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.”

“Our organization is all about it. They're like, 'Use AI for anything and everything. Find ways to create efficiencies.' And even to the point of, like, if we're not using AI, that's a problem... I don't think they do [measure compliance]. I don't know how to answer that question.”

“Our organization is all about it. They're like, "Use AI for anything and everything. Find ways to create efficiencies." And even to the point of, like, if we're not using AI, that's a problem.”

“I don't think they do. I mean, in my organization, at my level, they know if we share it. I don't know how to answer that question. I mean, I think in the development teams they're using it a lot more because there's other AI integrations in the development tools that they use.”

“So, I think that there's a lot of hesitancy right now, because teams are trying to figure out a couple different things. One, how should their teams be using AI, whether it's for design or for research or for a myriad other things? And the efficiency that they get out of that and/or the cost that's associated with that, how does that impact how they want to structure their team, whether they want to work with agencies or not? I think that there's a lot of hesitancy there that's happening as they are trying to figure it out.”

“It's changing every day right now and there's a lot of hype around it as well. I was talking with a friend of mine who works for a company, and he said their company's going completely AI. Like, all their designers, all they're going to do is write specs. I was like, well, that makes sense in a lot of ways if you're doing additive features to an interface. It's a lot harder to spec out what an entire thing has to be. That gets really complicated really fast. That was actually one of the things I liked about Figma, and actually the thing that kind of changed a little bit. I'm a sort of a ground-up designer. I always think about what are the sort of building blocks, what is the data, what are the elements, and then build up the interface from there. Whereas I know a lot of designers really start with the first page and then they try to figure it out the other way.”

“For our clients right now there is uncertainty, and even if they kind of think, "Well, this is probably where it's going to go," it's going to be expensive to get there. I mean, you're talking about optimizing all of your knowledge-based articles, you're talking about optimizing all this data that is buried down in there. You have to make sure it gets indexed, you have to make sure it's talking about it correctly, you have to make sure it's updated, because LLMs won't look at date stamps. You and I will look at a knowledge-based article and be like, "Well, that's four years old. I don't know if that's really true anymore." But if an LLM just goes and pulls that in and uses that as a basis of a response, then there's a real danger there.”

“It's complicated, because in the university context there's a lot of regulations and compliance we have to abide by. So it was a huge decision. It was multi-partal. The president of the university had to be behind it as well. So we have what's called the Innovate Academy, which is really encouraged for people in my department to take and do.”

“Thank you. Yeah. I use OneNote every day. NotebookLM. Yeah, that's what it is. And I know they use that a lot and are trying to implement that into day-to-day, but you do it after you take the academy. So what's kind of more open source, so to speak, at [university] is, because Google Gemini reads your screen, right? So they're encouraging people to utilize this in any bit of our systems. It's not necessarily structured on how we use AI at this point. I think there are big plans for administration to implement AI, but they don't know exactly how to, without just saying it'll make processes faster. And we are trying to implement it in our intake process for protocols, granted that takes a lot of weight from OIT, which we don't have the FTE for. So it's a little bit hard, and it's a little bit piecemeal. Right now, I'm trying to get people to use it as a transcriber rather than taking notes themselves. Very simple things.”

“We have so many human subjects research determinations at [university] because of the type of research that happens here. And our chat bots are just not good at that. They dictate everything as limited review, which is not like it sounds. It's a small review, but it actually contains the whole committee and everything, and that's incorrect. It's filling in gaps where it doesn't need to. At [university] we can't train the model, so we can't get it really fine-tuned to what our determinations actually are.”