2017 has been a year that has delivered on the promise of machine learning and AI technologies for the enterprise. Every year we set up our booth at some of leading industry conferences. We use this opportunity to interact with potential customers and also have conversations about machine learning and Enterprise AI.
In 2016 when we unveiled our product discovery bots and Enterprise AI solutions for Retail, a lot of people were skeptical. Most of them wanted to get an idea of what is AI, where has it come from and where it was headed. Only a few bold ones were willing to throw use cases at it. The majority of the attendees wanted to wait and watch.
This year, it was a completely different interaction. Everyone that we spoke to was already educated about Enterprise AI and how it can help them. Most of them also had ready use cases and problems that they knew machine learning could solve or automate for them. From image recognition, conversations or customer support management to everything that needed basic human cognitive skill was up for a discussion.
This year, we clearly saw AI adoption for enterprise become mainstream.
As the year draws to a close, a lot of your teams would start planning for the next year. I am sure AI or Machine Learning driven automation will be on a lot of your lists for 2018. Some of you might wonder where to begin? A good way to start planning for AI is with a strategy workshop. This is typically one or two week period of assessment which looks at your systems, data and organizational goals to determine the best way to start your AI journey. It usually concludes by recommending one of the following approaches:
#Start with Data – This strategy works when you do not have a specific AI goal in mind but you have a gold mine of past data. You want someone to figure out what can be achieved from this data? What can algorithms learn from this data to automate something that needs human effort today or what new insights can be extracted for improving a business process?
#Start with Goal – This approach works when you already have a fixed goal that you need to be achieved – for instance, cutting down your operational expense by 5% or improve your turn around time for pre-sales activities. In this case, your AI partner can evaluate what data you have and what, if any, external data needs to be sourced to train algorithms to give you the desired results.
#Start with Story – In some cases, businesses want to explore how AI can be used to solve a specific problem for a specific function. For instance, how to sell more or increase AOV for your customers? They already know that they have the right data to feed the algorithms.
If you have not thought about how to bring AI into your enterprise in the new year, one of these three approaches might be the right way to start.