The ROI on purchased software and subscriptions has always been a matter of high interest for C-level executives in profit and not-for-profit companies alike.

But with AI entering the fray, the measurement of ROI has become quite muddled.

In fact, a 2025 report by the IBM Institute for Business Value found that while enterprise-wide AI projects incurred a 10% capital investment, those same AI initiatives achieved an ROI of just 5.9%.

The reason for this is that many organisations across the world are still in the nascent stage of AI adoption. Those that have actively embraced AI into their business workflows are still learning to cope with the constant changes happening around AI and machine learning, particularly in relation to autonomous AI agents.

Understanding how to measure AI ROI, therefore, becomes just as important as adopting various AI tools across your business landscape. With the increase in spend on leading agentic AI tools such as Salesforce Agentforce and Atlassian Rovo, it becomes necessary to be able to quantify the impact agentic AI has on your bottom line.

In this blog, we’ll explore what AI ROI means, the difference between trending and realised ROI, how to leverage Salesforce Agentforce and Atlassian Rovo for measurable gains, frameworks to scale AI deployments, and pitfalls to avoid.

Who is this blog for?

The topics covered in this blog will be valuable to:

• CIOs and CTOs evaluating AI technologies and platforms.
• CFOs seeking transparent ROI models that capture cost savings, efficiency gains, and revenue opportunities from AI adoption.
• Operations managers responsible for aligning AI solutions to measurable operational improvements.
• Program managers overseeing AI rollouts and requiring clear measurement frameworks.
• Salesforce and Atlassian users looking to understand how Agentforce and Rovo can deliver tangible ROI and scale intelligently.

Looking into the AI ROI challenge

Proving the Return on Investment (ROI) for artificial intelligence (AI) remains one of the biggest obstacles to scaling enterprise adoption.

According to Gartner, 49% of executives identify the inability to demonstrate AI ROI as the primary barrier to advancing their AI initiatives. At the same time, McKinsey’s ‘The State of AI 2025’ report highlights a striking contrast — while leading organisations are capturing 20–30% improvements in EBIT through AI, the average company struggles to quantify results, which often leads to stalled or abandoned AI projects.

This challenge is rooted in several underlying factors. First of all, AI initiatives come with hidden costs such as system integration, employee training, and ongoing governance requirements that are not always visible at the outset. Secondly, AI creates diffuse benefits—including time saved, knowledge unlocked, and improvements in employee and customer satisfaction—that are harder to measure in financial terms (as compared to traditional IT systems). Thirdly, the payoffs of AI often lag, with many organisations needing between 6 to 36 months to realise measurable financial returns.

Because of these complexities, traditional ROI models—built for straightforward IT investments—fall short when applied to AI. To succeed, organisations must adopt smarter, AI-specific ROI frameworks that account for both the tangible and intangible value delivered by agentic AI systems (such as Salesforce Agentforce and Atlassian Rovo).

Defining ROI for agentic AI systems

AI ROI cannot be reduced to a simple dollars earned-dollars invested formula. Instead, organisations should distinguish between:

1) Trending ROI (which are short-term, leading indicators). These may include:

• Hours saved
• Rate of cases or incidents resolved
• Search queries answered correctly
• Levels of employee adoption

2) Realised ROI (these include longer-term, lagging indicators, such as):

• Reduced FTE costs
• Incremental revenue
• Improvements in customer retention
• Higher NPS (Net Promoter Score)

Moreover, a balanced AI ROI framework must also capture:

• Financial metrics, such as cost savings and revenue impact.
• Operational metrics that include productivity, time to market, and error reduction.
• Human impact metrics such as employee satisfaction, adoption rates, and customer trust.

How to create a framework to measure AI ROI effectively

Measuring AI ROI requires more than simple cost-versus-benefit calculations. As an organisation, you need to implement a structured framework that can capture both short-term indicators and long-term business impact. Four key approaches can help achieve this.

1. Establishing a baseline
Before implementing AI, it is essential to set a baseline by recording key performance indicators. These may include metrics such as the average cost per support ticket, the amount of time employees spend searching for information, or sales conversion rates. Establishing this pre-deployment data provides a reference point to measure the actual improvements brought by AI.

For instance, Salesforce’s own ROI calculator and Atlassian’s pricing calculator can help you collect and visualise these baseline metrics.

2. Performing before-and-after comparisons
A practical way to measure AI ROI is to run AI solutions in a pilot environment and compare results against a control group using traditional processes. For example, a support team could deploy Salesforce Agentforce to handle customer queries and then measure the case deflection rate compared with a similar team that does not use Agentforce. This approach isolates the AI’s contribution and ensures the results are directly attributable to the new system.

3. Including time variances for measuring AI ROI
The impact of both generative AI and agentic AI deployments unfolds over different time horizons, making it necessary to evaluate outcomes at multiple stages:

• 0–6 months: Focus on trending ROI, such as hours saved, adoption rates, and employee satisfaction. These indicators provide early signals of value, even if financial results are not yet realised.
• 6–18 months: Track realised ROI, including cost avoidance, improved productivity, and reduced manual workloads. This period often reflects the first measurable financial gains.
• 18–36 months: Evaluate strategic ROI, such as your organisation’s increased capacity for innovation, entry into new markets, or the creation of new revenue streams.

4. Implementing data governance and feedback loops
Finally, it is important to treat AI ROI measurement as a continuous process rather than a one-time exercise. Embedding AI ROI tracking into sprint retrospectives ensures that your IT, sales, marketing, service, and product teams regularly assess the value delivered by AI. On a broader level, quarterly executive reporting on AI KPIs helps align AI progress with business strategy and provides the leadership team with clear visibility into outcomes.

Together, these frameworks create a comprehensive measurement model that captures both the immediate and long-term value of AI investments.

But how do you measure the overall transformative value of AI? And more importantly, how do you scale it?

Below, we explore smart, actionable ways to measure and scale AI ROI in enterprise environments.

7 smart ways to measure and scale AI ROI in 2026

1. Define clear business objectives
The foundation of measuring AI ROI is clarity of purpose. Before deciding on metrics, your company should try to align future AI initiatives with specific business objectives.

For instance, your support team may want to focus on reducing ticket resolution times in Jira Service Management, your sales team may want to improve lead conversion rates in Salesforce, or perhaps your consulting team may be looking to automate repetitive administrative tasks. By clearly defining these objectives, you can ensure that you track meaningful KPIs rather than vanity metrics, making it easier to connect AI investments directly to business impact.

2. Use platform-specific metrics
Any software or cloud platform you use provides its own analytical capabilities. Therefore, tapping into these ensures precision when measuring the ROI of your agentic AI tools.

For example, Atlassian’s product suite can track metrics like issue resolution time, SLA compliance, and automation success rates, while Salesforce’s CRM system offers visibility into lead scoring accuracy, sales cycle duration, and customer satisfaction scores (CSAT). Using these platform-native metrics in conjunction with the use of Agentforce or Rovo AI agents will reveal how AI adoption directly improves both productivity and the customer experience.

3. Measure time and cost savings
One of AI’s most immediate contributions is operational efficiency. This means that you should look to quantify the hours saved by automation, such as auto-triaging support tickets or auto-generating reports.

Additionally, cost reduction due to AI adoption could also come from eliminating manual work or reducing reliance on third-party vendors. Another key dimension is resource reallocation—employing AI agents can help free up employees from repetitive tasks so they can focus on higher-value, strategic activities. Comparing metrics before and after AI deployment makes these improvements tangible.

4. Track adoption and engagement
Even the most powerful AI tools will fail to deliver tangible ROI if employees don’t actively use them. Adoption and engagement are crucial indicators of success. As a company, you should consider monitoring usage rates of AI-powered tools such as predictive analytics dashboards or customer chatbots, while also conducting user feedback surveys to surface any barriers to adoption. Training programs and enablement initiatives play an important role here, helping teams build confidence while maximising the value of AI tools.

5. Scale with modular AI solutions
Scaling AI effectively does not mean rolling it out across the entire enterprise all at once. A smarter approach is modular and iterative. Start with pilot programs in targeted business areas, such as IT service management (ITSM) or sales operations. From there, integrate modular AI apps from the Atlassian Marketplace or Salesforce AppExchange to extend functionality without requiring full-scale custom builds. As the system generates real-world data, you can then choose to refine models, optimise workflows, and scale AI gradually and sustainably.

6. Calculate AI ROI with a holistic formula
When calculating AI ROI, companies should adopt a holistic formula that accounts for both tangible and intangible benefits.

Tangible benefits include measurable outcomes such as revenue increases, cost savings, and improved productivity. Intangible benefits, such as higher employee satisfaction and stronger customer loyalty, are harder to quantify but equally important for understanding AI’s long-term value. Including both ensures that your organisation captures the full benefit of AI’s impact.

A simple approach is:

ROI (%) = [(Net Benefits from AI – Cost of AI Implementation) ÷ Cost of AI Implementation] × 100.

7. Leverage AI for strategic insights
The powerful tool that it is, AI goes beyond process automation—it can serve as a powerful strategic advisor. AI tools such as Salesforce Einstein can forecast demand, predict customer churn, or recommend next-best actions, while machine learning models in Atlassian Intelligence can highlight bottlenecks and inefficiencies in workflows. These insights elevate AI from being a tactical tool to a driver of strategic decision-making, enabling businesses to proactively adapt to market shifts and create new opportunities.

Maximising AI ROI with Salesforce Agentforce

Salesforce Agentforce is a next-generation suite of autonomous AI agents built on large language models and deeply integrated with Salesforce CRM, Data Cloud, and Salesforce Flow. By embedding AI directly into core business processes, Agentforce helps organisations scale efficiency, improve personalisation, and realise faster time-to-value.

What are some of the key AI ROI metrics Agentforce provides customers?

1. Reduction in customer churn

In tested enterprise deployments, Salesforce Agentforce has demonstrated the ability to reduce customer churn by up to 47%. The AI platform did this by proactively engaging customers, analysing sentiment, and predicting attrition risks, thereby enabling businesses to intervene early and retain valuable accounts.

2. Increase in implementation speed

Compared with Salesforce’s own Einstein AI, Agentforce’s agentic AI deployments are 67% faster. This acceleration comes from its native integration with Salesforce Data Cloud and Salesforce Flow, which reduces customisation needs and allows businesses to achieve outcomes more quickly.

3. Boost in sales productivity

Precina Health (a healthcare innovator in the United States) is using Agentforce to efficiently onboard, train, and provide ongoing coaching for practitioners and paraprofessionals. This includes therapeutic support, call training, and clinical visit guidance. Agentforce-supported sales coaching has thus led to measurable improvements in service outcomes. Specifically, diabetic patients saw average blood sugar levels drop from 9.6 to 6.4 within 12 weeks.

4. Improvement in service efficiency

Publishing giant Wiley achieved a 213% AI ROI by deploying Agentforce-powered service agents. They also reported a 40% increase in customer self-service efficiency, which helped free their service reps’ time to focus on complex cases while reducing overall support costs.

5. Increase in cost savings

A Fortune 500 company reported saving $2.1 million in their first year of adopting Salesforce Agentforce. The savings stemmed from faster automation rollouts, reduced reliance on external vendors, and higher process efficiency.

Maximising AI ROI with Atlassian Rovo

Rovo is Atlassian’s agentic AI platform designed to transform knowledge management and productivity across tools like Jira, Confluence, and other Atlassian products. By combining enterprise-grade search, contextual chat, and customisable AI agents, Rovo AI empowers teams to work smarter and faster with reduced friction.

What are some of the key AI ROI metrics Rovo provides customers?

1. Increase in time saved

Atlassian Jira and Confluence users are already seeing up to 2X AI ROI, saving more than 100 minutes per day when using Rovo. By eliminating the need to manually search for tickets, documents, or historical project data, Rovo accelerates daily workflows and allows employees to redirect time to higher-value activities.

2. Improvement in work quality

According to Atlassian, 85% of Rovo users state that the agentic AI platform improves the quality of their work. This improvement comes from more accurate information retrieval, context-aware responses, and reduced rework.

3. Enhanced productivity gains

With Atlassian Rovo, companies can typically achieve savings of 20–40% in daily search time. This is because Rovo combines AI-powered search with knowledge cards, Rovo chat, and customisable AI agents to provide contextually relevant, efficient knowledge access. When scaled across teams, these efficiency gains translate into thousands of dollars in annual savings per department.

4. Improvement in AI adoption

Unlike many other enterprise AI platforms, Rovo is bundled at no additional cost with Atlassian’s Premium and Enterprise plans. This significantly lowers the barrier to AI adoption, driving rapid agentic AI usage across businesses without requiring additional budget approvals.

Common pitfalls to avoid when measuring AI ROI

1. Overfocusing on cost savings while ignoring revenue and engagement benefits

Many organisations measure AI success purely in terms of cost reduction (such as reduced labour costs or lowered operational expenses). While these savings are definitely important, they represent only one dimension of AI’s value.

By overlooking revenue growth opportunities—such as higher conversion rates or upselling through AI-driven insights—and ignoring engagement benefits—like improved customer satisfaction or employee productivity—companies risk underestimating the full impact of AI and missing long-term gains.

2. Not focusing on AI adoption rates within the company

AI only drives ROI if people use it.

Even the most advanced AI systems deliver no value if employees do not adopt them into daily workflows. Low AI adoption often results from insufficient training, unclear communication of benefits, or resistance to change. Measuring adoption rates, gathering user feedback, and providing continuous enablement programs are critical to ensuring that teams embrace generative and agentic AI solutions and use them effectively.

3. Neglecting data and AI governance

AI hallucinations and bias can increase security and compliance risks while reducing credibility. Without proper governance, there is a risk of AI generating inaccurate results, perpetuating bias in decision-making, or exposing your business to compliance and regulatory risks. These issues can quickly erode stakeholder trust and undermine ROI.

Establishing strong governance frameworks—including validation checkpoints, bias monitoring, and regulatory compliance reviews—ensures that AI outputs remain reliable, ethical, and credible.

4. Implementing AI in silos

AI ROI diminishes if your AI agents aren’t integrated across your business workflows.

Deploying AI in isolation within a single department or tool limits its potential impact. Siloed implementations often lead to fragmented insights and reduced efficiency gains. To maximise AI ROI, ensure that your AI agents are integrated across your business ecosystem and that your data and processes from sales, service, IT, and operations are seamlessly connected.

Best practices that can help you scale AI ROI

1. Start with high-impact use cases
The best tip to achieve success with your company’s AI project is to focus on processes that generate visible and measurable outcomes.

Examples of measurable processes include handling customer queries around the clock with the help of AI chatbots or retrieving accurate information instantly using AI knowledge assistants. Not only do these high-impact use cases provide you with quick wins, but they will also help you demonstrate the value of AI to your stakeholders and help you build momentum for broader AI adoption across your business.

2. Align AI with your company’s strategic goals
AI initiatives deliver the strongest returns when they are directly tied to overarching business objectives. For example, if your company board prioritises customer experience, then your AI deployments should focus on reducing service resolution times and improving satisfaction scores.

Similarly, if operational efficiency is a priority for your stakeholders, AI can be used to streamline business workflows and reduce manual effort. Linking AI ROI to board-level priorities ensures that AI is not viewed as a side project but as a driver of enterprise transformation.

3. Use AI ROI dashboards
Real-time visibility into AI performance is essential for scaling AI ROI effectively. An AI ROI dashboard can help you track key performance indicators such as adoption rates, cost savings, revenue spikes, and other efficiency metrics. By presenting this data in a clear and accessible way, AI ROI dashboards help executive members and teams understand the progress of AI initiatives, identify areas for improvement, and make evidence-based decisions about scaling investments.

4. Regularly evaluate your AI use cases
AI ROI should never be treated as a one-time calculation but rather as a dynamic measure that evolves as models, workflows, and use cases mature. As an organisation, you should attempt to regularly evaluate AI performance, retrain models with updated data, and expand successful applications into new areas of the business. This can help ensure that AI remains relevant, accurate, and aligned with your company’s changing business needs.

Important ROI formulas to keep in mind

• Net Benefit = Total Benefits – Total Costs
• ROI (%) = (Net Benefit ÷ Total Costs) × 100
• Payback Period = Total Costs ÷ Annual Benefits
• Employee Productivity ROI = (Hours Saved × Hourly Cost × Adoption Rate) ÷ AI Cost

How Corptec can help you measure and maximise your AI ROI

When dealing with a technology as new as agentic AI, it understandably takes time to get the ball moving. An experienced technology solution partner like Corptec Technology Partners can help you set realistic AI ROI benchmarks while guiding you through the deployment of AI platforms that answers your specific business challenges.

With our long-standing technology partnerships with Atlassian, Salesforce, and Microsoft, as well as our deep expertise in AI technology solutions and custom GPT services, Corptec can help you deploy an AI platform that aligns with your operational, security, and compliance needs. Our team of experts can help you:

• Strengthen your AI foundation with data quality and governance
• Integrate AI into your current business processes
• Define your AI use case and help you build, test, and deploy your AI models
• Design and implement self-hosted AI and train AI models on proprietary data
• Ensure custom AI/GPT integrations as per your unique requirements
• Train and support your team to adopt AI effectively

Would you like to explore how AI can help you achieve enterprise-wide value while improving business processes and customer satisfaction? Book a free discovery session with us today!

Discover how your business can stay ahead in today’s AI-driven world! Book a meeting with Corptec’s AI expert today!