AI ROI: How artificial intelligence delivers business value

As organizations pour billions into artificial intelligence (AI), a pressing question emerges: Will these AI investments pay off? Business leaders seek tangible returns from AI initiatives, yet achieving and measuring the return on investment (ROI) from AI is challenging. In fact, ROI remains one of the most persistent challenges in AI adoption, often a long-term endeavor taking several years due to complex implementation and integration hurdles. Despite these difficulties, surveys show a majority of enterprises are beginning to see positive ROI from AI efforts. 

“72% are formally measuring Gen AI ROI, focusing on productivity gains and incremental profit,” noted the Wharton study above. “Three out of four leaders see positive returns on Gen AI investments.”

Understanding AI ROI in context

AI ROI refers to the net value or benefit an organization gains from its investment in artificial intelligence. Just like any ROI calculation, it compares the returns, such as increased revenue or cost savings, to the costs, such as money, time, and resources, of an AI project. 

However, measuring ROI for AI isn’t always straightforward. Traditional ROI metrics, such as pure cost reduction, may miss the full picture of AI’s impact. For example, AI might improve decision quality or customer satisfaction, which are valuable outcomes that are not immediately reflected on the balance sheet.

To understand AI ROI, companies are expanding their metrics. An effective AI ROI framework measures efficiency gains, quality improvements, and strategic benefits alongside financial returns. In other words, beyond asking “did we save money or make money with AI,” businesses look at factors such as faster workflows, better accuracy, improved customer experience, and competitive advantage. By quantifying both tangible and intangible benefits from AI, leaders assess whether an AI initiative is delivering value commensurate with its costs.

Why ROI is important for AI initiatives

Documenting a strong ROI is important because AI projects often require upfront investment, whether in data infrastructure, skilled personnel, or computing power. Stakeholders need to justify these investments with results. A positive ROI means the AI project is contributing value, through increased revenue, reduced costs, or other gains, greater than its expense. This, in turn, drives leadership buy-in and continued funding for AI programs.

On the flip side, if AI initiatives fail to show returns, organizations may scale back or abandon them, potentially losing out on the benefits AI could provide. So ROI becomes a barometer of success for AI adoption. It aligns AI efforts with business objectives: Rather than experimenting in a vacuum, teams focus on projects that improve business outcomes. 

In practice, demonstrating ROI helps turn AI from a buzzword into a sustained enterprise strategy. It assures executives that AI contributes to the company’s bottom line and strategic goals, whether by boosting productivity, increasing sales, improving customer retention, or mitigating risks. Especially in large enterprises, showing even early ROI builds momentum for broader AI integration.

Moreover, focusing on ROI encourages a mindset of delivering value. It pushes AI teams to ask at the outset: How will this model or system create value? That question leads to better project selection by choosing examples with clear business value and clearer success criteria. In essence, ROI keeps AI initiatives accountable to business performance.

Challenges in delivering AI ROI

Delivering consistent ROI from AI has proven difficult for many organizations. “AI rarely delivers value in isolation,” noted one Deloitte study. “It is typically introduced alongside efforts to improve data quality, reconfigure teams, or streamline operations, which makes it difficult to isolate its value.”

Here are some challenges that make AI ROI more difficult:

Many benefits are intangible

AI frequently produces outcomes that are important, such as better customer engagement, improved employee satisfaction, and stronger vendor relationships, but these are hard to quantify. 

For example, an AI-driven recommendation system might improve customer experience, yet the monetary impact, such as a lifetime value increase, is not immediately obvious. Because of this, early AI projects deliver improvements without showing results on traditional financial metrics. Organizations that focus only on short-term tangible gains may overlook these intangible benefits. This makes it harder to declare ROI, even if the AI is creating value in less obvious ways.

Siloed data and poor data quality

Many companies discover that data issues hamper AI ROI. Fragmented systems and siloed data make it challenging to conduct ROI measurement, particularly the before-and-after impact of AI deployments. In fact, executives often overestimate their data maturity; they invest in sophisticated AI models before fixing core data quality and infrastructure gaps, which delays meaningful results. 

When AI models are trained on incomplete or inconsistent data, their outputs are less useful, undermining potential ROI. Siloed data also means that an AI solution might not be fed all the information it needs, or the insights it produces don’t reach the right business units. All of this results in AI systems that don’t deliver on their promise, because the data foundation wasn’t there.

Technology is evolving faster than metrics

The AI field moves rapidly, with new tools and examples emerging regularly. This pace outstrips an organization’s ability to measure effect. Leaders in a survey described how hype and pressure lead to premature investments in the “next big AI,” before there’s a clear way to evaluate its success. 

“Executives described AI adoption as a business imperative driven by the fear of falling behind and the promise of improved performance,” Deloitte wrote. 

Meanwhile, traditional metrics often lag behind because they weren’t designed for AI-driven processes. For example, how do you quantify the value of an AI assistant improving employee decision-making? Companies sometimes find themselves with advanced AI capabilities but no agreed-upon key performance indicators (KPIs) to gauge their contribution. This misalignment makes ROI seem invisible or hard to prove. Without updated metrics, AI projects may be deemed failures or successes based on misguided measures, complicating the true ROI picture.

The human factor and adoption

Achieving ROI with AI isn’t just a tech challenge, but also a human one. New AI systems face cultural resistance or low adoption if not managed well. Employees might mistrust AI recommendations or fear that automation threatens their jobs. If an AI tool is not fully adopted by its intended users, expected efficiency or revenue gains won’t materialize. Deloitte’s research highlights that successful AI outcomes depend on how effectively people integrate these tools into workflows. 

“Leaders also recognize that AI’s success depends on a mature ecosystem, including integrated data platforms, reskilled workforces, scalable infrastructure, and strong governance frameworks,” the organization noted. 

Training staff and managing change are important. For instance, a sales team given an AI analytics platform might continue relying on instinct unless they trust and understand the tool. Overcoming this means investing in user education, change management, and demonstrating that AI is there to augment their work, not replace it. Organizations that neglect the human side by failing to address concerns or provide adequate training often see their AI projects stall, delivering little ROI.

Entanglement with broader transformation

AI initiatives are often rolled out alongside other big changes, such as moving to the cloud, reorganizing teams, or new operating models. This entanglement makes it hard to isolate AI’s contribution. If a bank implements an AI fraud detection system at the same time as overhauling its IT infrastructure, any reduction in fraud losses might be due to a mix of both efforts. Executives report difficulty in parsing out what portion of gains or losses to attribute to the AI system itself. 

This challenge is especially true for advanced “agentic AI” systems that automate end-to-end processes because they require extensive process re-engineering, which means any ROI comes hand-in-hand with broader ops changes. Consequently, agentic AI ROI takes longer to show up, and when gains do appear, they’re shared with other transformation efforts. This complexity means organizations must be patient and use nuanced evaluation methods to assess an AI transformation’s effect from other concurrent changes.

Despite these challenges, businesses are not shying away from AI. The fear of falling behind competitively is driving continued AI investment even when short-term ROI is unclear. The key is to address these hurdles so AI projects deliver on their potential.

Measuring AI ROI: Tangible vs. intangible benefits

Calculating AI’s ROI requires looking at both direct financial gains and softer benefits. Many early approaches to AI ROI focused narrowly on cost savings, such as automating a process to save labor costs. While cost reduction is important, it’s only one part of the value AI creates. Leading organizations now measure a mix of tangible and intangible outcomes from AI projects.

Tangible outcomes

These benefits directly improve financial performance or efficiency in measurable ways. The most common tangible metric is cost savings. AI automation of routine tasks reduces manual effort and errors, lowering operating costs. Many firms report time savings as well, with AI tools freeing five hours of employees’ work each week. For example, an AI system that automates data entry might save hundreds of employee hours, translating into labor cost savings or the ability to redeploy staff to higher-value work. 

Another tangible outcome is revenue growth driven by AI. This could come from personalization engines that boost sales conversion rates, or predictive analytics that help retain customers and increase their lifetime value. Efficiency gains are also tangible: AI streamlines workflows, leading to faster response times and throughput. In fields such as finance or legal, AI-powered analytics help professionals handle more cases or transactions than before, improving productivity. All these tangible benefits, including cost reduction, time saved, error reduction, increased sales, and productivity improvements, should be assigned monetary values or quantified to make them easier to plug into an ROI formula.

Intangible outcomes

Not all value from AI shows up immediately in dollar terms, yet these factors are important for long-term success; one is accuracy. AI reduces errors by catching fraud that humans miss, or making more precise predictions, for better decisions and outputs. Over time, higher accuracy leads to better customer trust and less waste, indirectly improving profitability. 

Customer experience is another area: AI supports personalized, responsive interactions, such as AI chatbots providing support or recommendation systems tailoring content to each user. These make clients happier and more loyal, which may not pay off immediately but builds a competitive advantage. 

Decision quality and strategic insight from AI are also intangible benefits. By analyzing large amounts of data quickly, AI uncovers patterns and solutions humans might miss. Better decisions in pricing, marketing, and risk management yield big payoffs down the line. 

Additionally, AI improves employee morale and talent retention; automating drudge work lets employees focus on more creative, fulfilling tasks, which boosts morale. Companies that use AI to augment employees often find it easier to attract and retain top talent, as they signal a forward-thinking and efficient workplace. 

While it’s harder to assign a dollar value to intangibles like customer satisfaction or employee happiness, they are important for sustained ROI. Mature organizations incorporate these into their AI ROI calculations, such as customer lifetime value models or employee turnover costs saved. This captures the full spectrum of AI’s value, not just the immediate dollars and cents.

In practice, a holistic ROI analysis for an AI project might combine both types: 

• “hard” ROI, such as the amount saved per year in infrastructure costs due to AI optimization

• “soft” ROI, such as an improved NPS customer score, which is expected to increase retention by a certain percentage 

Companies that measure only the tangible may undervalue their AI initiatives, while those that acknowledge intangibles make a stronger case for AI’s overall return.

Strategies to improve AI ROI

If demonstrating AI ROI is challenging, what do successful organizations do differently to get better returns? Research points to several best practices that AI ROI leaders use to turn their investments into real results. Here are some strategies:

Align AI projects with business strategy

One common trait among high-ROI organizations is that they treat AI as a strategic, enterprise-wide initiative, not just a series of ad hoc tech experiments. AI projects should be chosen and designed in alignment with the company’s core goals and pain points. By focusing on projects that drive revenue growth, cost efficiency, or competitive differentiation, firms use AI to target meaningful outcomes. 

In fact, top performers are more likely to define their AI successes in strategic terms, such as new revenue opportunities or business model transformation, rather than operational improvement. 

“AI ROI leaders are significantly more likely to define their most critical AI wins in strategic terms: ‘creation of revenue growth opportunities’ (50 per cent) and ‘business model reimagination (43 per cent).” Deloitte wrote.

This means when brainstorming AI applications, ask how it could open new markets, create new products, or improve the value proposition. 

Additionally, it’s important to make AI part of the corporate strategy and leadership agenda. In many leading companies, AI isn’t relegated to an R&D lab; it’s championed by the C-suite and even owned by the CEO or a chief AI officer as a strategic program. Top-level sponsorship helps support sufficient funding and integration across departments. 

“High performers are three times more likely than their peers to strongly agree that senior leaders at their organizations demonstrate ownership of and commitment to their AI initiatives,” noted McKinsey. “These respondents are also much more likely than others are to say that senior leaders are actively engaged in driving AI adoption, including role modeling the use of AI.”

The Thomson Reuters study found organizations with a detailed AI adoption roadmap were almost four times more likely to experience revenue growth from AI compared to those without a plan. The lesson is clear: to improve ROI, incorporate AI into business planning. Set clear objectives, such as “improve customer retention by 15% with AI-driven personalization”, secure executive buy-in, and make sure every AI project has a business case justification that ties to the company’s strategic metrics.

Invest in data quality and infrastructure

Because data issues are a top culprit for poor AI ROI, successful organizations tackle data readiness head-on. This involves breaking down data silos, improving data quality, and investing in robust data infrastructure to handle AI workloads. Architecture decisions at the outset are important because AI systems need to integrate with databases, data lakes, and pipelines to avoid inefficiencies. 

Leading AI adopters often update their data stack, such as adopting real-time databases or scalable cloud data platforms, so their AI models always have access to fresh, relevant data. They also implement strong data governance: clean, consistent data input leads to reliable model outputs. 

There’s a performance element too. AI, especially real-time or deep learning applications, is computationally intensive. Organizations that see high ROI frequently use high-performance data solutions to support AI systems. Each millisecond of latency or bottleneck in data delivery degrades an AI system’s effectiveness; think of a fraud detection model that needs to scan transactions in <100 ms. 

In fact, Aerospike case studies have shown that AI performance and efficiency start at the database layer. PayPal handles millions of transactions per second with sub-second latency by rethinking its data infrastructure, which in turn helps its AI to catch fraud in real time. By contrast, if data retrieval is slow or the system can’t scale to production volumes, the project won’t deliver promised value regardless of the AI model’s quality. The bottom line: improving AI ROI requires spending effort and budget on infrastructure such as fast databases, scalable pipelines, and high-quality data to support AI’s potential.

Foster a culture of adoption and learning

The human element makes or breaks AI ROI. Organizations that succeed treat change management and education as integral to their AI strategy. This starts with leadership setting a tone: leaders should communicate a vision where AI is a tool to augment employees, not replace them. 

Many AI ROI leaders invest in training their workforce. For example, 40% of them mandate AI training for employees to build AI fluency across the board. Training staff helps employees understand how to use AI tools effectively and creatively in their jobs. 

“Leading organizations are moving beyond voluntary education to embed AI understanding as a fundamental skill across their workforce,” Deloitte wrote.

It’s also important to address employee concerns. Transparent discussions about how AI will affect roles and involving users in AI implementation help reduce resistance. Some firms create AI champions or centers of excellence that disseminate best practices and support teams in adopting AI solutions. 

Integrating AI into existing workflows and sometimes re-engineering those workflows is important. It might involve adjusting job roles to take advantage of AI results or setting new performance metrics that encourage using AI outputs. Organizations reporting strong AI ROI often adjust their operations and processes to take advantage of AI, rather than forcing AI into existing processes. 

For example, an IBM study of AI implementation in Europe, the Middle East, and Africa found that 32% of senior leaders said they were already redesigning value streams around AI capabilities rather than automating existing steps. In addition, 36% said they had changed their operating model to speed innovation cycles to meet the productivity gains they want to deliver through AI. 

Another cultural aspect is experimentation and iteration: treating AI projects as learning experiences. Not every model demonstrates ROI immediately, so teams that iterate rapidly, learn from failures, and persist will eventually find approaches that yield value. By building a culture that is both AI-friendly and change-ready, companies improve the chances that their AI investments pay out. 

Broaden how ROI is measured

Leading organizations rethink their success metrics for AI. Rather than applying a one-size-fits-all ROI formula, they develop nuanced KPIs and timeframes appropriate to different AI projects. For example, a generative AI project aimed at improving product design speed might be measured on time-to-market for new designs or the innovation rate, rather than immediate revenue. 

McKinsey & Co. reported that 39 percent of respondents to a survey attributed any level of improvement to earnings before income and taxes (EBIT) to AI. “Most of those respondents say that less than 5 percent of their organization’s EBIT is attributable to AI use,” the company added. “However, respondents see other company-wide qualitative outcomes: A majority say that their organizations’ use of AI has improved innovation, and nearly half report improvement in customer satisfaction and competitive differentiation.”

AI ROI leaders explicitly use different evaluation frameworks for different types of AI, such as short-term metrics for efficiency projects and longer-term ones for transformative projects. 

To improve ROI, it’s important to set the right expectations. Some AI projects might intentionally prioritize learning and capability-building with payback expected in a couple of years. Companies that do well often identify interim metrics that indicate progress, such as model accuracy, user adoption rates, or customer satisfaction scores, as proxies for eventual ROI. They also factor in risk reduction and future options value, recognizing, for instance, that an AI system that prevents a compliance failure indirectly saved potentially millions. 

By measuring what matters, not just what is easy, organizations capture more of AI’s value. This might involve incorporating customer feedback, employee feedback, and other qualitative data into ROI assessments. The goal is to avoid prematurely declaring an AI initiative a failure just because traditional ROI accounting doesn’t capture its early contributions. Over time, as those contributions translate to financial outcomes, such as improved sales from happier customers or saving money from predictive maintenance, the ROI picture becomes clearer. 

Make sustained, strategic investments

Finally, improving AI ROI often requires a commitment to investing over the long term. Many organizations that see strong returns are those that did not dabble timidly in AI, but went in big and with resources and patience. According to research, 95% of top AI performers allocate more than 10% of their technology budget to AI.

“Moreover, they are more likely than other respondents to have significantly increased their AI spending in the past 12 months and are more likely to plan to do so again in the next 12 months,” Deloitte added.

This level of investment gives AI initiatives the necessary talent, technology, and R&D to reach maturity. 

These companies also differentiate their investment approach. They might use external AI tools for quick wins but simultaneously build in-house capabilities for core strategic areas. This way, they balance immediate ROI with building long-term proprietary advantages. Patience is part of this strategy. Leaders understand that ROI, especially from ambitious AI projects, may take multiple years. Many survey respondents expected significant returns only after three to five years for AI projects such as autonomous systems. 

During this time, sustained support is important. The payoff can be considerable. When AI is incorporated across operations, it drives compound benefits such as efficiencies across departments and new revenue streams. In contrast, organizations that treat AI as a one-off experiment or cut funding at the first setback often miss out on the later-stage returns that follow initial learning curves. In essence, to get a big ROI, one must be willing to think big and stay the course, treating AI as an ongoing process rather than a plug-and-play tool.

Companies that don’t commit to this level of investment are less likely to see success, warned analyst David Linthicum. “AI success favors the bold,” he wrote. “Organizations willing to prioritize AI as a cornerstone of their operations, invest heavily, and rethink their processes are positioning themselves for greater payoffs. The vast majority are navigating resource constraints that include insufficient funding, inadequate talent, and overburdened IT systems. It’s no wonder so few enterprises find success with AI when limited buy-in, poor strategy, and fragmented execution remain pervasive roadblocks.”

But by following these strategies of strategic alignment, data/infrastructure readiness, cultural adoption, smarter metrics, and sustained investment, companies improve their likelihood of seeing meaningful ROI from AI.

Building the foundation for AI ROI

Ultimately, achieving strong AI ROI is about combining innovative technology with practical execution. Organizations that succeed are those that pair AI models with the right data foundations, organizational support, and long-term vision. 

This is where Aerospike comes into the picture. Aerospike’s real-time data platform is the kind of high-performance infrastructure that moves AI initiatives from proof-of-concept to profitable deployment. By providing a database capable of millions of transactions per second with sub-millisecond latency, Aerospike helps keep AI models from being starved for data or slowed by bottlenecks, because AI performance and business efficiency often start at the database layer. In fact, a Forrester study projected that adopting Aerospike’s real-time database delivers a 446% to 574% ROI, thanks to millions of dollars in savings and lower infrastructure needs compared with traditional databases.

For businesses striving to improve their AI ROI, the takeaway is clear: you need the right strategy and the right tools. Aerospike provides a proven data foundation that addresses ROI blockers from data silos to scalability issues, allowing AI systems to run faster, more efficiently, and at lower cost. That means companies focus on extracting insights and value from AI, rather than worrying about infrastructure limitations. As AI moves from experimental to essential, ROI requires an enterprise-grade, real-time data backbone.

To learn more about how Aerospike’s high-performance data platform supports your AI initiatives for greater ROI, visit Aerospike.com and explore the solutions tailored for AI/ML applications. With the right approach and the right partner, turn AI’s promise into measurable business success.