14: AI Adoption is the Problem Everyone is Desperate to Solve — Dr. Molly Sands, Atlassian
episode
Product Impact Podcast | Secrets to unlocking the value of AI
31 min
1 speaker
8 chapters
transcribed 1 month ago
Transcript
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What is the overall problem with AI adoption that the episode tackles?
Welcome to the Product Impact Podcast with our guest, Dr. Molly Sands, the head of Teamwork Lab at Atlassian. Today, we are tackling the most important problem facing everyone working with AI. How can we increase adoption and leverage this massively powerful and expensive technology to deliver actual productivity gains? So the big takeaway from today's episode is that adoption is more difficult than anthropic and your employer ever imagined. There are specific approaches that we know work. So let's dive into those. As part of creating reports for AI leadership with my business, AI Value Acceleration, I've spent the last six months researching what's happening in the world's leading AI-powered organizations, as well as in mid-sized businesses.
How does the six‑month research reveal the split between high‑performers and those drowning in forced change?
And we tend to assume that big companies are further ahead, but honestly, a lot of them are not. And here's the reality that almost every business is facing. AI adoption is uneven. There are a handful of people who are seeing 10x or even 20x gains, but most are only just starting to see some movement. And there's still a significant portion of the workforce that is drowning in forced change.
Why are many workers feeling overwhelmed by new AI tools and mandates?
They're just trying to keep up with tools and mandates that they don't feel equipped to handle. And part of why this is so hard is that AI isn't just a tool. It's more like being forced to work with an entire new workforce. So you need to train them, give them structure, know how to fix when something breaks, and be able to recognize when they are delivering you junk. And that's a real and demanding new skill set. And it lands on top of every other responsibility you're already carrying. And most organizations have seriously underestimated that burden and under budgeted for it. From what I've been seeing, there are things that actually work and they're not complicated, but they're often not happening consistently enough.
So the organizations making real progress are inspiring their teams rather than just mandating AI use. Their leaders are leading by example, not just endorsing AI and in all hands, but using it openly, sharing their actual workflows, what those look like. They're being transparent about where they're failing. And they're building cultures where experimentation is genuinely encouraged. It's not just a buzzword. So this is where you can struggle out loud, where you are documenting your successes, but you're documenting failures for people to learn from. And you're building a shared knowledge base so that that learning compounds instead of disappearing when someone just walks out of the room.
What governance gaps (no CAIO, missing policies) are stopping AI experiments from compounding?
And they're honest about where they actually are in that AI journey. And that honesty can create permission for everyone else to stop pretending that they have it figured out for fear of what they'll look like. Where everyone is still struggling is harder to say clearly, but it is super important to talk about. One thing that you're going to start seeing a lot more is this idea of learning to work with context.
Why is the “token‑max” approach becoming too expensive and unsustainable for organizations?
This is a huge shift that's coming. And by context, I mean the background that you give your AI system so that it can actually do useful work. And this is what the task is, what we already know, what's not worked in the past, what a good output looks like, what the constraints are.
How do security and governance concerns arise when AI‑built applications lack proper guardrails?
So most people have never had to think that explicitly about their own work before. And I can tell you, it does not come naturally. And the incentives are either non-existent or they're fear-based. Like people are adopting AI because they're afraid of falling behind or they're afraid of being called out, not because there's a real reward for doing it well. And that's a terrible foundation for lasting habits. And adoption itself is just a poor measure of value creation. You can hit high adoption numbers and have almost nothing to show for it at a business level. Most organizations don't have a better metric, so they keep measuring the wrong thing. Now, before we get to consistent adoption and real value creation, there are a few things that have to get solved that haven't really been yet.
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Chapters
8 chapters
1
What is the overall problem with AI adoption that the episode tackles?
0:00–0:51
2
How does the six‑month research reveal the split between high‑performers and those drowning in forced change?
0:51–1:24
3
Why are many workers feeling overwhelmed by new AI tools and mandates?
1:24–3:00
4
What governance gaps (no CAIO, missing policies) are stopping AI experiments from compounding?
3:00–3:26
5
Why is the “token‑max” approach becoming too expensive and unsustainable for organizations?
3:26–3:44
6
How do security and governance concerns arise when AI‑built applications lack proper guardrails?
3:44–4:59
7
What are AI working agreements and how do they boost clarity, usage, and innovation?
4:59–10:50
8
What future insights does Dr. Molly Sands hope the 2027 State of Teams report will uncover?
10:50–31:39
Speakers
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