Why experimentation with AI tools is a leadership skill

Many leaders are curious about how to integrate AI into their businesses effectively. The rapid accessibility of tools such as ChatGPT, Gemini, DeepSeek, and Claude have sparked interest, but confusion remains about where to start and how AI aligns with business goals. The biggest challenge lies in moving beyond superficial adoption to strategic implementation that drives meaningful outcomes.

The reason AI initiatives stumble and to recover

A common pitfall is expecting immediate productivity gains after introducing AI tools like Microsoft Copilot without proper training or alignment with business objectives. Leaders must first define their 12- to 36-month goals—whether increasing revenue or reducing costs—and then identify how AI can support these aims. A strategic approach ensures technology adoption is purposeful rather than reactive.

Spotting high-impact AI opportunities in your workflows

Two approaches can help:

1. Top-down: Align AI initiatives with KPIs and business processes.
2. Bottom-up: Analyse workflows to pinpoint inefficiencies or redundant tasks.

Engage subject-matter experts to map processes and evaluate whether existing tools already incorporate AI capabilities. This dual approach ensures AI investments address genuine needs.

Evaluate tools and vendors wisely against ROI

Vendors frequently market AI features, but leaders must scrutinise their actual value. Conduct ROI assessments by involving IT specialists and end-users to test functionality and cost-effectiveness. The goal is to invest in solutions that deliver measurable improvements, whether through cost savings or new revenue streams.

Communicate about AI with clarity

AI adoption can unsettle employees who fear job displacement. Leaders should:

• Emphasise AI as a tool to enhance—not replace—human skills.
• Acknowledge their own learning curve to foster a culture of shared experimentation.
• Clearly articulate how AI will help teams thrive, such as automating mundane tasks like meeting minutes or data entry.

Building and leading hybrid teams of humans and AI

AI introduces new dynamics, requiring leaders to:

• Set quality standards for AI-generated outputs.
• Encourage critical thinking—employees shouldn’t blindly trust AI results.
• Upskill teams to interact effectively with AI, moving beyond traditional point-and-click interfaces to conversational workflows.

Hype & trends in the AI space

• Overhyped: Agentic AI, which remains in early stages for enterprise use.
• Underhyped: Training employees to use AI tools conversationally, a skill often overlooked despite its transformative potential.

Secure stakeholder commitment early. Without buy-in from business units, even promising initiatives can falter. Leaders must:

• Validate alignment before investing resources.
• Establish milestones to track progress and pivot if needed.
• Normalise failure as part of innovation, fostering a culture of experimentation.

Align AI projects to business goals

Stop projects that don’t contribute to business goals. Persisting with under-performing initiatives wastes resources and stifles progress.

How to create an AI-friendly culture

Encourage experimentation at all levels. Leaders don’t need to be AI experts but should:

• Model curiosity by trying tools themselves.
• Create forums for teams to share prompts, techniques, and success stories.

Key takeaways

1. Align AI with strategy: Start with business goals, not technology trends.
2. Communicate transparently: Address fears and emphasise AI’s role as an enabler.
3. Embrace failure: Not every AI project will succeed, but each offers lessons.
4. Invest in literacy: Equip teams to use AI critically and creatively.

Photo by Richard Horvath on Unsplash.