Across enterprises, the idea of having AI around is no longer a question of “if” but more of a “how?” and “when?”. As promising success stories ripple through every sector, leaders face the pressing question of: what is the real ROI on AI? Behind every pilot and deployment, executives need a clear answer to - will artificial intelligence actually pay off for us? And if so, how do you measure it?

This article explores the practical realities of ROI on AI for enterprises, decodes the basic formula that you need to know and illustrates how organizations from healthcare to finance are translating LLMs into true enterprise value.

Defining ROI on AI with a simple, universal formula

At its core, Return on Investment (ROI) is about one thing: what you get back for what you put in. Whether you’re investing in AI, new machinery or a marketing campaign, the fundamental question is the same: was it worth it?

The good news is that the ROI formula doesn’t need to be intimidating. In any business context, it’s simply:

ROI = (Gains from Investment (minus) Cost of Investment) (divided by) Cost of Investment

Expressed as a percentage, this formula is the clearest way to judge if an investment added value. For AI, the concept is the same:

• Gains from investment: These could be anything from new revenue streams, cost savings, process efficiencies, better customer experiences or risk reduction thanks to AI.

• Cost of investment: This includes everything you put into your AI initiative, from software, hardware, integrations, data collection, training and ongoing maintenance.

So, if an AI-powered chatbot saves your support team $500,000 in labor costs over a year, and the total cost of implementing and running it was $200,000, your ROI on AI is:

ROI = (Gain – Cost) / Cost
ROI = ($500,000 – $200,000) / $200,000 = 150%

or, as a percentage, 150% ROI on your AI investment.

This simple equation has profound implications for an enterprise. It cuts through the hype, giving you a clear method to decide if implementing Gen AI is right for your business.

Barriers to measuring true ROI on AI

Despite the straight forward formula, real-world ROI on AI sometimes proves elusive. Common challenges include:

• Undefined goals: Launching AI to tell your customers you have AI, without a needful business case or measurable target.

• Poor data quality: AI is only as good as the data it learns from. You must provide data that can be leveraged meaningfully.

• Siloed initiatives: If an AI project helps one department but creates costs elsewhere, the ROI on AI is overstated.

• Time-to-value aspect: Some AI benefits materialize over months or years, making up-front ROI estimates difficult but not impossible with careful planning.

Why you should calculate ROI on AI investments?

In the early days of AI adoption, the urge to “do something with AI” was almost universal. Today, business leaders face pressure to justify those investments and scale what works. Understanding your ROI on AI moves the conversation from theory to practical application:

• Prioritization: With dozens of possible AI use cases, which should get funding first? Calculating potential ROI on AI quickly separates high-impact ideas from distractions.

• Accountability: AI is not magic but an investment just like any other. Clear ROI metrics create transparency and foster better oversight.

• Scalability: Once companies prove positive ROI on one use case, they can confidently replicate and expand AI initiatives throughout the organization.

When measured properly, ROI on AI is about real business value, and not an imagined, hypothetical value. And, real business value will get your board approvals for further investments.

What goes into the “gains” of ROI on AI?

If the formula is simple, measuring the “gains” of AI is both an art and a science. For most enterprises, the ROI on AI comes from one or more of these sources:

1. Process automation and labor savings

AI excels at automating repetitive, rules-based tasks far more than any human ever can. Chatbots, document processing and robotic process automation (RPA) can save thousands of employee hours each year. The classic ROI on AI here is straightforward: reduced labor costs and fewer errors within a typical work timeline.

2. New revenue streams

Banks have begun using AI-driven models to launch new financial products for niche customer segments. Manufacturers employ AI to streamline their supply chain and maximise profits.

3. Better decision-making

AI enables faster, data-driven decisions like personalized marketing offers or dynamic pricing. This results in positive outcomes like higher conversion rates, more sales and improved customer retention. Gains here are measured as increased revenue, which feeds directly into the ROI on AI.

4. Reduced risk and losses

Fraud detection, quality control, compliance monitoring are all tasks that AI excels at. For insurers, banks and retailers, reducing fraudulent claims or product defects has a very real, quantifiable impact on their bottom line (which can easily be avoided with AI solutions).

5. Enhanced customer experience

AI-powered personalization engines, chatbots and recommendation systems create  much more relevant experiences for customers. This can translate into higher customer satisfaction, repeat business and greater loyalty, which are all measurable components of ROI on AI.

Common “costs” of AI investments

Capturing the real ROI on AI also means being clear-eyed about the total costs. These typically include:

• Development and deployment: Buying or building the AI technology, integrating it with existing systems, launching proof-of-concepts and scaling successful pilots.

• Data costs: Collecting, cleaning, and managing data, which are often the largest “hidden” cost in AI initiatives.

• Training and change management: Teaching employees and stakeholders to work with AI, updating processes and sometimes hiring new, relevant talent.

• Ongoing operations: Maintaining, updating and monitoring AI models in production.

For a true picture of ROI on AI, all these costs must be included while doing a cost-benefit analysis for the respective project.

Real-world examples of delivering ROI on AI investments

Healthcare

GoML deployed a Generative AI solution to assist ophthalmologists in retinal disease triage. The system reduced triage time by up to 85%, enabling clinical teams to rapidly identify high-risk patients and prioritize their care, while maintaining diagnostic accuracy. This is not the only success... we've collected multiple stories of RoI gains from using AI for diagnosis.

Finance

GoML partnered with a leading fintech to build an AI-powered fraud detection system that cut manual review by 67%, boosted transaction processing by 75% and accelerated fraud detection by 82%. In a modeled scenario - $3 million in fraud prevented against $900,000 in implementation costs - that’s a 233% ROI on AI. You can read the full report here.

Five practical steps to maximise ROI on AI

1. Tie every AI project to a business problem

The best ROI on AI starts when the goal is clear to begin with. Some common examples are to reduce churn, increase sales or automate a pain point.

2. Quantify both gains and costs

List all the possible benefits, even if they may be intangible. I

3. Pilot, measure, and keep iterating

Test AI projects on a small scale. Measure results objectively before full rollout. Find Gen AI solution providers like GoML who have done this successfully for many use cases.

4. Build for integration

AI works best when it seamlessly fits into a workflow and co-exists within the system.

5. Invest in change management

Help your teams understand and trust AI. Training and buy-in are essential parts of the ROI equation.

The “hidden” value of AI

While the ROI on AI is often calculated in dollars and percentages, AI’s value can extend beyond the initial formula. Consider benefits like -

• Organizational agility: AI can drastically offer you an edge before your competitors get around to it.

• Quicker innovation: AI can free up time for people to solve bigger problems, leading to higher chances of repetable breakthroughs.

• Employee satisfaction: While AI handles mundane tasks, employees can focus on creative and strategic work.

Including these “soft” benefits in long-range planning further supports the business case, even if exact figures are elusive at first.

The real ROI on AI for enterprises

No technology, including AI, is a silver bullet for any operation. However, countless enterprises are already demonstrating impressive, measurable ROI on AI.

The path to meaningful ROI on AI requires clear objectives, precise measurement and a willingness to evolve as results emerge. By applying the ROI formula to each use case, businesses can cut through the hype and invest where benefits are highly likely.

If you’re looking to deploy high yield AI solutions with experts in the game, reach out for an executive AI briefing that will impress your CFO.