What Kind of ROI Can You Expect From an AI Project?
A cute robot is developing in a laptop. AI-generated content.
TL;DR: Most companies start to see ROI from AI projects within 6 to 18 months when everything is set up properly and focused on solving a specific business problem.
Summary
AI can deliver impressive returns, but not every project does. Some companies save money, work faster, or boost revenue. Others have a hard time seeing any real value. What makes the difference? It usually comes down to what you’re building, how prepared your team and systems are, and whether your project has clear, measurable goals.
What is the return on investment for AI projects? It’s a fair question and one many teams are asking right now. With all the hype around AI, it’s natural to wonder whether the time and money you’re investing will actually pay off. For some businesses, the payoff is real: faster operations, lower costs, and even new revenue streams. For others, not so much.
The truth is, there’s no single answer that applies to everyone. AI can drive serious results, but it also comes with risks, unknowns, and hidden costs that aren’t always obvious at the start. What you get from it often depends on the type of project you’re running and how prepared your team and infrastructure are.
In this article, we’ll explore what ROI means in the context of AI, the key factors that can make or break your project’s success, typical timelines to expect, real-world benchmarks and examples, and practical strategies to maximize the business value of your AI investment.
What Is ROI in AI, and Why Is Everyone Asking About It Now?
Return on investment (ROI) in AI is exactly what it sounds like. It’s about how much you gain from using artificial intelligence compared to what you spend. That return might come in different forms: reduced costs, faster workflows, or increased sales. It can also mean making smarter decisions that help the business grow.
According to the 2019 McKinsey Global Survey, as cited in a Primo Bonacina article, 63% of respondents said the parts of their business using AI were already seeing more revenue. High performers were nearly three times more likely than others to grow revenue by over 10% in those areas. Besides that, 44% of respondents reported cost savings.
However, not every AI project gives a clear payoff, though. That’s why more companies are starting to ask serious questions before they dive in. In fact, Rheo Data noted that failure rates for deploying AI are estimated to be between 70% and 80%.
Moreover, Nasuni’s The Era of Hybrid Cloud Storage 2025 report, highlighted in a Technology Magazine article, found that only 27% of AI initiatives deliver measurable results. This raises valid concerns about how consistent AI project ROI really is.
With the recent buzz around generative AI, custom chatbots, and large language models (LLMs), businesses are moving quickly to explore what AI can do for them. But with all the excitement comes growing pressure to justify the cost. Leaders want to know: Is AI really worth the investment?
7 Factors That Make or Break Your AI Project’s ROI
The ROI of AI projects can look different for every business. It’s not just about the tools; several factors have a significant impact on AI investment returns. Getting these elements right early on can be the difference between solid results and a stalled rollout.
1. What Kind of Project You’re Running
Not all use cases deliver the same type of return. Automating manual tasks often leads to quick wins, while forecasting and personalization usually take longer but can unlock greater value over time. The clearer the goal, the easier it is to measure success.
For example, Omega Healthcare used AI-powered automation from UiPath to handle medical billing and other administrative tasks. The result? They saved over 15,000 employee hours each month, cut document turnaround time in half, and hit 99.5% accuracy. These changes helped their clients realize about a 30% return on investment.
2. Tech Setup Problems
Getting AI tools to work with your current systems isn’t always easy. Old software, messy data, or missing APIs can cause delays that last weeks or even months. These technical problems often require extra time and money to fix, meaning they can shrink your early gains.
Several companies have faced this challenge. One of them is Wells Fargo. Hakuna Matata Tech noted that the agency invested in an AI chatbot meant to assist customers, but it failed to integrate with their 1970s-era mainframe banking systems. As a result, Wells Fargo lost $9 million.
3. Upfront Cost vs. Long-Term Payoff
You’ll likely spend heavily early on data, infrastructure, and development. That can add up fast. However, the return on investment for AI projects tends to improve the longer they run. This is especially true once the model starts improving and becomes integrated into daily operations.
A Fortune 500 financial services company once spent about $850,000 over 18 months to upgrade its trading system with AI. At first, the returns were modest, with about 23% ROI in the first six months. Most of that came from saving time and speeding up reviews. But as the AI tools became part of daily work, the returns grew quickly.
By month 18, ROI had climbed to 187%, and they expect it to reach around 340% over five years. This shows that while AI can take time to deliver, a large upfront investment can lead to strong long-term gains once the system is fully in place.
4. Data Quality
If your data is unorganized, AI won’t perform well. It might give the wrong answers or miss important patterns. That makes it hard to trust or act on the results. In fact, 91% of companies admit that poor-quality data directly affects performance. On average, businesses lose $12.9 million each year due to bad data.
Clean, well-structured data leads to smoother rollouts, fewer surprises, and stronger AI implementation results down the line. It’s one of the most important foundations for getting real value from AI.
5. Employee Pushback or Lack of Training
Adopting AI for business growth isn’t just about the tech. It also depends on the people who use it. If employees don’t understand how it works or worry about losing their jobs, they might resist using it.
Sometimes, the tools feel confusing or disrupt their usual routines. That’s why training is important. Giving your team time to learn and letting them ask questions can be very helpful. When your team feels confident, you’re more likely to see a good outcome.
One example is Rent a Mac’s case. The company launched an AI system without giving sufficient training or getting the team on board. This led to a seven-week delay and about $85,000 in lost savings. After changing their approach and involving the team, usage jumped from 31% to 89% in just three months.
6. No One Leading the Project
If no one on your team takes the lead, the AI project can lose momentum. You need someone who understands the business, drives the project forward, and ensures it stays on track. Without a clear leader, even useful advanced technology can end up sitting unused.
For instance, a large retail company spent about $680,000 over 18 months building 15 different AI pilot projects. The tools worked as intended, but no one took charge of integrating them into daily operations. Because there was no clear leader or team responsible for pushing them forward, and no executive support to back them, the projects never took off. In the end, they weren’t used at all, even though they had real potential.
7. Ongoing Costs and Maintenance
AI isn’t something you set up once and then leave alone forever. Over time, models can become less accurate, and systems may need updates as your data or business changes. Without regular checkups, your AI might stop working well or even cause issues. To keep your AI investment returns steady, it’s crucial to plan and budget for ongoing upkeep.
JPMorgan Chase used AI to help assess credit risk. But when the pandemic hit, people’s spending and borrowing habits changed rapidly. The AI models couldn’t keep up and began making mistakes, putting the company at risk of losing a lot of money.
But JPMorgan didn’t abandon the AI; instead, they focused on keeping it up to date. They tested its accuracy, added fresh data, and used backup models to compare the results. Even though these extra steps took more time and money, they helped protect the company’s ROI. In the end, they avoided losses, approved more good loans, and held on to more customers.
This example shows that the return on investment from AI isn’t just about building a smart system. It also requires ongoing care and support. For companies wondering, “Is AI worth the investment?”, the answer depends on whether they’re ready to maintain and improve it as they go. With the right attention, the results can continue to improve.
What the Numbers Say: AI ROI Benchmarks
How long does it take to see results from your AI project? That varies based on what you’re building, but many companies start seeing returns within 6 to 18 months. Projects that address a clear problem often show results faster.
For example, a tool that helps with customer support or takes care of simple tasks can start paying off in just a few months. In these cases, the benefits of AI for business include faster replies, fewer support tickets, and lower staffing costs.
More advanced tools, like those that detect equipment problems early or offer smart product recommendations, take more time to build. But if done well, they can become even more valuable as the AI learns and improves.
Surveys and studies back this up. According to The CFO, 78% of senior business leaders at large companies expect to see a return from generative AI within one to three years. The 2025 EY AI Pulse Survey found that 75% of 500 US senior leaders using AI for business growth reported positive ROI after just one year. These gains came from things like better efficiency, higher productivity, and improved customer satisfaction.
AI investment returns also vary depending on how you measure success. Some teams track time saved; others look at higher sales, happier customers, or getting more done with the same resources. What matters most is setting clear goals early so you can measure real progress.
Bottom line: the return on investment for AI projects isn’t always immediate, but it’s real. When your project solves an actual problem and has a solid plan, you’re much more likely to see strong results.
4 Examples of AI ROI in Real Business Use Cases
Not every AI project is the same, and neither is the return. Some companies save time, others cut costs, and some grow their sales. Here are a few real examples that show how the business value of AI appears across different industries and goals.
Customer Support Automation
Customer support automation uses AI to answer questions and solve problems quickly. It can reply to common requests through chat, email, or online help pages without needing a person each time. This helps customers get faster support and lets teams focus on the harder, more sensitive issues.
In Malaysia, a leading retail enterprise implemented an AI-powered chatbot for customer service. Within six months, the company saw great AI implementation results.
Customer engagement went up by 40% within six months, and common questions were handled 60% faster. This led to better customer satisfaction and stronger retention. The AI also took over more than 80% of routine admin tasks, helping the team focus on higher-value work.
As a result, customer service costs dropped by around 50% per year.
On the revenue side, smart suggestions helped drive a 25% increase in sales within a year. With faster support and better service, customer retention grew by 30%. All in all, the company saw more than a 200% ROI from the AI project. This shows how the right solution can bring positive outcomes when built around real business needs.
Predictive Maintenance in Manufacturing
Predictive maintenance uses AI to keep an eye on machines and catch problems early. Instead of waiting for something to break, companies can fix it before they cause trouble. This helps avoid delays, lowers repair costs, and makes machines last longer.
In fact, predictive maintenance is one of the clearest examples of AI ROI in manufacturing. A recent ITCart LinkedIn post reports that manufacturers using AI for predictive maintenance were able to reduce downtime by 30% to 50%. Companies also experienced lower maintenance costs and longer equipment life.
For example, GE cut unplanned downtime in its factories by 30%, which helped them work more efficiently. Airbus lowered maintenance costs by 30% by using AI to better plan when their planes needed service. Ford increased how long its machines lasted by 20% by catching small problems early. And Siemens made its factories safer, cutting machine-related accidents by 15%.
Personalized Recommendations in E‑commerce
Personalized recommendations use AI to suggest products each shopper is more likely to want. It looks at things like past purchases, browsing history, and what similar customers liked. This helps people find what they need faster and often leads to more sales for the store.
Sephora uses AI to personalize the shopping experience across its website, app, and physical stores. One standout feature is its Virtual Artist tool, which lets customers try on products digitally. The results speak for themselves: people who used the tool spent 25% more, and Sephora saw better conversion rates and more engaged shoppers. This is a strong example of the business value of AI and how personalization can drive real results.
Sephora also makes privacy a priority and closely follows data protection rules. This careful approach has paid off with happier customers and a clear boost in revenue. It shows that when done right, using AI can lead to a strong return on investment.
What happened to Walmart is also one of the best examples of AI ROI. In 2024, the company began using AI to personalize the shopping experience with helpful product suggestions, custom landing pages, and marketing messages tailored to each shopper. The impact? A 20% jump in sales. Customers responded well to the personalized content. This case highlights how tailored experiences can directly support AI for business growth.
Fraud Detection and Risk Reduction
AI is also helping companies stop fraud before it causes damage. It can quickly scan thousands of transactions, spot anything unusual, and block fake payments. This helps businesses stay safe, protects customers, and prevents losses that could affect profits.
A great example is the online ticket marketplace TickPick. It implemented an AI tool from the software company Riskified to stop fraud and avoid rejecting real customers. Before using this advanced tech, TickPick mistakenly declined many legitimate orders. They would often deal with high-value tickets such as $20,000 Super Bowl passes.
Riskified’s tool is called Adaptive Checkout. It uses AI to analyze orders in real time, automatically assessing the risk of each order. It considers factors like the customer’s shopping history, real location, billing address, and other data from the company’s global network of stores.
This is one of the most impressive AI implementation results in recent years. After adopting Adaptive Checkout in late 2024, the ticket marketplace can make “intelligent decisions” for each purchase. Within just three months, TickPick successfully reclaimed $3 million in revenue by approving orders that would have otherwise been falsely rejected.
How to Maximize the Business Value of AI
Using AI to achieve a return on investment takes more than luck. It starts with a solid plan. These four steps can help turn an AI experiment into something that delivers real, long-term results.
1. Start with a Clear Business Problem
Before choosing tools or building anything, make sure you know what problem you’re solving. Is it too many customer support requests? Missed sales? Slow manual tasks? When the problem is clear, it’s easier to track progress and demonstrate the business value of AI.
2. Test It First Before Going Big
Don’t start with a full rollout. Build a small version first, test it with real users, and use their feedback to improve it. A quick win builds confidence, reduces risk, and helps you make better choices when it’s time to scale.
3. Use Metrics That Matter
How will you know if the AI is helping? Look for results such as getting work done faster, making fewer mistakes, or boosting sales. These metrics help show the benefits of AI for business and make it easier to secure support for future projects.
4. Keep Improving — Returns Get Better Over Time
The ROI of AI projects doesn’t end after launch. As the AI works with more data, it improves. Keep updating it, adjusting your process, and adding what works. These small updates often lead to bigger returns later, like compounding interest. The sooner you start, the more you can gain.
Here’s a simple checklist to help you get better results and long-term ROI from your AI projects.
Tips
Why It Matters
Start with a real business problem
Ensures the AI is solving something valuable
Pilot before scaling
Reduces risk and fine-tunes the approach
Track meaningful metrics
Helps prove ROI and enables early course correction
Keep improving as time goes on
ROI grows as models learn and adapt
Final Thoughts: Is AI Worth the Investment?
If it’s set up properly, AI can definitely be worth it. It can help reduce costs, improve processes, and create new ways to grow. The return on investment for AI projects can be substantial, but getting there takes more than just adding a new tool. You need solid targets, clean data, and a team that’s willing to learn and adapt along the way.
The business value of AI builds over time, especially when it’s tied to real problems and tracked with the right metrics. Whether you want to improve how things run, provide customers with a better experience, or find new ways to make money, the best AI projects are long-term efforts, not quick fixes.
AI isn’t magic, but with a smart plan and steady focus, it can deliver real impact.
Looking to use AI for business growth? Rio Data helps turn data into results that matter. Contact us today to get started.