Many business are AI curious but are far from ready to start a project. Most get stuck finding use cases relevant to their business. As a co-founder of KUNGFU.ai, an AI consultancy, I come across many. Though these AI and Machine Learning use cases are industry specific, the application can scale across industry. Here are the top ten most interesting that can make an impact for many types of businesses immediately.
Ten.
Companies like Salesforce and Hubspot are trying predictive lead scoring so we spend time on targets who are ready to buy:
https://www.wordstream.com/blog/ws/2017/07/28/machine-learning-applications
Nine.
Banks are stepping up and offering lines of credit by predicting emergency events that leave you strapped for cash.
https://www.mckinsey.com/industries/high-tech/our-insights/an-executives-guide-to-machine-learning
Eight.
Paypal is using machine learning to observe suspicious activity and prevent fraud.
Seven.
Sick of going to the store and leaving empty handed because they ran out something? Me neither because we all shop online to avoid this. Well step back into the light! Now we can predict inventory and transposition demands to ensure that's no longer an issue.
Six.
Disruptive tech company, Caavo, uses machine vision to see what's on your TV screen and execute correct commands. APIs you've been put on notice.
https://www.theverge.com/2017/2/15/14620510/caavo-tv-streaming-box-machine-vision-ai-gamechanger
Five.
Talent Management is using Natural Language Processing to predict which resumes will lead to successful employees with a high degree of accuracy.
https://www.mckinsey.com/industries/high-tech/our-insights/an-executives-guide-to-machine-learning
Four.
GE is a great example how many industries are using machine learning and IoT sensors to service machines before they fail.
Three.
Insurance companies are using AI to predict which customers driving behavior will lead to accidents to adjust premiums.
Two.
Stitch Fix augments design consultants with machine learning to better curate clothing recommendations.
One.
Universities are using student data and machine learning to anticipate which students need emergency financial assistance to reduce the dropout rate.
https://er.educause.edu/articles/2017/12/machine-learning-and-higher-education