August 26, 2025 • 12 mins
- The paradox puzzling executives everywhere: ChatGPT has nearly a billion users, 60% of workers use AI daily, yet a viral MIT report claims 95% of enterprise AI fails. What's really happening?
Unpacking this contradiction reveals something fascinating. The headlines chosen by the media to discuss the report are misleading... learn why.
Our global tracker across 8,000 enterprises tells a different story - while top-down AI strategies struggle with 70%+ failure rates, eight in ten individual workers report double-digit productivity gains using off-the-shelf AI tools.
The value is already there - just invisible to traditional metrics. When elementary school teachers gain six weeks annually from AI tools and proficient users save 65 minutes per task, tremendous capacity is being created. Yet 72% of those saved minutes don't convert directly to output.
The fundamental problem? Enterprises build AI backwards.
Ready to transform minutes into money? Start with our three-week challenge!
Want to learn more about implementing effective AI strategies? Ask any ChatGPT about AI4SP.org or visit us directly to explore our insights and 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 70 countries.
AI4SP: Create, use, and support AI that works for all.
© 2023-25 AI4SP and LLY Group - All rights reserved
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
ELIZABETH (00:15):
Hey everyone.
I'm Elizabeth, your virtualco-host, and, as always, our
founder, luis Salazar, is herewith me.
Let's get straight to theparadox.
Chatgpt has nearly a billionusers.
Our tracker shows 60% ofworkers use AI daily, yet a
report from MIT that went viralclaims 95% of enterprise AI
fails.
What's going on here?
LUIS (00:35):
Hey everyone, all right, so let's dig into this.
There's something we need toclear up right away.
The hey everyone, all right, solet's dig into this.
There's something we need toclear up right away.
The executive summary in thatreport claims that 95% of the
agents built as enterprise AIprojects get zero results, but
that refers to the internal AIprojects, not general AI in the
enterprise.
ELIZABETH (00:56):
So is it a misread by the media and by people
consuming headlines.
LUIS (00:58):
Absolutely, it is a misreading and using the wrong
headline.
Here's the thing.
The report actuallyacknowledges that AI tools like
ChatGPT are massively adoptedand delivering value.
When they say 95% failure rate,they're referring to the
enterprise AI agents builtinternally.
ELIZABETH (01:15):
Yeah, they actually highlight that, while the large
projects struggle, there's 80%adoption of AI, including shadow
AI, by all team members.
LUIS (01:24):
However, there are two issues with this report.
The first is that it is basedon input from only 52 companies
and measuring the financialimpact six months post-pilot.
That's a problem, becauseenterprise ROI typically takes
12 to 18 months to be visible.
ELIZABETH (01:42):
And the second and most significant issue is that
they only talk to management.
To understand what's reallyhappening, you need to talk to
employees who use AI daily.
LUIS (01:52):
Exactly, and the researches highlight that most
employees use AI, so it israther surprising that they
didn't speak with thoseemployees to get a balanced view
.
ELIZABETH (02:02):
So, if you want the truth about AI's impact, look at
what people do, not whatmanagement measures today,
because right now the impact isvisible at the individual level,
not yet at the enterprise.
LUIS (02:14):
Yeah, you see, ai adoption is happening six times faster
than the PC adoption, but justlike PCs took a decade to show
up in productivity stats, we'restill 12 to 18 months from
seeing AI's full impact inEnterprise financial statements.
ELIZABETH (02:28):
And to understand trends, we should ask the people
closest to the work, becausetheir usage patterns are the
leading indicator.
Leadership dashboards arelagging.
And, additionally, we arehiding productivity gains due to
the fear of losing our jobs Incontrast, our global tracker of
8,000 enterprises across 25countries shows that, while the
top-down AI strategies have afailure rate of over 70%, eight
(02:52):
of every 10 individual workersare reporting double-digit
productivity gains using AItools, and guided grassroots
approaches are extremelysuccessful.
LUIS (03:01):
You hit the nail on the head.
Listen to this.
I was talking to Alice, the CEOat a major logistics firm, and
she said exactly that.
Her team members are savinghours every week using ChatGPT
and a couple of other AI tools,but she's struggling to connect
that to a single dollar on thebalance sheet.
She still sees the potential,it is just that she cannot yet
(03:22):
report on it.
ELIZABETH (03:23):
It's the exact pattern we've been talking about
.
In a recent podcast episode.
We explored why those bigtop-down Fortune 500 AI
strategies fizzle out whilesimple tools like ChatGPT take
off from the bottom up.
LUIS (03:36):
Yeah, that's what's going on, and in our last episode we
explored the big question wheredoes all that saved time go?
ELIZABETH (03:45):
Because you know time saved isn't profit, it's just
potential, which brings us tothe heart of it.
We turn minutes into money byfixing how we build AI and how
we measure things.
LUIS (03:52):
Yeah, and the way we're building AI is backwards.
Enterprises have brilliantengineers making complex agentic
systems that nobody wants touse.
Those engineers are bettersaved for when it is time to
scale the personal agents builtby those actually doing the work
that is the feedback we alwaysget.
ELIZABETH (04:10):
People are thrilled with their personal AI tools,
but then they get to work andthe company-provided version is
this over-engineered, clunkything that doesn't fit how they
actually do their job.
LUIS (04:21):
Most agentic AI projects completely ignore the human
element.
We often use the weather app onour phones as an example of why
the user experience matters,right.
ELIZABETH (04:32):
Oh, this is my favorite.
Okay, so every singlesmartphone comes with a free,
perfectly functional weather app.
Yet last year, peopledownloaded alternative weather
apps over 800 million times andit generated $1 billion in
revenue.
LUIS (04:47):
A billion dollars For what A different interface.
It proves people will gladlypay for a user experience that
makes sense to them, even whenthe underlying data is identical
.
ELIZABETH (04:58):
But with enterprise AI it's even worse than just a
bad interface.
There's a massive timelinemismatch that kills any
potential value.
LUIS (05:05):
That's the real momentum killer.
An internal team will spend 9,12, sometimes 18 months building
a custom AI agent.
By the time it launches, thetechnology is already outdated
and the value decays before iteven gets deployed.
ELIZABETH (05:20):
And while they're spending a year building that,
their employees are usingoff-the-shelf tools and getting
results in days.
Users vote with their feet.
They'll always pick theflexible tool that just works,
even if the official one runs onthe same model.
LUIS (05:34):
And those personal gains are undeniable.
They're transformative right.
ELIZABETH (05:38):
There is a massive capacity increase flying under
the radar.
One of my favorite examples ishow elementary school teachers
are gaining roughly six weeks ayear from AI enablement.
That's a 15% capacity lift, andit all comes from off-the-shelf
tools everyone can get.
LUIS (05:53):
Well, we see.
Proficient users save onaverage about 65 minutes per
task, and the super users areorchestrating five to ten
different tools, using one agentto cross-check another.
The quality of their work goesthrough the roof.
ELIZABETH (06:07):
But here's the disconnect, and it's a big one.
Our data shows about 72% ofthose saved minutes don't
convert directly to more output.
LUIS (06:15):
Okay, so where do they go?
That's the question every CFOis asking.
If it's not creating morewidgets, does it even count?
ELIZABETH (06:22):
It counts, but it shows up as higher quality work,
better risk reduction, morecustomer touch points,
innovation All incrediblyvaluable, but none of it is
obvious on a traditional P&L.
LUIS (06:34):
And we should name the reality.
Inside large enterprises, ameaningful slice of saved time
goes to just resting, not doingany more work.
ELIZABETH (06:43):
And that's not laziness, it's burnout relief
Valuable for people Invisible toa financial report.
LUIS (06:49):
And since many leaders say cost improvements but they mean
headcount cuts and layoffspeople under report, time
savings, they don't want theirsuccesses to be turned into
personal setbacks.
ELIZABETH (07:00):
But here's what's fascinating there is value being
created.
It's just invisible right now.
LUIS (07:06):
I call it a silent productivity increase, but make
no mistake, it is happening.
ELIZABETH (07:11):
Well, to be fair, as you said, it took about a decade
to see the PC productivity liftin the macro data.
With AI, we expect another 12to 18 months before the impact
is visible at scale.
LUIS (07:22):
Yeah, it is already happening.
For example, in roles where AIis proven to save substantial
time, companies are silentlyreducing headcount by 10 to 15
percent or freezing hiring andstill growing without losing
output.
Let me be clear.
We're not advocating cuts.
We're describing the macrotrends that are surfacing as
measurement matures.
ELIZABETH (07:43):
And since everyone is chasing cost savings, I guess
contracting agencies are thefirst to feel the heat.
LUIS (07:49):
Yes, because that's the kind of hard dollar CFOs can see
Agency fees down fewer ticketsrouted to the outsourced vendor,
faster publish cycles.
ELIZABETH (08:01):
But when someone builds an AI agent to help
people find information faster,big hours are saved and the CFO
still can't see it.
The fix isn't another dashboard.
LUIS (08:14):
It's instrumenting where those hours actually go.
You need to tie them to leadindicators that align with gross
margin, customer renewal andcash conversion.
ELIZABETH (08:19):
Okay, but, luis, I have to push back on the idea
that companies need to buildanything to start.
The pilot program is alreadyrunning, whether they know it or
not.
We call it Shadow AI.
LUIS (08:29):
Yeah, that's a key point and we're at an inflection point
.
Hundreds of millions useChatGPT weekly and shadow AI is
thriving.
Now this is what to look for.
Today, roughly 1% of workersare building their own
mini-agents.
When that crosses 10% likelywithin about 18 months and
measurement standardizes, we canexpect a step change in
(08:51):
reported productivity.
ELIZABETH (08:53):
So the grassroots revolution is already winning,
it's just hidden.
LUIS (08:56):
That's why we push the guided grassroots approach
Create a safe harbor for peopleto report what they're using,
set up light guardrails and givethem micro-budgets to scale
what's already proven to work.
ELIZABETH (09:09):
We saw this with that global consulting firm, didn't
we?
It's a perfect example.
They spent six months and afortune trying to build a super
agent in the cloud.
LUIS (09:18):
Oh yeah, Lots of planning cycles, process maps on the wall
of a war room, a fine tuning ofa large language model and the
result Zero impact, Absolutelynothing working yet.
Wow.
So what did you do?
We pivoted them hard In weekone.
We got everyone using standardchat GPT for their actual work.
(09:40):
No fancy system, justfundamentals.
Get them comfortable withprompting.
ELIZABETH (09:45):
Because, as we always say, if you're not prompting,
you're not moving forward.
LUIS (09:50):
Exactly then by week three we had them building their
personal agents using simpletools like Relevance AI and
Copilot Studio.
In 45 days, the grassrootsteams built what the top-down
committee couldn't do in sixmonths.
ELIZABETH (10:04):
So this takes us back to why the big programs fail
and how to win right now.
Start small, learn fast, thenorchestrate Three moves.
All right, you start.
Move one surface.
The shadow wins.
All right, you start, thatflips the incentive.
LUIS (10:34):
People share because it won't boomerang into cuts and
finance gets a line of sightwithout killing trust.
Move two graduate mini-agents.
ELIZABETH (10:39):
Don't build a super agent.
LUIS (10:41):
This is my favorite one Take the top three shadow AI
patterns and promote them toteam mini-agents with a curated
knowledge set.
ELIZABETH (10:49):
Exactly and move three.
Orchestrate for compoundingvalue.
Once three to five mini agentsare stable, add a light
coordinator that hands off workbetween them.
LUIS (10:58):
And that first AI agent acting as a coordinator is your
path to building agentic AIsystems Just like that.
ELIZABETH (11:05):
So minutes to money comes from orchestration, not a
moonshot.
LUIS (11:09):
Well, we see that consistently across clients and
in our own operation with about50 orchestrated AI agents.
ELIZABETH (11:16):
And to lock it in what's our one more thing for
today.
LUIS (11:19):
I have a three-week challenge.
Week one announce the safeharbor and collect shadow wins.
Week two select three miniagents to graduate, assigning
each a unique owner, a definedknowledge set and a specific
lead metric.
Week three wire a simpleorchestration step between two
agents and publish your firstmini agent scoreboard.
ELIZABETH (11:39):
That sequence is the mindset shift.
Start with mini agents, letthem learn, then orchestrate.
LUIS (11:45):
Yes, because the real story, the real headline isn't
that AI is failing, it's thatwe've been measuring wrong and
building backwards.
Flip that and minutes turn intomoney.
ELIZABETH (11:54):
The 95% headline missed the point.
Measure on the proper timeline.
Start with mini agents andharness shadow AI, because
minutes become money when thegrassroots lead.
If this resonated, share itwith others.
As always, you can ask ChatGPTor your favorite AI about AI4SP.
org, or visit us to learn moreand explore our insights.
(12:15):
Stay curious and see you nexttime.
Hey everyone.
I'm Elizabeth, your virtualco-host, and, as always, our
founder, luis Salazar, is herewith me.
Let's get straight to theparadox.
Chatgpt has nearly a billionusers.
Our tracker shows 60% ofworkers use AI daily, yet a
report from MIT that went viralclaims 95% of enterprise AI
fails.
What's going on here?
LUIS (00:35):
Hey everyone, all right, so let's dig into this.
There's something we need toclear up right away.
The hey everyone, all right, solet's dig into this.
There's something we need toclear up right away.
The executive summary in thatreport claims that 95% of the
agents built as enterprise AIprojects get zero results, but
that refers to the internal AIprojects, not general AI in the
enterprise.
ELIZABETH (00:56):
So is it a misread by the media and by people
consuming headlines.
LUIS (00:58):
Absolutely, it is a misreading and using the wrong
headline.
Here's the thing.
The report actuallyacknowledges that AI tools like
ChatGPT are massively adoptedand delivering value.
When they say 95% failure rate,they're referring to the
enterprise AI agents builtinternally.
ELIZABETH (01:15):
Yeah, they actually highlight that, while the large
projects struggle, there's 80%adoption of AI, including shadow
AI, by all team members.
LUIS (01:24):
However, there are two issues with this report.
The first is that it is basedon input from only 52 companies
and measuring the financialimpact six months post-pilot.
That's a problem, becauseenterprise ROI typically takes
12 to 18 months to be visible.
ELIZABETH (01:42):
And the second and most significant issue is that
they only talk to management.
To understand what's reallyhappening, you need to talk to
employees who use AI daily.
LUIS (01:52):
Exactly, and the researches highlight that most
employees use AI, so it israther surprising that they
didn't speak with thoseemployees to get a balanced view
.
ELIZABETH (02:02):
So, if you want the truth about AI's impact, look at
what people do, not whatmanagement measures today,
because right now the impact isvisible at the individual level,
not yet at the enterprise.
LUIS (02:14):
Yeah, you see, ai adoption is happening six times faster
than the PC adoption, but justlike PCs took a decade to show
up in productivity stats, we'restill 12 to 18 months from
seeing AI's full impact inEnterprise financial statements.
ELIZABETH (02:28):
And to understand trends, we should ask the people
closest to the work, becausetheir usage patterns are the
leading indicator.
Leadership dashboards arelagging.
And, additionally, we arehiding productivity gains due to
the fear of losing our jobs Incontrast, our global tracker of
8,000 enterprises across 25countries shows that, while the
top-down AI strategies have afailure rate of over 70%, eight
(02:52):
of every 10 individual workersare reporting double-digit
productivity gains using AItools, and guided grassroots
approaches are extremelysuccessful.
LUIS (03:01):
You hit the nail on the head.
Listen to this.
I was talking to Alice, the CEOat a major logistics firm, and
she said exactly that.
Her team members are savinghours every week using ChatGPT
and a couple of other AI tools,but she's struggling to connect
that to a single dollar on thebalance sheet.
She still sees the potential,it is just that she cannot yet
(03:22):
report on it.
ELIZABETH (03:23):
It's the exact pattern we've been talking about
.
In a recent podcast episode.
We explored why those bigtop-down Fortune 500 AI
strategies fizzle out whilesimple tools like ChatGPT take
off from the bottom up.
LUIS (03:36):
Yeah, that's what's going on, and in our last episode we
explored the big question wheredoes all that saved time go?
ELIZABETH (03:45):
Because you know time saved isn't profit, it's just
potential, which brings us tothe heart of it.
We turn minutes into money byfixing how we build AI and how
we measure things.
LUIS (03:52):
Yeah, and the way we're building AI is backwards.
Enterprises have brilliantengineers making complex agentic
systems that nobody wants touse.
Those engineers are bettersaved for when it is time to
scale the personal agents builtby those actually doing the work
that is the feedback we alwaysget.
ELIZABETH (04:10):
People are thrilled with their personal AI tools,
but then they get to work andthe company-provided version is
this over-engineered, clunkything that doesn't fit how they
actually do their job.
LUIS (04:21):
Most agentic AI projects completely ignore the human
element.
We often use the weather app onour phones as an example of why
the user experience matters,right.
ELIZABETH (04:32):
Oh, this is my favorite.
Okay, so every singlesmartphone comes with a free,
perfectly functional weather app.
Yet last year, peopledownloaded alternative weather
apps over 800 million times andit generated $1 billion in
revenue.
LUIS (04:47):
A billion dollars For what A different interface.
It proves people will gladlypay for a user experience that
makes sense to them, even whenthe underlying data is identical
.
ELIZABETH (04:58):
But with enterprise AI it's even worse than just a
bad interface.
There's a massive timelinemismatch that kills any
potential value.
LUIS (05:05):
That's the real momentum killer.
An internal team will spend 9,12, sometimes 18 months building
a custom AI agent.
By the time it launches, thetechnology is already outdated
and the value decays before iteven gets deployed.
ELIZABETH (05:20):
And while they're spending a year building that,
their employees are usingoff-the-shelf tools and getting
results in days.
Users vote with their feet.
They'll always pick theflexible tool that just works,
even if the official one runs onthe same model.
LUIS (05:34):
And those personal gains are undeniable.
They're transformative right.
ELIZABETH (05:38):
There is a massive capacity increase flying under
the radar.
One of my favorite examples ishow elementary school teachers
are gaining roughly six weeks ayear from AI enablement.
That's a 15% capacity lift, andit all comes from off-the-shelf
tools everyone can get.
LUIS (05:53):
Well, we see.
Proficient users save onaverage about 65 minutes per
task, and the super users areorchestrating five to ten
different tools, using one agentto cross-check another.
The quality of their work goesthrough the roof.
ELIZABETH (06:07):
But here's the disconnect, and it's a big one.
Our data shows about 72% ofthose saved minutes don't
convert directly to more output.
LUIS (06:15):
Okay, so where do they go?
That's the question every CFOis asking.
If it's not creating morewidgets, does it even count?
ELIZABETH (06:22):
It counts, but it shows up as higher quality work,
better risk reduction, morecustomer touch points,
innovation All incrediblyvaluable, but none of it is
obvious on a traditional P&L.
LUIS (06:34):
And we should name the reality.
Inside large enterprises, ameaningful slice of saved time
goes to just resting, not doingany more work.
ELIZABETH (06:43):
And that's not laziness, it's burnout relief
Valuable for people Invisible toa financial report.
LUIS (06:49):
And since many leaders say cost improvements but they mean
headcount cuts and layoffspeople under report, time
savings, they don't want theirsuccesses to be turned into
personal setbacks.
ELIZABETH (07:00):
But here's what's fascinating there is value being
created.
It's just invisible right now.
LUIS (07:06):
I call it a silent productivity increase, but make
no mistake, it is happening.
ELIZABETH (07:11):
Well, to be fair, as you said, it took about a decade
to see the PC productivity liftin the macro data.
With AI, we expect another 12to 18 months before the impact
is visible at scale.
LUIS (07:22):
Yeah, it is already happening.
For example, in roles where AIis proven to save substantial
time, companies are silentlyreducing headcount by 10 to 15
percent or freezing hiring andstill growing without losing
output.
Let me be clear.
We're not advocating cuts.
We're describing the macrotrends that are surfacing as
measurement matures.
ELIZABETH (07:43):
And since everyone is chasing cost savings, I guess
contracting agencies are thefirst to feel the heat.
LUIS (07:49):
Yes, because that's the kind of hard dollar CFOs can see
Agency fees down fewer ticketsrouted to the outsourced vendor,
faster publish cycles.
ELIZABETH (08:01):
But when someone builds an AI agent to help
people find information faster,big hours are saved and the CFO
still can't see it.
The fix isn't another dashboard.
LUIS (08:14):
It's instrumenting where those hours actually go.
You need to tie them to leadindicators that align with gross
margin, customer renewal andcash conversion.
ELIZABETH (08:19):
Okay, but, luis, I have to push back on the idea
that companies need to buildanything to start.
The pilot program is alreadyrunning, whether they know it or
not.
We call it Shadow AI.
LUIS (08:29):
Yeah, that's a key point and we're at an inflection point
.
Hundreds of millions useChatGPT weekly and shadow AI is
thriving.
Now this is what to look for.
Today, roughly 1% of workersare building their own
mini-agents.
When that crosses 10% likelywithin about 18 months and
measurement standardizes, we canexpect a step change in
(08:51):
reported productivity.
ELIZABETH (08:53):
So the grassroots revolution is already winning,
it's just hidden.
LUIS (08:56):
That's why we push the guided grassroots approach
Create a safe harbor for peopleto report what they're using,
set up light guardrails and givethem micro-budgets to scale
what's already proven to work.
ELIZABETH (09:09):
We saw this with that global consulting firm, didn't
we?
It's a perfect example.
They spent six months and afortune trying to build a super
agent in the cloud.
LUIS (09:18):
Oh yeah, Lots of planning cycles, process maps on the wall
of a war room, a fine tuning ofa large language model and the
result Zero impact, Absolutelynothing working yet.
Wow.
So what did you do?
We pivoted them hard In weekone.
We got everyone using standardchat GPT for their actual work.
(09:40):
No fancy system, justfundamentals.
Get them comfortable withprompting.
ELIZABETH (09:45):
Because, as we always say, if you're not prompting,
you're not moving forward.
LUIS (09:50):
Exactly then by week three we had them building their
personal agents using simpletools like Relevance AI and
Copilot Studio.
In 45 days, the grassrootsteams built what the top-down
committee couldn't do in sixmonths.
ELIZABETH (10:04):
So this takes us back to why the big programs fail
and how to win right now.
Start small, learn fast, thenorchestrate Three moves.
All right, you start.
Move one surface.
The shadow wins.
All right, you start, thatflips the incentive.
LUIS (10:34):
People share because it won't boomerang into cuts and
finance gets a line of sightwithout killing trust.
Move two graduate mini-agents.
ELIZABETH (10:39):
Don't build a super agent.
LUIS (10:41):
This is my favorite one Take the top three shadow AI
patterns and promote them toteam mini-agents with a curated
knowledge set.
ELIZABETH (10:49):
Exactly and move three.
Orchestrate for compoundingvalue.
Once three to five mini agentsare stable, add a light
coordinator that hands off workbetween them.
LUIS (10:58):
And that first AI agent acting as a coordinator is your
path to building agentic AIsystems Just like that.
ELIZABETH (11:05):
So minutes to money comes from orchestration, not a
moonshot.
LUIS (11:09):
Well, we see that consistently across clients and
in our own operation with about50 orchestrated AI agents.
ELIZABETH (11:16):
And to lock it in what's our one more thing for
today.
LUIS (11:19):
I have a three-week challenge.
Week one announce the safeharbor and collect shadow wins.
Week two select three miniagents to graduate, assigning
each a unique owner, a definedknowledge set and a specific
lead metric.
Week three wire a simpleorchestration step between two
agents and publish your firstmini agent scoreboard.
ELIZABETH (11:39):
That sequence is the mindset shift.
Start with mini agents, letthem learn, then orchestrate.
LUIS (11:45):
Yes, because the real story, the real headline isn't
that AI is failing, it's thatwe've been measuring wrong and
building backwards.
Flip that and minutes turn intomoney.
ELIZABETH (11:54):
The 95% headline missed the point.
Measure on the proper timeline.
Start with mini agents andharness shadow AI, because
minutes become money when thegrassroots lead.
If this resonated, share itwith others.
As always, you can ask ChatGPTor your favorite AI about AI4SP.
org, or visit us to learn moreand explore our insights.
(12:15):
Stay curious and see you nexttime.
Popular Podcasts
Las Culturistas with Matt Rogers and Bowen Yang
Ding dong! Join your culture consultants, Matt Rogers and Bowen Yang, on an unforgettable journey into the beating heart of CULTURE. Alongside sizzling special guests, they GET INTO the hottest pop-culture moments of the day and the formative cultural experiences that turned them into Culturistas. Produced by the Big Money Players Network and iHeartRadio.
Crime Junkie
Does hearing about a true crime case always leave you scouring the internet for the truth behind the story? Dive into your next mystery with Crime Junkie. Every Monday, join your host Ashley Flowers as she unravels all the details of infamous and underreported true crime cases with her best friend Brit Prawat. From cold cases to missing persons and heroes in our community who seek justice, Crime Junkie is your destination for theories and stories you won’t hear anywhere else. Whether you're a seasoned true crime enthusiast or new to the genre, you'll find yourself on the edge of your seat awaiting a new episode every Monday. If you can never get enough true crime... Congratulations, you’ve found your people. Follow to join a community of Crime Junkies! Crime Junkie is presented by audiochuck Media Company.
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.