Steps to Transform AI Champions into Builders

Steps to Transform AI Champions into Builders

Moving from AI champion to builder is about turning enthusiasm and advocacy into tangible outcomes. Builders don’t just promote AI—they create AI solutions that improve processes, solve problems, and generate real value. With the right support, any organisation can cultivate builders from its champion pool.

Why Building Matters

Champions raise awareness and guide teams, but builders deliver results. They transform ideas into practical AI applications that:

• Speed up workflow efficiency

• Enable employees to solve challenges independently

• Unlock innovation across departments

• Ensure AI initiatives align with strategic goals

Without builders, AI initiatives risk stagnating, with champions promoting ideas but leaving implementation incomplete.

Step 1. Identify High-Potential Champions

Look for employees who have the curiosity, influence, and aptitude to take the next step:

• Understand business processes and can spot inefficiencies

• Are eager to experiment with new technologies

• Communicate ideas effectively and inspire peers

• Solve problems creatively and seek better solutions

• Are willing to invest time in learning and testing AI

• Hold respect and credibility within their teams

Even in organisations new to AI, there are often multiple employees ready to evolve into builders.

Step 2. Provide Tools and Training

Becoming a builder doesn’t require coding expertise. No-code or low-code platforms can allow employees to build AI solutions. Organisations should:

• Give champions access to AI development platforms

• Offer training, tutorials, and guides to build confidence

• Run internal workshops or collaborative sessions to practice building AI solutions

• Encourage projects that solve real business challenges, even small-scale pilots

Example: A team leader in operations identifies repetitive data-entry tasks. By using a low-code AI tool, the champion builds a workflow that automates data collection and report generation, saving hours each week.

Step 3. Foster a Supportive Environment

Builders need space to experiment, fail safely, and share lessons learned:

• Provide executive support to prioritise AI experimentation

• Celebrate successful AI projects and learning moments from failures

• Create forums or channels for champions to share experiences and solutions

• Encourage teams to rethink processes and test AI-driven improvements

Example: In a marketing department, a champion experiments with an AI tool to generate personalised email content. They test, refine, and then share the templates with the wider team, speeding up campaign creation.

Step 4. Encourage Continuous Learning

AI evolves quickly, so champions must keep learning to become effective builders:

• Host internal challenges, hackathons, or showcases

• Provide access to tutorials, courses, and learning resources

• Encourage peer mentoring and collaborative projects

• Recognise continuous improvement and applied learning

Example: In customer service, a champion regularly tests AI chat agents, learning from interactions and optimising their responses. Over time, they become the go-to expert for improving automated support across the team.

Step 5. Use Tools for Knowledge Management

Builders generate insights, best practices, and AI workflows that must be captured and shared. Knowledge management tools help organisations scale learning and avoid repeated mistakes:

• Implement shared repositories or intranets to store AI blueprints and templates

• Use collaborative platforms where teams can document experiments and results

• Encourage champions to write guides, tips, or case studies for wider use

• Track successful AI applications and lessons learned for future projects

Example: A finance team documents AI workflows for automated invoice processing. Other teams access the repository to adapt workflows to their processes, reducing trial-and-error and accelerating adoption.

Step 6. Establish Clear Goals and Metrics

Builders need direction to ensure their AI solutions align with business priorities. Define measurable objectives for AI projects and track progress. This helps champions understand what success looks like and encourages accountability.

• Set KPIs for efficiency gains, error reduction, or user adoption

• Align AI initiatives with strategic organisational outcomes

• Regularly review results and iterate on AI solutions

Example: A supply chain champion measures the reduction in manual order processing time after deploying an AI workflow.

Step 7. Promote Cross-Functional Collaboration

AI works best when knowledge is shared across teams. Encourage builders to collaborate with colleagues from different departments to combine domain expertise with technical skills.

• Create cross-functional project teams for AI experiments

• Facilitate regular knowledge-sharing sessions

• Encourage co-creation of AI solutions for multi-department workflows

Example: Marketing and customer support champions co-design an AI tool that improves personalised responses and reduces turnaround times.

Step 8. Provide Access to Data and Resources

Builders need clean, relevant data to experiment and develop AI solutions. Removing barriers to access ensures champions can work effectively without waiting for IT bottlenecks.

• Provide secure access to necessary datasets

• Offer templates or starter workflows to accelerate experimentation

• Ensure compliance with data privacy and governance policies

Example: A HR champion uses anonymised employee survey data to build an AI tool for predicting training needs.

Step 9. Recognise and Reward Contributions

Celebrating builders’ successes motivates continued innovation and demonstrates the value of AI adoption. Recognition can be both formal and informal.

• Highlight successful AI projects in internal communications

• Offer awards or incentives for innovation

• Encourage peer recognition and mentorship

Example: A champion who develops an AI workflow to reduce reporting time is acknowledged in a company-wide newsletter, inspiring others to build their own solutions.

Step 10. Integrate Feedback Loops

AI solutions improve when users provide input. Builders should establish mechanisms to gather feedback and continuously refine their agents or workflows.

• Collect user feedback on AI outputs and usability

• Monitor adoption metrics and identify improvement opportunities

• Iterate AI workflows based on practical insights

Example: A customer service champion refines an AI chatbot weekly based on frontline feedback, improving response accuracy and satisfaction scores.

Final Thought

Transforming champions into builders turns advocacy into action. By spotting potential, providing tools and training, fostering experimentation, supporting continuous learning, and capturing knowledge effectively, organisations empower employees to create AI solutions that generate measurable impact. Champions inspire; builders deliver.