AI is everywhere, and the pressure to adopt it is relentless. But what does responsible, practical AI adoption actually look like for database and software professionals – the people who live closest to the data?

Redgate brought together three experts to find out: Scott Sauber (Director of Engineering at Lean Techniques), Deborah Melken (Data Architect at Advisor360; Microsoft MVP), and our very own Kellyn Gorman.

The result was a candid, experience-led conversation that cut through the hype and got into what’s really happening on the ground – from data governance and security to vibe coding risks and the future of junior developers. Here are the 10 things we learned.

1. Having an “AI story” doesn’t mean building an AI product

Organizations are feeling the pressure to do something – anything – with AI, and it’s pressure coming from all directions: boards, CEOs, their competitors – to name a few.

Scott describes the pattern succinctly: “board members fire up ChatGPT, see something impressive, and the question flows down through the org until every team is being asked ‘what are we doing with AI?”

The result is teams falling for the hype and scrambling to create their own AI narrative rather than a solid strategy.

“A lot of clients come to us and are like, hey, we want to build this chatbot,” Scott added. “And we’re like, cool, but who’s the target user, what’s the use case? They don’t know, but they’re like, ‘we need a chatbot’.”

The panel agreed that, in reality, this knee-jerk response is unnecessary. If an organization doesn’t know why they ‘need’ AI, they don’t need AI.

Further, responding to the mounting pressure of AI adoption doesn’t have to mean building and shipping your own AI feature. Sometimes, they suggest, the smarter move is positioning what you already do as infrastructure that supports AI.

The key takeaway

Before asking what AI you can build, ask what specific problem you’re actually trying to solve. If you don’t know why you need AI, you don’t need AI. You don’t need to ship your own AI feature or build an AI narrative if there’s no reason for it.

2. Slow down to speed up!

That, for good reason, was a highlight quote of the panel. After all, the instinct right now is to move fast and adopt AI everywhere at once. People and organizations are diving in head-first. But, perhaps unsurprisingly, there’s growing evidence that organizations taking this approach are creating more work, and risk, for themselves – not exactly achieving their aim of reducing workload.

“Sometimes, organizations are throwing spaghetti at a wall (with AI),” Deborah commented. “However, if you stop and think about what you’re trying to do – think, ‘maybe we’ll just start in this one place’…once you figure that out, it makes it easier and faster to do something next time.”

Deborah did also address the AI contradiction hiding in plain sight. “AI is supposed to make everything faster, and then you miss the report that says ‘no, people are spending more time, because they’re having to do all the extra work on top of whatever AI is doing’.”

The key takeaway

The conflicting reports are out there for all to see – and perhaps you’ve read some already – but the answer isn’t to simply dismiss AI. Instead, it’s best to pick one concrete use case, learn it properly, and expand from there. That ‘something’ you create with AI will likely then be better for everyone.

3. AI is overconfident – and that’s your problem to manage

AI tools will tell you you’re absolutely right even when you’re not. Similarly, they’ll produce a wrong answer, be corrected…then produce a different wrong answer with equal confidence!

Deborah described the exhausting loop of talking – almost even arguing – with AI: “No, that’s incorrect. ‘Oh, you’re absolutely correct. Let me give you this answer.’ And a couple of times in, you’re kind of like, let me just do it myself – it’ll be faster.“

Kellyn is direct about this as well: “AI is incredibly optimistic. It’s very happy to tell you, ‘this is a great way to do this’. It will be overconfident – and it absolutely is overconfident.”

For tackling this optimism, Kellyn’s course of action is surprisingly simple: when AI gives you the wrong answer, ask it directly what you could have prompted differently, to receive the correct (or better) answer.

All of this is exactly why experts will still have jobs. Using code reviews as an example, Scott says: “70% of the comments (from AI) are valid, but that also means 30% are not. If you don’t know any better, you might just blindly accept everything it says.”

Knowing exactly which 30% to ignore and throw out, is exactly what human expertise is needed for.

Finally, the panel made it clear to not be afraid of using one AI to check another. And, if you have time, build reusable ‘prompt templates’ – markdown files that give the model its goal and context upfront – rather than starting from scratch every time.

The key takeaway

AI is often overconfident, highly optimistic, and will tell you you’re absolutely right when you’re really not. To tackle this, if you notice AI give you a wrong (or poor) answer, ask it directly what you could have done to receive the more accurate response. Use AI to help you with your own prompt engineering skills. Finally, use one AI to check another – and build reusable ‘prompt templates’ to speed up the process.

4. ‘Garbage in, garbage out’ – AI amplifies existing data problems

AI doesn’t create data quality and governance problems, but it certainly exposes the ones you already have – and makes the consequences even harder to ignore.

As Deborah put it plainly: “If you’re training your models on junk data, you’re going to get junk results. We now need to understand where our data is, where our PII lives, who has access to it, who can see what…All of this becomes amplified with AI.”

The silver lining is that AI is forcing organizations to finally care about things they’d been ignoring for years. The likes of semantic models, column definitions, data lineage (to name a few), are all suddenly urgent, simply because models need them to function.

“People like the phrase ‘semantic models’ now,” Deborah adds. “So now you’re going to tell me what that column, and that table, actually means…that I’ve been asking for, for five years…” Better late than never!

The key takeaway

AI exposes the data quality and governance problems that organizations have been ignoring for years – to a point where they now have to react. It’s never been more important to understand exactly where data lives, who has access to it, and how accessible it is.

5. ‘Zero trust’ applies to AI, too

Database professionals are instinctively cautious about access and permissions (for good reason), and the panel agreed that the same level of caution should be applied to every AI tool. It’s not paranoia to ask questions like, does GitHub Copilot send my schema outside the org? – or, what does Snowflake Cortex do with our data?

Deborah summarizes it as such: “As data people, we think about security first, and I think we think about it in ways that maybe other types of engineering don’t always think about.”

In fact, tooling now exists to block employees from uploading sensitive data to consumer AI products at the infrastructure level.

“If somebody says, ‘I’m going to get an Excel spreadsheet of your loan data and put it up in the free version of ChatGPT,’ and they go to do it – it just stops it,” Kellyn explained. “They can’t upload it. They can’t even open up ChatGPT if you say ChatGPT isn’t authorized.”

The key takeaway

“Only give it (AI) access to what it absolutely needs,” is Kellyn’s top advice. “Just like we would with any other user.”

6. Data retention is a risk on both ends

It’s just instinct to want to hold onto data indefinitely – after all, “data is the new oil” – but hoarding creates its own problems. As Scott says, “sometimes, if you hold on to data too long, it rots, like food on the shelf.”

That’s why defining and actually enforcing data retention policies matters now more than ever – even when it’s a fight. And it’s an issue that cuts both ways, as too much data is also bad for AI.

“Too much information is a problem for AI as well,” Kellyn points out. “If you give it (the AI) too much information, you’ve got old information. How is that going to impact the answers and the hallucinations you get?”

Kellyn’s answer to that question – how to get users to actually delete old data? “AI won’t like it. Just saying…”

The key takeaway

Hanging onto old data is not only bad for you and your team (in the context of strict data retention policies) – it’s also bad for AI. The older the data AI has access to, the less accurate its responses.

7. Vibe coding is creating a new generation of ‘self-coders’

Scott drew a sharp parallel to a past era of Microsoft Access, when business users got tired of waiting for IT and built their own production-critical systems, which then embedded themselves into workflows and became impossible to unpick. With AI and vibe coding, not only has this returned, but it’s accelerated the pattern dramatically.

“I almost feel like there’s some of that coming back,” he says, “the Access database running under Bob’s desk for 20 years. Business people are not wanting to wait for IT, so they’re just vibe coding some stuff. They’re taking an Excel spreadsheet exported from Power BI, importing it, doing some stuff with an app – and that’s all great when somebody’s just doing it themselves.

“But then, they insert it into a critical part of their business workflow – and what happens when that person leaves in six to twelve months?”

The key takeaway

Business people are getting tired of waiting for IT, and AI and vibe coding now allows them to create their own quick-fire solutions – with varying levels of quality. This is a problem for governance, as Deborah says: “We’re expecting the (AI) tool to take care of this stuff (governance) for us – that’s why it’s ‘no code, low code’. But we’re forgetting: ‘no, it’s just a tool’. It can’t do everything for us. We still have to take on that responsibility.”

8. The database world is still playing catch-up

AI tooling for Python and application code is genuinely impressive. For SQL and database work, however, it’s noticeably behind. With the stakes for getting database decisions wrong being so much higher, this is a problem. And while there are some exceptions – such as AI assistance tool SQL Prompt AI – the overall point remains.

Deborah is clear on her thoughts about the structural reasons for this. “I feel like the database is still kind of the afterthought – it’s all about the application”, she says. “The software engineers are making the decisions about which database platforms we’re using.”

Part of what drives the DBA instinct to verify everything is an understanding of permanence that application engineers don’t always share. As one of Deborah’s colleagues put it in a line she quoted: “It’s hard to think of the database as part of the application, because that database and the data in it is going to outlive your application code – it’s just there, it’s always going to be there.”

The trust issue with AI suggestions is real, too – and not only for simple things it does well, but for complex SQL. “I don’t know if it’s because AI is suggesting changes”, Deborah adds. “I think there’s still a trust issue.”

The key takeaway

Some AI tools are impressive. However, some others – such as many for SQL and database work – are lagging behind. There’s also still a trust issue around AI – especially with databases, where the stakes for getting things wrong are so much higher.

9. Nobody is hiring junior developers — and that should worry us

When the panel moderator asked the room who was actively hiring junior developers, nobody raised their hand. They were honest about why (and it’s something seen quite widely across the board): AI is handling a lot of what entry-level hires used to do, so the need to bring in junior talent has weakened considerably.

Of course, the longer-term concern is what this means for the people trying to break into the industry – and for the expertise pipeline as a whole.

Scott put it plainly: “How we all learned was we banged our head against a wall against something for a while, and then it got stuck in our head. But if AI is giving you all the answers, I’m just curious how that’s going to turn out long term.”

He also flagged that AI-generated code has a hidden cost, even for experienced developers: “I don’t know the code as well as if I hand typed it myself. When I’m in a meeting three weeks later, I can’t picture the code as clearly in my head.”

The key takeaway

AI is handling a lot of entry-level tasks, so there’s less need to onboard junior employees. It’s also impacting the way we learn, which has typically been by doing things ourselves – things that AI can now do with relative ease. The productivity gains are clear, but at what cost?

10. The AI tools that survive will be the ones with enterprise inertia

The current AI tool landscape is crowded and heavily funded, but the money won’t last forever. “Grok raised $20 billion, but their burn rate is over a billion a month,” Scott pointed out. “That means that $20 billion is only going to last them a little more than a year and a half. At some point, the private equity companies funding this want their returns.”

Kellyn added that it’s apparent that many of the players are essentially sharing the same investor money around, so significant consolidation could be coming. So, what’s the implication for anyone using these AI tools?

Put simply, the platforms most likely to survive are those already embedded in enterprise workflows, with Microsoft Copilot being an obvious example. While it’s not necessarily the best product, it’s the one with the most inertia behind it – so committing to it is safer than building critical processes around a tool that might not exist in two years. That, in its own right, would be a huge governance risk.

The key takeaway

Where possible, favor proven enterprise platforms for production workloads. For prototyping, meanwhile, use lighter, cheaper, or even offline models such as Llama, Ollama, or Qwen. This will keep costs down while you validate whether a use case is worth investing in properly.

In summary: AI is a tool, not a strategy

The common thread running through everything our panelists said is simple: AI is a tool, not a strategy. Used well — with clear intent, proper governance, and realistic expectations — it can genuinely transform how database and software teams work. Used badly, it amplifies every problem you already have. The difference, more often than not, comes down to slowing down long enough to ask the right questions before you start.