How Using ChatGPT Shaped Our Q2 Goal-Setting Process

We sit down, open a clean Notion page, and say the same thing:

“Okay, let’s plan the next three months properly.”

It sounds simple, and in many ways it is, but over time we realized that the process itself is never the hard part.

What’s hard is what happens once you start questioning your assumptions.

That’s exactly why our Q2 planning took longer than expected.

Not because we were disorganized, but because we allowed ourselves to slow down and properly challenge decisions that, in previous quarters, we might have accepted too quickly.

Why Q2 Planning Took Longer This Time

Once marketing and sales aligned on initial goals, we didn’t move straight into execution planning.

Instead, we started asking more difficult questions:

• What happens if a channel performs well, but lead quality drops?
• What if sales executes perfectly, but demand doesn’t convert as expected?
• And most importantly, where does ownership actually sit when outcomes are shared between teams?

At that point, planning stopped being about numbers and became about real operational clarity.

This is usually the moment where alignment either strengthens or breaks.

We Relied on ChatGPT for Goal Structuring

We used AI tools heavily during Q2 planning.

They helped us:

• structure conversations
• challenge assumptions
• explore alternative KPI models
• speed up early-stage planning discussions

In many ways, AI acted like a very strong “thinking partner”.

But we also hit its limit quite quickly. A concrete example was lead ownership between marketing and sales.

Both teams contribute to the same pipeline:

• Marketing creates demand and builds trust over time
• Sales converts that trust into conversations and deals

So the question became simple: Who owns the lead?

We asked ChatGPT for a recommendation, and it leaned toward a standard industry interpretation: that once a lead converts into a booked call or active sales conversation, it should typically be attributed as a sales-generated lead, with credit going to the sales team.

On paper, that answer makes sense and reflects common best practices. But in our case, it immediately raised a second, more important question:

What about the marketing team that designed the funnel, created the landing pages, and essentially built the system that made that conversion possible in the first place?

If you only attribute the result to sales, you lose visibility into the upstream work that enabled that outcome in the first place. This is where the limitation of AI became very obvious for us.

It can give you:

• frameworks that are widely accepted
• “correct” industry logic
• clean attribution models used in theory

But what it cannot understand is how your internal teams actually collaborate, what behaviors you want to encourage long-term, and how attribution decisions affect motivation, ownership, and alignment inside your company

In the end, we realized that this wasn’t a question AI could answer for us. So we had to make a conscious internal decision about how we define success and how we measure contribution across teams. And that moment was important for us, because it reinforced something we keep seeing in practice: AI can help you structure decisions, but it cannot take ownership of them for you.

Why We Changed Our Goal-Setting Model for Q2

In Q1, each channel had a fixed numerical target. On paper, this looked clean and easy to track.

In practice, it became difficult to evaluate performance properly, especially when channels influenced each other. It also created a problem we didn’t fully anticipate, which is optimization in isolation instead of optimization for the system.

So for Q2, we changed the structure.

Instead of multiple disconnected targets, we introduced:

• One main company goal
• Channels treated as contributors to that goal
• KPI systems built on ranges and multiple signals, not single rigid numbers

The main reason behind this shift was simplicity in focus. We wanted everyone in the company to always have one clear thing in mind that matters the most, instead of constantly switching between multiple smaller targets that can easily create fragmentation in thinking.

Because when you start your day and open your laptop, it should be immediately clear what you are optimizing for that day, not a list of ten different numbers that all feel equally important but pull you in different directions.

By reducing everything to one primary goal, we removed a lot of internal noise and made it easier for both marketing and sales to understand how their work connects to the same outcome, even if their day-to-day activities are very different.

This shift didn’t just change how we measure success; it changed how we prioritize work on a daily level.

When “Best Practice” Doesn’t Fit Your Stage

Another key realization during planning was how often we were influenced by AI-generated recommendations and so-called “ideal” growth models.

The problem wasn’t that these inputs were incorrect, in many cases they were based on solid logic and common industry patterns, but rather that they were not always relevant to our current stage, capacity, or priorities.

What we also noticed is something that tends to happen when you are working on complex systems under time pressure: you don’t always have the mental space to deeply question every detail in the moment. When your attention is spread across multiple moving parts, it becomes very easy to accept certain inputs just to move forward, even if they are not fully aligned.

That is exactly what happened in Q1.

We had a situation where we accepted a KPI suggestion that came from an AI-generated recommendation. At the time, it felt reasonable enough to include, and because we were in the middle of a broader planning effort, we didn’t challenge it as deeply as we should have.

Only later, during execution, did it become clear that this number didn’t really reflect our actual plans or our operational reality. It was more of a “good benchmark” than a meaningful target for our specific context.

That experience made us much more careful in Q2.

Instead of accepting suggested numbers at face value, we went back and re-evaluated every KPI in detail, asking a simple question each time: Does this actually reflect what we are trying to achieve right now, with our current capacity and constraints?

At one point, we realized that forcing certain targets would not improve performance; it would only increase pressure without improving outcomes.

So we made a decision that some goals are simply not for this quarter.

Not because we are lowering standards, but because timing is also a critical part of execution, and misaligned goals can quietly create more damage than progress.

A Growing Focus in Q2: YouTube and Technical Content

One clear direction for Q2 is increasing focus on our YouTube channel.

We’ve seen strong feedback on content where Aleksa shares insights from his perspective as a CTO and Head of Mobile, especially because it’s not theoretical advice, but real decision-making from actual delivery situations, the kind of things you only learn when you’ve been responsible for shipping complex products and scaling engineering teams under pressure.

The topics we’re focusing on are very practical and close to real problems teams are facing every day, such as:

• how to structure mobile architecture so it stays maintainable as the team grows
• what actually breaks when scaling from a small dev team to multiple teams
• how to make Kotlin Multiplatform work in real production environments, not just in demos
• how to reduce delivery risk when multiple teams are working on the same product
• and how technical decisions directly impact speed, stability, and long-term cost

The value we consistently see is that people don’t just watch these videos for inspiration, they use them as input for real engineering decisions, especially when they are stuck between multiple valid technical directions and need a clearer way to think through trade-offs.

On top of that, we already have several podcast appearances planned, and we’re looking forward to testing this channel more seriously in the coming months, especially as a way to open up more behind-the-scenes discussions about how delivery actually works inside real projects, not just how it looks in theory.

If you want to stay up to date with this type of content, you can subscribe to our CTO newsletter, where he shares much more detailed insights in written form before they ever turn into a video.

What Changed After This Planning Cycle

Looking back, Q2 planning felt less like goal setting and more like alignment design.

Some of the key internal shifts include:

• clearer boundaries between marketing and sales ownership
• more flexible KPI structures
• improved evaluation of cross-channel impact
• stronger focus on realistic execution instead of theoretical targets

These changes are not just documentation updates, but they directly impact how we operate week to week.

Final Thoughts

There is a common belief that goals should be intentionally unrealistic to push teams harder.

We’ve tried that approach.

In our case, it didn’t improve performance, it reduced clarity and created unnecessary tension inside the team, especially when effort didn’t feel connected to achievable outcomes.

That’s why our current approach is different:

We still set ambitious goals, but we make sure they stay connected to reality, team capacity, and timing. Because in our experience, motivation comes from clarity, ownership, and trust in the system.

Every quarter teaches us something new about how we operate.

Q2 reinforced something we already suspected: better planning doesn’t come from adding complexity, but from removing uncertainty.

We’re curious to see how this quarter unfolds, and even more curious about what it will change in how we approach Q3!

If setting goals and organizing company processes is something you’re interested in, you can follow me on LinkedIn where I share similar stories with my network.