Investing in and implementing an AI system isn’t as simple as pressing a button. AI systems require a heavy amount of training and customization to ensure that they perform up to the standard set by human employees, follow strict business processes, and improve continuously. As a result, determining your return on investment (ROI) in the short term can be quite tricky. We’ve written in the past about the costs associated with getting an AI system up and running. In short, you’ll be spending money on the solution, the people building the solution, and any tweaks needed to make the solution run perfectly, but the solution won’t be making any money for your business until it’s ready for prime time. This process could be as short as several weeks, or as long as three months, depending on what you’re planning to accomplish with AI.

Let’s review how you can chart ROI with your AI system at years one, three and five of an implementation.

The First Year of an AI Implementation

However, once the system is up and running (past testing and piloting stages), you should expect to begin saving money immediately by having the system handle simple and repetitive processes that were once handled by humans. For example: If you run a bank, your AI system should be able to verify accounts, state deposit balances, and even issue new credit and debit cards without human intervention. These automated processes are massive time-savers that allow your business to allocate human labor to more complex and creative work.

Hopefully, when human employees focus on other tasks, they can figure out new ways to help generate revenue. Whether this happens because employees are able to spend more time with customers, or because they’re able to move to sales-oriented roles, or because they’re able to help dream up new products and services, your employees can now dedicate time to improving the business rather than solving simple, repetitive issues.

The Third Year: New Implementations for Additional Services

If your first two years of AI adoption have been as fruitful as they’ve been for our customers, you’ll want to start thinking about new processes to automate. If your AI system has been focused on customer service issues, maybe it’s time to expand to fraud prevention. If it’s been focused on fraud, maybe it’s time to let your AI system start selling and recommending products.

You’ll have a digital colleague who is available 24/7, 365, on any channel, ready to provide help to customers for almost any issue.

The first two years of AI adoption are generally focused on experimenting and proving that AI can provide massive value for your organization. Now that you’ve done that, you should look to areas where AI can generate money, or prevent you from having to spend it. In the case of fraud prevention, AI can thwart phishing and denial-of-service attacks without manual intervention. These types of attacks could cost your company millions of dollars. Let AI help you make sure you’re never a victim. In the case of direct selling, you’ve already let AI interface with customers, and during many of these conversations your customers have expressed frustration or satisfaction with products. Those would be ideal moments for AI to make product recommendations for upselling and cross-selling.

Year Five: End-to-End AI Implementation

Now that your AI system is selling products, and preventing fraud, and slowly making its way through the architecture of your business, you should consider implementing AI end-to-end, across every customer touchpoint, and even as an internal tool to help your employees access and log information more quickly.

Such a goal requires integrating AI with every front and back-end tool you use to do business. This integration allows the AI system to learn everything it needs to know about your processes, your customers, and whatever it may need to collaborate with customers and employees. This is a time-consuming process that will require a lot of building, as well as trial and error, but once everything is finalized, you’ll have a digital colleague who can welcome customers to your website, guide them through the correct automated interactions, or escalate issues to the correct human agents. On the back-end, you’ll have a digital colleague who can retrieve data for employees at a faster rate that someone searching through Excel docs; you’ll have a digital colleague who can pull up historical customer interaction data to bring context to human-to-human service calls; and you’ll have a digital colleague who is available 24/7, 365, on any channel, ready to provide help to customers for almost any issue.

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