Let’s first review the successes. AI has been successfully deployed by Netflix, who uses data on its subscribers to recommend movies or shows they may want to watch in the future. And by Coca-Cola, who has smart vending machines that inform the company on when a machine needs a refill. Cover Girl has also implemented an AI solution that helps consumers pick the right foundation for their skin.
Now the failures. Microsoft did not consider the amount of negative sentiment used on Twitter when it released Tay, an AI chatbot intended to be an experiment in “conversational understanding.” Within a day, the Twitter bot, turned into a racist chat bot and had to be shut down. Facebook tried a similar experiment and found their AI chatbots started speaking to each other in only a language they understood, requiring human intervention.
In a recent presentation at the 2nd annual Ugam Customer Summit, our Guest Speaker, Forrester Principal Analyst Brandon Purcell, discussed his recommendations for businesses looking to successfully apply AI. The following represent our takeaways from his presentation.
2) Inventory your available data – can it be used for training? – Before taking on an AI solution, you need to understand the data you have so you can properly train the AI system. If you don’t have a lot of data to work with it’s better to go with a well-trained machine, if you have plenty of data to train the machine, then you will want to look at a system you can train. If you can, Purcell recommends training a system with your own data over going with an AI system that is already trained, as the pre-trained system will be more general in nature, and not necessarily support the unique needs of your business.
3) Select a narrow use case based on feasibility and perceived ROI – It’s always good to start with a small business problem and expand out as you go along. As Purcell notes, try to prioritize your efforts based on what you think the overall business impact will be.
4) Build if you have the right skills and data, otherwise buy – Not everyone is lucky enough to have a data scientist working for them, and if you don’t, a pre-built solution that you can buy may be better for your business. This is an important thing to consider before you buy.
5) Continuously measure impact against KPIs and customer experience – Keep an eye on your AI solution and continuously measure how its performing and make sure it’s not going off the rails and remains in line with the objectives you’ve set for the project.
6) Communicate results and build on your success! – Develop metrics for success before implementing the project and promote the project within the organization. And continue to champion the successes as you reach and surpass your goals – communicating your project and championing it across the business will only help to open more doors for how you can leverage AI for additional projects in the future.
For more insights on AI from Brandon Purcell, view his presentation from the 2018 Ugam Customer Summit Click here.