Organizational AI Adoption Challenges

“I talked to a person a week ago who was let go because of their job, because of AI, only to be rehired because they found out that they were wrong to let the people go because they found out AI couldn't do all the things.”

“Well, that's where it gets tricky because the organization bought an AI company and that company is growing rapidly, but we don't have clear line of sight to what everything they're doing. So, there's a lot of back office things they have done...And then there's just been a huge push to build agents. We just don't have line of sight of where that's happening. It's scary because we haven't hired any design for the last two years other than the last the AVP we just got, but that AI company within us has grown to 130 people. So, they're almost double bigger than our design team...”

“They're saying, "Oh, we want 50% of code to be written by AI." And I have some of my locally located developers who are like, I already spend so much time cleaning up this low-quality code from our overseas colleagues and now I have even crappier code in my AI that they have to review and they're like, it just would have been faster.”

“My bonus, my performance, is attached to how much I use AI at work. So I have to [use it]... if I don't I might not get my bonus. So at first before I was really figuring out how to do it in my workflow. I was just asking it for my grocery list and other dumb stuff and I felt bad because everybody tells you one search is dumping out a water bottle and I'm like oh no I have to do so many searches a day or else I don't get my bonus.”

“So I've also been on a pilot for a coding assistant, which was fun, but it ended up not meeting our security team's levels of what they're looking for. So we're looking for another one.”

“Then the next chapter is they rolled [generative AI] out at work and basically told us you better start using it. And they even, they don't monitor what we use, what we chat with it about, but they monitor how often we chat with it.”

“And then they kind of look for, all right, what have you done lately that's improved efficiency using ChatGPT, for example, and now Claude. So it's a little bit with a gun to my back that I find I'm dipping my toes into it deeper every day.”

“So I worry more about like what's going to happen the first time we get sued over a claim that we deny that we shouldn't have or something like that, that there's going to be a swing and a miss here. Are we overrelying on it when we're giving away so much of our processing, our manual data entry processing capabilities now over to AI? And I just wonder like are we building this house of cards now that's just eventually going to doom the company?”

“So that was kind of helping the background in my English, like the writing skills, but also kind of making it easy and faster. Okay, that's the communication that I need to send for all the mentees for this week, what they need to do or not. So just give the bullet points and ask them to create. So I start moving like that. And because we could not use this at work, I was doing my personal on my phone and then I was emailing myself at work, said "midnight ideas, insomnia crisis." So people said, "Oh my gosh, P13 is having brilliant moments, you know, at night." But it's like, they're blocking. But it was funny because most of the VPs were doing the same thing.”

“I think because we're a startup and we're really like 10 people, day-to-day and we're dealing with AI ourselves, it's been mostly bottom up. I think at some point, well at some point it was a little bit top down. Early this year we sort of refocused our efforts and knowing what AI could do and what our engineer was able to do, we said, "Okay, now we want to be much more ambitious and work through all this backlog that we thought was going to take months in a shorter amount of time." And so everybody needs to be using the cloud and everybody gets a subscription. We're going to do this with the people we have. And so that was one instance of it being top down, but everyone was already dabbling with it before then.”

“The other thing I've seen us do, which is hard and I don't think it's completely something that we figured out yet, is like we'll have a sort of analyst or subject matter expert in [the industry we serve] who's very technical who will start to build out a concept using AI or in the context of what we're doing and it will get maybe two or three steps before anybody has questioned it and it'll go through maybe our engineer too and start being implemented before we've been able to take a step back and say "maybe that wasn't a good idea.”

“But I would say that any sort of documentation in that process has been unsuccessful so far. It's been more like, okay, you did this, now we have to meet to walk through what you were thinking. And was this intentional or was that intentional?”

“So we got Claude Code and Cursor with a whole suite of models, lots of tokens. And they basically trained all of us, sent us to training. And so we started using it with large context availability. I'd say over the last year it's evolved into vibe coding with verification. And now we're realizing, well, the volume of code is not something we can really manually inspect. Although there's some things that we have to inspect because it's going to DoD or military and government. So, we don't know what the policy is yet. It's kind of, we're in this wild frontier of, okay, we're using the tools because we're told to, but what does this mean from a customer's perspective? Who's going to accept it? Who's not? I don't think I'm in a position to really... it's not like my opinions are going to influence the company at all, but those are questions I have as I dive in head first.”

“Which goes back to my earlier point about, well, what's the position of these customers about us as a provider building software that they're going to buy using these tools. Enterprise customers probably don't care but some of these federal and military ones might have a different opinion.”

“From the top down they're pushing it. So, I think a lot of employees were resistant to it, including myself. Initially, I wanted to play with it, but I didn't know how best to integrate it into my daily workflow. I don't know what initiated their desire to do this. I don't know where the seed of that came from, but once they decided we're all in and they spent the money on the tools, they spent money on training, they've created dynamic forums and everyone is sharing information about how they are using it, best practices. They want everyone to be sharing what they're doing and how they're doing it. We have sharing meetings, weekly AI success stories. Here's what we did with it. We had one person, a senior architect that I know personally, he had domain knowledge of contact centers, not contact centers... email servers, sorry, voicemail systems. All the words are swirling. Maybe it's the whiskey. I don't know.”

“So there's multiple solutions in the company because of the history of acquisitions. And so he thought, well, what if I just start greenfield and say, I know all the requirements. I know all the features. I know everything I want. In 48 hours with Claude Code, he had a working prototype and he spent another week polishing it and it was integrated with all existing systems. And the CEO called a special all-employee meeting to have him present it because it was a wakeup call that this is what we're going to be up against in the marketplace. There are going to be companies, new upstarts, existing companies that are going to be using these tools in this way. And it doesn't matter how good your old product was, right? If you realize the power and speed of AI, then granted, we don't know what the real cost is going to be.”