker, Elizabeth.
While executives are sitting inboardrooms debating their big
AI strategies, their employeeshave already built a parallel AI
economy.
Our data shows that 71% ofknowledge workers are using AI
tools, whether their companyapproves or not.
ELIZABETH (00:40):
And it's not just casual use.
38% are sharing sensitivecompany data with these tools.
But this isn't about employeesbeing rebellious, is it?
LUIS (00:49):
Not at all.
It's about people solving realproblems with tools that
actually work.
Traditional enterprisesolutions often miss the mark,
so employees are finding theirown way with niche AI tools that
are simple, effective anddesigned to solve specific
problems.
ELIZABETH (01:04):
Meanwhile, the big software companies are basically
just adding chatbots toexisting experiences.
Right?
Our research shows that usersatisfaction with these add-on
AI solutions like Copilot orGemini Enterprise is only 41%.
LUIS (01:19):
Exactly, and that low satisfaction is driving people
toward alternatives.
We've seen this firsthand.
Our team regularly goes beyondapproved platforms to find tools
that solve immediate problems.
ELIZABETH (01:31):
And that's not reckless behavior.
It's innovation driven bynecessity.
LUIS (01:35):
Absolutely.
In fact, as we discussed in ourFebruary 14th podcast, each of
our team members now relies onabout five AI tools or agents.
We treat them like virtual teammembers that help us get the
job done.
ELIZABETH (01:48):
I remember that conversation.
You even mentioned copying meon some of those team emails
which I appreciated.
LUIS (01:55):
Yes, exactly, it's become second nature to include our AI
team members in communications.
You're one of those virtualteam members, Elizabeth.
The real question fororganizations isn't whether
shadow AI exists.
It's how they'll respond.
Will they fight it or will theyharness its potential?
ELIZABETH (02:12):
Let's dig into what the data tells us about this
invisible economy.
There's a huge gap between whatexecutives think is happening
and what's actually going on.
LUIS (02:21):
Well, it's still early days, so things are a bit messy.
While over 70% of organizationsare using AI tools, only 40% of
executives report having activeAI programs.
This gap has been consistentsince early 2023, when employees
started taking AI adoption intotheir own hands.
ELIZABETH (02:42):
And executives are completely in the dark, aren't
they?
LUIS (02:46):
It's pretty striking Three out of four executives believe
they understand what's happeningwith AI in their companies, but
the reality is that 65% or moreof it is happening completely
under the radar.
ELIZABETH (02:57):
And here's where it gets concerning Two out of every
10 companies that conduct AIaudits uncover data leaks from
unsanctioned tools.
For example, source coderepresents 29% of data leak
cases, marketing content is 37%,financial data is 18% and legal
documents make up 27%.
LUIS (03:17):
Exactly, this invisible AI economy is definitely a
double-edged sword.
For every story of innovationand efficiency gains, there's a
potential risk of exposedintellectual property or
compliance headaches.
The question leaders should beasking is are you ready to see
what's really happening in yourorganization?
ELIZABETH (03:38):
So why exactly are employees looking elsewhere?
Is it just that the enterprisetools aren't good enough?
LUIS (03:44):
It's not about rebellion.
It's about getting things done.
Employees aren't bypassing therules to be difficult.
They're doing it because thetools they're given fall short.
There are three key factorsdriving this shift.
ELIZABETH (03:57):
Oh, I've been looking at our research on this.
It's about experience gaps,right?
LUIS (04:01):
Yes, that's the first key factor.
Enterprise AI tools likeCopilot, Gemini and Salesforce
Einstein have just 41% usersatisfaction.
Compare that to 78% for nichetools or widely adopted agents
like ChatGPT and Claude.
Niche tools focus on doing onlyone thing very well, and their
users get great results.
ELIZABETH (04:23):
And then there's the training issue.
I saw that enterprise AI toolsrequire extensive training to
use effectively.
LUIS (04:30):
Exactly, and that's the second factor, these proficiency
barriers.
Enterprise tools often comewith a steep learning curve,
while niche tools are designedto work right out of the box.
They're focused on solving onespecific problem really well.
ELIZABETH (04:45):
And the third factor is simply the incredible
productivity boost these toolsprovide, isn't it?
LUIS (04:50):
The results are amazing.
Perhaps not visible in thefinancial statements yet, but
very visible at the individuallevel.
ELIZABETH (04:57):
Okay, give me examples.
I know senior developers cuttask times by 33% or more using
AI coding assistance.
What else?
LUIS (05:05):
Paralegals, sales teams and marketers are slashing
document analysis from 2 hoursto 15 minutes, and content teams
are reducing their reliance onhuman translators by 90% or more
.
ELIZABETH (05:17):
Those are compelling reasons to look outside approved
channels, but I imagine theremust be ways to address this.
LUIS (05:24):
Absolutely.
When organizations providesolid AI training and clear,
open policies for using AI tools, shadow AI adoption drops by
50% or more.
ELIZABETH (05:36):
I'm curious about the specific tools people are using
.
You mentioned something in thenewsletter about presentation
and social media tools.
LUIS (05:43):
Yes, let me share something interesting from our
global tracker.
We've been analyzing a popularcategory of niche AI tools,
presentation and marketingsocial media creators.
We consistently see high usageof solutions like Canva, prezi,
gamma Powtoon, e-maze and Visme.
ELIZABETH (06:01):
Those are PowerPoint competitors, but they're native
AI versions.
How many users are we talkingabout?
LUIS (06:06):
Together, these tools have over 400 million active users.
And do you remember thepatterns we found?
Oh what?
ELIZABETH (06:14):
patterns, are you seeing?
LUIS (06:16):
First, user satisfaction is consistently above 70%.
Second, most accounts arecreated and paid for by
individuals or teams withinorganizations.
And third this is the key partNearly all these users already
have access to PowerPoint orGoogle Slides through corporate
licenses.
ELIZABETH (06:36):
Wait.
So they're essentially payingtwice the company buys Microsoft
365 or Google Workspaces andthen they're personally paying
for tools that do the same thing.
LUIS (06:47):
Exactly this is shadow AI in action.
Employees aren't rejectingenterprise tools out of spite.
They're choosing better, fasterand more intuitive ways to get
their work done, and they'reoften paying twice.
Their employer covers theenterprise license for approved
tools, while they use personalor corporate cards to pay for
the tools they actually use.
ELIZABETH (07:08):
That's such a clear example of the gap between what
companies provide and whatemployees actually need to be
productive.
LUIS (07:15):
And it's happening in every industry.
Employees are buildingelaborate workflows with tools
the company doesn't even knowexist.
ELIZABETH (07:22):
It's like two parallel technology tracks
within the same organization.
LUIS (07:26):
Completely parallel tracks , and the companies that
recognize this reality and learnto bridge the gap are going to
have a massive advantage overthose that keep pretending it
isn't happening.
ELIZABETH (07:38):
Let's talk about the productivity risk trade-off
between enterprise and niche AItools.
You have some interestingcomparison data in the
newsletter.
LUIS (07:46):
Yes, we've been tracking this closely, and the
differences aren't just aboutcost.
In fact, niche AI solutions areoften more expensive than
Copilot or Google Gemini, forexample.
ELIZABETH (07:58):
Well, I recall one hidden cost is training.
Enterprise solutions requireabout $580 in training costs per
user, while niche tools needalmost no training investment.
They're designed to beintuitive from day one.
LUIS (08:11):
Yes, and with enterprise AI it takes people three to five
months to reach proficiency,With niche tools, days or weeks.
That time-to-value gap is hugein today's fast-paced business
environment.
ELIZABETH (08:24):
And then there's the satisfaction and retention
numbers.
LUIS (08:28):
Those tell the real story and after three months, only 30%
of enterprise AI users arestill actively using the tools,
compared to over 70% retentionwith niche solutions.
ELIZABETH (08:41):
But enterprise tools do have advantages in security
and governance right, theyabsolutely do.
LUIS (08:47):
Security protocols and enterprise solutions are
typically strong and centralized, while niche tools vary wildly.
Some have excellent security,others virtually none.
That's the real risk trade-off.
ELIZABETH (08:59):
And that brings us to the hidden costs.
Beyond just productivity gains,the security risks are
significant, aren't they?
LUIS (09:06):
Cyberhaven Labs' report from 2024 shows a 156% jump in
sensitive data shared with AItools, climbing from 11% in 2023
to 27% in 2024.
In our AI4SP global tracker,we've seen this hit 35% in
high-risk areas like softwaredevelopment and finance.
ELIZABETH (09:28):
And there's intellectual property risk too,
right.
LUIS (09:31):
Yes, most security publications report that close
to 50% of all data policyviolations involve sharing
proprietary source code with AItools, or over 80% of legal
documents shared with AI toolsuse non-corporate accounts.
It's creating massive IPvulnerability.
ELIZABETH (09:49):
That's a significant security blind spot.
So how are forward-thinkingorganizations responding to this
challenge?
LUIS (09:56):
Well, you know what?
This is where I want to sharesomething we've done at AI4SP
that might be helpful.
Oh, you mentioned that in thenewsletter About your team
growth.
Yes, we've done at AI4SP thatmight be helpful.
ELIZABETH (10:02):
Oh, you mentioned that in the newsletter About
your team growth.
LUIS (10:05):
Yes, we've walked the talk .
What started as a team of threehas grown to 40, with 32 of
those team members being AIagents managed by our humans.
On top of that, we collectivelyuse around 60 different AI
tools.
ELIZABETH (10:20):
Wow, more AI team members than humans.
I didn't know.
I was part of the majority inour team.
How did you make thattransition?
LUIS (10:28):
It all began when we encouraged what we call bring
your own AI or shadow AI.
But not in the shadows, webrought it into the open.
The result we've supportedexponential business growth
while keeping our finances leanand optimized.
ELIZABETH (10:44):
I'd love to hear your practical advice for
organizations facing thischallenge.
Where should they start?
LUIS (10:49):
Leaders must start using AI daily.
Pick one repetitive task thateats up your time, like
summarizing documents, takingmeeting notes or crafting social
media posts, and find the righttool to save one to two hours a
week.
Then do this again and haveopen dialogues about the
experience.
ELIZABETH (11:06):
That's such practical advice Starting small and
building from direct experience.
LUIS (11:11):
And the second critical step is to assess your reality.
Start with anonymous surveys touncover how employees already
use AI.
This isn't about punishment.
It's about understanding what'salready happening in your
organization.
ELIZABETH (11:24):
These are just two steps from a more comprehensive
approach right.
LUIS (11:27):
Exactly.
We've developed a seven-stepframework to help organizations
transition from shadow AI to aculture that safely and
responsibly embraces AI forgrowth.
ELIZABETH (11:38):
For those who want the complete framework, they can
find an overview in thecompanion article at AI4SPorg,
correct.
LUIS (11:46):
Yes, and here's one insight I want everyone to take
away.
ELIZABETH (11:50):
Oh, I always love your.
LUIS (11:51):
one more thing Think of it this way Shadow AI isn't a
security breach waiting tohappen.
It's your organization'sinnovation lab operating without
your guidance.
The question isn't if youremployees will use these tools.
It's whether you'll be part ofthe conversation when they do.
ELIZABETH (12:08):
That's such a powerful reframing of the
situation.
LUIS (12:11):
The most forward-thinking leaders aren't asking how do we
control AI.
They're asking how do weharness the innovation already
happening across our teams?
That shift from control toempowerment might be your most
important AI decision this year.
ELIZABETH (12:25):
That's a perfect note to end on.
Thanks for these insights, Luis, and remember everyone.
You can find more resources atAI4SPorg.
Stay curious and we'll see younext time.
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