AI Can Do More for Project Management Than Note-Taking: 4 Ways to Level-Up Your Usage

TL;DR

• AI is more than just a summary or synthesis tool for project managers, and the best PMs will use it to become a better strategic partner

• PMs can use AI to spot ongoing friction and pinpoint the root cause, or look for and resolve broader patterns across your client accounts
• Use AI as a sounding board to find the gap in your idea before bringing it to the client
• Leverage internal expertise and documentation to build client-ready deliverables with minimal manpower
• AI can't interpret human behavior or responses, and that's where PMs can shine when they offload busywork to AI 

Are PMs Extracting the Maximum Value from AI?

By now, most project managers are using AI to pull action items from a call, draft a status update, or help you write an SOP.

But if the tactical execution part of the job has gotten cheaper and faster thanks to AI, what's left for a PM to be good at?

I think the answer is the thing most of us got into this work hoping to do in the first place: being a genuine strategic partner to the client, not just the person who keeps the trains running on time.

Here are 4 ways I’ve started using AI to level-up my client strategies and build stronger client relationships.

1. Catching the Pattern You'd Never Notice Call by Call

Our CEO, Wil Reynolds, says something that’s stuck with me: use AI for the manual tasks your brain power far exceeds, and spend that brain power on strategy and moving the needle for the client. I didn’t fully get what that meant until I started actually doing it.

Now it's part of my process. After almost every client call, I ask AI one question based on the transcript:

THE PROMPT
How can we be a better strategic partner here, based on what the client said?

Just asking AI to dig for subtext instead of a summary isn't a differentiator anymore. Plenty of PMs have figured out that part. What's more impactful is what you do with whatever AI surfaces.

In practice: With that prompt, AI helped me understand the root of recurring campaign delays for one client: they didn’t know where our campaigns stood, and approval requests were scattered across Slack threads with no clear queue. They weren’t stalling on purpose, they just had no single place to understand what was needed from them.

After AI picked up on the pattern, I worked on a solution and built a live campaign tracker. Now, the client had:

• A scheduled weekly update via Slack on where every campaign stood
• A dedicated approvals-only Slack channel for our team to share exactly what we needed from them
• Updates formatted so the client could scan, review, and approve in minutes

The client stopped being an accidental bottleneck and started closing out approvals in minutes, and our team stopped losing time chasing sign-off across multiple channels.

A single call would never have told me that on its own. It took AI flagging the same friction call after call before the real fix became obvious.

TRY THIS
After your next few client calls, run each transcript through the same question. Focus not on what was said, but on how you could be a better strategic partner. Don't act on the first answer. Watch for the same friction showing up more than once before you build anything around it.

2. Seeing Across Accounts, Not Just Within One

Most PMs manage more than one relationship, and the thing AI is uniquely good at that a busy human isn't, is holding all of them in view at once.

So I started asking a version of the same question I use on individual calls, but pointed at my whole book of business: is there anything showing up across more than one of my accounts that I'm not connecting?

In practice: AI surfaced that GEO test implementation was stalling for a similar reason across a few accounts that, on paper, had nothing in common. It wasn't a technical blocker. It was resourcing; client teams didn't have the bandwidth to execute anything complex, and that was only visible once I stopped looking at each account in isolation.

Seeing that bigger issue led me to change how I approached a GEO project for one client in particular. Implementation had already stalled. Tests weren't moving because the work assumed more bandwidth than the client's team actually had. 

Instead of continuing to push on the same complex tests, I raised the resourcing question directly. The client confirmed their team's bandwidth was limited right now, which told me exactly what needed to change: I had my internal team shift focus to easy, low-lift test ideas the client could realistically execute. That shift is what got the project back on track.

That pattern only showed up because AI helped me look beyond my individual accounts when I didn’t have the bandwidth to hold everything in view all at once.

TRY THIS
If you manage more than one account, ask AI a version of this before your next resourcing or roadmap conversation: is anything showing up across more than one of my accounts that I've been treating as separate? Do it before something stalls, not after.

3. Using AI to Pressure-Test Yourself Before the Client Does

AI has a default instinct to agree with you. It wants to be helpful, which in practice often means it wants to validate whatever you just said. Left unchecked, that's the opposite of useful when you're trying to stress-test a strategy. 

So I set a standing rule for how mine should behave: 

• Don't agree by default
  

• Find the weakest point in what I've said before you affirm any of it
  

• Tell me what someone who disagrees would say 

When I bring AI a strategy or ask for a read on a situation, its first move is to argue with me, not validate me. That changes the conversation completely. Instead of a tool that confirms what I already think, it's one that makes me defend my idea first.

In practice: A new contact on one of my accounts wanted the relationship with our team to feel more collaborative. She was interested in less status reporting and more working through things together.

My first instinct was straightforward: change our current meeting format. Turn the recurring status call into more of a strategic discussion.

I ran that call's transcript through AI afterward, and it didn't just validate the plan; it found the gap in it. Buried in the same conversation, my own team had voiced frustration about how campaigns were getting launched: almost no input from us on strategy, and asset timelines so tight there was no room to do more than execute whatever landed. 

AI's read: changing the meeting format was a reasonable start, but it wouldn't touch the actual problem. If we wanted to be real collaborators, the meetings needed space to shape campaign strategy together, talk through how assets should be positioned by platform, and get ahead of sizing and formatting before assets showed up with days to spare.

That's the version we actually built: a new SOP centered on recurring strategy sessions, not a lighter-touch status call. The relationship is strong enough now that the client's asking about expanding our scope to additional divisions because we became the partner she wanted.

My instinct wasn't wrong. But the fix needed to go further, and I wouldn't have caught that on a single read of my own notes.

TRY THIS
Before you bring your next recommendation to a client, run it past AI with the same standing instruction: don't agree, find the weakest point, tell me what someone who disagrees would say. If it doesn't push back at all, that's worth noticing as it usually means the instruction didn't take, not that your plan is airtight.

4. Spearheading the Work Instead of Coordinating It

The clearest example of what this actually changes for a PM happened when a client asked for a full deck to present in a CMO meeting that had landed on their calendar with about 24 hours' notice. My team was stretched thin elsewhere, and we didn’t have the bandwidth to pull together a normal cross-functional build.

In practice: I built the deck myself by taking our existing analysis decks, call transcripts, and Slack threads for the account, feeding all of it into AI, and specifying the story I wanted the deck to tell. Not “summarize this,” but the actual narrative: here’s what matters to this CMO, here’s the through-line I want a stakeholder walking away with. 

The point wasn’t about AI being fast. It was that I had the full context on the account: the relationship history, the priorities, and the political read on the room. AI closed the gap between having that context and having a polished deliverable, without needing to pull in three other people to get there. 

It used to take a full cross-functional build to turn scattered account knowledge into a client-ready strategic narrative. Increasingly, a PM who already holds that knowledge can spearhead it, carrying the team’s work into the room rather than convening the room to package it. 
To be clear: AI wasn’t replacing our specialists, but rather leveraging the work our specialists had already done to inform the output. 

Those analysis decks, the account knowledge, the read on the CMO all came from the team’s expertise. Because I already had the information, I was able to turn it into a polished, client-ready narrative on a 24-hour turnaround. 

The layer this shrinks is synthesis and assembly, not subject-matter expertise. And that’s the opportunity: a lot of cross-functional time gets spent packaging what someone already knows. If the PM can spearhead that layer, our specialists get more of their time back for the deep work only they can do. 

TRY THIS
Next time you’re under deadline with everything you need already scattered across decks, transcripts, and Slack, don’t ask AI to summarize it. Tell it the actual narrative you want a stakeholder to walk away with, and let it build toward that. See how far you get solo before asking for backup.

What AI Still Can’t Do: Interpret Human Behavior or Responses

None of this makes the PM optional. 

For example, go back to that campaign tracker and approvals channel from Pattern 1: AI could see that requests were stuck and tell me exactly where. But it couldn't tell me whether raising that with the client directly would land as helpful or harmful.

That read came from a dozen calls with the client, understanding: how he takes feedback, what the relationship could hold at that point, whether this was a conversation for a call or a quiet Slack message. None of that lives anywhere AI can find it.

AI is good at telling you something's there. It has no way of telling you how it'll land with this specific person, right now. That part is still entirely yours.

Where This Leaves the Project Manager Role

For years, the job rewarded whoever could juggle the most balls. That was never the point.

The PMs who get the most out of AI are using the space that creates to actually think: about the account, about the client's business, about what they're not being told directly.

Here's where I think this actually takes the role over the next year: 

• Less time spent proving the work got done
  

• Reading what a client isn't saying
  

• Knowing when a relationship needs a different kind of attention than a status update

• Building the judgment calls that no prompt can make for you 
  

The PMs building habits like the ones above will spend more time doing the work that made them want this job in the first place, while the busywork keeps quietly shrinking around them.

That's the opening in front of every PM right now: less of your day spent holding it together, more of it spent actually thinking, and finally the room to become the strategic partner you always wanted to be.

If you're looking for a strategic partner to level-up your marketing activities, Seer can help. Chat with our team to learn what we can achieve together.