Ways to Ensure AI Credibility and Adoption

By Jyoti Nigania |Email | Apr 12, 2018 | 9483 Views

Artificial Intelligence and Machine Learning technologies have made impressive strides in recent years, and thanks to platforms such as cloud, AI and machine learning capabilities are now widely available to organizations of all types and sizes. But as any seasoned technology leader knows, having technology on the shelf doesn't mean it will get accepted or used. It could simply just end up staying on the shelf.
Recently caught up with Jack Berkowitz, vice president of products and data science for Oracle Adaptive Intelligence, whose job it is to make sure the technology doesn't end up as shelf ware, but is put to good use for the business. He says he sees AI and machine learning are now being embedded into a range of applications and functions, from supply chain and manufacturing applications to ERP, finance, procurement, human capital management, and customer experience applications for sales, service, marketing and commerce. The impact is nothing less than transformative, he adds, as some AI or machine learning driven applications now in use today are helping to unearth rich business insight and create greater efficiencies across the entire organization.
Driven by these hopes of transformation to the positive side, the pace of artificial intelligence and machine learning adoption is accelerating. "AI is possible now because of the plethora of data, sophisticated algorithms, and lightning fast computing power."

Gradually introduce features even small ones helpful to end users:  Berkowitz says his own products at Oracle, for example, are designed "to send users subtle cues slide outs and alerts to let them know of new AI features within the familiar user interface of the application." He predicts other vendors will follow suit.

Ensure the right data is being applied to the right business problem: Along with user comfort with AI, there's another concern that needs to be addressed having good, quality data from the right sources.  "The data that is needed for AI and machine learning is determined by the problem being solved," Berkowitz explains. "For example, if you're trying to identify customers to target a digital ad, then you can break this problem into different areas. First, how do I find out who are my most trusted customers, can I generate a customer importance score using their past purchases, lifetime value, or their activities on social networks? The next area is what data do i have about the campaign, such as metadata and target demographics. Once you have the data, then you can use techniques such as machine learning, deep learning and learning to rank to find and rank the customers most suitable for this ad campaign."

Make sure data is of the best quality, and is double checked: "The challenge starts with bringing in the fixed and transient data from various disparate sources into a common platform," says Berkowitz. "Not only do you need to bring in the data, but you need to make sure the data is clean, changes are synchronized, and records denormalized. Maintaining the data quality and having checks at the data ingestion stage is critical.

Test diligently: Exploratory data analysis and feature engineering is an important part of AI and machine learning success, Berkowitz says. These approaches play a major role in driving important key features and insight that are not available from the data in its raw form. Various steps of feature scaling and feature transformations need to be done before the data can be passed into the algorithms. One of the key things the model does is generalize the patterns from past data in order to predict patterns in future data points.

Source: Forbes