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How to Map Out a Successful Artificial Intelligence Strategy Posted on : Feb 07 - 2019

A mortgage lender enables a new customer to onboard for a loan from his mobile device. An insurance customer submits and manages a claim via her smart device with swipes and clicks instead of manually inputting data. A transportation carrier manages all its invoices, bills of lading and customs documents digitally, transforming that data into a single pane of glass to optimize routes, inventory, schedules and closing out loads. All of these examples were the result of artificial intelligence (AI) projects that went right, said Anthony Macciola, chief innovation officer for ABBYY.

Content was digitized, and unstructured data was transformed into structured actionable information and automated into various business processes. Then, a level of learned intelligence was added. It all went off without a hitch, more or less.

But sometimes, maybe often, an AI project doesn’t deliver as originally conceptualized. Then you have angry customers whose apps are not working as they should. You have angry colleagues that were expecting results from your AI project and aren’t getting them. And you have an angry boss.

The best way to avoid all of that is to have a plan in place for the project from the very beginning. While this may seem obviously intuitive, in truth many AI projects start out without a discernible goal — the cornerstone of any plan. And even if there is a goal, too often there are misunderstandings about other aspects of a project. “Do you have a business problem you are trying to solve? Do you have a use case in mind? Can you articulate it clearly? [If not], you’re likely to struggle with project scope, metrics and any definition of success,” said Indico CEO Tom Wilde. For a plan to succeed make sure it has the following elements.

A Solid Use Case

A detailed use case is essential for an AI project. “Many organizations approach AI with the notion that it will tell them what the right answer is inside a large pool of data,” Wilde said. In reality, AI is great at discovering what maps to or matches an already defined desired state — for example, if it is shown what compliant contract language looks like, AI can then automate the process of identifying which contracts are compliant and which are not. “If you can’t define the desired state, don’t expect AI to do it for you,” Wilde said.

The Right Data

“The golden rule of AI is that 90 percent of the work in any AI project is in getting the data,” said Paul Brown, co-founder and CPO of Koverse. “Data is fuel for the algorithms, so you need all the necessary data in a timely manner in one place for processing.” Also remember, he added, the data technology must support requirements for performance, security and scale.

Another tip, from TIBCO’s chief analytics officer Michael O’Connell, is that big data or more data is not necessarily better. “There are two different types of data: data that represents the business problem and data that doesn’t. The former is essential.” View More