Measuring ROI in AI: Finding Value that Isn’t Financial

Alex Thompson

May 20, 2022

It’s crucial to consider your return on investment in artificial intelligence endeavors. When you know your potential ROI, you can plan and customize your production plan approach based on what you want to get out of the deployment. The ROI of any AI project will determine where you allocate your resources and invest your time.

You must note that AI systems require plenty of experimentation, and calculating ROI requires more than an all-or-nothing approach. Plenty of estimates come into play, differing according to industry, making a return on investment analysis essential early on.

Business leaders can justify some AI use cases by studying noticeable potential gains, but other cases will need more to determine worth. Intelligent prioritization means putting high-value products first, but you have to decide what that means to your company.

Correctly Measuring the ROI of AI

It’s only natural that business leaders have begun to look to capitalize on AI opportunities. Still, predicting future returns can be challenging, as well as determining which part of your business the investment should target. Business owners have to understand which AI capabilities can enable better business performance overall before they attempt to measure the true ROI of AI systems.

There are ways to measure ROI without only limiting the process to financial returns. There are varying ways for business leaders to think about success regarding AI projects, and they’re not as hard to implement as one might think. So yes, while financial gains in utilizing AI are essential, plenty of other factors make AI well worth the investment of time and money.

Assessing the Future Value of AI Systems

Artificial intelligence is all about the future, including assessing the future value of the AI systems we implement today. When business leaders think about what AI can do for business, it’s usually highly well-marketed instances that stem from very well-defined pieces of the sector, such as the world’s best chess player losing to an AI program.

However, it’s important to note that while AI can solve a problem like chess, it’s because the game has a distinct endpoint. Unfortunately, most issues that pop up in business and Fortune 500 companies do not have a definite measurable outcome. So, you can see where the primary circulating examples of AI and what it can do for your company could be very different.

Most businesses face real-life problems, such as successful product launches and improving customer experience. In short, the topics are sketchy and, at the very least, complex, with the potential for various undefined outcomes. The challenge comes in gauging the ROI on an acquisition when the result of that investment in itself is unclear.

If business leaders don’t understand the core of the business problem that they want to solve, then it’s impossible to determine ROI from a perspective that isn’t financial. The framing of your problem is essential, as there are open-ended issues where AI and ML were not previously in use.

To eradicate questions involving your ROI for AI, you have to pinpoint exactly what your business question entails. Knowing if AI can positively add to your solution is the first step in determining if it’s worth your time.

Scaling AI-Related Problem Solving

Companies of all sizes focus on solving problems on a scale that will impact that functional area (such as development or operations) as well as the business overall. To gain deeper insight into what you want AI to solve, you must frame and reframe the issue at hand, and it’s a nonnegotiable prerequisite to determining your ROI in AI.

You’ve got to pinpoint whether your problem is inefficiency or an improved customer journey. What do you hope to solve or gain by employing artificial intelligence in your company systems and applications?

Problem-solving on a scare contains three solutions after you’ve efficiently framed the issue at hand.

When scaling your AI-related problem solving, keep in mind that every decision your business makes will impact a human in one way or another. For these three problem-solving elements to come together, solving problems at scale, you need to establish improved sophistication within your algorithm and engineering and embrace a better overall understanding of human behavior.

Once you’ve made an effort to take these steps, you’ll have a better idea of what AI can do for you. At this point, you’ve probably noticed that artificial intelligence can’t work for you if you don’t put the research and effort in first. Lack of preparation is why so many businesses fail at the correct utilization of AI and never see a return on their investment

Finding AI Success

Finding the success you want for your company with AI depends on several factors. First, you have to understand that there isn’t one way to get everything right. The use of AI comes with testing, learning, experimenting, and failing. However, business leaders must also pick up what they’ve learned from past failures and understand that those lessons will be important in the near future.

For example, it’s not unheard of to execute 30 to 40 different AI initiatives in a six to eight-week time to show progress. When you focus on working through various AI solutions in a relatively short amount of time, your company will quickly define problems within the software and execution and determine progress and potential future success.

From these 30 to 40 choices, you could come away with four or five that you work into your company at scale. It’s a distinct process of elimination.

AI Requires a Thirst for Innovation

In general, AI and digital modernization require a thirst for innovation and a desire to make your company operations better, more manageable, and provide improved outcomes for your business, employees, and consumers. Your ROI on your AI endeavors comes from an initiative for success and the drive for teams from various areas of expertise to work together.

AI projects succeed when the approach comes from a collaborative framework, and an agile work mode typically yields better outcomes. Also, documenting and compiling past results increases the probability of success, and AI helps businesses become less linear.

The approach to business that will probably always prevail over human intelligence and futuristic machine algorithms is the combination of humans and machines. The value of the success of your AI initiative comes from realizing that AI asks for many business aspects to come together to improve customer experience. If you’re achieving this, can you justify that as an overall improvement on your ROI?

Looking at return on investment has to be cognitive in a way that we look at financial gains from implementing modern software and when the moving parts of a company come together to add value. Measuring ROI in any artificial intelligence journey should focus on how the opportunity affects your business, and financial ROI is only one part of a much more intricate story.

Improving Your ROI from AI

Staying dedicated to digitalization and automation is part of the ROI puzzle. Your business depends on it, and it’s crucial never to stop looking for solutions. You can commit yourself to maximize your efficiency and ROI while focusing on the areas of your business where ROI makes a difference. Regardless of your ROI focus, you’ll always want to be able to demonstrate your success areas.

ROI from Artificial Intelligence

The correct implementation of AI takes plenty of work and a lot of trial and error, and it’s a risk for almost any company, no matter how established. If you concentrate on how AI assists your company in moving forward, you’ll find that those financial gains will also come, and you’ll cast yourself far above your competition. Find the value your AI brings, and place your focus on every area that shows improvement.


Alex Thompson

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