The study titled “ROI of Artificial Intelligence,” developed by IBM in partnership with Lopez Research and Morning Consult in December 2024, provides deep insights into how 2,413 IT Decision Makers (ITDMs) from 12 countries assess and maximize the ROI of Artificial Intelligence. These professionals represent organizations with more than 100 employees and have influence in IT-related decisions, including product selection, advisory services, and business consulting. Conducted online between October 30 and November 13, 2024, the study carries a margin of error of ±2 percentage points. Its aim is to shed light on both the benefits and the difficulties of integrating AI into complex business environments, focusing on ROI metrics, obstacles to adoption, and emerging trends. The findings offer strategic direction for entrepreneurs, executives, and professionals who wish to make informed AI-related decisions, revealing how the drive toward innovation, diverse approaches to ROI calculation, and skill gaps can either accelerate or slow down the transition from pilot projects to full production.
How to Maximize ROI from Artificial Intelligence: Global Insights
According to the research, 85% of participants say they have made tangible progress in carrying out their AI strategies, with 43% indicating they have already achieved significant results. These figures suggest a robust inclination toward integrating AI-based solutions into well-established business processes. Remarkably, 41% of those surveyed state they are equally motivated by the promise of direct ROI and by a desire to explore new technological capabilities. Another 31% focus primarily on the pursuit of innovation, whereas 28% emphasize more quantifiable economic outcomes. This balance between creative development and financial returns underscores that AI is increasingly viewed not just as a cost-savings tool, but also as a catalyst for productivity, workforce satisfaction, and operational efficiency.
Large enterprises—those with over 1,000 employees—often move faster in implementing AI because they can invest in advanced infrastructure and develop specialized skill sets. Countries like India and Brazil stand out for reporting higher rates of meaningful results, driven in part by organizational agility and strong leadership commitment. Some respondents mention that their businesses transitioned from experimental testing to fully operational AI solutions in under a year, particularly when upper management provided both the financial resources and the strategic support needed to integrate AI throughout the organization.
It is important to note that cost reductions are not the sole benchmark for measuring AI success. The study points out that metrics such as faster software development or improved productivity time savings often surpass mere financial savings. About 25% of ITDMs prioritize software development speed, 23% focus on rapid innovation, and 22% consider the reduction of time spent on operational tasks as their leading metric. Only 15% highlight large-scale cost efficiencies. This finding points to a widespread strategic perspective: AI investments are increasingly judged by how they boost competitiveness, streamline workflows, and speed up product or service launches.
Even though most companies are investing heavily, actual ROI may still be elusive for many. Less than half (47%) of respondents confirm achieving positive returns in 2024, 33% say they are about breaking even, and 14% note negative returns. Another 6% struggle to collect sufficiently systematic data to determine outcomes. While many organizations recognize AI as a high priority, they often lack coherent measurement methodologies or processes that integrate AI seamlessly into existing systems. Despite these gaps, a majority plan to expand AI projects, demonstrating that the path to fully realized gains may require deeper technical competencies, robust infrastructure, and organizational alignment.
Accelerating AI ROI: Metrics and Experimentation Strategies
The survey further illustrates how AI deployment strategies differ according to company size and reveals the pace at which pilot projects evolve into full-scale implementations. Approximately 58% of ITDMs can transition from a test phase to production within one year, with 10% finalizing this step in fewer than six months. Organizations with over 5,000 employees show higher agility in integrating AI solutions with legacy systems—an attribute particularly valuable to senior executives seeking to cut go-to-market timelines and keep products and services constantly updated.
The data highlights a preference for running multiple pilot projects in parallel: 71% of respondents say they initiated over 10 pilots in 2024 alone, and in certain regions like Brazil and India, more than 20 pilots were launched by one-third of the sample. This reflects a highly exploratory approach, especially in markets that see AI as a source of new industrial and service opportunities. Despite this enthusiasm, only half of the pilots initiated in 2024 reached full operational status by year’s end. Such a gap underscores the complexities of integrating AI beyond the conceptual phase, emphasizing the need for clear governance, employee training, and system compatibility.
Open-source has a special place in these adoption patterns. About 61% of respondents rely on open-source ecosystems for at least some of their AI tools, while 67% prefer to buy or rent solutions from third-party vendors. The study also notes that 55% combine in-house development capabilities with external resources, creating a hybrid approach that merges both flexibility and ready-made solutions. In markets like Mexico, Spain, Indonesia, South Korea, and India, open-source adoption frequently surpasses 70%. Open-source frameworks can drive rapid experimentation and customization, giving organizations more room to build AI models that are precisely tailored to their business goals. For executives, the cost advantage of skipping hefty license fees is clear, but the real benefit may lie in the flexibility to adapt and extend AI features quickly.
Among the leading ROI metrics, 62% measure the success of AI initiatives in terms of productivity time savings, and 61% monitor speed of innovation. About 52% focus on how quickly problems can be resolved, and 62% track software development speed. Traditional cost-savings in dollars, by contrast, interest only 43% of participants. This is a vital clue for decision-makers: many companies judge ROI not purely on financial outcomes but through a multifaceted lens that includes reduced bottlenecks, accelerated product cycles, and overall organizational agility. From a managerial standpoint, adopting AI thus becomes part of a broader strategy for sustainable growth, in which efficiency gains and creative momentum are both essential.
Overcoming Challenges to Boost AI ROI
Despite the excitement around AI, organizations report a range of technical and organizational issues that may hamper the journey from pilot project to large-scale deployment. Data quality is the leading concern, cited by 50% of respondents as a critical obstacle. Robust machine learning models require consistent, clean, and readily available data; if datasets are fragmented or lack uniform formatting, AI algorithms struggle to deliver accurate insights. Companies with headcounts between 101 and 5,000 often find it particularly difficult to unify data sources and eliminate duplication.
A second major barrier is integrating new AI systems with older technologies, mentioned by 44% of ITDMs. Organizations that have built up multiple generations of software infrastructure often need extensive custom engineering to bridge AI solutions with enterprise resource planning (ERP) or customer relationship management (CRM) systems. This can slow down data pipelines and divert time away from real AI value creation. Moreover, the shortage of skilled AI professionals—flagged by 23% of respondents as “very challenging”—creates stiff competition in the job market. Those firms capable of training and retaining specialized talent typically achieve more reliable outcomes because in-house experts can spot inefficiencies and tailor AI models more precisely.
The survey also reveals that 22% of ITDMs struggle with AI governance, meaning they lack clear guidelines on accountability, ethical oversight, and data protection. Without well-defined processes, AI initiatives risk legal or reputational backlash, causing some executives to hesitate in extending AI throughout the organization. Another concern is “employee adoption,” highlighted by 16% of participants: if staff don’t see the tangible benefits of AI or feel it has been forced upon them, they may resist or underuse new technologies. This reluctance can significantly reduce the impact AI might otherwise deliver. In practical terms, an initiative to automate administrative tasks might stall if data assets remain disorganized, if staff are untrained, or if the AI’s outputs do not integrate smoothly with existing workflows.
Interestingly, companies that leverage open source tend to face more frequent governance challenges. Because open-source AI tools allow for deeper customization, teams need meticulous coordination to prevent project fragmentation. While open-source frameworks grant developers freedom to innovate and refine code, managers must ensure consistent security protocols, compliance measures, and documentation. This juggling act between autonomy and control is a common theme in organizations that adopt open platforms extensively.
The Future of AI ROI: Strategic Investments for Growth
Looking ahead, the study shows strong momentum in AI investments for 2025. Some 62% of participants plan to allocate more resources to AI than in 2024, with India, Brazil, Mexico, and South Korea displaying particularly high levels of optimism. In India alone, 93% of IT leaders intend to boost AI budgets. This trend, reflecting both faith in AI’s potential and a desire to capitalize on previous experiences, emphasizes the critical need to improve the conversion of pilot projects into full-scale, revenue-generating solutions.
About one-third of respondents foresee launching more than 20 new AI pilots in 2025. This surge in experimentation spans multiple enterprise functions—from IT operations and data management to product innovation and software development. For business executives, these findings imply a need for careful planning to avoid letting numerous experiments run uncoordinated. Instead, pilot initiatives should be synchronized via a well-defined roadmap with clear objectives, performance indicators (KPIs), and skill-sharing mechanisms. Success in one domain—such as an AI-driven marketing pilot—can yield insights and best practices that transfer to another area, like operations or logistics.
An additional highlight is the intent to expand open-source usage. The share of AI solutions based on open frameworks could rise from 37% to 41% in 2025. India is again at the forefront, with an anticipated 70% of AI applications expected to be open source. The main attractions are continued flexibility and reduced licensing fees, though the benefits are contingent on robust governance and ongoing education. Indeed, 51% of surveyed organizations plan to increase their reliance on cloud-managed services, 48% intend to hire more AI talent, and 48% want to enhance their open-source adoption—indicating parallel strategies to upgrade technical infrastructure, workforce skills, and organizational processes.
Senior leaders often find immediate value in focusing on areas like IT operations, a nerve center affecting security, infrastructure reliability, and service continuity. An AI-powered approach to system monitoring and predictive maintenance can slash downtime and minimize disruption. For companies reliant on uninterrupted service, trimming just a few minutes of downtime can translate into substantial financial and reputational benefits.
Leveraging Open-Source for Better AI ROI
The research suggests that organizations already witnessing positive returns on AI frequently exhibit a combination of strong internal capabilities, engaged managers who understand both technical and business complexities, and a well-structured data governance framework. With many companies preparing to increase their AI investments in 2025, these top performers may move toward more advanced machine learning models or adopt cloud-managed solutions to streamline project life cycles.
However, firms that have yet to see a clear ROI often lack consistent metrics or conflate mere experimentation with genuine innovation. While a quarter of those failing to achieve ROI anticipate turning things around within a year, the majority believe it will take one to three years or more, largely depending on how mature their projects are and whether organizational bottlenecks can be removed.
For business owners and executives, these insights point to several key steps: align AI pilots with actual commercial goals, ensure well-trained teams, maintain consistent data quality, and cultivate a company culture that values AI as a strategic asset. The survey shows that more than 30% of respondents regard business value and vision as the greatest contributor to a successful AI ROI. This underscores the necessity of identifying the precise problems AI can solve—such as anticipating production-line defects or creating hyper-personalized customer experiences—while ensuring that technology investment fits into a long-term growth strategy.
One illustrative case is a manufacturing firm that deploys AI-based monitoring to detect machine defects on the assembly line, thereby cutting scrap and boosting output quality. Here, business objectives are concrete, the IT division aligns closely with production, data inputs from sensors are kept clean and current, and management incorporates AI outputs into strategic decision-making. While open-source frameworks provide the agility to adapt models swiftly, robust oversight is needed to maintain code security and clarity around who is responsible for each aspect of the project.
AI ROI: Key Takeaways and Future Opportunities
The results of this 2024 study portray an industry in flux, where AI plays a pivotal role in addressing market complexity and upgrading internal processes and services. Drawing on the participation of ITDMs from 12 different countries, the research notes a shift toward speed, innovation, and productivity as the chief metrics of AI effectiveness—beyond mere cost reduction. Although many organizations have yet to arrive at a verifiable positive ROI, the majority plan to invest more in AI, signaling broad confidence in the potential of these technologies.
From an entrepreneurial perspective, AI can be seen as more flexible and predictive than legacy analytics or automation solutions. Well-structured AI integration, backed by good data hygiene, has the potential to streamline interdepartmental workflows, anticipate failures, and lower testing costs. It could even serve as a unifying force among different corporate functions, accelerating both innovation and continuous feedback loops.
Still, a successful AI rollout demands clarity about objectives, a workforce that grasps the technology’s practical benefits, and rigorous governance to avoid pitfalls around bias, data security, and compliance. As more organizations ramp up their AI budgets, a clear divide may form between those that implement AI methodically—establishing a mature, data-driven culture—and those that approach AI ad hoc, risking fragmentation and underwhelming results.
For executives, the data gathered in 2024 suggests that companies with advanced technological capabilities enjoy a competitive edge, particularly if they can handle integration hurdles and cultivate widespread support for AI projects. Challenges like data management, legacy system compatibility, and stakeholder buy-in remain critical. Ultimately, managers are urged to pursue realistic goals, addressing use cases where rapid returns are attainable, and simultaneously planning for long-term human capital development. It is fair to say that AI, if carefully supported by strategy, ownership, and measurable performance indicators, can become a stable growth vector for organizations navigating the complexities of modern markets.
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