The rapid adoption of artificial intelligence (AI) is transforming business landscapes, with approximately 78% of companies integrating AI into at least one function. The global market for AI now exceeds $244 billion USD. Despite this growth, many organizations are falling short in realizing the expected return on investment (ROI) from their AI initiatives. According to recent findings, only 25% of AI projects have met their ROI expectations, leading to questions about how businesses can bridge this gap.
A study by the IBM Institute for Business Value (IBV) reveals that around 18% of companies have achieved an ROI that surpasses their cost of capital. This raises a critical issue: how can the remaining organizations improve their performance? Successful companies often share common strategies, including a comprehensive approach to integrating AI with hybrid cloud infrastructure, optimizing for cost and performance, and actively managing data security and compliance.
Strategic Integration of AI and Hybrid Cloud
To maximize AI’s benefits, businesses need to adopt a platform approach that leverages shared capabilities across various IT environments. This holistic management of hybrid cloud infrastructure is vital for achieving sustainable ROI. Organizations that lack intentional design often find themselves with fragmented architectures that lead to inefficiencies. Such hybrid-by-default systems can impede innovation and complicate data management, ultimately restricting the organization’s potential.
The IBV study highlights that about one in three businesses have paused AI deployments after initial pilot phases. The challenge lies in recognizing that AI must be integrated within a broader data ecosystem, encompassing data centers, cloud environments, and edge computing. For instance, a financial services company using AI chatbots may see operational improvements, but these gains are limited if the chatbots operate in isolation, lacking access to comprehensive data from other departments.
To unlock greater value, companies must develop an enterprise-wide AI strategy that facilitates data sharing across departments. This integration allows for more effective AI applications, such as predicting customer behavior and optimizing inventory management, resulting in superior outcomes.
Addressing Infrastructure and Security Challenges
Currently, approximately 80% of organizations function with outdated technology infrastructures. Transitioning to a robust IT framework requires a thorough understanding of how various tools—cloud, mainframe, and edge—interact to drive ROI and control costs. This understanding is essential for fostering collaboration among different teams within the organization.
For example, a large retail chain could use AI-driven insights from its marketing and merchandising teams to enhance targeted campaigns and optimize inventory, thereby driving sales and improving ROI. Such collaborative efforts depend on IT decision-makers aligning on business needs and developing scalable systems.
Data security also plays a crucial role in realizing AI investments. The IBM Cost of a Data Breach report indicates that the average cost of a data breach in the U.S. surpassed $10 million for the first time in 2025. This underscores the importance of having a well-designed hybrid architecture that secures data throughout its lifecycle, particularly in highly regulated sectors such as finance and healthcare. Organizations must stay vigilant about evolving legal frameworks and ensure that data privacy protections are integrated into their AI strategies from the outset.
To navigate diverse regulatory environments, enterprises should implement a layered framework that addresses data privacy, residency, and governance. This comprehensive strategy will help maintain compliance and avoid unnecessary costs while ensuring data security.
Building Partnerships for Growth
A hybrid-by-design approach that enhances data agility while minimizing risk is essential for turning AI from a promise into significant profit. Strategic partnerships have emerged as vital for growth in today’s landscape, with industry leaders often collaborating to unlock value that is unattainable alone. This trend highlights the importance of collaboration in driving innovation and progress.
By engaging with other organizations, companies can leverage shared insights and resources to strengthen their AI initiatives. Those that embrace this collaborative spirit are likely to find themselves at the forefront of the evolving AI landscape, setting the standard for industry advancements.
In conclusion, while AI adoption is on the rise, many companies face challenges in maximizing their investments. By intentionally integrating AI within a hybrid cloud framework, addressing infrastructure shortcomings, and fostering interdepartmental collaboration, organizations can better position themselves to realize the full potential of AI technologies. Such efforts not only enhance ROI but also provide a roadmap for others in the industry to follow.