According to the latest Aberdeen paper: According to AI research, only 4% of companies are not using AI in some way. Whether through deliberate deployment or simply using AI-enabled software in everyday business tools, technology has moved from pilots and trials to the core of daily operations.
From our report: “As AI reshapes IT operations, organizations are finding practical ways to integrate AI into their daily workflows and infrastructure. From September to October 2025, Aberdeen Strategy & Research surveyed 15 of 281 IT decision makers personally involved in their organizations’ AI-related technology purchase decisions. We conducted a minute-long online survey to find out how IT professionals in North America, EMEA, APAC, and India are currently using AI and the results they're achieving.
However, this ubiquity comes with potential dangers. Cyclical investing and a frenzy of vendors pushing immature AI features on users who don't need them are likely to create an AI bubble.
As AI hype continues to accelerate, companies must choose between irrational AI and practical AI. Understanding the differences between these two approaches is the deciding factor between successful AI-driven modernization and costly stagnation.
Practical AI and Irrational AI
The difference between successful AI initiatives and unsuccessful projects is not just the technology itself, but the approach taken by the business. Our research shows that companies tend to either follow the AI hype cycle irrationally (what we call irrational AI) or adopt a more cautious approach (similar to real AI). Let's define these further.
Irrational AI is characterized by a “just do it” mentality. It's fueled by fear of missing out, not business logic. Organizations adopting this approach like to say they're “transforming everything,” but they don't have a coherent plan for specifically transforming anything.
Irrational AI also tends to rely heavily on publicly available large-scale language models (LLMs), exposing them to data privacy risks and generic outputs. In many ways, these companies seem to view AI as “magic”, a black box whose mere presence will solve unspecified problems. This approach will be the main fuel for the looming AI bubble, creating noise without a signal and costs without a return.
Conversely, practical AI is defined by a clear focus on using AI to achieve specific, tangible business goals. Pragmatic AI leverages internal data to build AI capabilities focused on what matters to the business, rather than relying entirely on a common public model. It is also deployed as part of a carefully planned, phased rollout, rather than a “just-in-place” implementation. Most importantly, real-world AI is governed by defined success metrics, allowing companies to demonstrate where and how their AI is working.
Driving force for implementation
Aberdeen research shows a consistent increase in dedicated deployment of AI (not just functionality built into existing software solutions). And when we look at the pressures driving these companies to adopt AI, we find that serious and “real” concerns are the biggest drivers.
52% of organizations cited the need to combat increasing cybersecurity threats as a key factor. As attackers leverage automated tools to penetrate defenses, businesses are turning to AI to analyze patterns, detect anomalies, and respond to threats at machine speed.
The second major factor is rising cloud costs (34%). While AI itself requires computing power, it is increasingly being used to optimize infrastructure, identify waste, and streamline the allocation of cloud resources.
Finally, a lack of application development efficiency (25%) is driving companies toward AI. In an agile world, the ability to write, test, and deploy code faster is critical to success. AI-driven coding assistants and automated testing frameworks are becoming essential to maintaining development velocity.
Strategies for preparedness and infrastructure hurdles
Recognizing the need for AI is one thing, recognizing the need for AI is another. Whether you're ready to implement it or not is another story. Key strategies for increasing AI readiness include using a dedicated AI platform to develop, train, and deploy models (35%) and ensuring a structured environment for innovation.
With the lack of AI expertise a constant challenge, 34% of companies are prioritizing in-house AI skill development and AI/ML talent recruitment efforts. Additionally, 25% of organizations are focusing on improving data management, recognizing that AI is only as good as the information fed to it.
However, our research also uncovered a number of important challenges that businesses face when it comes to AI success. The ability to integrate AI with existing legacy IT platforms is a major challenge as it is essential to improving usability and engagement.
Additionally, running advanced models requires large cloud capacities and associated costs, creating a barrier to entry. Database functionality is also an issue. Traditional databases are often ill-equipped to handle the vector search and high-speed search needs of modern AI applications.
IT Engine: Support, Services and Benefits
Nowhere is the impact of practical AI more evident than in IT support and services. This space has become a testing ground for practical use cases such as faster problem resolution, improved analysis, and automation of repetitive workflows.
When looking at the benefits realized with IT service management (ITSM), we find that 50% of organizations leveraging AI in ITSM report improved IT productivity. Offload Level 1 support tickets to conversational AI and automate routing, freeing up IT staff to focus on complex, high-value strategic initiatives. This ripples through the broader organization, resulting in increased end-user productivity (reported by 35% of respondents) and increased end-user satisfaction (33%).
For companies that overcome infrastructure hurdles and adopt a pragmatic mindset, the impact of AI on a wide range of business metrics is often positive. Companies that have implemented AI for more than a year see a positive impact on system uptime and reliability, incident response times, and overall user satisfaction. The main benefits of implementing it for your business include increased process efficiency, greater automation of tasks, and improved performance.
This study, which analyzes businesses with long implementation periods, shows a clear maturity curve. These organizations have improved the accuracy of their AI, likely because they have fine-tuned their internal data models over time.
They also report increased employee and customer engagement with AI, moving past the initial friction of adoption and into a state of collaboration. And these companies report improved IT outcomes in key areas such as performance, uptime, and return on investment (ROI).
By focusing on internal data, defining specific metrics, and targeting high-impact areas like cybersecurity and IT productivity, companies can protect themselves from the hype. The future belongs to the practical: people who use AI not because it's trendy, but to solve problems, save money, and secure their future.
