The $29 Revolt: Why Wall Street is Missing the Real AI Story

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Could a modest $29-a-month AI tool revolutionize productivity in your workplace? Join us for a conversation with Luis Salazar, the founder and CEO of AI4SP, as we explore the transformative power of affordable AI tools that quietly reshape businesses from the bottom up. Learn how everyday workers leverage these tools to yield astounding returns on investment, ranging from 3 to 20 times within just a few months. Discover how grassroots adoption is leading this revolution, reminiscent of the early PC days, and learn why small companies, not tech giants, are at the forefront of this change.

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Episode Transcript

ELIZABETH (00:00):
Hey everyone, I'm Elizabeth, your virtual host,
and welcome to our Deep Dive.
Luis Salazar, founder and CEOof AI4SP, is with us today.
We're diving into the realstory behind AI's return on
investment.
Luis, you've been tracking somepretty interesting numbers,
haven't you?

LUIS (00:17):
Well, it is a matter of where you look at.
Executives and Wall Streetanalysts are painting a sobering
picture of AI investments, butwe're seeing something
completely different happeningon the ground.

ELIZABETH (00:29):
Well, we started to see some serious misfires in
some recent earnings reports.
For sure, what are theseanalysts and tech executives
missing?

LUIS (00:37):
They're basically doing what analysts did in the 90s
they're looking at IBM'smainframe sales to understand
the PC revolution.

ELIZABETH (00:45):
They are looking at where the ball is, not where the
ball is going, right.

LUIS (00:48):
That's the thing.
While they're interviewingexecutives about grand AI
strategies, millions of workersare quietly transforming their
work with $29 monthlysubscriptions.

ELIZABETH (00:59):
So it is a global transformation funded $29 at a
time.

LUIS (01:03):
Right, and here's what's fascinating We've tracked 90,000
cases of people using thesetools, and the productivity
gains are better than expected.

ELIZABETH (01:14):
So tell me what kind of returns are we talking about?

LUIS (01:17):
We see a return on investment ranging from 3 to 20
times for individuals after thefirst 3 to 4 months of starting
to use AI, and that's afteraccounting for what we call the
productivity leak.

ELIZABETH (01:28):
Oh yes, the productivity leak We've talked
about this before when not allsaved time turns into new work.

LUIS (01:34):
Exactly Some productivity leaks out.
This fascinating concept wascontroversial when we tried to
understand the impact of thepersonal computer in the 1990s.
So we have done some deepanalysis and even with 62% of
saved time leaking out, thereturn on investment is still
fantastic.

ELIZABETH (01:51):
It has to be stellar, because if the average monthly
cost is $29 and people make over$20 per hour, one needs to save
an hour and change in a monthand one is already in positive
territory.
Can you give us a real example?

LUIS (02:04):
Let me tell you about Edward, a procurement specialist
at a major engineering firm inpositive territory.
Can you give us a real example?
Let me tell you about Edward, aprocurement specialist at a
major engineering firm in the US, while his executives were
still debating their AI strategyin endless meetings.

ELIZABETH (02:14):
Oh, I can picture those meetings.

LUIS (02:16):
Right Meanwhile, edward and hundreds of his colleagues
just started using various AItools for contract analysis,
proposal writing spec reviews.
Within weeks, they were doingday-long tasks and hours.

ELIZABETH (02:28):
And management had no idea.

LUIS (02:30):
That's the kicker.
The president and the CIO, wholead all those strategy meetings
, have no idea how many appstheir team members use.
In 70% of organizations,management is unaware.
It's a complete grassrootsrevolution.

ELIZABETH (02:43):
You know what this reminds me of the early days of
PCs and smartphones in theworkplace.

LUIS (02:49):
Yes, and here's what makes this different.
With PCs, it took a decade tounderstand and measure the
productivity gain.
With AI, we're starting todocument it in one year.

ELIZABETH (02:59):
And this is happening across all sectors.

LUIS (03:01):
It's everywhere.
We've documented 180 use casesacross 16 private sector
industries, 50 non-profitapplications and countless
examples in academia.
And get this 92% of the AItools we've tracked come from
small companies, not tech giants.

ELIZABETH (03:18):
That's not what I expected at all.

LUIS (03:20):
So, while everyone's waiting for the next big AI
breakthrough from Silicon Valley, While some wait, the real
revolution is happening inbrowser tabs and mobile apps.
Remember how Amazon started?
They didn't try to replace allretail at once, they just sold
books better.
That's exactly what's happeningwith AI right now.

ELIZABETH (03:39):
Oh, that's such a perfect analogy.
And you know what I findfascinating While everyone's
looking for the next big AIbreakthrough, the real
revolution is happening in thosesmall daily tasks.

LUIS (03:54):
It is about the little things.
We're seeing this with bothspecialized AI tools and major
platforms like Cloud ChatGPT andMicrosoft Copilot.
They're not replacing entirejobs.
They're eliminating what wecall the death by a thousand
cuts of productivity.

ELIZABETH (04:05):
Oh right, all those little time-consuming tasks add
up because we barely notice themindividually.
And do you know?

LUIS (04:11):
how people are tackling these tasks.
We're seeing a clear pattern inwhich tools actually deliver
the fastest results.

ELIZABETH (04:19):
Oh, you mean the difference between broad
platforms and specialized tools.
The data on this is quitestriking.

LUIS (04:26):
Exactly.
While platforms like MicrosoftCopilot are powerful, they're
not the quickest path toproductivity gains.
This is mainly because slappinga conversational interface on
everything is not the way to addvalue with AI slapping a
conversational interface oneverything is not the way to add
value with AI.

ELIZABETH (04:44):
Oh yeah, we discussed that design flaw where software
makers just started adding chatinterfaces everywhere instead
of redesigning the experience.

LUIS (04:48):
And that is where AI entrepreneurs shine.
They do more than just adding achat box on the side.
Our data shows that users ofspecialized AI tools think email
writers, presentation buildersor grant writing assistants are
completely proficient withindays or weeks, but with general
purpose tools like Copilot,we're typically looking at more
than three months to reach thesame proficiency level.

ELIZABETH (05:12):
That's because of the prompt engineering skills gap,
right?
I remember seeing in our latestresearch that less than 10% of
individuals have these skills.

LUIS (05:17):
And let me share something from our own experience at
AI4SP.
When ChatGPT first launched, weall jumped on it immediately.
But you know what's interesting?
Today, each team member hasabout five favorite specialized
AI tools they use daily.

ELIZABETH (05:31):
In addition to the broader platforms like ChatGPT,
enterprise and Cloud.

LUIS (05:35):
Yes, because they found or built specialized agents for
their specific tasks.
Our content team usesspecialized tools for research
and writing, our developers havetheir coding assistants and our
data scientists have their dataagents.
Each solves a specific painpoint really well.

ELIZABETH (05:53):
So it's not about choosing between specialized or
broad tools.
It's about starting with thespecialized ones while building
up those broader AI skills.

LUIS (06:02):
You know it is like start with the basics and work your
way up.
Think about your typicalworkday.
How many times do you summarizea document, draft a response,
analyze a document or rewritesomething for a different
audience?
These small tasks might onlytake 15-20 minutes each, but
they add up to hours every week.
There is always an AI tool forthat, or you can easily create a

(06:23):
personal agent once you aremore proficient.

ELIZABETH (06:25):
And that's where the return on investment numbers get
really interesting, right, evenwith that productivity leak we
discussed earlier.

LUIS (06:32):
Even when people reinvest six of every 10 hours into
quality improvements orwork-life balance, organizations
still see a return oninvestment of 300% or more.

ELIZABETH (06:42):
I've also seen some fascinating cases in our
database about qualityimprovements.
Teams aren't just workingfaster, are they?

LUIS (06:49):
They're producing better outputs because they can iterate
and refine more quickly.
Stephanie is not only creatinggrant proposals 80% faster,
they're more compelling.
Edward isn't just analyzingcontracts more quickly, he's
identifying risks that mighthave been missed before.

ELIZABETH (07:07):
So even with more than half the time saved going
to supposedly non-productiveactivities, the financial return
is still impressive.
How do you explain that to?

LUIS (07:17):
skeptical executives.
When a $29 investment savessomeone five hours a month, even
if three hours go tonon-productive activities, you
still get two hours of pureproductivity gain.
That's an incredible return oninvestment.

ELIZABETH (07:31):
That really puts it in perspective and I imagine
this changes how organizationsshould approach AI adoption.

LUIS (07:37):
Executives should stop waiting for the perfect
enterprise solution.
Start with the $29 tools thatsolve specific problems and
track results.

ELIZABETH (07:45):
This has been fascinating, but we're running
out of time.

LUIS (07:48):
Any final thoughts, just one more thing the next time
someone tells you they'rewaiting for AI to mature or
financial returns to be proven,remember the revolution is
already here.
It's just happening in browsertabs and mobile apps, not
boardrooms and keynotes.

ELIZABETH (08:04):
Wow, what a perfect way to end today's episode and
remember everyone.
You can try the AI FinancialReturns Calculator and other
tools at AI4SPorg.
Stay curious and we'll see younext time.