Does Your Business Need an AI Strategy?

Building an AI strategy for your business

Artificial intelligence is projected to contribute $15.7 trillion to the global economy by 2030, a statistic that reinforces the fundamental role AI is already playing in business. Shifts to AI-powered services are changing how consumers discover and interact with businesses as well — and it’s easy to see why leveraging the benefits of AI is top of mind for many brands.

But the term AI strategy is woefully unspecific, and it can be difficult to determine the concrete benefits to key business objectives. Calls to “Invest in AI!” speak more to the prevalence of a buzzword than to the individual technologies powered by artificial intelligence.

So does your business need an AI strategy? Here are three fundamental benefits of the AI revolution to consider when deciding how — and if — to make AI work for your business.

Boost efficiency by automating workflows.

Industries like financial services, healthcare, and logistics have much to gain from AI implementations. Why? Because some of the most robust applications of AI and machine learning have to do with 1. automating complicated workflows and 2. detecting fraud.

AI process automation tools can simplify employee workflows, and while this may not be worth the investment for small businesses, enterprises can see a significant benefit in investing upfront in order to redirect their frontline employees’ time elsewhere.

Take a hiring example, for instance: Large corporate job openings may attract 250+ resumés a piece, and sorting through these applications requires valuable time from HR employees and department heads. AI-powered tools can help.

“Software quickly sifts through thousands of job applications and can shortlist candidates who have the credentials that are most likely to achieve success at the company,” according to the Harvard Business Review. “Care must be taken not to reinforce any human biases implicit in prior hiring. But software can also combat human bias by automatically flagging biased language in job descriptions, and by detecting highly qualified candidates who might have been overlooked because they didn’t fit traditional expectations.”

Similar examples abound in cases of corporate approvals, logistics planning, and more.

Reducing instances of fraud is another valuable way in which AI tools can boost efficiency. In terms of detecting payment fraud, as FICO states in a blog post, AI and machine learning can be incredibly useful in helping data scientists efficiently determine over time which transactions are most likely to be fraudulent — while reducing false positives for fraudulent activity. These techniques are extremely effective in fraud prevention and detection, as they allow for the automated discovery of patterns across large volumes of transactions.

Companies like IBM, Altoros Labs, and many more provide solutions aimed at helping businesses improve their fraud detection and build greater transparency around AI models — tactics that businesses operating in the financial sector (or simply processing a vast number of online payments) should certainly consider.

Deal with vast reserves of customer data.

While brands have proclaimed data as the “holy grail” for years, it’s only valuable when it helps you take an action that benefits your business — like attracting new customers, or retaining existing ones by effectively targeting them with relevant content that attracts their interest.

Customer data is difficult to sort through and analyze when it comes to processing the behaviors and engagement of thousands — or millions — of people. This is a challenge for mid-market and enterprise businesses across verticals, who may have billions of data points about customer store visits, online engagements, and more, but who suspect it could be leveraged more effectively. This is a point at which embracing AI or machine learning technology can help you make better use of data to reach and retain your customers.

When companies can effectively mine customer actions and online engagements for preferences and sentiment, they can make major strides in retention and loyalty. If your business has many customers and vast reserves of data but is struggling with losing customers to the competition, an AI strategy aimed at identifying and predicting which customers are most likely to leave might be a smart investment.

For example, if a national gym chain were to use AI-powered tools to analyze a massive data set, it might determine something like this: Perhaps customers who work out less than twice a week are more likely to let their membership lapse. If the gym then systematically targets these customers with offers for a free personal training session or an invitation to a special event, they may succeed at bumping up their workout frequency — and retaining them as a customer for longer on average. These kinds of use cases are where AI tools can really shine, making it so that employees don’t have to spend countless hours mining and analyzing data sets.

Meet your customers where they are.

Not every business has enough employees, or processes enough transactions, to make automating workflows or fraud detection a top investment priority. But every business can and should think about how to meet their customers’ expectations as AI-powered consumer-facing services, like Amazon Alexa, change behaviors across the board.

Google’s Maria Wang-Faulkner stated at ONWARD18 that voice queries are 40x more action-oriented than traditional searches. Research whether or not your customers are making voice searches related to your business category. If they are, prioritizing discoverability in those searches is paramount. Independent of vertical, it’s a smart bet get your digital brand information ready for consumption by the AI-powered assistants, like Alexa and Siri, that consumers use every day.

Businesses should be thoughtful about when — and how — to invest in AI-powered technology. AI is changing the world, and it could change your business. But focusing on the benefit to your key objectives and your customers’ needs is the place that all decisions should come from. Dedicating resources to building an AI strategy won’t accomplish much if you don’t know exactly which problems you’re trying to solve.

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