Last week, DeepLearning.AI invited a group of learners to our Palo Alto office’s courtyard. We had a good time chatting about paths into AI, career trajectories, applications people were working on, and challenges they were facing. You can see the group below.
A few people mentioned the challenge of persuading others to try a machine learning solution. Even at leading tech companies, it’s not uncommon for someone to say, “Yes, machine learning may work well for other applications, but for what we’re doing, non-learning software works fine.”
Still, machine learning might work better. If you believe that a learning algorithm can help optimize server allocations, improve product recommendations, or automate some part of a business process, how can you push your idea forward?
Here are some tips that have worked for me:
- Ask everyone who would be affected for their perspective, and share yours with them. AI projects can be complex, and many things can go wrong. Colleagues can alert you to issues you’ll need to address, such as difficulty gathering data, complexity of software integration, the need to reorganize workflows, how to manage the occasional incorrect prediction, as well as safety, fairness, and regulatory concerns.
- Bring evidence that a machine learning system could work. You might build a quick proof of concept. Or you might find related work, either in the academic literature or reports of other companies, to persuade others that it could work for your organization, too.
- Bring in outside consultants, advisors, or speakers. Their expertise can help persuade your team. (True story: I’ve met several people who have asked their non-technical teammates to take the AI for Everyone course. They’ve found that things move forward more easily when everyone involved has a basic business understanding of AI).
- Find allies. One forward-thinking partner can make all the difference! Persuading the first person is usually the hardest part. The first can help you persuade the second, and together you can persuade the third.
Throughout this process, be open to learning that your idea isn’t sound after all or that it might need to change before it can be successful. I would guess that almost every successful AI application you read about in The Batch required someone to persuade others to give machine learning a shot.
Don’t let the skeptics shut you down. Don’t give up, keep pushing, and . . .