Using AI in a company generally involves two approaches: using vendor software or creating in-house models. The first one is the most prevalent—and may be enough for a large number of companies. The irony is that you may already be using software, say from Salesforce.com, Microsoft, Google, Workday, Adobe, or SAP, that already has powerful AI capabilities. In other words, a good approach is to make sure you are taking advantage of these to the fullest.
To see what’s available, take a look at Salesforce.com’s Einstein, which was launched in September 2016. This AI system is seamlessly embedded into the main CRM (Customer Relationship Management) platform, allowing for more predictive and personalized actions for sales, service, marketing, and commerce. Salesforce.com calls Einstein a “personal data scientist” as it is fairly easy to use, such as with drag and drop to create the workflows. Some of the capabilities include the following:
- Predictive Scoring: This shows the likelihood that a lead will convert into an opportunity.
- Sentiment Analysis: This provides a way to get a sense of how people view your brand and products by analyzing social media.
- Smart Recommendations: Einstein crunches data to show what products are the most ideal for leads.
However, while these prebuilt features make it easier to use AI, there are still potential issues. “We have been building AI functions into our applications during the past few years and this has been a great learning experience,” said Ricky Thakrar, who is Zoho’s customer experience evangelist. “But to make the technology work, the users must use the software right. If the sales people are not inputting information correctly, then the results will likely be off. We also found that there should be at least three months of usage for the models to get trained. And besides, even if your employees are doing everything right, this does not mean that the AI predictions will be perfect. Always take things with a grain of salt.”3
Now as for building your own AI models, this is a significant commitment for a company. And this is what we’ll be covering in this chapter.
But regardless of what approach you may take, the implementation and use of AI should first begin with education and training. It does not matter whether the employees are non-technical people or software engineers. For AI to be successful in an organization, everyone must have a core understanding of the technology. Yes, this book will be helpful but there are many online resources to help out as well, such as from training platforms like Lynda, Udacity, and Udemy. They provide hundreds of high-quality courses on many topics about AI.
To give a sense of what a corporate training program looks like, consider Adobe. Even though the company has incredibly talented engineers, there are still a large number who do not have a background in AI. Some of them may not have specialized in this in school or their work. Yet Adobe wanted to ensure that all the engineers had a solid grasp of the core principles of AI. To this end, the company has a six-month certification program, which trained 5,000 engineers in 2018. The goal is to unleash the data scientist in each engineer.
The program includes both online courses and in-person sessions, which not only cover technical topics but also areas like strategy and even ethics. Adobe also provides help from senior computer scientists to assist students to master the topics.
Next, early on in the implementation process, it’s essential to think about the potential risks. Perhaps one of the most threatening is bias since it can easily seep into an AI model.
An example of this is Amazon.com, which shut down its AI-powered recruiting software in 2017. The main issue was that it was biased for hiring males. Interestingly enough, this was a classic case of a training problem for the model. Consider that a majority of the resume submissions were from men—so the data was skewed. Amazon.com even tried to tweak the model, but still the results were far from being gender neutral.4
In this case, the issue was not just about making decisions that were based on faulty premises. Amazon.com was also probably exposing itself to potential legal liability, such as with discrimination claims.
Given the tricky issues with AI, more companies are putting together ethics boards. But even this can be fraught with problems. Hey, what may be ethical for one person may not be a big deal for someone else, right? Definitely.
For example, Google closed down its own ethics board in about a week of its launch. It appears the main reason was the backlash that came from including a member from the Heritage Foundation, which is a conservative think tank.5
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