Strategy Trendslop: Why AI Needs Decision Discipline

Many leaders are discovering something uncomfortable: AI is becoming exceptionally good at sounding strategic.

The language is polished. The recommendations feel balanced. The reasoning appears thoughtful. But there is an increasingly important question beneath all of this:

What if AI is getting better at producing strategy language rather than better at producing strategy?

A recent Harvard Business Review article discussing “strategy trendslop” raised exactly this concern. The core argument was unsettling: large language models (LLMs) often lean toward currently fashionable management ideas rather than identifying what is strategically right for a specific situation.

That matters because strategy has never been about selecting the most socially acceptable answer.

Strategy is about choice.

And choice means trade-offs.

What Is Strategy Trendslop?

The article describes strategy trendslop as a tendency for AI systems to reproduce dominant ideas in management discourse rather than evaluate what actually fits the situation.

For example, LLMs may naturally favour recommendations like:

• Collaboration over competition

• Long-term thinking over short-term focus

• Differentiation over cost leadership

• Human augmentation over automation

None of these positions are inherently wrong.

The problem emerges when they become default answers.

A recommendation can sound intelligent and still be strategically weak if it ignores market conditions, competitive realities, timing constraints or execution capability.

The danger for leaders is subtle because polished advice often feels persuasive.

But persuasive is not the same as correct.

Strategy Has Always Been About Difficult Choices

Many strategic failures happen not because organisations lacked ideas, but because they avoided choices.

Michael Porter famously argued that strategy requires trade-offs. Trying to pursue incompatible positions simultaneously often leaves organisations “stuck in the middle.”

Yet AI models appear naturally inclined toward compromise.

The HBR article highlighted what could be called the "hybrid trap."

When models were not forced to choose, they frequently attempted to combine strategic alternatives:

• Differentiate and pursue cost leadership.
• Invest in both radical innovation and incremental optimisation.
• Optimise for short-term gains while maximising long-term transformation.

At first glance these recommendations appear sophisticated.

But experienced executives recognise the problem immediately.

Many strategic tensions cannot simply be merged.

Sometimes choosing one path means explicitly declining another.

And organisations frequently avoid making that decision because saying “both” feels safer.

AI can unintentionally reinforce this instinct.

Why Better Prompting Is Not Enough

Many people assume that AI bias can be fixed through prompt engineering.

Just provide more context.

Ask better questions.

Use clever instructions.

The article tested this assumption and found something interesting.

Adding context helped only modestly.

Changing prompts reduced biases inconsistently.

Even more concerning, the order in which options appeared could influence recommendations.

In other words, AI sometimes appears to reason while remaining surprisingly sensitive to presentation.

That creates an important lesson for organisations building AI-enabled strategy processes:

Prompt design matters.

But prompt design alone is not enough.

You cannot engineer reliable strategic judgement purely through wording.

You need systems and workflows designed to challenge assumptions.

The Difference Between AI Assistance and AI Theatre

Many AI strategy tools risk becoming what could be called: “ChatGPT in a strategy wrapper.”

The experience feels impressive.

Ask a question.

Receive polished recommendations.

Generate attractive outputs.

Move on.

But if AI recommendations remain unaudited, unchallenged and disconnected from evidence, organisations may simply accelerate poor decisions.

The issue is not that AI lacks intelligence.

The issue is that strategy requires accountability.

• Who owns the recommendation?
• What assumptions drove it?
• What evidence supports it?
• What alternatives were rejected?
• What trade-offs were considered?

Without answers to these questions, strategic outputs become difficult to trust.

And trust matters because strategy ultimately shapes investment decisions, organisational priorities and execution effort.

Why StratNav Was Built Differently

This challenge strongly reinforces the direction we have taken with StratNav.

The objective was never to build AI that acts as “the strategist.”

The objective is to build AI that strengthens strategic thinking.

That distinction matters.

StratNav is designed around the idea that better strategy comes from structured thinking, evidence and decision discipline.

That means helping organisations:

Surface strategic alternatives

Rather than assuming one obvious answer exists, strategy should examine competing options.

Capture rationale and debate

Decisions rarely emerge fully formed.

Good strategy often develops through discussion, disagreement and iteration.

Link recommendations to evidence

Outputs should not simply appear.

Leaders should understand why recommendations exist.

Maintain human accountability

AI can support analysis.

Humans remain responsible for judgement.

Support execution as much as planning

A strategy only creates value if it changes behaviour.

Execution discipline matters as much as strategic insight.

These principles become increasingly important as AI capabilities improve.

Because the better AI becomes at sounding convincing, the more important structured decision-making becomes.

The Missing Layer: Decision Discipline

The article implied something deeper than model bias.

The weakness of LLMs is not eloquence.

It is lack of groundedness.

Models generate outputs based on patterns.

Strategy requires commitment.

Those are different activities.

This suggests an opportunity for systems like StratNav to go beyond recommendation generation.

Future strategy workflows should include:

Evidence trails

Users should see sources and supporting information linked directly to insights.

Forced comparisons

Alternatives should be examined explicitly rather than hidden.

Counter-argument generation

Systems should generate the strongest opposing case automatically.

Human checkpoints

Leaders should actively accept or reject recommendations and explain why.

Decision histories

Organisations should track changes over time and identify shifts in AI outputs.

Because strategic judgement does not improve by hiding uncertainty.

It improves by exposing it.

The Question Leaders Should Ask

The real question may not be: "Can AI create strategy?"

Increasingly, the answer is yes.

The better question is: "How do we stop AI-generated strategy becoming polished nonsense?"

Because if strategy is fundamentally a set of decisions, organisations need systems that make difficult decisions unavoidable.

The future probably does not belong to AI replacing strategists.

It belongs to AI helping leaders think more rigorously, challenge assumptions and make stronger choices.

That is a very different proposition.

And perhaps a much more valuable one.

Interested in exploring how AI and structured strategy can work together?

Schedule a demo of StratNav or try StratNav for free.

You can also book a discussion at Chris C Fox Consulting if you'd like to explore how strategy decision systems could strengthen your organisation’s planning and execution process.