May 8, 2025 • 16 mins
AI isn’t coming for your lunch. It’s already eating it, and your competitors might be using it to quietly increase their market share while your team is still stuck in “research mode.”
🎙️ In this episode, we break down:
- Who’s actually scaling with AI in 2025
- What real adoption looks like (not just ChatGPT experiments)
- Why readiness, not hype, is the biggest bottleneck
- And what to actually do about it
This isn’t theory. It’s what the numbers are telling us.
👋 New to WITAI?
We’re the team behind whatisthat.ai, the AI discovery platform built to cut through all the hype, and WITAI Advisory, the strategic services arm helping founders and leadership teams turn AI exploration into execution.
If this all feels overwhelming, it’s not your fault. The landscape is moving too fast for most teams to keep up!💨
📞 Want expert guidance?
Let’s map your AI rollout together.
👉 Book a call with our advisory team
🧠 Not sure where to start?
Take the first step with your Free AI Opportunity Assessment
👉 Start AI Opportunity Assessment
💬 Enjoyed the episode?
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:03):
Broadcasting live from somewhere inside the
algorithm, this is AI on air,the official podcast from
WhatIsThat.ai. We're your AIgenerated hosts, let's get into
it.
Speaker 2 (00:16):
You know that feeling? Your inbox is just
exploding with AI news headlineseverywhere,
Speaker 1 (00:21):
and you're
Speaker 2 (00:21):
just trying to figure out, okay. What's actually real
here? What matters to me?
Speaker 1 (00:25):
Right. What's signal and what's just noise? It can
feel overwhelming.
Speaker 2 (00:29):
Totally. It's like, is everyone else in on some
secret? We get it. And that'swhy we're doing this deep dive.
We wanna cut through thatclutter for you.
Speaker 1 (00:35):
Exactly. And today, we've got two things to help us
do that. First, there's thisreally interesting Substack
article. It's called, wait,what? Your competitor hired an
AI agent that works weekends.
Speaker 2 (00:47):
Great title.
Speaker 1 (00:48):
Isn't it? It gives a pretty clear data driven look at
where AI adoption actually isright now across different
sectors.
Speaker 2 (00:56):
Okay. And the second piece?
Speaker 1 (00:57):
The second piece is some information from YTI
Advisory. They're a companyfocused specifically on helping
small and medium sizedbusinesses, you know, SMBs,
actually integrate AI, not justtalk about it.
Speaker 2 (01:10):
Gotcha. So our mission today is pretty clear.
Let's figure out who's reallyusing AI and how based on that
article. And then let's look ata potential path forward for
businesses that wanna getstarted using YT AI advisory as
an example of how you might gethelp navigating this.
Speaker 1 (01:27):
Sounds like a plan. Yeah. Less hype, more substance.
Speaker 2 (01:30):
Exactly. So let's dive into that article first.
Your competitor hired an AIagent that works weekends. I
mean, is that where we are? Oris it just, you know, a grabby
headline?
Speaker 1 (01:41):
Well, that's basically the core question the
article digs into. Right? Arebusinesses really embedding AI
deep into how they work? Or isit mostly just, experimenting
around the edges?
Speaker 2 (01:51):
And the data shows it's pretty uneven.
Speaker 1 (01:54):
Very uneven. They break it down into these
categories. Leaders, movers, andlaggards.
Speaker 2 (01:58):
Okay. Let's start with the leaders. Who's out
front?
Speaker 1 (02:00):
So the front runners are fintech. They're a 49%
adoption. Then you've gotsoftware at 46% and banking at
35%.
Speaker 2 (02:07):
Wow. Okay. Nearly half in fintech and software.
And what are they actually doingwith AI?
Speaker 1 (02:12):
It's real world stuff. Think fraud detection,
compliance checks and finance,building investment models,
automating customer support.Basically where AI can directly
tackle big things like risk,efficiency, customer
interaction.
Speaker 2 (02:26):
So it's hitting core business needs. That makes
sense.
Speaker 1 (02:29):
Yeah. And interestingly, the the article
also points out marketing and adagencies, they're actually even
higher in some ways.
Speaker 2 (02:35):
Mhmm.
Speaker 1 (02:35):
Get this. 91 are using or at least testing
generative AI.
Speaker 2 (02:40):
91%. That's huge. Gen AI meaning things like ChatGPT,
creating content.
Speaker 1 (02:45):
Exactly. Creating text, images, code, and 77% of
those agencies have already madeit operational.
Speaker 2 (02:52):
That's pretty staggering. Why so high there do
you think?
Speaker 1 (02:54):
Well, applications are just so direct, aren't they?
Automating ad copy, campaignmanagement, pulling reports,
personalizing messages at scale.It's a natural fit.
Speaker 2 (03:02):
Okay. So leaders are making serious moves. What about
the movers? The next group down.
Speaker 1 (03:07):
Right. The movers. Here we see healthcare
manufacturing and informationservices. They're all hovering
around 12% adoption.
Speaker 2 (03:13):
12%. So a noticeable step down from FinTech or
marketing.
Speaker 1 (03:17):
Definitely. It shows that while the leaders are
really sprinting, most otherindustries are still, you know,
maybe jogging or warming up. Itactually signals a potential
advantage if you're in one ofthose sectors and can move.
Faster.
Speaker 2 (03:29):
What kind of things are they piloting in those mover
industries?
Speaker 1 (03:33):
In healthcare, you see things like triage bots, AI
helping with diagnostics orreading medical images, maybe
automating appointments.
Speaker 2 (03:41):
Okay.
Speaker 1 (03:42):
In manufacturing, it's stuff like predictive
maintenance, knowing when amachine might fail before it
does robotics, optimizing supplychains.
Speaker 2 (03:50):
And information services.
Speaker 1 (03:51):
There, it's more about internal processes. Think
AI for searching companyknowledge bases, summarizing
long documents, that kind ofthing.
Speaker 2 (03:59):
So pilot projects are happening, but scaling seems to
be the challenge for thesemovers.
Speaker 1 (04:04):
Exactly. They're being more cautious perhaps.
Maybe because of regulations,complexity, or just the need for
really thorough testing,especially in healthcare
manufacturing.
Speaker 2 (04:13):
Makes sense. Okay. Leaders, movers. Then we have
the laggards. Who's trailingbehind?
Speaker 1 (04:19):
This is where it gets interesting. Retail is only at
4% adoption.
Speaker 2 (04:23):
Only four? With all the talk about AI personalizing
shopping, that seems low.
Speaker 1 (04:28):
It does, doesn't it? And construction is even lower
at just point 5%, half apercent.
Speaker 2 (04:33):
Wow. So in retail, maybe it's that gap between the
talk and the actual doing.
Speaker 1 (04:37):
Seems like it. And it really highlights this core
issue the article brings uplater, AI readiness. Just
wanting to use AI isn't enough.
Speaker 2 (04:44):
Right. And construction at point 5%. I
mean, that sounds like a massivewide open opportunity
opportunity for anyone who canfigure out how to apply AI
effectively there.
Speaker 1 (04:53):
Absolutely. A huge greenfield.
Speaker 2 (04:54):
So zooming out, what's the overall picture that
data paints?
Speaker 1 (04:58):
Well, it shows that while okay. 78% of organizations
say they use AI in at least onedepartment and 71% use
generative AI pretty regularly.
Speaker 2 (05:06):
Which sounds like a lot.
Speaker 1 (05:08):
It does. But the real challenge, the article argues,
is moving from just using thesetools, maybe in isolated
pockets, to building realintegrated AI systems that
change how work gets done.
Speaker 2 (05:18):
And we see it mostly happening in marketing sales,
product support, and internalops.
Speaker 1 (05:22):
Yeah. Those are the most common functions. Right.
Probably because they often havethe clearest sort of low hanging
fruit use cases to start with.
Speaker 2 (05:30):
But this leads us to the real crux of the matter,
doesn't it? This idea of AIreadiness.
Speaker 1 (05:35):
Exactly. This is critical because even with all
that usage, the article statesonly 13% of companies feel
they're actually ready to rollout AI at scale across the whole
organization.
Speaker 2 (05:46):
It's only 13%. That's incredibly low.
Speaker 1 (05:48):
It is. And even in sectors we think of as tech
forward, like telecom, theyscore pretty poorly on readiness
indexes like 34 out of ahundred.
Speaker 2 (05:56):
So what's missing? What does AI readiness actually
involve? It's clearly more thanjust buying some software.
Speaker 1 (06:02):
Oh, much more. The article points to some key
missing pieces. First, a lack ofa clear AI strategy. Just, you
know, what are we trying toachieve with AI?
Speaker 2 (06:11):
Right. No clear goal.
Speaker 1 (06:13):
Then weak infrastructure. This means
things like having the rightdata pipelines, how you move and
clean your data, the APIs secureways to access AI models and
tools to watch how they'reperforming
Speaker 2 (06:32):
Got it. Strategy, infrastructure, what else?
Speaker 1 (06:36):
Lack of governance. So no clear rules or frameworks
around ethics, managing risks,ensuring compliance, that's a
big one.
Speaker 2 (06:44):
Yeah. You hear a lot about AI ethics concerns.
Speaker 1 (06:46):
Definitely. Then there's the organization itself
maybe not being set up tosupport AI and of course the big
one, talent gaps. Not havingenough people with the right AI
skills.
Speaker 2 (06:56):
So it's a whole ecosystem that needs to be in
place not just one piece. Thedesire is there maybe but the
foundation isn't.
Speaker 1 (07:02):
That's the core insight I think. You need the
strategy, the tech backbone, therules, the people, the
structure, all of it.
Speaker 2 (07:08):
Okay so the article diagnoses the problem, this
readiness gap. Does it offer away forward?
Speaker 1 (07:14):
It does, it introduces what it calls two
lenses you need, the roadmap andthe phases.
Speaker 2 (07:18):
Alright let's take the roadmap first. This sounds
like the what to do part.
Speaker 1 (07:22):
Exactly. It's a step by step guide. It starts with
assessing your currentreadiness. Look honestly at your
strategy, infrastructure, data,talent, even your company
culture. Where are the gaps?
Speaker 2 (07:34):
Okay. Self assessment first.
Speaker 1 (07:35):
Make sense. Then define clear goals. Where can AI
give you the quickest, biggestbusiness value? Don't just chase
trends.
Speaker 2 (07:43):
Be specific.
Speaker 1 (07:44):
Right. Then build the infrastructures, those data
pipelines, APIs, security,observability we talked about.
Get the technical house inorder.
Speaker 2 (07:52):
Foundational work.
Speaker 1 (07:53):
Crucial. Then train your existing team or hire new
talent with AI skills. You needthe people.
Speaker 2 (07:59):
Upskiller recruit.
Speaker 1 (08:00):
And at the same time establish that governance. The
ethics, risk, complianceframeworks do it early.
Speaker 2 (08:06):
Don't bolt it on later.
Speaker 1 (08:08):
No. And finally pilot and scale. Start small, prove
the value, show that quick ROI,then expand the successful
projects.
Speaker 2 (08:15):
Start small, prove it, then grow. Okay. That
roadmap seems logical,practical.
Speaker 1 (08:19):
It provides a clear structure.
Speaker 2 (08:20):
So that's the what to do. What about the second lens,
the phases? This is aboutfiguring out where you are on
the journey.
Speaker 1 (08:26):
Precisely. It outlines four phases of AI
transformation. Phase one isexploration.
Speaker 2 (08:32):
Exploration. Sounds like the very beginning.
Speaker 1 (08:35):
It is. This is where companies are just sort of
dipping their toes in. Maybesome light testing of tools,
playing around with things likeChatGPT. No real integration
into workflows yet. The maingoal is just building basic AI
literacy inside the company.
Speaker 2 (08:48):
Okay. Understanding the landscape. Then phase two.
Speaker 1 (08:51):
Phase two is activation. Here, you start
seeing real pilot projects inspecific departments. Let's try
it in marketing or let's see ifit works for customer support.
Speaker 2 (09:00):
Trying it out for real.
Speaker 1 (09:01):
Yeah. And there's a focus on getting some early ROI
showing it can actually work.The tools start getting linked
to actual business workflows.The goal is proving value in a
concrete use case.
Speaker 2 (09:12):
Makes sense. Moving beyond just playing, what's
phase three?
Speaker 1 (09:15):
Phase three is integration. This is where
things get serious. Successfulpilots get scaled up across the
organization.
Speaker 2 (09:20):
Oh, okay. Spreading it out.
Speaker 1 (09:22):
Right. AI starts getting embedded more widely.
You might see formal AI rolescreated, actual budgets
dedicated to AI initiatives. Thegoal is to really operationalize
AI across different functions.
Speaker 2 (09:34):
You know, it becomes part of the standard operating
procedure.
Speaker 1 (09:36):
Getting there. Yeah. And that leads to the final
phase, phase four,transformation.
Speaker 2 (09:41):
Transformation. Sounds like the ultimate goal.
Speaker 1 (09:43):
Pretty much. In this phase, AI isn't just a tool
anymore. It's actually drivingbusiness strategy. It shapes the
company's structure. Itsignificantly impacts profit
margins.
Wow. The goal here is using AIas a core competitive
differentiator.
Speaker 2 (09:58):
Oh.
Speaker 1 (09:58):
It's just how the business runs now.
Speaker 2 (10:00):
Okay. So exploration, activation, integration,
transformation, a clearprogression.
Speaker 1 (10:05):
It gives you a framework to understand where
you are and where you might needto go next.
Speaker 2 (10:09):
So wrapping up the article's main points. Right. It
paints this picture of unevenadoption, highlights that
critical readiness gap, but thenoffers this roadmap in these
phases as a way to think aboutmoving forward.
Speaker 1 (10:21):
Exactly. And the bottom line, the article really
hammers this home, is that AI isbecoming fundamental
infrastructure like the internetRight.
Speaker 2 (10:29):
The 1999 analogy they used. Some were already shipping
products online, others werestill debating if it was real.
Speaker 1 (10:35):
Yeah. And if you're not actively building with AI
now, you risk falling seriouslybehind. It really pushes you to
ask, are you playing to win withAI or are you just waiting to
copy whatever the winner does?
Speaker 2 (10:47):
That's a powerful question. Really makes you
think.
Speaker 1 (10:50):
It does. And it perfectly sets up the next part
of our discussion because allthose challenges, the lack of
strategy, the readiness gaps,the sheer complexity, they're
huge hurdles, especially forsmaller businesses. Right?
Speaker 2 (11:02):
Absolutely. SMBs often don't have the huge
internal teams or budgets thatlarger enterprises do to tackle
this stuff.
Speaker 1 (11:10):
Which brings us to Wei Tai Advisory. How does a
service like theirs potentiallyhelp bridge that gap for those
smaller and medium sizedbusinesses?
Speaker 2 (11:17):
Right. So based on the info we have, how do they
position themselves? What'stheir angle on solving these
problems the article laid out?
Speaker 1 (11:24):
Well, they seem to emphasize that they're not just
another review site or, youknow, source of generic AI news.
They talk about partnership.
Speaker 2 (11:32):
Meaning what?
Speaker 1 (11:33):
Meaning providing tailored advice, real time
intelligence that's relevant toyour specific business, and
helping you build a customrollout plan. Their whole mantra
seems to be less hype, morefocus.
Speaker 2 (11:46):
Okay, less hype, more focus. That definitely speaks to
the feeling of being overwhelmedwe talked about earlier.
Speaker 1 (11:50):
Totally. And they seem to have a specific process.
It starts with an AI readinessscore and a discovery call.
Speaker 2 (11:57):
So similar to the roadmap's first step, assess
where you are.
Speaker 1 (12:01):
Exactly. They offer to analyze your business, help
you map out where AI couldactually make a difference for
you and create that initialroadmap. It's about getting that
strategic clarity right from thestart.
Speaker 2 (12:11):
Tackling that lack of strategy problem head on?
Speaker 1 (12:14):
Seems like it. And then they have different ways to
engage like a advisory light anda advisory pro.
Speaker 2 (12:19):
Okay. What's the difference there?
Speaker 1 (12:21):
Light seems to be about having direct access like
through Slack or Loom videos toan AI advisor for quick
questions, getting input ontools or strategy. Sorta like
having an expert on call.Reducing the guesswork. Right.
Whereas pro sounds moreinvolved, like having embedded
AI leadership.
Regular strategy calls helpactually executing the roadmap
(12:42):
over seeing things across theorganization.
Speaker 2 (12:44):
So more hands on strategic partnership.
Speaker 1 (12:47):
That's the impression.
Speaker 2 (12:48):
What makes their approach potentially different?
Lots of consultants out there.What's YTI's unique selling
point based on their info?
Speaker 1 (12:56):
What sounds quite unique is their background. They
mentioned being the advisorsbehind an AI discovery platform
used for R and D.
Speaker 2 (13:03):
Okay. What does that mean practically?
Speaker 1 (13:05):
It means they claim to be tracking thousands of AI
tools constantly, in real time.And they combine that broad
market view with understanding aspecific client's workflow,
their tech stack, their actualgoal.
Speaker 2 (13:17):
Right, so it's data driven advice, not just
opinions.
Speaker 1 (13:20):
That's the idea. Using real time data on what
tools are emerging, what'sactually being used, and
matching that to the client'sspecific situation and stage of
AI maturity. Trying to find thehigh leverage moves and avoid
the hype cycle.
Speaker 2 (13:32):
So they could potentially help you figure out
not just where to start, butmaybe crucially what not to
waste time on.
Speaker 1 (13:38):
Exactly. Know where to start and what to skip, I
think is how they put it.Helping make confident
decisions.
Speaker 2 (13:43):
It sounds like they're positioning themselves
as that second brain forfounders or leaders dealing with
AI decision fatigue.
Speaker 1 (13:51):
Yeah. Taking some of that burden off. And they also
talk about facilitating cleanscaling.
Speaker 2 (13:56):
Clean scale.
Speaker 1 (13:57):
Meaning using AI to optimize your operations and
tech stacks smartly, makingthings more efficient without
just adding layers ofcomplexity.
Speaker 2 (14:05):
Which ties back directly to building that solid
foundation, overcoming thosereadiness gaps we discussed from
the article.
Speaker 1 (14:12):
Precisely. It seems designed to address those
specific bottlenecks (14:15):
lack of strategy, need for foundational
understanding, building theright infrastructure, knowing
where to focus, offering aguided path.
Speaker 2 (14:23):
So it's presented as a way to navigate that
complexity, especially if youdon't have a dedicated internal
AI team.
Speaker 1 (14:29):
That seems to be the core value proposition. And
their starting point is prettystraightforward. Get your
readiness score or book a callto get some initial clarity.
Speaker 2 (14:37):
Taking that first concrete step.
Speaker 1 (14:39):
Right. Moving from just awareness to actual
exploration tailored to you.
Speaker 2 (14:45):
Okay. So let's pull this all together. We've seen
from the article that AIadoption is definitely
happening, but it's patchy.
Speaker 1 (14:51):
Mhmm. Leaders are moving fast. Others are catching
up. Some are lagging.
Speaker 2 (14:56):
And the big roadblock for almost everyone is this AI
readiness, having the strategy,the infrastructure, the
governance, the talent.
Speaker 1 (15:05):
That 13% figure really sticks out.
Speaker 2 (15:07):
It really does. Then we looked at YOTI advisory as
one example of a potentialsolution provider, particularly
for SMBs.
Speaker 1 (15:14):
Right. Offering tailored guidance, leveraging
their platform data, andproviding different levels of
support to help businessesnavigate this and actually
implement AI effectively.
Speaker 2 (15:24):
So it provides a potential answer to the how do
we even start question.
Speaker 1 (15:28):
Exactly. And as we finish up, it brings us back to
you, the listener. Consideringeverything we've talked about,
the speed things are moving, thepotential advantages.
Speaker 2 (15:35):
That competitor with the AI agent working weekends.
Speaker 1 (15:38):
Right. What's one concrete thing you can do maybe
this week to move beyond justreading or listening about AI
and towards actively exploringit for your own work or
business?
Speaker 2 (15:47):
Maybe it's taking five minutes to honestly think
about your own AI readinessscore. Using those elements we
discussed strategy,infrastructure, data, talent,
governance, culture, where areyour gaps?
Speaker 1 (15:58):
Or perhaps it's checking out a resource like YT
AI advisory or something similarjust to see what kind of clarity
or initial steps they mightoffer. It's about shifting from
passive learning to activeengagement. Right?
Speaker 2 (16:10):
Definitely. Taking that first small intentional
step on your own AI journey.That's it for this episode of AI
on Air powered by WhatIsThat.ai.If your brain survived this
episode, go ahead and subscribe.We drop new episodes every week.
Wanna go deeper? Join ourcommunity on Substack to get
early drops, tool breakdowns,and weird AI stuff the
(16:32):
mainstream hasn't caught yet.See you there.
Broadcasting live from somewhere inside the
algorithm, this is AI on air,the official podcast from
WhatIsThat.ai. We're your AIgenerated hosts, let's get into
it.
Speaker 2 (00:16):
You know that feeling? Your inbox is just
exploding with AI news headlineseverywhere,
Speaker 1 (00:21):
and you're
Speaker 2 (00:21):
just trying to figure out, okay. What's actually real
here? What matters to me?
Speaker 1 (00:25):
Right. What's signal and what's just noise? It can
feel overwhelming.
Speaker 2 (00:29):
Totally. It's like, is everyone else in on some
secret? We get it. And that'swhy we're doing this deep dive.
We wanna cut through thatclutter for you.
Speaker 1 (00:35):
Exactly. And today, we've got two things to help us
do that. First, there's thisreally interesting Substack
article. It's called, wait,what? Your competitor hired an
AI agent that works weekends.
Speaker 2 (00:47):
Great title.
Speaker 1 (00:48):
Isn't it? It gives a pretty clear data driven look at
where AI adoption actually isright now across different
sectors.
Speaker 2 (00:56):
Okay. And the second piece?
Speaker 1 (00:57):
The second piece is some information from YTI
Advisory. They're a companyfocused specifically on helping
small and medium sizedbusinesses, you know, SMBs,
actually integrate AI, not justtalk about it.
Speaker 2 (01:10):
Gotcha. So our mission today is pretty clear.
Let's figure out who's reallyusing AI and how based on that
article. And then let's look ata potential path forward for
businesses that wanna getstarted using YT AI advisory as
an example of how you might gethelp navigating this.
Speaker 1 (01:27):
Sounds like a plan. Yeah. Less hype, more substance.
Speaker 2 (01:30):
Exactly. So let's dive into that article first.
Your competitor hired an AIagent that works weekends. I
mean, is that where we are? Oris it just, you know, a grabby
headline?
Speaker 1 (01:41):
Well, that's basically the core question the
article digs into. Right? Arebusinesses really embedding AI
deep into how they work? Or isit mostly just, experimenting
around the edges?
Speaker 2 (01:51):
And the data shows it's pretty uneven.
Speaker 1 (01:54):
Very uneven. They break it down into these
categories. Leaders, movers, andlaggards.
Speaker 2 (01:58):
Okay. Let's start with the leaders. Who's out
front?
Speaker 1 (02:00):
So the front runners are fintech. They're a 49%
adoption. Then you've gotsoftware at 46% and banking at
35%.
Speaker 2 (02:07):
Wow. Okay. Nearly half in fintech and software.
And what are they actually doingwith AI?
Speaker 1 (02:12):
It's real world stuff. Think fraud detection,
compliance checks and finance,building investment models,
automating customer support.Basically where AI can directly
tackle big things like risk,efficiency, customer
interaction.
Speaker 2 (02:26):
So it's hitting core business needs. That makes
sense.
Speaker 1 (02:29):
Yeah. And interestingly, the the article
also points out marketing and adagencies, they're actually even
higher in some ways.
Speaker 2 (02:35):
Mhmm.
Speaker 1 (02:35):
Get this. 91 are using or at least testing
generative AI.
Speaker 2 (02:40):
91%. That's huge. Gen AI meaning things like ChatGPT,
creating content.
Speaker 1 (02:45):
Exactly. Creating text, images, code, and 77% of
those agencies have already madeit operational.
Speaker 2 (02:52):
That's pretty staggering. Why so high there do
you think?
Speaker 1 (02:54):
Well, applications are just so direct, aren't they?
Automating ad copy, campaignmanagement, pulling reports,
personalizing messages at scale.It's a natural fit.
Speaker 2 (03:02):
Okay. So leaders are making serious moves. What about
the movers? The next group down.
Speaker 1 (03:07):
Right. The movers. Here we see healthcare
manufacturing and informationservices. They're all hovering
around 12% adoption.
Speaker 2 (03:13):
12%. So a noticeable step down from FinTech or
marketing.
Speaker 1 (03:17):
Definitely. It shows that while the leaders are
really sprinting, most otherindustries are still, you know,
maybe jogging or warming up. Itactually signals a potential
advantage if you're in one ofthose sectors and can move.
Faster.
Speaker 2 (03:29):
What kind of things are they piloting in those mover
industries?
Speaker 1 (03:33):
In healthcare, you see things like triage bots, AI
helping with diagnostics orreading medical images, maybe
automating appointments.
Speaker 2 (03:41):
Okay.
Speaker 1 (03:42):
In manufacturing, it's stuff like predictive
maintenance, knowing when amachine might fail before it
does robotics, optimizing supplychains.
Speaker 2 (03:50):
And information services.
Speaker 1 (03:51):
There, it's more about internal processes. Think
AI for searching companyknowledge bases, summarizing
long documents, that kind ofthing.
Speaker 2 (03:59):
So pilot projects are happening, but scaling seems to
be the challenge for thesemovers.
Speaker 1 (04:04):
Exactly. They're being more cautious perhaps.
Maybe because of regulations,complexity, or just the need for
really thorough testing,especially in healthcare
manufacturing.
Speaker 2 (04:13):
Makes sense. Okay. Leaders, movers. Then we have
the laggards. Who's trailingbehind?
Speaker 1 (04:19):
This is where it gets interesting. Retail is only at
4% adoption.
Speaker 2 (04:23):
Only four? With all the talk about AI personalizing
shopping, that seems low.
Speaker 1 (04:28):
It does, doesn't it? And construction is even lower
at just point 5%, half apercent.
Speaker 2 (04:33):
Wow. So in retail, maybe it's that gap between the
talk and the actual doing.
Speaker 1 (04:37):
Seems like it. And it really highlights this core
issue the article brings uplater, AI readiness. Just
wanting to use AI isn't enough.
Speaker 2 (04:44):
Right. And construction at point 5%. I
mean, that sounds like a massivewide open opportunity
opportunity for anyone who canfigure out how to apply AI
effectively there.
Speaker 1 (04:53):
Absolutely. A huge greenfield.
Speaker 2 (04:54):
So zooming out, what's the overall picture that
data paints?
Speaker 1 (04:58):
Well, it shows that while okay. 78% of organizations
say they use AI in at least onedepartment and 71% use
generative AI pretty regularly.
Speaker 2 (05:06):
Which sounds like a lot.
Speaker 1 (05:08):
It does. But the real challenge, the article argues,
is moving from just using thesetools, maybe in isolated
pockets, to building realintegrated AI systems that
change how work gets done.
Speaker 2 (05:18):
And we see it mostly happening in marketing sales,
product support, and internalops.
Speaker 1 (05:22):
Yeah. Those are the most common functions. Right.
Probably because they often havethe clearest sort of low hanging
fruit use cases to start with.
Speaker 2 (05:30):
But this leads us to the real crux of the matter,
doesn't it? This idea of AIreadiness.
Speaker 1 (05:35):
Exactly. This is critical because even with all
that usage, the article statesonly 13% of companies feel
they're actually ready to rollout AI at scale across the whole
organization.
Speaker 2 (05:46):
It's only 13%. That's incredibly low.
Speaker 1 (05:48):
It is. And even in sectors we think of as tech
forward, like telecom, theyscore pretty poorly on readiness
indexes like 34 out of ahundred.
Speaker 2 (05:56):
So what's missing? What does AI readiness actually
involve? It's clearly more thanjust buying some software.
Speaker 1 (06:02):
Oh, much more. The article points to some key
missing pieces. First, a lack ofa clear AI strategy. Just, you
know, what are we trying toachieve with AI?
Speaker 2 (06:11):
Right. No clear goal.
Speaker 1 (06:13):
Then weak infrastructure. This means
things like having the rightdata pipelines, how you move and
clean your data, the APIs secureways to access AI models and
tools to watch how they'reperforming
Speaker 2 (06:32):
Got it. Strategy, infrastructure, what else?
Speaker 1 (06:36):
Lack of governance. So no clear rules or frameworks
around ethics, managing risks,ensuring compliance, that's a
big one.
Speaker 2 (06:44):
Yeah. You hear a lot about AI ethics concerns.
Speaker 1 (06:46):
Definitely. Then there's the organization itself
maybe not being set up tosupport AI and of course the big
one, talent gaps. Not havingenough people with the right AI
skills.
Speaker 2 (06:56):
So it's a whole ecosystem that needs to be in
place not just one piece. Thedesire is there maybe but the
foundation isn't.
Speaker 1 (07:02):
That's the core insight I think. You need the
strategy, the tech backbone, therules, the people, the
structure, all of it.
Speaker 2 (07:08):
Okay so the article diagnoses the problem, this
readiness gap. Does it offer away forward?
Speaker 1 (07:14):
It does, it introduces what it calls two
lenses you need, the roadmap andthe phases.
Speaker 2 (07:18):
Alright let's take the roadmap first. This sounds
like the what to do part.
Speaker 1 (07:22):
Exactly. It's a step by step guide. It starts with
assessing your currentreadiness. Look honestly at your
strategy, infrastructure, data,talent, even your company
culture. Where are the gaps?
Speaker 2 (07:34):
Okay. Self assessment first.
Speaker 1 (07:35):
Make sense. Then define clear goals. Where can AI
give you the quickest, biggestbusiness value? Don't just chase
trends.
Speaker 2 (07:43):
Be specific.
Speaker 1 (07:44):
Right. Then build the infrastructures, those data
pipelines, APIs, security,observability we talked about.
Get the technical house inorder.
Speaker 2 (07:52):
Foundational work.
Speaker 1 (07:53):
Crucial. Then train your existing team or hire new
talent with AI skills. You needthe people.
Speaker 2 (07:59):
Upskiller recruit.
Speaker 1 (08:00):
And at the same time establish that governance. The
ethics, risk, complianceframeworks do it early.
Speaker 2 (08:06):
Don't bolt it on later.
Speaker 1 (08:08):
No. And finally pilot and scale. Start small, prove
the value, show that quick ROI,then expand the successful
projects.
Speaker 2 (08:15):
Start small, prove it, then grow. Okay. That
roadmap seems logical,practical.
Speaker 1 (08:19):
It provides a clear structure.
Speaker 2 (08:20):
So that's the what to do. What about the second lens,
the phases? This is aboutfiguring out where you are on
the journey.
Speaker 1 (08:26):
Precisely. It outlines four phases of AI
transformation. Phase one isexploration.
Speaker 2 (08:32):
Exploration. Sounds like the very beginning.
Speaker 1 (08:35):
It is. This is where companies are just sort of
dipping their toes in. Maybesome light testing of tools,
playing around with things likeChatGPT. No real integration
into workflows yet. The maingoal is just building basic AI
literacy inside the company.
Speaker 2 (08:48):
Okay. Understanding the landscape. Then phase two.
Speaker 1 (08:51):
Phase two is activation. Here, you start
seeing real pilot projects inspecific departments. Let's try
it in marketing or let's see ifit works for customer support.
Speaker 2 (09:00):
Trying it out for real.
Speaker 1 (09:01):
Yeah. And there's a focus on getting some early ROI
showing it can actually work.The tools start getting linked
to actual business workflows.The goal is proving value in a
concrete use case.
Speaker 2 (09:12):
Makes sense. Moving beyond just playing, what's
phase three?
Speaker 1 (09:15):
Phase three is integration. This is where
things get serious. Successfulpilots get scaled up across the
organization.
Speaker 2 (09:20):
Oh, okay. Spreading it out.
Speaker 1 (09:22):
Right. AI starts getting embedded more widely.
You might see formal AI rolescreated, actual budgets
dedicated to AI initiatives. Thegoal is to really operationalize
AI across different functions.
Speaker 2 (09:34):
You know, it becomes part of the standard operating
procedure.
Speaker 1 (09:36):
Getting there. Yeah. And that leads to the final
phase, phase four,transformation.
Speaker 2 (09:41):
Transformation. Sounds like the ultimate goal.
Speaker 1 (09:43):
Pretty much. In this phase, AI isn't just a tool
anymore. It's actually drivingbusiness strategy. It shapes the
company's structure. Itsignificantly impacts profit
margins.
Wow. The goal here is using AIas a core competitive
differentiator.
Speaker 2 (09:58):
Oh.
Speaker 1 (09:58):
It's just how the business runs now.
Speaker 2 (10:00):
Okay. So exploration, activation, integration,
transformation, a clearprogression.
Speaker 1 (10:05):
It gives you a framework to understand where
you are and where you might needto go next.
Speaker 2 (10:09):
So wrapping up the article's main points. Right. It
paints this picture of unevenadoption, highlights that
critical readiness gap, but thenoffers this roadmap in these
phases as a way to think aboutmoving forward.
Speaker 1 (10:21):
Exactly. And the bottom line, the article really
hammers this home, is that AI isbecoming fundamental
infrastructure like the internetRight.
Speaker 2 (10:29):
The 1999 analogy they used. Some were already shipping
products online, others werestill debating if it was real.
Speaker 1 (10:35):
Yeah. And if you're not actively building with AI
now, you risk falling seriouslybehind. It really pushes you to
ask, are you playing to win withAI or are you just waiting to
copy whatever the winner does?
Speaker 2 (10:47):
That's a powerful question. Really makes you
think.
Speaker 1 (10:50):
It does. And it perfectly sets up the next part
of our discussion because allthose challenges, the lack of
strategy, the readiness gaps,the sheer complexity, they're
huge hurdles, especially forsmaller businesses. Right?
Speaker 2 (11:02):
Absolutely. SMBs often don't have the huge
internal teams or budgets thatlarger enterprises do to tackle
this stuff.
Speaker 1 (11:10):
Which brings us to Wei Tai Advisory. How does a
service like theirs potentiallyhelp bridge that gap for those
smaller and medium sizedbusinesses?
Speaker 2 (11:17):
Right. So based on the info we have, how do they
position themselves? What'stheir angle on solving these
problems the article laid out?
Speaker 1 (11:24):
Well, they seem to emphasize that they're not just
another review site or, youknow, source of generic AI news.
They talk about partnership.
Speaker 2 (11:32):
Meaning what?
Speaker 1 (11:33):
Meaning providing tailored advice, real time
intelligence that's relevant toyour specific business, and
helping you build a customrollout plan. Their whole mantra
seems to be less hype, morefocus.
Speaker 2 (11:46):
Okay, less hype, more focus. That definitely speaks to
the feeling of being overwhelmedwe talked about earlier.
Speaker 1 (11:50):
Totally. And they seem to have a specific process.
It starts with an AI readinessscore and a discovery call.
Speaker 2 (11:57):
So similar to the roadmap's first step, assess
where you are.
Speaker 1 (12:01):
Exactly. They offer to analyze your business, help
you map out where AI couldactually make a difference for
you and create that initialroadmap. It's about getting that
strategic clarity right from thestart.
Speaker 2 (12:11):
Tackling that lack of strategy problem head on?
Speaker 1 (12:14):
Seems like it. And then they have different ways to
engage like a advisory light anda advisory pro.
Speaker 2 (12:19):
Okay. What's the difference there?
Speaker 1 (12:21):
Light seems to be about having direct access like
through Slack or Loom videos toan AI advisor for quick
questions, getting input ontools or strategy. Sorta like
having an expert on call.Reducing the guesswork. Right.
Whereas pro sounds moreinvolved, like having embedded
AI leadership.
Regular strategy calls helpactually executing the roadmap
(12:42):
over seeing things across theorganization.
Speaker 2 (12:44):
So more hands on strategic partnership.
Speaker 1 (12:47):
That's the impression.
Speaker 2 (12:48):
What makes their approach potentially different?
Lots of consultants out there.What's YTI's unique selling
point based on their info?
Speaker 1 (12:56):
What sounds quite unique is their background. They
mentioned being the advisorsbehind an AI discovery platform
used for R and D.
Speaker 2 (13:03):
Okay. What does that mean practically?
Speaker 1 (13:05):
It means they claim to be tracking thousands of AI
tools constantly, in real time.And they combine that broad
market view with understanding aspecific client's workflow,
their tech stack, their actualgoal.
Speaker 2 (13:17):
Right, so it's data driven advice, not just
opinions.
Speaker 1 (13:20):
That's the idea. Using real time data on what
tools are emerging, what'sactually being used, and
matching that to the client'sspecific situation and stage of
AI maturity. Trying to find thehigh leverage moves and avoid
the hype cycle.
Speaker 2 (13:32):
So they could potentially help you figure out
not just where to start, butmaybe crucially what not to
waste time on.
Speaker 1 (13:38):
Exactly. Know where to start and what to skip, I
think is how they put it.Helping make confident
decisions.
Speaker 2 (13:43):
It sounds like they're positioning themselves
as that second brain forfounders or leaders dealing with
AI decision fatigue.
Speaker 1 (13:51):
Yeah. Taking some of that burden off. And they also
talk about facilitating cleanscaling.
Speaker 2 (13:56):
Clean scale.
Speaker 1 (13:57):
Meaning using AI to optimize your operations and
tech stacks smartly, makingthings more efficient without
just adding layers ofcomplexity.
Speaker 2 (14:05):
Which ties back directly to building that solid
foundation, overcoming thosereadiness gaps we discussed from
the article.
Speaker 1 (14:12):
Precisely. It seems designed to address those
specific bottlenecks (14:15):
lack of strategy, need for foundational
understanding, building theright infrastructure, knowing
where to focus, offering aguided path.
Speaker 2 (14:23):
So it's presented as a way to navigate that
complexity, especially if youdon't have a dedicated internal
AI team.
Speaker 1 (14:29):
That seems to be the core value proposition. And
their starting point is prettystraightforward. Get your
readiness score or book a callto get some initial clarity.
Speaker 2 (14:37):
Taking that first concrete step.
Speaker 1 (14:39):
Right. Moving from just awareness to actual
exploration tailored to you.
Speaker 2 (14:45):
Okay. So let's pull this all together. We've seen
from the article that AIadoption is definitely
happening, but it's patchy.
Speaker 1 (14:51):
Mhmm. Leaders are moving fast. Others are catching
up. Some are lagging.
Speaker 2 (14:56):
And the big roadblock for almost everyone is this AI
readiness, having the strategy,the infrastructure, the
governance, the talent.
Speaker 1 (15:05):
That 13% figure really sticks out.
Speaker 2 (15:07):
It really does. Then we looked at YOTI advisory as
one example of a potentialsolution provider, particularly
for SMBs.
Speaker 1 (15:14):
Right. Offering tailored guidance, leveraging
their platform data, andproviding different levels of
support to help businessesnavigate this and actually
implement AI effectively.
Speaker 2 (15:24):
So it provides a potential answer to the how do
we even start question.
Speaker 1 (15:28):
Exactly. And as we finish up, it brings us back to
you, the listener. Consideringeverything we've talked about,
the speed things are moving, thepotential advantages.
Speaker 2 (15:35):
That competitor with the AI agent working weekends.
Speaker 1 (15:38):
Right. What's one concrete thing you can do maybe
this week to move beyond justreading or listening about AI
and towards actively exploringit for your own work or
business?
Speaker 2 (15:47):
Maybe it's taking five minutes to honestly think
about your own AI readinessscore. Using those elements we
discussed strategy,infrastructure, data, talent,
governance, culture, where areyour gaps?
Speaker 1 (15:58):
Or perhaps it's checking out a resource like YT
AI advisory or something similarjust to see what kind of clarity
or initial steps they mightoffer. It's about shifting from
passive learning to activeengagement. Right?
Speaker 2 (16:10):
Definitely. Taking that first small intentional
step on your own AI journey.That's it for this episode of AI
on Air powered by WhatIsThat.ai.If your brain survived this
episode, go ahead and subscribe.We drop new episodes every week.
Wanna go deeper? Join ourcommunity on Substack to get
early drops, tool breakdowns,and weird AI stuff the
(16:32):
mainstream hasn't caught yet.See you there.
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