It depends on what the technical benefits are. I think many forms of improvement are easy to explain to stakeholders. The new model trains faster? This translates to less money spent on training. The new model predicts faster? This can mean that the bounce rate on your website decreases since people hate waiting for something to load. The new model is interpretable? The stakeholders will probably trust it more since they understand how it creates its prediction. Also, sometimes interpretability is necessary to meet regulatory requirements.

When pitching a new machine learning (ML) model to non-technical decision-makers, keep it simple and focused on the benefits. As a Senior Application Support Analyst, I once introduced an ML-powered reporting tool to our management team. Instead of diving into technical details, I highlighted how it would: Save Time: It cut report prep from days to hours. Boost Accuracy: It minimized human error, which we often struggled with. Drive Better Decisions: It revealed user trends to enhance support. Using visuals and plain language made it relatable, and I encouraged questions to keep it engaging. This approach not only excited the team but also made the tech easier to embrace. The key takeaway? Show the real-world benefits to get buy-in!

Here’s how you can break it down for non-technical decision-makers: ? Storytelling: Use relatable stories or analogies to explain how the model works and its impact. Think of it like describing how a GPS helps in navigating a city. ? Focus on Results: Highlight the concrete benefits like increased efficiency, cost savings, or improved customer satisfaction. Show them the "what" before diving into the "how." ? Visual Aids: Charts, graphs, and visualizations can make data and outcomes more digestible. People often grasp visual information faster than raw numbers. ? Simplify Terms: Avoid jargon. Use simple, everyday language to explain complex concepts.

Al presentar un nuevo modelo de ML a responsables de la toma de decisiones no técnicos, es crucial comunicar los beneficios de manera clara y concisa. Algunas estrategias efectivas para hacerles ver las ventajas del nuevo modelo de ML: 1. Comunicación en Términos No Técnicos 2. Demostración Práctica 3. Enfatizar Beneficios Tangibles 4. Impacto en la Toma de Decisiones 5. Comparación con el Estado Actual 6. Casos de éxito y Ejemplos Prácticos 7. Análisis de Costo-Beneficio 8. Resaltar la Escalabilidad y Adaptabilidad 9. Sesiones de Preguntas y Respuestas Al seguir estas estrategias y enfoques, es posible comunicar de manera efectiva los beneficios del nuevo modelo de ML a los responsables de la toma de decisiones no técnicos.

When presenting a new ML model to non-technical decision-makers, focus on explaining practical benefits in simple terms. Start by describing the problem it solves and how the model addresses specific business needs, such as increasing sales, improving customer satisfaction, or reducing costs. Use visuals like charts to show the model’s impact compared to previous methods or baselines. Share real-life examples or scenarios where the model’s predictions provide clear value. Highlight any metrics that resonate with business goals, such as improved accuracy or efficiency, and avoid technical jargon. Finally, explain the model’s ease of integration and how it will support better decision-making overall.