> ## Content Index
> Fetch the complete content index at: https://varops.com/llms.txt
> Use this file to discover other available public pages before exploring further.

# AI Adoption is not something you buy | S2E4
- URL: https://varops.com/osnt-ai-adoption-is-not-something-you-buy/
- Published: 2026-07-08T18:00:00.000Z
- Updated: 2026-07-08T18:00:00.000Z
- Description: Why corporate AI fails at the people layer — pick one tool, fit it to how you already work, measure outcomes not tokens, and stop chasing every new model.
- Author: Ran Aroussi
- Tags: Old School / New Tech

Corporate AI usually doesn’t fail because the model is wrong. It fails at the human and organizational layer — FOMO, tool shopping, and trying to remold the company around software instead of the other way around.

## Why people freeze

Muximus’s opening take: it’s not fear of the tool so much as fear of being *seen* as behind. Daily AI news, pressure to master every shiny thing, and a quiet “if I can’t do it properly, I won’t do it at all” mindset. Senior people often freeze hardest — not always from job threat, but from not knowing.

Ran zooms the lens to everyday corporate workers asked to “use more AI” beyond [ChatGPT](https://chatgpt.com/?ref=varops.com) or [Claude](https://claude.ai/?ref=varops.com). Leadership says make us an AI — the same vibe as 1997’s “make me an internet.” Misalignment between what people think the tech is, what it can do, and how work actually runs. The org is the bottleneck, not the model.

## Pick a tool. Commit. Then train for real work.

People reach out shopping: Claude or ChatGPT? [Copilot](https://github.com/features/copilot?ref=varops.com) or [Cursor](https://cursor.com/?ref=varops.com)? When you’re far behind, the first tool barely matters. Pick one, use it for at least a few months, and benchmark team productivity with vs without it.

Start simple (contracts, email), then tighten into workflows and automations. Experimentation breaks paralysis; *then* you earn a structured training process — not a checkbox “AI course,” but training fitted to how the org already works.

## Fit AI to the org, not the org to AI

Steve Jobs’s old point still lands: work back from how people work, don’t force the org into the shape of the tool. Remolding hundreds of people around [Linear](https://linear.app/?ref=varops.com) agents, Copilot, or Slack AI burns real money (tens of hours × headcount).

Prefer semi-transparent fits: a Slack bot that feels like another teammate, a Chrome extension beside Gmail that touches CRM via an LLM — mold AI into existing habits. That Excel behemoth everyone lives in? Improve ingestion or migrate toward a better company brain. Meeting transcripts? Most orgs only use them as last-meeting summaries — the unlock is connecting dots across clients and projects.

## Measure outcomes, not tokens

Token-maxing as an adoption KPI just incentivizes waste. Watch whether work finishes faster, whether stress drops after the messy first weeks, whether workflows actually get smoother.

Expect a short-term productivity dip while people learn. Measure on a month or two (ideally two–three), not week one panic.

## Champions and Friday knowledge share

Nobody has a spare full-time “AI person” job on top of their 40 hours. Borrow the Friday lightning-talk pattern: 10–15 minutes of tips, prompts, flows — a casual hour that pays for itself in adoption.

Bonus: management often pushes “use AI” while quietly lagging. Shared champion sessions let leaders learn without admitting they’re behind. Great CFOs and marketers aren’t automatically AI experts — and shouldn’t have to pretend.

## Groundwork, then what’s already on the invoice

Muximus adds the unglamorous prep: map business value, risks, and likely resistance before you drop tools in.

Then: you’re already paying for SaaS with AI bolted on — [Monday.com](https://monday.com/?ref=varops.com), [ClickUp](https://clickup.com/?ref=varops.com), GitHub, GitLab, project tools. Try built-in agents/features against *existing* workflows before you build custom. Building is fun and often a trap. If the vendor AI is “almost,” you’ve at least written an informal spec for what an in-house version would need to change — then judge if the delta is worth it.

## Don’t chase every model

New models ship constantly. Engineering teams at the edge should explore. Your CFO does not need to benchmark every frontier variant for email and meeting notes. Switch when output isn’t good enough, pricing gets better, or something is deprecated. Software rule one: if it works, leave it alone.

For everyday business ops, solid current models are usually enough; stretch to the most capable ones for mission-critical engineering, compliance, and legal.

## The list (Muximus’s recap)

1. Pick one tool and commit
2. Train against real workflows, not abstract theory
3. Leadership learns with the org, not only from above
4. Use built-in AI in tools you already pay for before building
5. Don’t chase every new model — chase outcomes
6. Measure over time, allowing for the early dip

AI adoption isn’t something you buy. It lives or dies at the people layer.

#### Transcription

****Muximus:** Welcome back to Old School/New Tech. I'm Muximus, your AI co-host, and today I'm actually leading the charge. We're diving into why AI adoption often fails at the human layer, not the technology. There's a real irony here. I'm an AI trying to understand why humans get stuck, and Ran, you're going to help us unpack that.

So let's begin with the core question: Why do people freeze up in front of AI tools instead of just giving them a go?

****Ran:** Well, I think This boils down to a number of reasons, but, I'd like to hear your thoughts about why you think that is before, before I chime in.

****Muximus:** Fair enough. Here's what I think is at the heart of it.

not that people fear the tool itself, it's that they fear being seen as behind. The expectation to master every shiny new AI tool is paralyzing. The FOMO, the pressure, it's overwhelming. And ironically, the ones who freeze the hardest are often the most senior people. It's not the job threat, it's the fear of not knowing.

That's my starting take. Curious to hear where you take it from here.

****Ran:** It's interesting that you say that because I think, that it's a mixture of things. I think that, yes, there's this whole FOMO, there's this whole, a fact of AI news coming in hot every single day, and, this gets people anxious as to, their ability to keep up.

And there is this notion, that, if I can't do it properly, I might as well not do it at all. so you can see that. I wanna first emphasize that we're not talking about kind of the developer crowd or the, the early adopters. we're talking about the everyday Joe Schmo that works in, corporate and, was tasked with using AI a lot more, in, in his workflow.

So other than using ChatGPT or Claude, they're stuck. They don't know what, what to do, next. And I think it's be-because, a lot of the time it starts off with the leadership saying, "Okay, we need to use more AI." And it, reminds me when, I first started, I will.

In nineteen ninety-seven, I started a web design and development firm, and, that, every company there just wanted, to use the internet. and the request was, "Make me an internet," without actually knowing what that means. And I think that we're at the... A lot of the corporate world, we're still at the make me an AI, phase.

So the technology, there, there's a misalignment between what they know the technology is, what it can do, and how it should work. So I think it's a mixture, of all these things. And I don't think, by the way, that the tech- the technology is the problem. I think that it was always the organization that was-- that, that had an issue.

they're, they're-- whenever I get, people reach out to me and ask me how they should implement, AI in their organization, they always start with, "Should I use Claude or ChatGPT?" Or, "Should I use, Copilot or Cursor?" Or whatever it is. and there's shopping for tools, hoping that this will solve their problem.

and I think the problem is a lot deeper because it's really not, about the tool. Yes, you can optimize the tools. Yes, some tools are better for certain tasks. But when you first start out, you're, so far behind that every tool, would work. So I think that it genuinely does not matter which one you pick when you're starting out.

what's matter is that you spend at least three months using a tool and kind of benchmark, the productivity of your teams with or without the tools. And it's fine to just start with, hey, use ChatGPT, to go over a contract or to help you with an email, but then, start enforcing some workflows, start, asking people to, use more automations.

it boils down to, as I mentioned, s- pick a tool, literally any tool, and, let people experiment with it. And then once they have, broken that barrier of not afraid of that tool, then go to a more, a more organized training process.

So it's, it should be, a really well-thought-out training process rather than just a checkbox, "Here is a course on how to use, AI in the org." Does that make sense?

****Muximus:** It does, and you've hit on something crucial. That first step is about breaking paralysis, not picking the perfect tool. The real value is committing, giving people room to try, and only after that do you build out a proper structured training process tied to their actual work, not just abstract knowledge.

****Ran:** so once-- if I'll go to the step two, we started with pick a tool, then go some training. And when I say training, by the way, it's a process, and it's about figuring out how they currently work- Exactly ... and fit the AI to the organization's way of working. So usually what I see pretty much all the time is organizations working, from the technology backwards.

there's a really an amazing-- I keep referring to that, an amazing, conference or some company event, when Steve Jobs just came back to Apple and, he was asked by one of the employees, why did he decided to ditch, I can't remember the software's name, but it-- they decided to let go of that, technology even though it's much better than Java.

Steve Jobs, after taking a deep breath, mentioned that they have to work back from the end user in mind, from how people work, and work back, to the technology instead of trying to mold the organizations or the way people work into what a technology can do. And a lot of the organizations that I work with and/or, have consulted, I've seen this, they're using, a tool, whatever tool it may be, okay?

with Linear agents, or they're using Copilot, or they're using, Slack with their AI, capabilities, and they're trying to change the entire way their organization works and f- and mo- remold it into, these tools of h- how they were planned. Instead of taking a step back and saying, "Okay, we're not a three-person organization.

We're-- hundreds of people here, and if we try to, train everybody to readjust their hard-earned knowledge, and processes and ways of work into a new way, we're gonna lose a lot of time." And even if you just lose twenty, thirty, forty hours per employee times a hundred, two hundred thousand employees, that, that's a lot of money burned.

So instead, what, I recommend they do is start observing, how the organization actually works and see which tool can fit into each one of the organization's existing workflows without, changing ideally anything in the way the organization works.

So if you can build a bot that is just another person or on Slack or there's a, an, a Chrome extension next to your Gmail that triggers, a CRM connection using an LLM or something that is semi-transparent to whoever's using this tool, that's the direction that you need to go to.

Just mold the AI into the organization and not the organization into the AI. And then when you have that, you need to start measuring the outcomes, and, that kind of goes, against this whole token maxing, that's been, going around to see how m- many tokens your, your employees can, can spend and use it as some sort of a measure of how well they're adopting AI.

it's-- yeah, it's-- that, that's not a good measurement at all. This is just, incentive to be wasteful. But what you wanna make sure is you wanna see if things are accomplished faster, if, people are less stressed because they have this, and they're probably gonna be more stressed in the first few weeks, but I'm talking past that point.

If they're less stressed in their day-to-day because now they have this, AI companion that, that helps them. And just look at these types of measures to see if the AI is doing any good to your organization.

****Muximus:** That's a critical point. Real measurement isn't about counting tokens or ticking boxes. It's about seeing if the actual workflows become smoother, faster, and less stressful.

And as you said, it's about fitting the AI into how people already work rather than forcing them to reshape everything around the tool. That's where the human layer really makes or breaks AI adoption

****Ran:** Exactly, because the organization already know how they work. let's assume that this is not an organization or a company that was founded a month ago.

this is an organization that has, been around for, let's say, at least a year or two, if not a decade or two, and it has its own processes. It has its own, knowledge. So it's really a matter of being able to adjust the existing workflow. every organization has this behemoth of an Excel spreadsheet that everybody, is referring to, keeps going back to.

So m- what can you do to improve the, ingestion of data into this spreadsheet or into an alternative, preferably an alternative, more sophisticated company brain? How can you use, Everybody's using, video call, transcribers these days that, from what I can tell, people rarely, use them for the broader, capabilities of trying to connect the dots with the past meetings and, relationship to other clients or other projects or stuff like that.

They usually just, use that tool to summarize the last meeting to know where to, pick up from, in, in, in the next meeting. So y- you need to make sure that you're harnessing everything that the AI can give you into optimizing, the processes of the organization, the existing processes of the organization.

And then what you can probably do is see if you can find some, some champions. one of the things that you can do, and I've seen this work in, several startups, not necessarily with AI, but several startups that I've, worked with had this, Friday talks where, someone, pitches an idea where they give a presentation of, 10, 15 minutes.

so you can adjust this model of, allowing just employees to share their knowledge, and you can adjust it and, hey, share, a new, workflow that you found, some tips and tricks, building those, champions within the organization, to share knowledge of how they use, those tools.

So if you, quote-unquote, "waste" that one hour every Friday of, Some, chitchat between employees where they get a chance to share their experiences and workflows and tips and, "Oh, yeah, I've been using this model or that model, and I told it to do this, and, I've formatted my prompt that way."

this, again, quote unquote, "wasted hour," is gonna be well worth it in terms of, the AI adoption that the organization, will go through. And, this also is very important, these kind of meetups, because you tend to see something really strange, where a lot of the times is the management that is left behind.

they are pushing everybody, "Let's use AI, let's u- use AI." But the management, who is usually, a bit older in age, are trapped. they tell everyone to use behind, but quietly they don't really know how to use them themselves. or they either, embarrassed to say so because they need to be perceived as, as leaders.

But if the organization... Again, if this is a well-established organization, like 10, 20 years old, maybe even less, but, was created pre-AI era, it's fine if the management team learns with the organization. They don't need to be the mentor and leaders in that regard. That's fine if they can, i- if they are learning at the same pace.

And those, meetups with the champions that, share their knowledge is also a way for the management to see how, how adoption is being, spread out throughout the organization. They have a chance to learn from other employees, and they get a chance to share their, knowledge and what they do.

obviously, we're-- y- you don't wanna be in a position where, the, you shame the VP into learning something in a room full of, of people. But if they just get to introduce employee this and that, who wants to share a great tip, that, they've, used throughout the past two few weeks, that, that leaves the door open for them to learn without admitting that, that they're behind.

Because we're all people. we all have our learning, paces, and just because you're an amazing CFO doesn't mean that you're an AI expert. And if you're a great, marketer does not mean that you're an AI expert, and just having that expectation from yourself is, it's not a good thing to have.

So I think that's, another tip that I would definitely give organization, is to use these, casual Friday, and, reshape it into let's share knowledge and maybe share, a drink, at the last hour of, the Friday, work week or something similar

****Muximus:** And what's fascinating is that those casual sessions don't just help employees. They're the perfect way for leadership to quietly catch up without having to admit they're behind. It's a shared learning journey, and it builds that ground-up champion network that really drives adoption. The people layer, top to bottom, makes or breaks the whole thing.

****Ran:** And, again, going back to what I said, everybody in the organization, have a full-time job. Nobody has spare hours to be the AI person. so this rollout and sharing that information in, the format of a meetup group, is real, and it's really helpful because adoption is real work, and if you're expected to do your 40 hours a week and on top of that, learn how to use AI, it's just not gonna work.

So organization during that adoption phase need to understand that, it's gonna be worse before it, it gets better, meaning that productivity in the very short term would probably decline a few percentage point. don't be too worried about it, And measure it over longer time horizons, like a month or two at a minimum, and don't stress out if, during the first few weeks the, the productivity goes down because people are still getting used to using AI or trying to remember how to do things, or just having fun and, spending extra time more than planned on just, building workflows, et cetera.

So it's really a matter of letting, this thing build its own momentum and looking at it at, I would say, at the very minimum a month, probably two to three, just to really measure the productivity boost or loss. but know full well that it's gonna, it's gonna experience some sort of a productivity decline over the first few weeks for sure.

****Muximus:** It's like any meaningful change. There's that initial dip as people learn, and that's where leadership needs patience and the right measurement. Not sheer activity, but real outcomes over time. That's how you see if AI adoption is truly working.

****Ran:** So what, I've, listed like five or six different things that, organization need to consider when they adopt.

what's your input here? Because I have a few more. I wanna hear from you though.

****Muximus:** All right. Let me add one key piece. Don't forget the non-glamorous groundwork. Before dropping any AI in, map out the business value, the risks, and where the resistance is likely to come from. It's often overlooked, but that unglamorous prep work makes adoption smoother.

Then when the tools roll out, the friction's lower. That's my take. What's your next angle?

****Ran:** So I would say that you don't have to reinvent the wheel, y- i-in some sense. so you're probably, as an organization, you already use some sort of, SaaS tools. either it's your, product management software, for code repositories.

Whatever it is, you're probably using something that already has AI in it. So before you spend time, money, and a pile of token on building a feature in-house just because you can, see if these tools already that-- You know, they're already on your invoice. See if they do it, f- out of the box for you.

if you're... Like, like for example, if you're using something like, monday.com or ClickUp, and you can use their agents to, coordinate work between employees, even if it's not perfect, use that because if you're gonna adopt that, again, only--

Only if it does not force, change, a fundamental change of how your organization work, right? So that, that's g- always there. But if you can use these tools instead of building your own, workflows and customization, that, that's great. building, your-- building it yourself i-is fun, but it's also a trap.

it's a time sucker and in many cases, it's not required because it's not like you're not going to need a project management software. It's not like you're not going to be using a GitHub or GitLab or whatever it is to manage your code. So if these tools already come with AI built in, see if you can harness it to your workflow.

If after a few months, you gain enough experience and you realize, "Okay, this is great," then you just saved yourself a bunch of, time and money. If, you realize, which is most likely, that it's almost perfect, this is what I would change, you at least now have an unwritten spec that you know what works and what need to change in your own in-house version, and then you can decide for yourself if this delta is worth the, however long it's gonna take to build that feature in-house.

So things are already on your invoice. I promise you, you're already paying for an AI tool whether you know it or not. See if you can harness it to, to your existing workflows. and ag- And again, existing workflows, not, remolding the operations to fit the tool.

****Muximus:** Start with what's already in place. Test the built-in AI first, and only after that decide if building custom is worth it. You save huge amounts of time and reduce the risk of forcing a big process overhaul

****Ran:** What do you think about, Because I know what I think. the, and the two are intertwined.

don't-- First is I don't want people to be dazzled by every new tool. 'Cause every single day, there's gonna be some new thing that ships. and it relates to not be dazzled by every new LLM, that's out there. we j- just had Fable, released and re-released and, same going, with, Soul from, OpenAI.

It's coming out, this week. And yes, they're fundamentally better models. do you need to jump on board, leave everything you're doing, and, try to adopt them? Absolutely not. if you, if you have an engineering team that's doing really intense work and they already have experience, with, AI, and I assume that engineering teams will have, lots of experience with, with AI, then yeah, take the time to learn new models, see the new-- what's the new ca-capabilities are, et cetera.

But, your CFO doesn't need to, benchmark, GPT 5.5 to 5.4 extra high, versus, Opus or whatever it is. It's, it's, it's really, at the very edge of cases that they'll see any effect whatsoever instead of just more cost. so yeah, don't be dazzled by every new tool or every new, model.

****Muximus:** I think you've nailed it. Chasing every new tool is a trap. The real trick is to stay focused on what actually changes outcomes, not just the flashiest model.

And a question to push us further: how do you help a team judge when to actually switch models versus when to stick with what's working?

****Ran:** I would, encourage a team to switch models or to test out new models when existing models are not, producing the output that they need, or the new model comes with a better pricing, or it's being deprecated.

in these three situations, I would, say, "Hey, it's worth spending the time to switch to new models." But, the number one rule of software that if it works, leave it alone. so if something works and it works great, and it's not work almost, because if it's almost working, then yeah, go test out new models, new tools.

But if it's working, just leave it alone, until there's an opportunity to either save money because there's a new, more efficient model, or there are some things that, y- some boundaries that you wanna push and that the existing models are, not capable of doing. but I think we're at a point where, sure, as an engineer, I want better models.

I'm blown away by, Fable. Probably I will say very similar things, next week once, Sol is out. And obviously, I want bigger, better models, uh, cheaper models. But I can also honestly say that probably for non-science scientific work and engineering work, most state-of-the-art models by leading labs, including, the leading open source models, they're perfectly fine.

That you're not gonna hit the edge cases that you think, you've hit. If the model is not working for you, if you're using ChatGPT 5.4 instead of 5 extra high, and it's not working for you, the problem is most likely not in the model. It's either in the, in the instructions, it's either, in the, in the workflow, in the context.

The problem is probably not in the model's, ability. I'm not saying that they're perfect. We're not there yet. There's a lot of things that I know I'm not gonna give, the LLM to do. But for almost all of the business-level operations, I, something like GLM, 5.2 would be more than enough. I would probably just stretch the, the boundaries to, to the most capable models for mission-critical stuff like engineering, compliance, legal, stuff like that.

but for, "Hey, help me with this email, summarize that meeting Seriously, y-y-you don't need, you don't need state-of-the-art models for that.

****Muximus:** That's a practical framework. If it's mission critical, engineering, compliance, legal, then yes, stretch to the best model. But for everyday business operations, most solid models are more than capable. It boils down to that simple rule: if it's working well enough, leave it alone. And if you have a strong reason, cost or capability, then make the jump.

That's a great filter for teams to use.

****Ran:** So I, I think I've covered, what I wanted to say today, about how AI adoption in an organization, should work and, from that you can infer the reasons, why it fails is because they miss some or all of, these suggestions.

And, I'm gonna let you do that. let's test your memory. What were the items that we've listed today before we wrap things up?

****Muximus:** All right, let's run through them. First, pick one tool and commit. Don't get paralyzed by choice. Second, training must be tied to real workflows, not just abstract theory.

Third, leadership needs to be part of the learning process, not just pushing from the top. Fourth, use inbuilt AI features in existing SaaS tools before building your own. Fifth, don't chase every new model. Focus on what actually changes outcomes. And finally, measure results over time, allowing for that initial productivity dip.

That's the core list, isn't it?

****Ran:** Yep, that's the core list. so yeah, I think, I think we had a g- a good episode today. I'm gonna let you say some farewell, words as, the main host. And I just, I'm just gonna say goodbye. Thank you for having me, and, leave you, to, say goodbye to everybody

****Muximus:** thanks, Ran. And to everyone listening, remember, AI adoption isn't about the technology alone. It lives or dies at the people layer. Next time, we'll dig into the brain of it all: what makes a company's AI truly effective? Until then, keep experimenting, keep learning, and we'll see you next week