Top-Down vs. Bottom-Up AI Adoption

Top-Down vs. Bottom-Up AI Adoption

When organisations begin their AI journey, one of the first strategic questions is how adoption should spread. Should it come from leadership pushing change across the company (top-down), or from employees experimenting and sharing results at the grassroots level (bottom-up)?

The truth is, both approaches have strengths and limitations. Understanding how they differ—and how to blend them—can make the difference between stalled experiments and lasting transformation.

The Top-Down Approach

In top-down adoption, leadership sets the vision, allocates resources, and drives AI priorities across the organisation.

Strengths

• Clear alignment with strategic goals

• Easier to secure budgets, tools, and training

• Company-wide standards and policies established early

• Strong governance around ethics, compliance, and data security

Challenges

• Risk of disconnect if leaders push tools without showing relevance to day-to-day work

• Employees may see adoption as a mandate rather than an opportunity

• Slower to capture grassroots innovation happening in real workflows

The Bottom-Up Approach

In bottom-up adoption, individuals and teams explore AI on their own, testing use cases and sharing wins across the organisation.

Strengths

• More organic adoption as employees see peer-led value

• Champions emerge naturally, spreading practices that already work

• Faster experimentation and iteration

• Builds trust as people adopt AI in ways that make sense for their roles

Challenges

• Adoption can remain inconsistent and siloed

• Risk of shadow AI tools without proper oversight

• Harder to align with broader business strategy if uncoordinated

• Gaps in governance and data security may emerge

Striking the Right Balance

The most successful organisations don’t choose one or the other—they combine both approaches.

What balance looks like

• Leadership provides the vision, guardrails, and resources

• Employees and champions drive experimentation, adoption, and feedback

• Regular communication ensures that bottom-up successes inform top-down strategy

• Governance frameworks evolve alongside grassroots use cases to scale responsibly

Practical Steps to Blend Approaches

1. Set a clear AI vision from leadership, aligned with business priorities

2. Encourage experimentation by empowering employees to test and share use cases

3. Create a champions network to connect grassroots innovation with leadership support

4. Provide governance and standards so adoption remains ethical, secure, and scalable

5. Celebrate wins at all levels—from small team automations to enterprise-wide impact

Final Thought

AI adoption doesn’t succeed by mandate alone, nor by uncoordinated experiments. It thrives when leaders set the direction and employees shape the practice. By combining top-down structure with bottom-up innovation, organisations can build AI adoption that is both strategic and sustainable.