What AI can really do for your business (and what it can’t)

Artificial intelligence, machine learning, and deep learning are no silver bullets. A CIO explains what every business should know before investing in AI

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Start your AI program with fundamental business goals

The hype around AI and machine learning is driving some technology and business leaders to dive into a technology-first strategy. If you are starting your journey by experimenting with machine learning libraries or courting vendors that are beating the AI drums, you’re missing some key starting steps.

Instead, start by looking at business problems and opportunities with significant upside to offset the costs for research and development. These opportunities should be backed by very large data sets that you already have or that you can acquire and data integrate in easily. Draw inspiration from what other industries where other nontech companies are demonstrating success.

One reason to start with a defined business opportunity is that you might find solutions that don’t require the latest AI technologies. If some form of AI is required, this defined-business-opportunity approach lets you classify the type of solutions and evaluate the overall maturity of the AI required.

For example, if you’re trying to automate a highly manual business process involving visual inspection of parts coming off an assembly line, you might identify that a combination of image recognition and robotic process automation as part of the solution set. Both are more mature AI areas, as evidenced by the variety of successes and vendor solutions in this area.

On the other hand, if the solution requires significant cognitive evaluation and thinking, you’re heading into an immature AI space.

One way to gauge AI maturity is look at the various vendor landscapes published on AI startups, such as the ones from Venture Scanner, the current state of machine intelligence, O’Reilly’s bot landscape, and the AI fintech landscape. Reviewing these lists, you’ll see that many startups have focused solutions around discrete problem sets rather than generalized cognitive solutions.

Don’t be fooled when a vendor says something like, “Just throw your data at our AI” and expect expert intelligence to be returned. It won’t happen.

To truly work, your AI will need lots of data

That brings up the second prerequisite for running AI successfully: You need large amounts of relatively clean data to train AI solutions and evaluate outputs.

One reason autonomous vehicles are possible is the 4,000 GB generated from one hour of driving generated from lidar and other sensors found in these cars. That’s a lot of data being used to make what are really just a handful of fundamental decisions on whether the car should turn, speed up, slow down, or fully stop.

Many successful AI solutions fall into this same category of churning large amounts of data into a finite number of decisions. In image recognition, for example, am I looking at a picture that contains you in it, or not? In collaborative filtering, is a newly published article more relevant to you based on your past reading experiences versus other reading options? When evaluating a transaction, does it have similar patterns to fraudulent transactions?

The AI “inside the box” is trying to approximate a curve to make these decisions. In deep learning, for example, the number of layers and neurons in the network can approximate highly complex curves to differentiate outcomes. To develop this network, you need a large, tagged data set so the network can be trained by comparing its computed results against your tagged result with the desired outcome. The errors are then used to tune the network using backpropagation or other learning algorithms, and the exercise is repeated multiple times across all tagged data until the network stabilizes to an optimized curve. These are supervised learning solutions, developed using a training set.

If the data isn’t tagged, networks can use unsupervised learning approaches that rely on entropy expressions that evaluate the outcome. For example, when Google’s DeepMind was used to learn to play the Atari game Breakout, it used the score to evaluate outcomes.

Going beyond data sets, your organization needs a data integration and automation capability so you can move data into and out of any AI processing engine. If your organization is used to having people run scripts manually to push data around, I strongly suggest first investing in automation before diving into AI solutions.

Your options for experimenting with AI

Once you have business opportunities identified and large, cleansed data sets available, you are ready to consider an AI journey. Those two steps are prerequisites for preparing your organization for artificial intelligence. The main next steps are to consider the type of AI solution and implementation. If you have the talent, you can experiment with TensorFlow or one of the other AI engines. If you don’t have the expertise, think twice about trying to recruit for it; the tech giants are paying huge salaries for scarce AI talent, and so the costs are enormous to just get into the game.

A second option is to use vendors that have embedded AI in their solutions. One example is Salesforce Einstein, an AI platform that can perform forecasting and other functions on top of CRM data stored in Salesforce. Likewise, you can look at industry-specific solutions, such as Synechron’s Neo for financial tech (fintech).

Once you settle on one or more approaches, it’s important to set realistic expectations with stakeholders. Investing in AI requires a commitment to agile experimentation because you’re likely to encounter many dead ends and experiments that require many runs before they are optimized. Set those expectations for budget, timeframe, and talent upfront.

Copyright © 2017 IDG Communications, Inc.

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