AI Transformations

From Headcount to Leverage: A Practical View on AI in Tech

January 11, 2026

by Michal Szymaniak

TABLE OF CONTENT

For years, growth in technology teams followed a familiar pattern: more scope meant more people. When delivery slowed, headcount increased. When complexity rose, teams expanded. AI is changing that equation-not by replacing humans, but by changing how much leverage a small, well-aligned team can have. The most effective teams we work with today are not the largest ones. They are the ones that understand where AI genuinely adds value, and where human judgment still matters most.

Smaller Teams, Higher Output

One of the clearest shifts we observe is team size versus output. Teams that successfully adopt AI do not grow faster—they grow smarter.

Instead of scaling headcount, they scale capability:
• one engineer does the work of several
• QA focuses on risk and strategy rather than manual repetition
• product discussions move faster because information is easier to surface

This doesn’t eliminate the need for collaboration. It increases the importance of it. Smaller teams require stronger alignment, clearer ownership, and better communication.

AI amplifies both strengths and weaknesses.

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Productivity Comes From Reducing Friction

AI’s biggest impact is not speed for its own sake-it’s removing friction from workflows that were never designed for scale.

Examples we see repeatedly:
• generating and refining acceptance criteria
• assisting with test coverage and edge cases
• accelerating debugging and investigation
• summarising documentation and legacy context
• supporting code reviews and refactoring decisions

Used well, AI shortens feedback loops. Used poorly, it creates noise and false confidence.

The difference lies in intent and governance.

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Why AI Adoption Often Fails

Many organisations approach AI as a tool rollout instead of a working model change.

Common failure patterns:
• tools introduced without clarity on ownership
• expectations of instant productivity gains
• lack of guardrails or quality standards
• teams unsure when to trust AI-and when not to

AI doesn’t remove the need for leadership. It increases it.

Without clear principles, teams either over-rely on AI or avoid it entirely. Both limit impact.

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How We Help Teams Transition

Our role is not to sell tools or replace teams. It’s to help organisations use AI intentionally.

We help teams:
• identify where AI creates real leverage
• integrate AI into existing workflows without disruption
• maintain quality, security, and accountability
• adapt roles and expectations for a smaller, higher-impact team model

AI transformation is not a one-off initiative. It’s an evolution in how teams think about work, ownership, and delivery.

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AI as a Multiplier, Not a Shortcut

AI rewards clarity. It exposes gaps. It accelerates good practices and magnifies bad ones.

The teams that benefit most are not chasing trends—they are refining how they work. They use AI to support human decision-making, not replace it.

The future of tech is not built by larger teams.
It’s built by teams with more leverage.

And that’s where AI, used responsibly, makes the difference.

Michal Szymaniak is a hands-on technology leader and Head of Technology at AMILI, where he builds AI-driven, bio-informatics and data platforms. He is also co-founder of Aagee, advising startups and leadership teams on scaling engineering organisations, improving delivery, and optimising costs in the age of AI. Michal has established multiple engineering hubs in Vietnam and brings deep experience in building and hiring high-performing teams across Southeast Asia. Writes about AI, engineering leadership, and delivery at Aagee.vn

Michal Szymaniak, Aagee Co-CEO