How to maximize AI ROI in 2026

So why are most businesses struggling to profit from AI solutions? And how can they cut through the hype to achieve business objectives in 2026?

It turns out that having technological AI capabilities 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. Achieving positive ROI on an AI transformation requires a more thoughtful approach.

Since the generative AI boom erupted in late 2022, organizations have raced to implement AI initiatives. Leaders have been on the hunt for scalable AI strategies that streamline operations, inform data-driven decision-making, reduce costs and turbocharge product development. Now, AI agents are the hot topic. But the financial payback for such solutions remains elusive. Here’s why:

According to discussions from IBM’s Q4 2025 Think Circle, the primary challenge is not a technology problem, but an organizational one. Culture, governance, workflow design and data strategy are the main constraints on realizing ROI, and leaders agreed that AI ambitions often collide with internal realities long before technical limitations do.

The Think Circle report highlights that although many executives are investing in AI, few can reliably measure ROI today—with only about 29% saying they can measure ROI confidently. Meanwhile, 79% see productivity gains, meaning operational value exists, but translating short-term productivity into financial impact is still hard.

An IBM CEO study found that only around 25% of AI initiatives deliver expected ROI, just 16% have scaled enterprise-wide, and CEOs are balancing pressure for short-term ROI with longer-term innovation goals. This supports the broader narrative that AI often starts as experimentation first, value realization second. This is typical of emerging technology adoption cycles.

It’s still early days, and many AI deployments are still experimental or narrow solutions. This is OK, because early experimentation should be narrow, but these are less likely to be applications of AI that will yield massive ROI in the near term. The real value, however, will be in deeper integration into core workflows throughout the organization, not just one-off projects.

IBM research shows that paying down technical debt from legacy systems can improve AI ROI by up to 29% because it reduces friction and rework. But many organizations are still not where they need to be in their digital transformation journey to realize the full benefit of AI integration. Technical debt remains a hurdle, but AI can help on that front as well.

ROI calculations can be difficult because many of the beneficial impacts of AI are abstract, indirect and don’t materialize in the short term. For example, if an organization uses AI to streamline data analysis and data visualization so that business leaders can make more informed decisions, those results might not be felt for years. 

The real-time ROI of AI adoption is often challenging to detect. And any immediate gains might be deceiving. A company that announces plans to automate workflows and reduce its workforce with AI might see a quick bump in share prices, but that’s no guarantee of how customers and employees will ultimately react.

Financial analysts divide ROI into two categories: hard and soft. 

• Hard ROI covers tangible effects directly related to profitability. As an example, using AI to automate IT can lead to fewer outages and quicker response times, increasing operational efficiency and improving customer satisfaction for potentially greater user retention.

• Soft ROI includes other benefits that, while not immediately linked to profits, are still good for the organization. These can include increased employee morale and an improved customer experience. For example, employees might report greater satisfaction when companies choose an ethical approach to AI adoption.

Because ROI is a measurement, it requires numerical data on business outcomes to calculate. The key metrics for AI ROI, both hard and soft, include numerous key performance indicators (KPIs) that can be measured and quantified. Choose the right KPIs to most accurately calculate the ROI of AI on cybersecurity initiatives, content marketing, forecasting and more.

Hard ROI KPIs pertain to concrete financial data: costs saved or profits gained.

KPIs relevant to cost savings include: 

• Labor cost reductions such as hours saved due to enterprise automation and increased productivity when using AI tools. 

• Operational efficiency gains such as a reduction in resource consumption as a result of streamlined AI workflows. 

KPIs relevant to increased profits include: 

• Increased traffic, lead generation and conversion rates due to enhanced customer engagement, data-driven marketing personalization and AI-powered product recommendation engines.

• Revenue growth and new revenue streams from new AI-powered applications, faster development cycles and new business opportunities.

Soft ROI KPIs are less straightforward to measure against business performance in the short term, but tend to affect long-term organizational health. Such KPIs are often measured with surveys and qualitative research initiatives and can include: 

• Employee satisfaction and retention linked to AI projects, such as an organization successfully overcoming internal AI adoption challenges or proving its dedication to sustainability in AI. 

• Better decision-making as executives and team leaders make more accurate decisions in less time with the use of AI-powered data analytics. 

• Improved customer satisfaction, such as if AI-driven personalization campaigns reduce churn or by using an AI customer experience chatbot to handle more customer service inquiries.

The IBM Institute for Business Value has undertaken a series of studies into how organizations and teams can achieve optimal financial returns on their AI initiatives. Though case studies are industry-specific, teams in any sector can generalize the takeaways to suit their respective needs. 

Product development teams that followed the top four AI best practices to an “extremely significant” extent reported a median ROI on genAI of 55%. Teams wishing to replicate their results should incorporate the following practices into their workflows: 

1. Celebrate feedback: An AI transformation is an ongoing work in progress. Encouraging stakeholder feedback helps personnel feel comfortable speaking out, while reducing wasted time and resources on ineffective processes. 
   
   
2. Work iteratively: Introduce AI into the product development cycle in small stages to prevent fatigue and reduce risk. Tweak AI implementation over time as teams realize what works and what is ineffective. AI scaling is often best in small pieces, rather than all at once. 
   
   
3. Learn from user data: Mine and analyze user data to identify opportunities where generative AI technologies can bring the most value. Data quality is as important as quantity. Rather than attempting to actively shape user behavior, adjust project roadmaps to meet users where they are.
   
   
4. Build multidisciplinary teams: Take advantage of diverse skillsets and areas of expertise to reduce bottlenecks. Cross-functional teams mutually support each other, while siloing leads to communication blockers and project slowdowns. 

Organizations that adopt a holistic big-picture view for AI and content report an ROI 22% higher for CSC development and 30% for genAI integration.⁸ Three pillars drive ROI success with AI and CSC: 

1. Adopt a bird’s eye view to prioritize effectively: Examine all aspects of how machine learning touches the CSC, including strategic planning, budgeting, human resources and proactive change management. Note the interdependencies between teams and departments and focus on AI use cases with the greatest ROI potential. 
   
   
2. Don’t neglect change management: Introducing new processes and technologies is never simple, especially when they are as polarizing as AI. Employee buy-in is critical for the success of a new AI initiative. A cross-functional strategy centered on key advocates of change can keep enthusiasm high throughout the AI transformation. 
   
   
3. Minimize risk to unleash creativity: AI risk management engenders creative freedom when teams don’t need to worry about the AI getting things wrong. Let AI systems take care of low-risk routine tasks so that creatives can do what they do best: make amazing content.

Although the rate of innovation in the AI space is so high, it’s still very early days, and it may be more advantageous for firms to think of this period as a time for messy experimentation rather than with an eye toward ROI. Nvidia CEO Jensen Huang argued as much at the Cisco AI Summit in February. He compared forcing engineers to justify AI work with hard ROI up front to asking a child to make a business plan for a hobby.²

“When your kids tell you they want to try something, you should say yes. We never ask questions at home like ‘What is the return on investment here?’”

This approach will require leaders to obtain widespread buy-in across the organization, with an understanding that ROI might yet be years away. Trying to force control through short-term ROI evaluation risks missing transformative opportunities.

Furthermore, Huang also advised companies to develop their own understanding of AI infrastructure rather than relying entirely on third-party service providers, so they can truly grasp what works and what doesn’t.

“Let a thousand flowers bloom,” said Huang, encouraging a spirit of broad, exploratory experimentation rather than rigidly filtering ideas based on early ROI metrics. It may not be easy to convince financial stakeholders, but how can one argue with the man who runs an AI innovator now worth USD 4 trillion?