In many ways, the upcoming year is shaping up to be one of opportunity and innovation as IT leaders see more benefits and options around AI than ever before in running the enterprise. By the same token, this progress is creating new complexities and choices for organizations to navigate. Through conversations with ScienceLogic customers, leading industry analysts, partner companies and key executives, several AI-related themes have emerged moving into 2025. Here are six predictions that will define the IT operations landscape in 2025 and guide transformation teams seeking to leverage AI over the next year.
Prediction #1: Overall investment in AI will continue to increase.
Positive economic conditions and recent macroeconomic moves like the Federal Reserve lowering interest rates will continue to spur more business investment overall, and that includes AI. But in a classic example of a “good problem to have,” this is creating urgency in choosing the right solutions and combinations of traditional and Generative AI (GenAI) technologies. Teams will need to prioritize the use cases that matter most, as well as the configuration choices that ensure the most security and trust (see Prediction #6) around AI processes, data and outputs. And, to ensure the sustainability of these investments, enterprises must achieve extensibility at the data layer with robust data standards providing a secure and future-proof approach.
Prediction #2: More best-of-breed architectures.
The 2025 IT marketplace will continue to grow exponentially when it comes to AI options and paradigms. A wider range of software and services dedicated to AI means there will be far fewer monolithic projects, and instead tighter API integrations with a broad array of AI partners. This best-of-breed ecosystem can be beneficial to the extent organizations ensure their monitoring solutions remain comprehensive and vendor-agnostic; and that their underlying data layer is standardized and flexible enough to accommodate any multitude of emerging technologies from vendors. With enhanced tools capable of supporting specialized tasks while maintaining flexibility, enterprises will reap the benefits of the adaptability necessary to drive innovation.
Prediction #3: Digital mentors powered by GenAI will further close the skills gap.
Digital mentors will upskill IT staff to a position of making better, more informed, and more timely decisions than they might otherwise be able to. By automatically analyzing KPIs and teeing up the right business and IT operational context, these digital mentors powered by GenAI, will help users more quickly to access relevant information, historical data and best practices in ways they can easily understand and act upon. GenAI further enhances the user experience by allowing IT staff to ask clarifying questions in natural language and receive practical guidance and recommendations throughout the process. This enables level 1 engineers to access the knowledge and resources necessary to troubleshoot complex errors on their own, gaining critical IT experience while more senior engineers are free to focus on business priorities, strategy, and innovation.
Prediction #4: Causal and Agentic AI will drive IT focus from efficiency to strategy.
Related to Prediction #3, causal and agentic AI in IT will transform problem-solving by predicting issues before they arise and autonomously enacting solutions. This proactive AI approach will enable systems to understand and adapt to underlying causes rather than react to symptoms, enhancing reliability, reducing downtime, and empowering IT teams with insights for optimized, self-correcting infrastructure management. Additionally, the causal nature of such AI Advisors showcases the technology’s reasoning and process to support human-in-the-loop intervention as needed and instill greater trust in such solutions, further discussed in Prediction #5.
Prediction #5: AI trust measures will become more essential.
New use cases mean new forms of data. And as confidence in AI leads to more autonomous and more mission-essential functions – such as managing a public utility or supporting a DoD project for national security – the trust in AI’s data management and decisions becomes more important. Against this backdrop, investments in prompt engineering, retrieval augmented generation (RAG), and other trust-building technologies will increase. The same is true for private AI deployments, which enhance trust and security by positioning AI and large language models (LLMs) to operate in protected enclaves and train exclusively on company data.
Prediction #6: Regulatory considerations will become increasingly front and center.
This prediction stems both from the increase in domain-specific regulations in key areas like healthcare and finance, and from the increased regulation of AI itself i.e., Digital Operational Resilience Act (DORA) and the AI Act. Such an environment raises the stakes for ensuring AI remains compliant even as it automates and scales a broader swath of enterprise operations. Teams must also architect the right public/private AI integrations so that their proprietary ML processes can continually and securely pull in public data on new or changing regulations to inform operations.
Taken together, these six predictions shape the field for innovation in 2025 as organizations expand their investments in AI and operations. For additional insights on how real-world transformation teams are navigating this changing landscape, see ScienceLogic’s recent survey of 400 global IT professionals on “The Future of AI in IT Operations: Benefits and Challenges.” Access the white paper here.
