AI adoption is reaching unprecedented levels: 78% of companies use AI in at least one business function and the global market is valued at over $244 billion USD.
But it’s not just about “do you use AI?” anymore. Rather, it’s about “how do you use it?”
Over the last three years, CEOs say only 25% of their AI initiatives have delivered the ROI they expected. Companies are continuing to invest in AI, but they’re struggling to demonstrate its financial benefit to stakeholders.
General Manager for IBM Power and Platforms, and CTO for IBM Infrastructure.
The organizations that truly stand apart from the crowd and are meeting ROI benchmarks have a few things in common: they are taking a holistic approach to integrating AI with hybrid cloud infrastructure, optimizing systems for cost and performance, rigorously managing data security and sovereignty, and even partnering with other businesses to advance their goals.
In doing so, they’re charting a path to next-level business transformation and demonstrating the true value of AI to investors and stakeholders—all while setting a prime example for others to follow.
Integrate AI and hybrid cloud intentionally
Taking a platform approach to AI—one that leverages and provides a common set of capabilities across multiple IT environments—is the first step in holistically managing hybrid cloud infrastructure to achieve ROI.
A thoughtfully planned architecture, known as hybrid by design, optimizes infrastructure and the capabilities atop it for consistency, scale, trust, and performance, rather than just allowing independently designed environments.
Without intentionality, businesses fall into hybrid-by-default architectures which emerge opportunistically and have different capabilities, operations, and security across each environment.
These are riddled with limitations, challenges, slower innovation, and weaker data management, and therefore hinder organizations’ ability to meet their true potential.
A recent IBM Institute for Business Value (IBV) study found that one in three businesses have paused an AI deployment after the initial pilot phase.
Why? The technology decision makers at these companies – the CTOs and CIOs – are realizing that AI doesn’t exist in a vacuum but resides in data centers, in the cloud, and at the edge with exponentially growing demands on power.
Without proper infrastructure, there’s no way to scale these deployments beyond one-off use cases with limited ROI.
Let’s think about a financial services company that is using AI chatbots to improve customer service. The chatbot performs well in isolation, reducing wait times and lightening the load on human agents.
But the gains are limited, because the chatbot is drawing from a narrow data pool. It can’t assist with more complex issues like fraud detection, loan application status, or investment portfolio insights because it isn’t integrated with those departments’ systems.
With a more intentional, enterprise-wide AI infrastructure strategy that enables data-sharing across departments and speeds time to discovery and deployment of AI, the company could unlock dramatically more value and achieve better results. To get the most ROI from AI, enterprises need to bring AI to the data, as opposed to the other way around.
Redesigning in collaboration
Right now, about 80% of organizations have outdated tech infrastructure – meaning their compute, storage, and network cores are due for an upgrade. However, the transition to a robust intentional IT estate doesn’t happen overnight.
This modernization needs to start with an understanding of how different tools work together with the cloud, mainframe, and the edge, each playing a valuable role in driving ROI and keeping costs low.
To understand how these different tools work together, each part of the business needs to communicate with one another. For example, at a large retail chain, the marketing team could use AI to predict customers’ purchase behaviors, while the merchandising team uses AI to plan peak product demands and inventory levels by region.
With a common data science and AI stack connecting them, the company can have a unified view into how to create targeted campaigns for items that are in stock in specific stores, driving sales and boosting ROI across both teams.
Such coordination is only possible if IT decision makers from across the organization come together to identify their precise business needs and align on systems that can scale with their strategy.
Maintaining data security and regulatory compliance
A well-planned hybrid-by-design structure is also paramount for mitigating the risks of increasingly sophisticated cyberattacks.
According to IBM’s latest Cost of a Data Breach report, the average cost of a U.S. data breach surpassed the $10 million mark for the first time in 2025, as threat actors increasingly exploit unsecured AI systems and fragmented data architectures.
Compounding the financial damage, 35% of U.S. organizations hit by breaches over the past year reported regulatory fines exceeding $250,000. These are devastating consequences for companies hoping to realize meaningful returns from their AI investments.
A carefully coordinated architecture can help ensure data is securely stored, processed, and governed in a consistent way from end to end—especially in highly regulated sectors like financial services, government, and healthcare, where reputational and legal consequences can be severe.
Organizations must stay current with evolving legal frameworks and ensure privacy and compliance protections are baked into AI and data strategies from the start.
This coordination and planning must extend globally to align with the specific regulations of the territories in which the organization operates. Enterprises should approach sovereignty as a layered framework—one that integrates data privacy, data residency, service locality, and governance into a holistic Sovereign Cloud strategy.
This layered approach allows organizations to adapt to diverse regulatory environments while maintaining control, security, and compliance across borders and avoiding unnecessary costs.
Partnering for success
A hybrid-by-design approach that maximizes data agility while minimizing risk enables deployment across an entire IT estate – giving enterprises the ability to turn AI from a promise into significant profit.
Strategic partnerships with entities outside the organization have also become indispensable for growth in today’s landscape. Industry leaders—and even competitors—are increasingly joining forces to unlock value that would be impossible to achieve alone.
Collaboration has become the new standard for innovation and growth. Companies that embrace these partnerships will be best positioned to thrive in the evolving world of AI.
Enterprise infrastructure is made stronger through partnerships, and by collaborating with other like-minded organizations, businesses can do more than just maximize their ROI—they can lead the entire industry forward.
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