??Educate the team on realistic machine learning capabilities to ground their claims. ??Set up a review process for all client-facing materials to ensure accuracy. ??Encourage open discussions with clients about both strengths and limitations of ML. ??Use case studies and examples to illustrate realistic outcomes without exaggeration. ??Align pitches with achievable goals to maintain trust and manage expectations. ??Highlight continuous improvement and learning, showing progress over inflated promises.

To address overpromising of ML capabilities, establish clear guidelines for client communications about model capabilities. Create standardized presentation templates with accurate, validated claims. Implement peer review for client-facing materials. Provide training on ethical sales practices and technical limitations. Share case studies showing realistic outcomes and timelines. Foster a culture where honesty builds long-term client trust. By promoting transparent communication and setting clear boundaries, you can maintain credibility while effectively showcasing ML's true potential.

To manage your team member’s overpromising, emphasize the importance of setting realistic client expectations. Encourage discussions focused on what machine learning can achieve versus its limitations, underscoring the importance of transparency in building trust. Provide examples of successful, realistic outcomes to guide discussions, and offer resources on common challenges and pitfalls in machine learning projects. Regularly review client communications together to ensure accuracy and alignment with the team’s capabilities. Lastly, position yourself as a supportive resource, highlighting the benefits of measured claims to long-term client satisfaction and repeat business.