Do the wrong things faster.

AI has not fixed bad strategy. It has industrialized it.

Most teams think AI is making them more effective. In reality, it is letting them do the wrong things faster.

Across almost every company we work with, the pattern is the same:

• More content

• More campaigns

• More experiments

• More “models”

• Very little signal.

AI has not fixed bad strategy. It has industrialized it. Here are the three traps I see all the time that are quietly destroying ROI, decision quality, and long-term advantage.

Trap 1: Scale Without Signal

What teams think is happening:

AI is increasing output and coverage, which should increase results.

What is actually happening:

AI is amplifying low-quality inputs into high-volume noise. Most AI systems are trained on averages. When you feed them vague prompts, weak positioning, or unclear ICP definitions, they do exactly what they are designed to do.

They generate the statistically safest output possible. That is why so much AI-generated marketing feels interchangeable.

The core issue is not creativity. It is signal density. When teams scale content, ads, outbound, or SEO without first locking in:

• A sharp point of view

• Clear market boundaries

• Real customer language

• Proven distribution mechanics

They are not compounding advantage. They are compounding irrelevance.

AI makes it easy to ship more. It does not make it easier to ship better.

At scale, volume without signal does not just underperform. It actively damages trust.

Trap 2: Randomized Execution Masquerading as Experimentation

What teams think is happening:

“We are testing more ideas than ever.”

What is actually happening:

They are running disconnected bets with no underlying system. AI has lowered the cost of trying things. That sounds good until you realize most teams are no longer testing hypotheses. They are sampling randomness.

You see this in:

• Content calendars with no topical spine

• Ad tests disconnected from buyer stages

• Messaging experiments untethered from positioning

• GTM motions that change every quarter

When everything is easy to spin up, nothing is forced to earn its place. Real experimentation has structure:

• A clear assumption

• A defined constraint

• A measurable outcome

• A decision that follows the result

Most AI-driven “testing” today produces activity, not learning. Teams end up with dashboards full of metrics and no narrative explaining why anything worked or failed.

Without a system-level view, AI increases motion but decreases understanding.

Trap 3: High-Confidence Models Built on Zero Ground Truth

What teams think is happening:

AI models are helping them make better decisions faster.

What is actually happening:

They are building precise-looking answers on top of broken data. AI is extremely good at pattern completion. It is terrible at telling you when the underlying pattern is wrong.

If your inputs include:

• Incomplete attribution

• Noisy CRM data

• Self-reported intent

• Content engagement without downstream validation

The model does not push back. It fills in the gaps.

That is how you end up with:

• Lead scoring models that optimize for demos that never close

• Content models that reward engagement instead of demand

• Forecasts that look coherent and fail consistently

The danger is not that the models are wrong. The danger is that they are wrong with confidence. AI makes bad data feel authoritative. That is a new failure mode most teams are not prepared for.

The Underlying System Most Teams Miss

AI is not a strategy layer. It is a force multiplier. It multiplies:

• Your clarity or your confusion

• Your signal or your noise

• Your discipline or your chaos

Teams that win with AI are not the ones producing the most output. They are the ones that:

• Narrow before they scale

• Define systems before tools

• Demand evidence before automation

• Slow down decisions that shape direction

AI rewards teams that already know who they are, who they serve, and what problem they uniquely solve. For everyone else, it just accelerates what they are already failing at.

Practical Implications

If you are leading growth, marketing, or GTM in 2026, the shift is not to “use AI more.” It is to ask better questions before you do.

Questions worth sitting with:

• Where do we have real signal versus inferred signal?

• What decisions are we automating that we do not fully understand?

• What would break if we cut our output in half?

• Which systems actually learn over time and which just produce?

What to stop doing:

• Shipping volume without a clear theory of impact

• Treating AI output as insight

• Confusing activity with progress

AI is not the edge. Judgment still is. The teams that win will not be the ones doing more. They will be the ones doing less, with intent.

Thanks for reading!

Adam

PS: If you are trying to scale GTM with AI without turning it into noise, this is exactly the work we do at Growth Union. We help teams design the strategy and systems first, then apply AI where it actually compounds. If that is useful, you know where to find us.