Your team oversold the results of a Machine Learning project. How do you salvage your credibility?
If your team has oversold the results of a Machine Learning (ML) project, it's crucial to act swiftly to maintain trust. Here’s how you can navigate this tricky situation:
- Acknowledge the issue: Be transparent with stakeholders about the overestimate and explain the root cause.
- Recalibrate expectations: Provide a realistic timeline and outcomes based on current data and analysis.
- Offer solutions: Suggest actionable steps to improve the project, such as additional training or better data collection.
How would you handle an oversold project? Share your strategies.
Your team oversold the results of a Machine Learning project. How do you salvage your credibility?
If your team has oversold the results of a Machine Learning (ML) project, it's crucial to act swiftly to maintain trust. Here’s how you can navigate this tricky situation:
- Acknowledge the issue: Be transparent with stakeholders about the overestimate and explain the root cause.
- Recalibrate expectations: Provide a realistic timeline and outcomes based on current data and analysis.
- Offer solutions: Suggest actionable steps to improve the project, such as additional training or better data collection.
How would you handle an oversold project? Share your strategies.
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Overselling an ML project can damage trust, but transparency and corrective actions can restore credibility. Here are some best practices: Acknowledge the Oversight: Admit the gap between expectations and actual results to build trust. Clarify the Current Status: Present realistic data on what the model can and cannot do. Set Realistic Next Steps: Outline a plan to improve accuracy and refine the model. Engage Stakeholders: Keep open communication with clients and leadership to align expectations. Document Lessons Learned: Implement measures to prevent future overpromises. By owning mistakes and taking corrective actions, you can rebuild trust and credibility.
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To salvage credibility after overselling the results of an ML project, transparency and proactive measures are key. Acknowledge the overstatement openly, recalibrate expectations with realistic outcomes, and propose actionable solutions like improving data quality or retraining models. Involving stakeholders throughout the process and maintaining clear communication will help rebuild trust. Additionally, implementing governance systems to ensure future accuracy and transparency can prevent similar issues from arising.
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To rebuild credibility after oversold ML results, start with transparent communication about actual performance and limitations. Present clear data showing current outcomes versus expectations. Create realistic improvement plans with measurable milestones. Document steps being taken to enhance capabilities. By combining honest assessment with concrete solutions, you can restore trust while setting appropriate expectations.
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Adjusting expectations is a slippery slope 📈🔻slow is fast ✅ Focus on their top goals - the conversation needs to be about focusing on successfully delivering 1 or 2 strategic features vs building Rome in a day. ✅ Benchmarking - A very important tool to set real expectations is to benchmark other industry players in your domain. A lot of business stakeholders are out of touch with Industry performance besides chatGPT or Fad topics (Agentic Paradigm? GenAI? Haha) ✅ Build trust - under commit and over deliver for a milestone while parallely building up to it
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Regaining credibility after overselling a machine learning project requires transparency and swift action. Acknowledge any discrepancies between expectations and reality, and communicate the actual capabilities of the model clearly. Shift the focus from what's missing to what can still be achieved by setting realistic goals and a clear improvement roadmap. Engage stakeholders with data-driven insights on performance, limitations, and potential enhancements. Strengthen internal review processes to prevent future over promises. By demonstrating accountability and a commitment to delivering value, you can rebuild trust and salvage your reputation.