It’s imperative to consider how you will achieve and measure the ROI of your AI investments. If ROI pathways are not clear from the beginning, it can be difficult to determine whether your project is worth the time and money you’re investing in it, or if you should shift focus to another area of your business instead. While countless companies across the globe are investing billions of dollars into AI projects, how to achieve ROI for AI products and services isn’t always clear. Whether you work in one of these organizations or are simply interested in learning more about the business value and commercialization of artificial intelligence, here is a comprehshive guide to achieve ROI for AI projects.
Part I: ROI Fundamentals
What is ROI?
Return on investment (ROI) is a measure of how much profit a company earns relative to its total investment. Before diving into calculations, it’s important to note that there are two different ways to interpret ROI calculations for AI projects: hard ROI and soft ROI.
The hard approach is purely financial. It involves calculating whether or not an AI project will produce any cash flow over time, but it doesn’t take into account any non-monetary benefits—like smarter customer service agents or more accurate predictions—that come from AI projects.
By contrast, soft ROI calculation takes into account factors like these by measuring how they affect people’s perception of your business. For example, if you implement AI technology in your marketing functions and provide better service as a result, soft ROI might be measured by asking customers about their satisfaction with their experience.
In general, hard ROI is easier to calculate than soft ROI because you can tie tangible numbers to monetary outcomes like sales volume and operating costs. But it can be harder to quantify intangible factors such as employee morale or brand recognition via ROI estimation.
How to Calculate ROI for beginners?
Let’s talk about two key ROI calculations that affect all types of technology projects, including artificial intelligence: payback period and annual percentage rate (APR). These metrics give you an idea of how long it will take you to see results from a particular project as well as just how much those results will be worth once they do arrive. Calculating these figures also gives you valuable information about whether or not any given project makes sense financially—which is critical if you want your company to succeed.
The first step when calculating ROI for any type of project is to determine its payback period. The formula looks like this:
Payback Period = Initial Investment / Annualized Savings
Payback periods are typically measured in years, but they can vary depending on factors like initial investment and interest rates. For example, if you invest $5 million into a new software system that saves your business $1 million per year, then your payback period would be 5 years ($5 million divided by $1 million). Once you know how long it will take to recoup your initial investment costs, then you can move on to calculating APR.
APR is similar to a car loan’s interest rate, except it applies to any form of capital expenditure. It’s calculated using the following formula:
APR = [Annualized Savings] x 100 / [Total Cost]
In our previous example, our payback period was five years; therefore, our APR would be 20% ([$1 million annual savings] x 100 / [$5 million total cost]). If you’re looking at multiple investments with different payback periods and APRs, then you’ll need to calculate their net present value (NPV) instead. This helps businesses compare investments with varying time horizons and returns so they can figure out which ones make the most financial sense. NPVs are calculated using the following formula:
NPV = [Present Value of Annual Cash Flows] - [Initial Investment]
To get a better understanding of NPVs, let’s look at another example. Say you have three options for investing money:
- Option 1 – Invest $100,000 today and receive $10,000 annually for 10 years
- Option 2 – Invest $200,000 today and receive $20,000 annually for 5 years
- Option 3 – Invest nothing today and receive nothing now
You can use an NPV calculator to find out which option provides you with more value. Here’s what each option looks like through an NPV lens: So while Option 1 might seem like a good deal at first glance, it actually provides less value than Options 2 and 3 because of its longer payback period.
ROI Time Dimension
Other things to keep in mind for ROI estimates include, ensuring that your time frame matches up with the expected value of revenue generation; quantifying results based on hard numbers (e.g., cost savings, increased sales); considering risk/reward ratios; running scenarios against different time frames; building as much flexibility as possible into your business model; and so forth.
In particular, you need to define exactly what constitutes a successful project. For example, does success mean achieving your key performance indicators (KPIs) within a set time frame? Or is it simply achieving those KPIs? This distinction is critical because one metric may be easier to achieve than another.
For example, if your goal is reaching $1 million in annual recurring revenue within two years then that might be considered an easier target than generating $10 million in net profit over five years. While both are certainly ambitious goals, one would require more upfront investment for marketing purposes while also relying heavily on customer retention rates throughout those two years. The other has a lower initial outlay but relies more heavily on positive word-of-mouth recommendations to grow organically over time. There’s no right answer here—it just depends on your situation.
Next, you should decide whether there will be any additional costs associated with running your project after its completion. In other words, will there be any ongoing support costs associated with maintaining and updating your product or service? If so, you should factor those costs into your calculation as well. This is extremely critical for AI projects which do not have a deploy-and-forget approach, but rather follow an active product or service management.
Part II: Step by step guide to achieving ROI for AI
Define Your AI Project Goal
Before you even think about commercializing your AI initiatives with expected returns, you need to know exactly what your objective is. The problem with most AI projects is that there’s not a clear goal or end product in mind. Do you just want to deploy a data science software stack? Are you aiming to enhance an existing AI model’s performance? Or do you want something more specific like AI models that can classify items or events and trigger a certain automation chain? It’s all about knowing what you want out of it first. That way, when it comes time to determine its business value, you’ll have a concrete idea of whether or not it helped increase profits or customer engagement.
Once you define your AI project’s goal, then come up with some metrics around how to measure its success. Is it increasing sales? Improving customer retention rates? Finding ways to cut costs? All three of those goals are very different and will require their own unique measures of success.
A few other perspectives to consider are, how much work is it going to take? What are my team’s strengths and weaknesses? Are we capable of pulling off a machine learning product or service in a reasonable amount of time and with reasonable funds? You may also want to consider using lean startup principles.
One such principle is customer development — essentially, talking to potential customers before you move forward. Customer development can help you understand whether there’s actually a market for your product and how much it might cost to acquire users, etc. If you don’t have early adopters willing to pay for what you’re offering yet, then maybe now isn’t the right time to build out your AI business.
Link Investments to Business Outcomes
Artificial intelligence investments will likely prove unsuccessful if they aren’t linked to business outcomes. That means AI projects should ultimately improve some aspect of your business model. Ideally, you should be able to show how a project improved cost efficiency, customer satisfaction, or brand perception, among others.
Artificial intelligence efforts that focus on making processes more efficient are often easier to quantify — in part because they can reduce costs and increase profitability (which can lead directly back to ROI). Nonetheless, it’s still possible to find ways of measuring improvements in other areas like customer satisfaction and brand perception.
Measuring employee productivity is also possible with AI; however, there are additional issues here since it’s challenging for companies at scale in real-time. At the very least, AI may help managers make better decisions about staffing levels and resource allocation — which could potentially have an impact on overall productivity. In any case, it’s important to consider all potential business impacts when calculating ROI from AI investments.
Get Everyone on Board (Up-and-Comers and Executives)
Most organizations have made AI investments — often sizable ones — but even more so, many don’t actually realize it. Given that every person has a different definition of what AI is and isn’t, companies can run into a lack of executive support. If you want to maximize your company’s AI ROI, be sure your colleagues understand exactly how they fit into your plans and expectations.
It doesn’t hurt to lay out what their role will be in achieving company-wide AI goals in order to help maintain buy-in across departments. This also helps set realistic expectations about AI performance and avoid costly surprises down the line. Don’t forget: AI investment success depends on everyone being on board.
Build vs Buy Decision for AI
There are two main factors to consider when trying to decide whether or not you should invest in an off-the-shelf solution, such as a service, or develop a custom-made AI solution instead.
The first factor is how quickly you need to get results, and what your timeline looks like. Building a custom AI solution will typically require more time before showing any ROI than going with an off-the-shelf option, but you may also be able to save money by building yourself rather than paying someone else.
The second factor is determining how much customization your specific project requires. If your requirements are very specific and only match those offered by one off-the-shelf solution, then it’s probably worth it to go with that option instead of building something from scratch. In order to make sure your AI projects bring real value to your business, make sure you can answer these questions as a start, among other profitable AI investment steps:
- What do I want my AI systems to do?
- How much do I have invested in AI?
- What’s my expected return on investment?
- What’s my ROI timeline?
- Is there a commercialized product available for my needs?
- Can I tweak that product for maximum benefit for me and my business?
- Do I know enough about machine learning models to build myself or use prebuilt solutions?
Identifying Qualitative Factors
If you’re using an AI system to solve a business problem, there are a number of qualitative factors you can use to measure success. These factors include how much easier it is for your team to identify and fix issues, how many customers have reported better customer service experiences, and your ability to scale your business faster or with fewer resources.
Qualitative indicators aren’t as measurable as quantitative ones; they’re more subjective and harder to analyze (particularly if you don’t keep data on those metrics), but at least getting them on paper will help determine what steps are necessary for achieving them.
For example, if one of your goals is to improve customer satisfaction by 10%, then you should set up surveys that ask questions like how likely are you to recommend our product? and how likely would you be to buy from us again? You could also try surveying some customers before implementing AI and others after implementing AI. This way, you can see whether or not their answers change over time—and hopefully in a positive direction!
Quantitative Factors that Affect ROI
The biggest differentiators between AI projects are measurable quantitative factors. It’s important to establish how valuable your ai project will be to both your business and customers as soon as possible, which is why we’ve put together a list of measurable data that affect AI return on investment. The earlier you know these key factors, the sooner you can start thinking about how you’ll use them for ROI estimates—and ultimately get it closer to one than zero. Here’s what to consider when doing AI ROI calculations:
1. Cost of AI Project – How much does your AI initiative cost? This should include both initial cost and run budgets associated with developing, testing, deploying, and maintaining an AI product or service over its lifetime. If you’re not sure how much something will cost, ask someone who knows, be it internal or external support. If you have several different models for calculating cost then choose the model that results in a higher number for calculating ROI. (Note: You might also want to look at How to Calculate ROI for beginners? for more information.)
2. Benefits of AI Project – How do you plan to measure benefits? Are there any quantifiable metrics related to customer satisfaction or other qualitative factors? Again, if you don’t know what these are yet, it’s okay—just write down whatever comes to mind. It’s important to think about these things as soon as possible so that when it comes time to calculate ROI, you’ll be able to get more accurate numbers by using actual data instead of estimates.
3. Time frame – How long do you expect your AI project to take from start to finish? For example, if you’re building a chatbot and it takes 6 months from the start date until the launch date, then 6 months would be your time frame.
4. Costs after AI Project – What additional costs will there be after you’ve launched your AI system? These could include ongoing maintenance fees, upgrades, support calls, etc. The longer your project lasts and/or more people use it, the higher these costs can potentially become.
5. Additional Factors – There are many other factors that affect AI return on investment but aren’t necessarily measurable quantitative factors such as risks involved with bringing an AI product or service to market and business strategy decisions like whether or not to commercialize your AI technology before measuring ROI. We recommend writing them down as well so that you’ll have everything in one place when it comes time to figure out your ROI.
Make Sure You Track All the Right Data
Beyond the identification of qualitative and quantitative factors, specific monitoring metrics to track ROI from AI projects include: how much was spent on AI initiatives; how many hours were saved by the AI system; how much revenue was generated through AI; and whether there were any unexpected costs associated with implementing AI systems. You can also track things like overall productivity levels at work or customer satisfaction scores if applicable.
The more mature your data management and monitoring framework for your artificial intelligence and data science projects, the easier it will be to determine if an AI investment is paying off or not. Once you’ve got all of your data tracked, ask yourself three questions: Have I enhanced customer experience and satisfaction? Is my company making money? Is my company saving time? If yes, then keep doing what you’re doing! If not, then consider altering some aspect of your AI strategy before moving forward.
For example, maybe you need to rethink which type of AI solution would actually benefit your business. Or perhaps there are some unnecessary costs getting in the way of generating a return on investment. Either way, asking these questions should give you clarity about where to go next with AI and increase its chances for success.
Note that different stages of the AI product lifecycle should be mapped to the varying depths of metrics. However, while it’s not uncommon to see organizations define AI success by a number of factors, all too often their metrics rely on soft data. For example, team members might consider an AI program successful if they can prove it increased customer satisfaction by 5%.
While a balance is a must, companies should proactively close the loop by asking questions that are based on hard data and performance indicators — such as how many new customers have we won? or are our average sales increasing month over month? To achieve positive ROI for AI projects, make sure you’re focusing on these types of commercial AI metrics.
Measuring AI ROI: 6 key steps
While it’s important to know how customers are engaging with your AI product or service, it’s even more critical to know where they dedicate their time and investments. For example, if you can prove that a pilot customer from a specific product campaign will purchase your product (you do have proof of conversion), then knowing what processes, messaging content, etc. drove those people to your site is worthwhile. Or if you get a lot of returning users from a particular piece of content on your site, investing in more such content makes sense.
This data enables you to commercialize AI by using performance indicators as a proxy for broader business goals, instead of making business decisions directly based on AI metrics. Here are six ways to help you achieve AI ROI:
Attributable Outcomes: When an AI system takes an action, you need to be able to attribute that action back to a human who made it happen. If AI drives sales or leads but doesn’t drive revenue or profit, there’s no way to tell whether AI is effective at driving these outcomes — because humans aren’t taking responsibility for its actions. You need systems in place so that when AI does something meaningful — like driving sales — you can attribute that outcome back to a human who made it happen. Otherwise, you’ll never be able to show how much value your AI investment brings to your organization.
Use AI As A Complement To Human Decision Making: It’s easy to fall into a trap of relying too heavily on AI decision-making. The reality is that artificial intelligence should complement human decision-making, not replace it. Humans make better decisions than machines most of the time.
Use Performance Indicators As A Proxy For Business Goals: Instead of solely relying on direct technical outputs, use performance indicators as proxies for business goals — and align them with actual business results whenever possible.
Measure ROI Over Time And Across Teams: By definition, long-term success requires looking beyond short-term successes or failures — which means tracking AI over time to see how it performs over extended periods in different environments with different teams working with it.
Look At AI Through The Lens Of Your Customers: AI has been described as black box technology, meaning it’s hard to understand why AI algorithms take certain actions — especially since many companies don’t share why they’re building AI in the first place. But customers don’t care about all that; they just want answers to their questions. Make sure you’re building AI that provides useful and transparent answers to customer questions or challenges — and keep asking yourself whether your current approach is helping or hurting customer experience along the way.
Get Help From An Expert: Building a successful AI program requires significant expertise across multiple areas of expertise including technical skills, marketing skills, business acumen, and more. Don’t try doing everything yourself; depending on your maturity level, hire experts who can help guide you through each step of the process.
Concluding remarks
Companies investing in artificial intelligence should be looking at balancing both efficiency (ROI inputs) and innovation to generate business value and gain a competitive edge. Those that attempt to innovate without first focusing on efficiency are often doomed from their start. For example, your business might think it’s a good idea to invest in artificial intelligence just because other companies are doing so. However, companies that focus on innovating AI solutions without looking at cost-effectiveness will almost always run into trouble in terms of funding and ROI.
A successful AI investment is one that brings value while also acting as a revenue generator — even limited at the start. This is far more sustainable than seeing an initial burst of higher profits followed by years of stagnation (or worse). On the flip side, those who simply focus on efficiency may end up stifling AI potential by making decisions based solely on top-line or bottom-line growth.
How do companies make ROI for AI initiatives though? What makes the high-tech giants really successful with AI? We could say that they are just trying different technologies or that they possess unique talent and assets; but in reality, they all know what they want and they use new technology in order to achieve their goals. It’s not enough just to start applying AI technologies without knowing exactly what you want.
If your goal is commercializing AI implementations, you need to tap into the benchmarks and strategies used by already established players and transform the blueprint to your specific industry. When it comes to the return on investment, there are several factors and best practices to consider, as shared in this article. If you keep these tips in mind, you’re sure to see an uptick in ROI for your artificial intelligence projects.
As always, be sure to consult with financial and AI experts before making any decisions – they can help ensure you’re making the best choices for your company’s success. And don’t forget to let us know what other tips you would add to this list in the comments below.
