What's the ROI of AI? You won't know until you've learned how to make the most of it

Unlike the case for most tech tools, leaders don’t need to be convinced to prioritize AI spending – they’re already bought in. In fact, research group IDC forecasts that global spending on AI will more than double in the next five years, to reach a staggering $632 billion by 2028. But with that spending comes a desire to prove it’s all been worth it. And the elephant in the room is, it’s way too early to measure the true ROI of AI.

The introduction of generative AI is the biggest inflection point we’ve seen in modern times. It’s on par, if not more important than the internet, mobile, and cloud transformations. There’s a big opportunity to change how we approach age-old business problems with AI to gain a competitive edge. But leaders today still need more guidance and experience in understanding how to get the most out of their AI investments. 

Where we are – the current state of affairs of AI ROI

We’re still at a stage where there’s no one right way to work with AI. Yet there has already been considerable obsession over how to determine its ROI in the enterprise. We get it, and in working with our teams and our customers, we’ve considered a wide variety of metrics to best assess the value of AI.

We've analyzed performance indicators such as hours saved and productivity gains. We even developed an internal workforce planning tool that gives us a comprehensive overview of all employees and resources allocated to specific projects related to AI initiatives and beyond.

Taking steps like this to quantify value and impact is important. But it’s equally important to realize that we, like many other enterprises, are still early in our working relationship with AI. Right now is the time for testing and experimentation and ensuring the right structure and culture are in place for AI to thrive, providing a foundation for far greater ROI in the future.

We should focus more on AI’s practical applications and encourage workers to use it rather than getting bogged down in the minutiae like minutes or hours saved. Until people and organizations become proficient in their use of AI, any ROI will be muted.

By understanding AI’s role on the team, shifting their mindset, and staying committed to a spirit of continuous learning, leaders can be miles closer to unlocking AI's full potential in their organizations.

Where we’re headed – establish AI’s role on your team

To get more value from your AI investment, it's a good idea to start by determining where AI fits into your organization -– in other words, assigning it a role.

Here’s what this can look like. Have AI automate workflows to minimize repetitive tasks, and keep humans at the center by focusing on employee productivity. For instance, Virtual Service Agents in Jira help cut human intervention by 85% for our IT team, meaning these folks can spend more time on what moves the needle and less time answering repetitive questions or completing basic work.

Efforts like this can be particularly helpful for new(er) employees as they get started but are also relevant for experienced employees as they handle their most complex work.

But this is just the start. From here, dig deeper and partner with each business unit – from engineering, sales, marketing, HR and legal – to identify top use cases where AI can help increase efficiency and productivity.

Mindset matters in maximizing AI’s value

Today, most teams view AI as a tool they use on occasion for simple tasks, such as getting quick answers or summarizing a page. To derive real value from AI, leaders need to go beyond encouraging more AI adoption – they need to promote a mindset change. Instead of viewing AI as a simple tool, consider encouraging them to look at it as a teammate, helping humans deliver better output. One way to do this is by treating AI as a creative sparring partner that can support more complex planning and decision-making rather than simply relying on it for one-off, disparate tasks.

Atlassian’s latestAI Collaboration report indicates that “strategic AI collaborators” experience greater benefits compared to “simple AI users.” These collaborators not only save up to a full workday each week but also experience enhanced work quality and are more inclined to reinvest their time in acquiring new skills.

If at first you don’t succeed (with AI), try, try again

Last but not least, encourage – and enable – AI experimentation. Encouraging testing helps employees more seamlessly integrate AI into their daily workflows, making it easy to move along the continuum toward becoming a strategic collaborator.

I recommend promoting a culture of continuous learning by keeping team members clued into the latest on AI developments and giving them the space they need to test and refine applications. It can be as simple as setting up a Slack channel dedicated to sharing the latest AI news and industry developments. At Atlassian, we ask employees to record and share quick videos on Loom of simple and effective ways they incorporate AI in their work and hold tech talks by machine learning engineers on their work to cross-pollinate the team's knowledge.

The key to widespread AI experimentation at every level is making it as easy and secure as possible to access the tools. Twice a year, employees across the company are invited to try something new or work on a project they’ve always wanted to see through during our ShipIt hackathon. Last year, we made AI one of our key focus areas for ShipIt and made it super easy for developers to access different LLMs and AI tools within a safe playground environment. By taking the friction out of the setup, we had more than three dozen teams work on a variety of AI ideas – some of them got shipped as real product features!

Conclusion

All in all, realizing maximum ROI from your AI investment starts with a clear strategy that centers this tech as a long-term investment that can deliver lasting benefits. Measuring ROI of these investments will get easier and more straightforward, but in the meantime, it’s important to take an iterative, learning-focused approach while continuing to invest in this tech.

AI isn’t going anywhere anytime soon, so it behooves us as knowledge workers to collaborate alongside it, adopt the right mindset, and stay committed to forward progress while working towards the ROI we need to deliver.