How can you persuade decision makers with Machine Learning communication?

1 Know your audience

The first step to effective ML communication is to understand who you are talking to and what they care about. Different audiences may have different levels of technical knowledge, expectations, and goals. You need to tailor your message and style to suit their needs and preferences. For example, if you are talking to a business executive, you may want to focus on the bottom-line benefits and the return on investment of your ML solution, rather than the details of the algorithms and the data. On the other hand, if you are talking to a technical expert, you may want to provide more evidence and justification for your choices and assumptions, and show how your ML solution compares to other alternatives.

2 Use storytelling techniques

One of the most effective ways to communicate your ML project is to use storytelling techniques. Storytelling can help you engage your audience, create a narrative structure, and highlight the key points and insights. A good ML story should have a clear beginning, middle, and end, and answer the following questions: What was the problem or opportunity that motivated your ML project? How did you approach the problem and what methods did you use? What were the results and what did they mean? What are the implications and recommendations for action? You can also use storytelling techniques to make your ML project more relatable and memorable, such as using analogies, metaphors, examples, and anecdotes.

4 Anticipate questions and objections

No matter how well you communicate your ML project, you are likely to face some questions and objections from your audience. This is not necessarily a bad thing, as it shows that your audience is interested and engaged. However, you need to be prepared to answer them confidently and respectfully, and address any concerns or doubts that they may have. To do this, you need to anticipate the possible questions and objections that your audience may have, and have some ready answers and evidence to support your claims. You should also be honest and transparent about the limitations and assumptions of your ML project, and acknowledge any uncertainties or trade-offs that may exist. By doing this, you can build trust and credibility with your audience, and show that you have considered different perspectives and scenarios.

5 Use clear and concise language

The final tip for improving your ML communication is to use clear and concise language. This means avoiding jargon, acronyms, and technical terms that your audience may not understand, or explaining them in simple terms if necessary. It also means using short and simple sentences, avoiding passive voice and complex structures, and using transitions and signposts to guide your audience through your ML story. You should also use positive and assertive language, and avoid hedging, apologizing, or undermining your own expertise. By using clear and concise language, you can make your ML communication more accessible and understandable, and more likely to persuade your audience.