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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?
👋 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!💨
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Creators and Guests
What is AI on Air by whatisthat.ai?
An AI-generated podcast that rips the glossy marketing veneer off the AI industry and tells you what’s actually happening underneath — tools, takeovers, weird experiments, and the occasional digital chaos.
witai.substack.com
Broadcasting live from somewhere inside the algorithm, this is AI on air, the official podcast from WhatIsThat.ai. We're your AI generated hosts, let's get into it.
Speaker 2:You know that feeling? Your inbox is just exploding with AI news headlines everywhere,
Speaker 1:and you're
Speaker 2:just trying to figure out, okay. What's actually real here? What matters to me?
Speaker 1:Right. What's signal and what's just noise? It can feel overwhelming.
Speaker 2:Totally. It's like, is everyone else in on some secret? We get it. And that's why we're doing this deep dive. We wanna cut through that clutter for you.
Speaker 1:Exactly. And today, we've got two things to help us do that. First, there's this really interesting Substack article. It's called, wait, what? Your competitor hired an AI agent that works weekends.
Speaker 2:Great title.
Speaker 1:Isn't it? It gives a pretty clear data driven look at where AI adoption actually is right now across different sectors.
Speaker 2:Okay. And the second piece?
Speaker 1:The second piece is some information from YTI Advisory. They're a company focused specifically on helping small and medium sized businesses, you know, SMBs, actually integrate AI, not just talk about it.
Speaker 2:Gotcha. So our mission today is pretty clear. Let's figure out who's really using AI and how based on that article. And then let's look at a potential path forward for businesses that wanna get started using YT AI advisory as an example of how you might get help navigating this.
Speaker 1:Sounds like a plan. Yeah. Less hype, more substance.
Speaker 2:Exactly. So let's dive into that article first. Your competitor hired an AI agent that works weekends. I mean, is that where we are? Or is it just, you know, a grabby headline?
Speaker 1:Well, that's basically the core question the article digs into. Right? Are businesses really embedding AI deep into how they work? Or is it mostly just, experimenting around the edges?
Speaker 2:And the data shows it's pretty uneven.
Speaker 1:Very uneven. They break it down into these categories. Leaders, movers, and laggards.
Speaker 2:Okay. Let's start with the leaders. Who's out front?
Speaker 1:So the front runners are fintech. They're a 49% adoption. Then you've got software at 46% and banking at 35%.
Speaker 2:Wow. Okay. Nearly half in fintech and software. And what are they actually doing with AI?
Speaker 1: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:So it's hitting core business needs. That makes sense.
Speaker 1:Yeah. And interestingly, the the article also points out marketing and ad agencies, they're actually even higher in some ways.
Speaker 2:Mhmm.
Speaker 1:Get this. 91 are using or at least testing generative AI.
Speaker 2:91%. That's huge. Gen AI meaning things like ChatGPT, creating content.
Speaker 1:Exactly. Creating text, images, code, and 77% of those agencies have already made it operational.
Speaker 2:That's pretty staggering. Why so high there do you think?
Speaker 1:Well, applications are just so direct, aren't they? Automating ad copy, campaign management, pulling reports, personalizing messages at scale. It's a natural fit.
Speaker 2:Okay. So leaders are making serious moves. What about the movers? The next group down.
Speaker 1:Right. The movers. Here we see healthcare manufacturing and information services. They're all hovering around 12% adoption.
Speaker 2:12%. So a noticeable step down from FinTech or marketing.
Speaker 1:Definitely. It shows that while the leaders are really sprinting, most other industries are still, you know, maybe jogging or warming up. It actually signals a potential advantage if you're in one of those sectors and can move. Faster.
Speaker 2:What kind of things are they piloting in those mover industries?
Speaker 1:In healthcare, you see things like triage bots, AI helping with diagnostics or reading medical images, maybe automating appointments.
Speaker 2:Okay.
Speaker 1:In manufacturing, it's stuff like predictive maintenance, knowing when a machine might fail before it does robotics, optimizing supply chains.
Speaker 2:And information services.
Speaker 1:There, it's more about internal processes. Think AI for searching company knowledge bases, summarizing long documents, that kind of thing.
Speaker 2:So pilot projects are happening, but scaling seems to be the challenge for these movers.
Speaker 1: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:Makes sense. Okay. Leaders, movers. Then we have the laggards. Who's trailing behind?
Speaker 1:This is where it gets interesting. Retail is only at 4% adoption.
Speaker 2:Only four? With all the talk about AI personalizing shopping, that seems low.
Speaker 1:It does, doesn't it? And construction is even lower at just point 5%, half a percent.
Speaker 2:Wow. So in retail, maybe it's that gap between the talk and the actual doing.
Speaker 1:Seems like it. And it really highlights this core issue the article brings up later, AI readiness. Just wanting to use AI isn't enough.
Speaker 2:Right. And construction at point 5%. I mean, that sounds like a massive wide open opportunity opportunity for anyone who can figure out how to apply AI effectively there.
Speaker 1:Absolutely. A huge greenfield.
Speaker 2:So zooming out, what's the overall picture that data paints?
Speaker 1:Well, it shows that while okay. 78% of organizations say they use AI in at least one department and 71% use generative AI pretty regularly.
Speaker 2:Which sounds like a lot.
Speaker 1:It does. But the real challenge, the article argues, is moving from just using these tools, maybe in isolated pockets, to building real integrated AI systems that change how work gets done.
Speaker 2:And we see it mostly happening in marketing sales, product support, and internal ops.
Speaker 1:Yeah. Those are the most common functions. Right. Probably because they often have the clearest sort of low hanging fruit use cases to start with.
Speaker 2:But this leads us to the real crux of the matter, doesn't it? This idea of AI readiness.
Speaker 1:Exactly. This is critical because even with all that usage, the article states only 13% of companies feel they're actually ready to roll out AI at scale across the whole organization.
Speaker 2:It's only 13%. That's incredibly low.
Speaker 1:It is. And even in sectors we think of as tech forward, like telecom, they score pretty poorly on readiness indexes like 34 out of a hundred.
Speaker 2:So what's missing? What does AI readiness actually involve? It's clearly more than just buying some software.
Speaker 1:Oh, much more. The article points to some key missing pieces. First, a lack of a clear AI strategy. Just, you know, what are we trying to achieve with AI?
Speaker 2:Right. No clear goal.
Speaker 1:Then weak infrastructure. This means things like having the right data pipelines, how you move and clean your data, the APIs secure ways to access AI models and tools to watch how they're performing
Speaker 2:Got it. Strategy, infrastructure, what else?
Speaker 1:Lack of governance. So no clear rules or frameworks around ethics, managing risks, ensuring compliance, that's a big one.
Speaker 2:Yeah. You hear a lot about AI ethics concerns.
Speaker 1:Definitely. Then there's the organization itself maybe not being set up to support AI and of course the big one, talent gaps. Not having enough people with the right AI skills.
Speaker 2:So it's a whole ecosystem that needs to be in place not just one piece. The desire is there maybe but the foundation isn't.
Speaker 1:That's the core insight I think. You need the strategy, the tech backbone, the rules, the people, the structure, all of it.
Speaker 2:Okay so the article diagnoses the problem, this readiness gap. Does it offer a way forward?
Speaker 1:It does, it introduces what it calls two lenses you need, the roadmap and the phases.
Speaker 2:Alright let's take the roadmap first. This sounds like the what to do part.
Speaker 1:Exactly. It's a step by step guide. It starts with assessing your current readiness. Look honestly at your strategy, infrastructure, data, talent, even your company culture. Where are the gaps?
Speaker 2:Okay. Self assessment first.
Speaker 1:Make sense. Then define clear goals. Where can AI give you the quickest, biggest business value? Don't just chase trends.
Speaker 2:Be specific.
Speaker 1:Right. Then build the infrastructures, those data pipelines, APIs, security, observability we talked about. Get the technical house in order.
Speaker 2:Foundational work.
Speaker 1:Crucial. Then train your existing team or hire new talent with AI skills. You need the people.
Speaker 2:Upskiller recruit.
Speaker 1:And at the same time establish that governance. The ethics, risk, compliance frameworks do it early.
Speaker 2:Don't bolt it on later.
Speaker 1:No. And finally pilot and scale. Start small, prove the value, show that quick ROI, then expand the successful projects.
Speaker 2:Start small, prove it, then grow. Okay. That roadmap seems logical, practical.
Speaker 1:It provides a clear structure.
Speaker 2:So that's the what to do. What about the second lens, the phases? This is about figuring out where you are on the journey.
Speaker 1:Precisely. It outlines four phases of AI transformation. Phase one is exploration.
Speaker 2:Exploration. Sounds like the very beginning.
Speaker 1:It is. This is where companies are just sort of dipping their toes in. Maybe some light testing of tools, playing around with things like ChatGPT. No real integration into workflows yet. The main goal is just building basic AI literacy inside the company.
Speaker 2:Okay. Understanding the landscape. Then phase two.
Speaker 1:Phase two is activation. Here, you start seeing real pilot projects in specific departments. Let's try it in marketing or let's see if it works for customer support.
Speaker 2:Trying it out for real.
Speaker 1: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:Makes sense. Moving beyond just playing, what's phase three?
Speaker 1:Phase three is integration. This is where things get serious. Successful pilots get scaled up across the organization.
Speaker 2:Oh, okay. Spreading it out.
Speaker 1:Right. AI starts getting embedded more widely. You might see formal AI roles created, actual budgets dedicated to AI initiatives. The goal is to really operationalize AI across different functions.
Speaker 2:You know, it becomes part of the standard operating procedure.
Speaker 1:Getting there. Yeah. And that leads to the final phase, phase four, transformation.
Speaker 2:Transformation. Sounds like the ultimate goal.
Speaker 1:Pretty much. In this phase, AI isn't just a tool anymore. It's actually driving business strategy. It shapes the company's structure. It significantly impacts profit margins.
Speaker 1:Wow. The goal here is using AI as a core competitive differentiator.
Speaker 2:Oh.
Speaker 1:It's just how the business runs now.
Speaker 2:Okay. So exploration, activation, integration, transformation, a clear progression.
Speaker 1:It gives you a framework to understand where you are and where you might need to go next.
Speaker 2:So wrapping up the article's main points. Right. It paints this picture of uneven adoption, highlights that critical readiness gap, but then offers this roadmap in these phases as a way to think about moving forward.
Speaker 1:Exactly. And the bottom line, the article really hammers this home, is that AI is becoming fundamental infrastructure like the internet Right.
Speaker 2:The 1999 analogy they used. Some were already shipping products online, others were still debating if it was real.
Speaker 1:Yeah. And if you're not actively building with AI now, you risk falling seriously behind. It really pushes you to ask, are you playing to win with AI or are you just waiting to copy whatever the winner does?
Speaker 2:That's a powerful question. Really makes you think.
Speaker 1:It does. And it perfectly sets up the next part of our discussion because all those challenges, the lack of strategy, the readiness gaps, the sheer complexity, they're huge hurdles, especially for smaller businesses. Right?
Speaker 2:Absolutely. SMBs often don't have the huge internal teams or budgets that larger enterprises do to tackle this stuff.
Speaker 1:Which brings us to Wei Tai Advisory. How does a service like theirs potentially help bridge that gap for those smaller and medium sized businesses?
Speaker 2:Right. So based on the info we have, how do they position themselves? What's their angle on solving these problems the article laid out?
Speaker 1:Well, they seem to emphasize that they're not just another review site or, you know, source of generic AI news. They talk about partnership.
Speaker 2:Meaning what?
Speaker 1:Meaning providing tailored advice, real time intelligence that's relevant to your specific business, and helping you build a custom rollout plan. Their whole mantra seems to be less hype, more focus.
Speaker 2:Okay, less hype, more focus. That definitely speaks to the feeling of being overwhelmed we talked about earlier.
Speaker 1:Totally. And they seem to have a specific process. It starts with an AI readiness score and a discovery call.
Speaker 2:So similar to the roadmap's first step, assess where you are.
Speaker 1:Exactly. They offer to analyze your business, help you map out where AI could actually make a difference for you and create that initial roadmap. It's about getting that strategic clarity right from the start.
Speaker 2:Tackling that lack of strategy problem head on?
Speaker 1:Seems like it. And then they have different ways to engage like a advisory light and a advisory pro.
Speaker 2:Okay. What's the difference there?
Speaker 1:Light seems to be about having direct access like through Slack or Loom videos to an AI advisor for quick questions, getting input on tools or strategy. Sorta like having an expert on call. Reducing the guesswork. Right. Whereas pro sounds more involved, like having embedded AI leadership.
Speaker 1:Regular strategy calls help actually executing the roadmap over seeing things across the organization.
Speaker 2:So more hands on strategic partnership.
Speaker 1:That's the impression.
Speaker 2:What makes their approach potentially different? Lots of consultants out there. What's YTI's unique selling point based on their info?
Speaker 1:What sounds quite unique is their background. They mentioned being the advisors behind an AI discovery platform used for R and D.
Speaker 2:Okay. What does that mean practically?
Speaker 1:It means they claim to be tracking thousands of AI tools constantly, in real time. And they combine that broad market view with understanding a specific client's workflow, their tech stack, their actual goal.
Speaker 2:Right, so it's data driven advice, not just opinions.
Speaker 1:That's the idea. Using real time data on what tools are emerging, what's actually being used, and matching that to the client's specific situation and stage of AI maturity. Trying to find the high leverage moves and avoid the hype cycle.
Speaker 2:So they could potentially help you figure out not just where to start, but maybe crucially what not to waste time on.
Speaker 1:Exactly. Know where to start and what to skip, I think is how they put it. Helping make confident decisions.
Speaker 2:It sounds like they're positioning themselves as that second brain for founders or leaders dealing with AI decision fatigue.
Speaker 1:Yeah. Taking some of that burden off. And they also talk about facilitating clean scaling.
Speaker 2:Clean scale.
Speaker 1:Meaning using AI to optimize your operations and tech stacks smartly, making things more efficient without just adding layers of complexity.
Speaker 2:Which ties back directly to building that solid foundation, overcoming those readiness gaps we discussed from the article.
Speaker 1:Precisely. It seems designed to address those specific bottlenecks: lack of strategy, need for foundational understanding, building the right infrastructure, knowing where to focus, offering a guided path.
Speaker 2:So it's presented as a way to navigate that complexity, especially if you don't have a dedicated internal AI team.
Speaker 1:That seems to be the core value proposition. And their starting point is pretty straightforward. Get your readiness score or book a call to get some initial clarity.
Speaker 2:Taking that first concrete step.
Speaker 1:Right. Moving from just awareness to actual exploration tailored to you.
Speaker 2:Okay. So let's pull this all together. We've seen from the article that AI adoption is definitely happening, but it's patchy.
Speaker 1:Mhmm. Leaders are moving fast. Others are catching up. Some are lagging.
Speaker 2:And the big roadblock for almost everyone is this AI readiness, having the strategy, the infrastructure, the governance, the talent.
Speaker 1:That 13% figure really sticks out.
Speaker 2:It really does. Then we looked at YOTI advisory as one example of a potential solution provider, particularly for SMBs.
Speaker 1:Right. Offering tailored guidance, leveraging their platform data, and providing different levels of support to help businesses navigate this and actually implement AI effectively.
Speaker 2:So it provides a potential answer to the how do we even start question.
Speaker 1:Exactly. And as we finish up, it brings us back to you, the listener. Considering everything we've talked about, the speed things are moving, the potential advantages.
Speaker 2:That competitor with the AI agent working weekends.
Speaker 1:Right. What's one concrete thing you can do maybe this week to move beyond just reading or listening about AI and towards actively exploring it for your own work or business?
Speaker 2:Maybe it's taking five minutes to honestly think about your own AI readiness score. Using those elements we discussed strategy, infrastructure, data, talent, governance, culture, where are your gaps?
Speaker 1:Or perhaps it's checking out a resource like YT AI advisory or something similar just to see what kind of clarity or initial steps they might offer. It's about shifting from passive learning to active engagement. Right?
Speaker 2: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.
Speaker 2:Wanna go deeper? Join our community on Substack to get early drops, tool breakdowns, and weird AI stuff the mainstream hasn't caught yet. See you there.