Why Burnout Is the Silent Killer of Product Teams—and How AI Can Help

A handy, AI-powered guide to boost team engagement.

Did you know employee engagement recently hit its lowest point in over a decade?

Product teams are drowning, not thriving, today.

They spend most of their time dealing with the system, not delivering value. And this wreaks havoc on their level of engagement.

• No autonomy: they get told what to do and how to do it.
• No purpose: their purpose is to crank out task after task.
• No mastery: no time, space, or guidance to build their craft.
• No relatedness: limited team bonding, belonging, and support.

But product teams aren’t the only ones suffering. The issue is much broader. It’s affecting every employee in every type of team.

Let’s dive into this downward burnout trend. And how an unlikely ally—Artificial Intelligence—can help us reverse it.

Recent alarming trends from Gallup on low employee engagement are a wake-up call.

The latest 2024 Gallup Employee Engagement surveys paint a bleak picture.

In Q1, they reported engagement had hit an 11-year low. Only 30% of employees are finding meaningful engagement at work.¹ While this inched up to 32% in Q2, it remains at a precarious low point.²

Stop, and let that sink in.

70% disengagement is a frightening statistic. And 16-17% are actively disengaged, spreading havoc throughout your organization’s culture.

This is alarming from my perspective.

I’ve found the best predictive indicator of future performance is employee engagement. An engaged team with high autonomy, purpose, mastery, and relatedness is unstoppable. And the disengaged team is a pale shadow of its ultimate potential.

So, I hunt out ways to ensure it doesn’t plummet.

Gallup agrees with this notion. They found the top-performing companies buck the trend, fueled by 70% employee engagement.¹

High engagement, while powerful, is rare these days. It’s time to act.

But the real question is: what’s causing this decline?

Is remote work to blame?

Gallup hints at remote work destroying the culture. It reduces recognition, opportunity to show strengths, and role clarity.

This is all true, but we need to go deeper.

I see what product teams deal with day in and day out (and I’ve seen this in other teams as well).

Remote work has amplified an existing underlying condition for product teams. The system was already broken before remote work started. Virtual work has intensified the cracks in the system, and it’s starting to crumble. Employees are getting pummeled by the debris.

It’s a broken system.

It’s a heavy, oppressive environment that repels engagement. The uninspired purpose of many product teams is to crank out more work better, faster, and cheaper.

• They have no customer connection and no time to breathe.
• Their days fill with meetings and endless administrative busy work.
• They work solo on their siloed tasks, disconnected from their team.
• They have a growing list of improvements with no space to do them.
• They build off a fixed backlog at a frantic pace toward a fixed deadline.

No wonder engagement is low.

Believe it or not, AI can help us improve our burnout problem. Here are 4 steps you can take today.

You wouldn’t think AI could help engagement.

Most employees fear AI will strip away more of their purpose at work. And send their disengagement deeper. But I’ve found some ways AI can help to amplify engagement, instead of destroying it.

It comes down to the speed AI can give us in making change happen.

We all face deadlines and a never-ending stream of work. And we have limited space and time to change our broken system. Why not use AI to use the sparse time we have more effectively for improvement?

Below you’ll find 4 actions you can take with AI to help expedite change. Let’s dig in.

Step 1: Identifying engagement-sucking waste in the broken system.

A heavy, bloated system plummets our engagement.

Many product teams feel stuck in a broken system and unable to zoom out and see the root cause. And managers don’t always have expertise in spotting waste or knowing what is wasteful.

This is where AI can help. It can give you “eyes for waste” if you don’t have them.

I look for 11 key lean wastes in a system of work.

You can feed these into a Large Language Model chat tool and have it help you identify waste in your system. Here are 3 ways I have done this:

1. Analyze your process for waste.  Get your team together and outline all the activities and steps you follow to take an idea from concept to cash. Note actors, hand-offs, time in process, and wait times. Feed these to your LLM and ask it to identify wasteful parts of the process. Reflect on those and identify the most painful. Ask for help to change it (or even better, change it yourself).
2. Ask it to educate you on waste. Prompt the LLM with questions on how to identify waste. This will help you to better spot the sparks that spawn disengagement. Then, you can extinguish them before they start a fire.
3. Feed it your process analytics to spot patterns. Most teams use a ticketing system to track their work. Mining the data from these tools to spot waste patterns is a great use case for AI. It will find patterns much faster than you can.

AI can be your fast lean waste analyst.

Step 2: Automating what you repeat.

You know those repetitive, manual tasks that drive you nuts? It’s time to squash them.

One team I worked with recently used AI as a research assistant. They fed it all the user research and asked it questions to find patterns and insights. This took a giant burden off the team. They no longer had to endure painful searches through the data to find insights.

AI is great at making the mundane disappear by handling it for us, but it has a catch.

You must be careful not to remove your human judgement from what it produces. AI does not have human reasoning (yet). So, you must check its work and apply the human touch and a sanity check.

You shouldn’t bank your career on AI hallucinations.

So, use AI to reduce manual burden, but don’t take your hands off the wheel.

Step 3: Ideating solutions to fix what kills our engagement.

Let’s say you have used AI to identify waste and automate mundane tasks.

But now, you need ideas to solve the waste.

You have a few ideas of your own, but you want to generate more. This is a great use case for AI. Here are some prompts I use for this:

• “Give me novel solutions to solving (a waste you are trying to remove).”
• “I have these solutions to (a waste you are trying to remove). Give me other solution ideas I may have missed. (List the solutions you came up with).”
• “What would (some expert) think about my solutions for (a waste you are trying to remove). What advice would they give? Would they recommend alternatives to try? Here’s my solutions (list of your solutions).”

Be prepared. AI may generate some lame ideas. Yet, I’ve found it suggests a few winners from time to time. Give it a try.

Now, you have all these great ideas. But some may require you to convince others to change. AI can help here too.

On to step 4.

Step 4: Justifying a case for change

You’re going to have to beg and plead to change a rigid, embedded system.

That’s the way things go. Most organizations mandate a justification for changing. This remains true even though it’s obvious the problem is destroying team engagement. It’s an unfortunate reality.

But AI can help you prepare to make your case for change. Here are some prompts I have used to prepare me and my team for this cage fight.

• “What are the common arguments against my proposed solution? How can I counter them? My suggested solution: (your solution). Problem to solve (your problem).”
• “Let’s role-play. You are an executive who is skeptical of change. You want to justify every action for improvement. I will bring you problems and proposed solutions. Challenge me on them.”
• “Help me prepare a business case for addressing (waste you are trying to solve) with (your suggested solution). What are the main elements I need to cover? What are some compelling ways I can show the value?”

I’m sure you hate the act of making a case for an obvious problem as much as I do.

The good news? AI can ease the pain of this necessary burden.

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And that’s how I’m using AI to help combat the poor environment of today’s product teams.

It’s not a silver bullet. But it does help.

We need all the help we can get to improve employee and team engagement. The alternative of accepting our fate and hitting a new engagement low year after year is not an option. We have to take action, and AI greases the wheels a bit.

AI can help us:

1. Identify engagement-sapping parts of our process we can’t see.
2. Automate painful, mundane tasks to make space for meaningful work.
3. Ideate solutions to the parts of our process that drive us mad.
4. Justify a case for change and spur action to make it happen.

This is a great use case for AI. It’s better than a future where AI strips all meaning from our work by doing it all for us.

So, when will you start using AI to change your system for the better and improve your engagement? I’d say today is a good time.

Do you have other ideas on how to use AI to reduce burnout? Please share them in the comments.

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References

1. Jim Harter, “U.S. Engagement Hits 11-Year Low,” Gallup, 10 Apr. 2024, https://www.gallup.com/workplace/643286/engagement-hits-11-year-low.aspx.
2. Jim Harter, “U.S. Employee Engagement Inches Up Slightly After 11-Year Low,” Gallup, 26 July 2024, https://www.gallup.com/workplace/647564/employee-engagement-inches-slightly-year-low.aspx.