December 20, 2019

Using AI Best Practices to Solve Business Problems

ETI Software executives Pete Pifer, CEO and Tom Taylor, Chief Strategy Officer, recently attended the AI Summit for Business in New York. The mission? To learn about the latest events in the Artificial Intelligence world as well as to understand better how to position ETI to improve services for its customers.

Many of the speakers highlighted that while AI is important, the first step is to understand the business problem to be solved. Too many times, AI Engineers and Data Scientists jump in with their tool of choice without first understanding the problem at hand. Once you have jumped that hurdle, consider these factors:

Data Amounts & Quality:

Does a great set of data exist? If there is no data, there will be no AI solution.
Is the data clean? Most likely it is not completely clean data. Several speakers got laughter from the audience when ask if there is old, legacy data in their data mix. There is almost always old data and this data will most probably not be in the best format. Data cleansing will be an important step to maximize the value of the data.
How should the data and the problem be approached? Different people will approach the data differently. Data Scientists will take a disinclined approach to get as much out of the data as possible. Statisticians will take a more rigorous approach to try to obtain specific binary or variable results. Analysts will take more of a blue-sky approach and ask questions like; What can the data tell us? What are the unknown, unknowns? Therefore, always know who is approaching the problem and what their bias perspective is.

Some Best Practices:

There is no question that AI is growing quickly and having a big impact on businesses that are doing it well. If you are not doing some sort of AI work, get started. The early leaders will grow their business significantly, the laggards will lose out.
Think big but start small. Small pilots that impact only a part of the business are the best place to “fail fast”. Once there is a pilot project that is working well, it will be easier to spread to other parts of the company and begin to build an enterprise-wide Data Culture.
People will always be a part of the AI environment. People make the data, people use that data/AI results and mostly importantly people handle the problems that need creative solutions. Don’t forget about the human element.

How much human interaction will be needed? A good definition is below:

Partial AI – there is a human in the loop
Supervisory AI – there is a human on the loop
Full Autonomy – the human is out of the loop

Use Case: Should you Replace your CSRs with Bots?

A good use case was given to encourage thinking correctly about the AI applications and the problems they solve.

There are many customer calls that a Bot could handle, such as: What are you hours? How do I reset my password? However, CSR agents should handle the most complex questions that require some measure on creativity; such as “Why is A not happening when I try B; but only on Wednesdays?”

A good comment to keep in mind with conversational Bots vs. Humans – AI does not handle sarcasm well. If there is a chance for sarcasm in this interaction, have a human in the loop.

Human and AI Bot interaction. Many people think that it is a one-way street; the Bot starts to get the easy information and later hands over to a human. However, consider having a Bot start the conversation, getting the easy intro information. The human can then control the middle phase to make sure all was well and to do some upselling. Finally, the Bot finishes with the standard (boring) payment section.

Fair Warning:

Speakers stressed that using AI is a long term, multi-year solution requiring very powerful computing equipment and facilities. AI is NOT about wiggling your nose and asking for the moon, and a magic wizard making it happen. There is still a lot of work on your part… so take a hard look at your options, resources and commitment. Three specific and different skills are required to run any good AI Project:

Subject matter expert. Someone who understands the problem from a technical perspective that is specific to your business and your business ecosystem.
AI technical expert. This person will understand the dataset, pick the AI toolset and determine what AI processes will be run.
Project manager. This person needs general project management skills, but also must be able to relate to the Business, the Subject matter expert and the AI technical expert. They do not have to be experts in all these fields, but they must be able to be the communication glue that holds the project together.

If you understand the business problem and you have a team in place, begin and then keep iterating!

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