University of Kwazulu-Natal

How Are Businesses Calculating Roi On Ai Investment

Bonisiwe Shabane
-
how are businesses calculating roi on ai investment

Access the top developers across Asia, fully compliant, ready to start. Here is a striking reality: while 78% of enterprises now use AI in at least one business function, only 23% actively measure their return on investment. This disconnect has created what analysts call the “AI accountability crisis “billions invested with little visibility into actual business impact. But 2026 marks a turning point. As AI budgets face increased scrutiny and CFOs demand clearer justification for technology spend, enterprises are adopting sophisticated frameworks to quantify AI value. According to Gartner research, organizations with structured ROI measurement achieve 5.2x higher confidence in their AI investments.

This guide explores the metrics, methodologies, and measurement frameworks that leading enterprises are using to track AI ROI in 2026 and how your organization can implement them to maximize returns on your AI development... Traditional return on investment calculations work well for predictable technology investments. You spend X on a new system, it saves Y in labor costs, and the math is straightforward. AI investments rarely follow this pattern. Executive leadership hub - What’s important to the C-suite? Global AI Lead; US Innovation Lead, Emerging Technology Group, Boston, PwC US

Artificial intelligence has a problem: a lackluster return on investment (ROI) that affects many companies that deploy the technology. And while our most recent AI survey found that businesses are beginning to reap AI benefits, the reality is they’re not often seeing a financial return — or worse, not even covering their investments. Compounding the challenge is the fact that many organizations struggle to define ROI for AI in the first place. Most people probably think they know what AI is and does, but it’s a term that encompasses many technologies, processes and functions, so it’s difficult to pin down. It’s definitely not a one-size-fits all field. That can make it challenging to determine a return on investment.

In its simplest form, ROI is a financial ratio of an investment’s gain or loss relative to its cost. In other words, when you invest in AI, the benefits of your investment should outweigh the costs. Since the generative AI boom erupted in late 2022, organizations have raced to implement AI initiatives that enhance their business objectives. Leaders have been on the hunt for scalable AI strategies that streamline operations, inform data-driven decision-making, reduce costs and turbocharge product development. But though the hype surrounding AI implementation continues to surge, many organizations are finding that the return on investment (ROI) of their AI solutions is falling short. A 2023 report by the IBM Institute for Business Value found that enterprise-wise AI initiatives achieved an ROI of just 5.9%.

Meanwhile, those same AI projects incurred a 10% capital investment1. So why are most businesses struggling to profit from AI-driven solutions? And how can they achieve a better ROI in 2025? It turns out that having AI isn’t nearly enough. Some business leaders jumped on the AI bandwagon in a FOMO-driven, short-term impulse move to stay ahead of their competitors. Others envisioned enterprise AI as the business strategy hammer for every nail.

Both groups forgot the importance of nuance and planning. “People said, ‘Step one: we’re going to use LLMs (large language models). Step two: What should we use them for?’” remarked Marina Danilevsky, Senior Research Scientist, Language Technologies at IBM. Her comment is a warning to companies potentially falling into the same shortsightedness trap with AI agents in 2025. Achieving positive ROI on an AI transformation requires the inverse approach. Fortunately, there’s a sunrise on the horizon for businesses and artificial intelligence.

It’s not only possible, but likely, to achieve measurable ROI gains when implementing AI systems correctly—when organizations let strong data quality and AI strategy take the lead. The surge in artificial intelligence (AI) investments is creating significant market turbulence. Analysts are increasingly cautioning that we may be approaching an "AI-driven bubble." In this environment, it is no surprise that boardroom discussions are shifting from excitement to scrutiny. As a leader, you are likely facing two critical questions: Before answering these, you must confront a foundational impediment. AI’s potential is indisputable, but without a disciplined playbook to define and manage ROI, even the most sophisticated solutions risk becoming costly experiments.

Current research underscores this challenge. According to recent data, 39% of enterprise decision-makers worldwide view quantifying AI’s business impact as a formidable hurdle. Furthermore, Gartner reports that nearly 50% of IT leaders—those overseeing AI execution—struggle to measure its impact. In my role, leading large deals and strategic transformation programs for some of the most complex digital engagements globally, I often share this perspective with CXOs: The "AI Bubble" isn’t necessarily a reflection of... To silence the clamor and establish AI as a strategic enabler, you need to move beyond tactical experiments and architect a results-driven strategy. Companies are pouring billions into artificial intelligence (AI), but is it returning the worth of all that money?

Short answer — yes, but not all companies can harvest what it has to offer, and most companies fail to capture the ROI of AI. They see pilots and prototypes, but not profits. For medium-to-large enterprises, the real question isn’t whether to invest in AI — it’s how to ensure those investments deliver measurable business value. ROI isn’t just about cost savings; it’s about driving efficiency, productivity, and long-term strategic advantage. This guide explores what drives the ROI of AI, practical metrics, proven frameworks, and use cases — arming the leaders of emerging businesses with a clear roadmap to maximize impact. Before we talk about models or tooling, connect every initiative to a measurable business lever.

The next section shows a simple, CFO-friendly way to quantify ROI—so you can compare pilots, prioritize roadmaps, and scale what works. We are a partner in confidently building, scaling, and evolving software products backed by 11+ years of experience. In April 2024, Arun Chandrasekaran, Distinguished Vice President Analyst at Gartner, whose research focuses on artificial intelligence, wrote in a Gartner blog about a prediction: By 2027, more than 50% of the GenAI models... Additionally, in 2023, businesses began spending money much more actively, as confirmed by a report from Statista. Based on these facts, he calls for planning to deploy and manage multiple domain-specific GenAI models. However, before doing so, he suggests looking for off-the-shelf, domain-specific models that can be trained or tuned to meet enterprise needs.

This sounds like a plan, but I think it's very important to have one's own data. Reports about the ROI of AI that has been implemented, or predictions of future plans for implementing AI, are crucial before starting to invest in popular solutions or trying new optimization methods with AI. 49% of business leaders struggle to estimate what their AI initiatives deliver. You’re investing in artificial intelligence, rolling out pilots, and watching competitors race ahead. But, are you actually making money from AI, or just burning money? AI ROI isn’t just another buzzword, it’s the difference between transformation and expensive experimentation.

While enterprise AI initiatives currently achieve a median return of just 5.9% against 10% capital investments, novel organizations are enhancing their bottomline. In this blog you’ll learn exactly how to measure AI ROI, calculate tangible business value, and make data-driven decisions that will make a difference. AI ROI measures the financial return you get from AI investments compared to what you put in. Both matter. A customer service chatbot might save 40 hours weekly in support costs (hard ROI) while simultaneously boosting customer satisfaction scores by 23% (soft ROI). Calculating the return on investment (ROI) for AI initiatives represents one of the most critical yet challenging aspects of digital transformation.

While 85% of executives believe AI will give them a competitive advantage, only 23% have successfully measured its actual business impact. This comprehensive framework provides a systematic approach to understanding, calculating, and optimizing AI ROI across your organization. Traditional ROI calculations, designed for tangible assets and linear processes, often fail to capture the full spectrum of AI's impact. Unlike conventional technology investments, AI implementations generate value through multiple channels: direct cost savings, revenue enhancement, risk reduction, and strategic positioning benefits that compound over time. The complexity deepens when considering AI's iterative improvement nature. Unlike static systems, AI solutions become more valuable as they process more data and learn from interactions.

This creates a value curve that accelerates over time,a phenomenon traditional ROI frameworks struggle to accommodate. Furthermore, AI investments often require fundamental changes to business processes, organizational structures, and employee capabilities. These transformation costs and benefits extend far beyond the technology itself, creating a web of interconnected impacts that demand a more sophisticated measurement approach. Understanding AI ROI begins with comprehensively mapping all associated costs across the implementation lifecycle. Our analysis of 200+ AI implementations reveals that organizations typically underestimate total costs by 40-60%, leading to unrealistic ROI expectations and project failures.

People Also Search

Access The Top Developers Across Asia, Fully Compliant, Ready To

Access the top developers across Asia, fully compliant, ready to start. Here is a striking reality: while 78% of enterprises now use AI in at least one business function, only 23% actively measure their return on investment. This disconnect has created what analysts call the “AI accountability crisis “billions invested with little visibility into actual business impact. But 2026 marks a turning po...

This Guide Explores The Metrics, Methodologies, And Measurement Frameworks That

This guide explores the metrics, methodologies, and measurement frameworks that leading enterprises are using to track AI ROI in 2026 and how your organization can implement them to maximize returns on your AI development... Traditional return on investment calculations work well for predictable technology investments. You spend X on a new system, it saves Y in labor costs, and the math is straigh...

Artificial Intelligence Has A Problem: A Lackluster Return On Investment

Artificial intelligence has a problem: a lackluster return on investment (ROI) that affects many companies that deploy the technology. And while our most recent AI survey found that businesses are beginning to reap AI benefits, the reality is they’re not often seeing a financial return — or worse, not even covering their investments. Compounding the challenge is the fact that many organizations st...

In Its Simplest Form, ROI Is A Financial Ratio Of

In its simplest form, ROI is a financial ratio of an investment’s gain or loss relative to its cost. In other words, when you invest in AI, the benefits of your investment should outweigh the costs. Since the generative AI boom erupted in late 2022, organizations have raced to implement AI initiatives that enhance their business objectives. Leaders have been on the hunt for scalable AI strategies ...

Meanwhile, Those Same AI Projects Incurred A 10% Capital Investment1.

Meanwhile, those same AI projects incurred a 10% capital investment1. So why are most businesses struggling to profit from AI-driven solutions? And how can they achieve a better ROI in 2025? It turns out that having AI isn’t nearly enough. Some business leaders jumped on the AI bandwagon in a FOMO-driven, short-term impulse move to stay ahead of their competitors. Others envisioned enterprise AI a...