How to prove return on investment in AI

The rapid adoption of Artificial Intelligence (AI) is driven by its ability to streamline IT operations, enhance decision-making, and uncover new growth opportunities. However, the market hype surrounding AI has amplified the pressure to prove its return on investment (ROI). Despite its transformative potential, AI’s evolving complexity makes its value harder to quantify, meaning clear metrics and effective strategies are essential to cut through the noise and demonstrate its tangible and long-term impact.

Understanding ROI in AI

AI implementations are inherently different because their payoff often extends beyond immediate, tangible gains. For example, automating repetitive testing tasks through AI can reduce testing time, though increase costs through additional tooling. The true return on investment is more likely to materialise over time as development cycles become shorter, defects are caught earlier, and overall quality assurance processes improve. This kind of layered value creation must be carefully measured to ensure stakeholders understand the scope of AI’s impact.

AI adoption in IT invariably comes with a shift in processes, infrastructure, and, occasionally, team culture. For Chief Technology Officers (CTOs) and IT managers advocating for AI projects, demonstrating ROI is essential to gaining buy-in from stakeholders across business units. Investors and senior executives want clear answers to crucial questions, such as, “How does this reduce operational bottlenecks?” or “What tangible benefits are we seeing in productivity or customer outcomes?”

Measuring and proving ROI accomplishes two things:

1. It provides transparency around the effectiveness of resource allocation.
2. It helps build trust among stakeholders by aligning AI projects with broader business goals, such as accelerating time-to-market for products or ensuring high scalability in cloud and DevOps workflows.

Challenges of Measuring ROI in AI

Calculating ROI in AI isn’t always straightforward. One major challenge is that traditional metrics often fail to capture AI’s ability to create intangible and indirect benefits. While automation of functional testing might show immediate savings in the form of reduced testing time, indirect benefits—a team that can focus on higher-value problem-solving tasks instead of repetitive quality checks—are harder to measure. These so-called “soft gains,” such as enhanced employee productivity or improved client satisfaction from faster issue resolution, typically unfold over time.

Another challenge lies in predicting the adaptability of AI systems in dynamic IT landscapes. Some AI models improve as they learn, meaning their full impact may not be realised until six months or even years into deployment. For instance, a machine-learning model embedded into CI/CD pipelines could significantly enhance build success rates by identifying potential issues in code commits. However, the associated return—streamlined DevOps cycles translating into faster feature delivery—might only become measurable over multiple project iterations.

Further complicating the matter and increasing the importance of proving ROI, AI projects often necessitate upfront investment in data preparation, algorithm training, and integration with legacy systems. These initial costs may appear high if the roadmap of how they’ll be offset by future efficiencies isn’t communicated to stakeholders. Consider a project deploying AI for cloud cost optimisation — the ROI may seem minor when compared to the setup expenses. However, as the AI continues reducing idle resource usage and improving load balancing, these efficiencies compound into significant financial advantages.

Key metrics for measuring ROI

AI initiatives in IT environments often impact multiple areas, from operational efficiency to customer satisfaction. To quantify and prove ROI, you must focus on a set of key performance indicators (KPIs) tailored to track both immediate outcomes and long-term benefits. Below are the primary metrics that can provide a comprehensive view of AI’s return on investment.

Cost efficiency

The most tangible metric for assessing AI’s ROI is cost efficiency. AI-driven automation can significantly reduce expenses related to manual testing, repetitive cloud operations, and continuous monitoring in DevOps. For example, AI-powered test automation frameworks eliminate repetitive tasks, reducing the number of manual testing hours required. To measure this:

• Compare pre- and post-implementation efforts in terms of human resource utilisation.
• Track the reduction in testing time and labour costs without compromising accuracy.
• Calculate cumulative savings over time as AI solutions optimise workflows further.

Productivity gains

AI excels at increasing team productivity by automating routine IT tasks and augmenting decision-making processes. Just look at Google, which uses AI systems to generate over 25% of its new code. Measurable productivity gains include faster code reviews, shorter build cycles, and expedited testing processes. To quantify this:

• Monitor throughput by comparing the volume of tasks or projects completed before and after deploying AI tools.
• Record time saved across critical workflows, including CI/CD pipelines or deployment processes.
• Use tools to track team speed and efficiency improvements brought by AI-driven recommendations and predictive insights.

Error reduction

AI’s ability to identify patterns and anomalies makes it an invaluable asset for improving quality assurance. Machine learning algorithms can detect errors earlier in development cycles, helping to avoid costly fixes post-deployment. Metrics to track in this context include:

• The number of defects caught during development versus those caught in production before and after implementing AI tools.
• Defect recurrence rates and severity of issues post-AI adoption.
• Calculating avoided downtime costs or the financial impact of mitigating critical vulnerabilities early.

For example, an AI solution applied to regression testing could consistently flag unanticipated outcomes, reducing bug rates while improving code stability.

Scalability benefits

AI can optimise cloud resource usage and improve DevOps efficiency, enabling IT projects to scale seamlessly. Scalability can be measured by the adaptability of AI systems to handle increasing workloads without incurring proportional cost increments. To measure this:

• Analyse resource utilisation metrics, such as compute hours or storage, before and after AI-driven optimisation.
• Evaluate system response times and stability during periods of peak demand.
• Track the efficiency of automated processes, such as task scheduling or shifting workloads across distributed systems.

AI-enhanced orchestration tools, for instance, can significantly improve resource allocation, minimising overhead costs while ensuring smooth scalability across cloud and DevOps ecosystems.

Time-to-market acceleration

AI’s predictive and automation capabilities help IT departments deliver software and services faster. AI-assisted DevOps tools, for instance, streamline testing, debugging, and deployment processes. To quantify this benefit:

• Measure the time taken to complete specific IT projects or product releases before and after AI adoption.
• Track delays avoided due to AI’s predictive capabilities in build verification or infrastructure provisioning.
• Use historical data to reflect the consistency of faster releases over multiple project life cycles.

For example, by employing AI solutions for real-time monitoring, teams can respond to build issues immediately, preventing delays and ensuring that new features are deployed according to schedule.

Proving the long-term impact of AI

The commercial deployment and utilisation of AI is still a relatively new frontier, which makes proving its long-term impact a complex task. Unlike other established technologies, AI’s evolving nature, combined with the lack of extensive historical data, presents a challenge for IT leaders aiming to quantify sustained benefits. Metrics like productivity gains or cost savings may provide initial insights, but it’s much harder to project these advantages over years, especially as AI systems adapt and improve.

Many AI projects face the hurdle of requiring time to deliver results iteratively. For example, machine learning models embedded in DevOps practices might only start delivering noticeable predictive accuracy after several iterations. The purported ‘ease’ of AI systems has also inadvertently created the notion in some users’ minds that it can simply be written into existing business systems and ‘switched on’ to deliver immediate results. This makes it difficult to gauge and communicate long-term value to stakeholders early in the process.

To address this, organisations must set realistic expectations about AI’s maturity and impact timelines. Adopting flexible, ongoing evaluation methods can help refine ROI measurements, ensuring AI’s contributions remain transparent and aligned with evolving business objectives.