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how ai transforms it operations amid privacy and compliance pressures

Artificial Intelligence

How AI Transforms IT Operations Amid Privacy and Compliance Pressures

Released on

Tuesday, December 16, 2025

Johannes Biesing

Artificial Intelligence is no longer a futuristic concept. It’s rapidly reshaping how organizations manage IT operations. Yet despite strong interest and growing adoption, many companies still struggle with unclear regulations, data privacy questions, and governance challenges.

To better understand how IT leaders are approaching AI by the end of 2025, USU commissioned Forrester Consulting to conduct an extensive AI study with 400+ IT decision makers from Europe and North America. The goal: uncover how organizations are adopting AI in IT operations, where they hit roadblocks, and what strategic priorities will define AI maturity in the years ahead.

Below, I summarize the key findings from the Forrester study AI in IT Operations: Balancing Potential With Data Privacy And Compliance and what they mean for IT decision makers evaluating their next steps.

Management Summary

The AI study shows that organizations see artificial intelligence as a foundational capability for staying competitive, even if many are still early in their journey.

AI is needed to stay ahead of the competition

According to the study, organizations believe AI will be critical for driving productivity, innovation, and better decision-making. While many companies are still early in their journey, IT leaders see AI as a foundational capability for staying competitive in the years ahead.

Compliance, privacy, and governance barriers slow progress

Data privacy and regulatory uncertainty are now major operational risks. Many companies admit they lack clarity or internal capabilities to manage AI responsibly at scale.

Leaders must address these challenges quickly

To realize AI’s full potential, organizations must treat governance as a foundation—not an afterthought. Trust, transparency, and compliance readiness are prerequisites for success.

Curious to see how USU can help?

We build our products on an AI platform designed for strong data privacy and governance. You’ll find more details at the end of this blog: How USU Helps

Detailed Findings From the Forrester AI Study

AI in IT Operations: Over 50% of Organizations Already Use It for Core Processes

The study confirms: AI has moved from experimental pilots to real operational impact. Organizations are enhancing data analytics, IT service management, and IT monitoring through AI shifting from reactive operations to proactive, insight-driven teams.

AI Compliance: over 75% Say Regulatory Alignment Is Now Critical

Most organizations see data protection, governance and compliance as a top priority for the next twelve months, influencing how AI projects are planned and executed.

IT Objectives Over The Next 12 Months...

AI Regulatory Readiness: Only 39% Feel Fully Prepared for Regulations

Awareness of the GDPR, CPPA and upcoming AI regulations is high. When it comes to execution, the picture changes: only 39% feel well prepared overall, with slightly higher confidence in Europe and lower levels in the United States.

AI Roadblocks: 71% Cite Lack of Privacy Guidance, 70% Face Cross-Border Challenges

The biggest inhibitors to AI scaling are tied to regulation: lack of clear privacy guidelines, cross-border data compliance, and the risk of data breaches. Skill shortages and the rising cost of compliance add complexity.

The Primary Challenges Organizations Face...

AI Scaling Confidence: Only 35% Believe They Can Successfully Scale AI in IT Ops

Only about one-third of organizations feel confident in their ability to scale AI across IT operations. Legacy systems, fragmented governance, and insufficient AI skills hold back progress.

How confident is your organization in its ability...

AI Challenges: 75% Struggle With Unclear Regulations

IT leaders must balance speed and responsibility. Transparent governance, ethical AI decision-making, and cost management are now central to AI success and not optional add-ons.

Which of the following challenges is your organization...

AI Benefits Outlook: 76% Expect Efficiency and Agility

Despite challenges, the expected benefits of AI remain powerful: improved efficiency, better decision-making, increased innovation, and enhanced customer and employee experiences.

What level of improvement do you expect...

AI Governance Investments: 75% Rely on Cross-Functional Teams and Compliance Tools

Organizations investing in cross-functional governance and AI-specific compliance tooling are better positioned to scale AI responsibly and build trust with stakeholders.

Actions that would be beneficial to ensure...

How USU Helps

The findings of the Forrester Consulting AI study make one thing clear: without strong governance and compliant AI foundations, organizations will struggle to scale. This highlights the need for technology that embeds compliance from the start. This is where our USU AI Platform can help.

USU AI Platform: Built for Data Privacy, Security, and Compliance

USU’s AI Platform is built from the ground up with GDPR and emerging AI regulations in mind. You can choose between SaaS or on-premises deployment, allowing you to meet your data privacy, security, and compliance standards without compromise.

--> Learn more about the USU AI Platform.

AI Agents that enable practical, compliant AI adoption

The AI Platform is the foundation for the AI agents integrated into our products. Discover what these agents can do for your organization:

Together, these tools help organizations turn AI potential into operational value without compromising privacy or compliance.

Download AI Study

Download the full study AI in IT Operations: Balancing potentials with data privacy and compliance from Forrester Consulting for detailed charts and actionable insights.

Frequently Asked Questions

What does AI in IT operations (AIOps) actually mean?

AI in IT operations uses machine learning and automation to analyze operational data such as logs, metrics, tickets and asset records. It detects anomalies, correlates events, predicts incidents and automates routine tasks. The aim is to move IT teams from reactive firefighting to proactive, data-driven operations.

Which IT operations use cases benefit most from AI?

The most common use cases are:

Incident and event correlation in monitoring
Ticket classification and routing in IT service management
Virtual agents for first-level support
Anomaly detection and capacity forecasting
License and asset data normalization in IT asset management

These areas share high data volumes, repetitive tasks and clear success metrics.

Why do privacy and compliance matter so much for AI in IT operations?

IT operations data often contains personal or sensitive information, such as user names, device identifiers, ticket descriptions and access logs. Feeding it into AI models can trigger obligations under data protection law. A poorly governed AI project can also lead to data leaks, regulatory fines and loss of stakeholder trust.

Which regulations should organizations consider?

In Europe, the main frameworks are the GDPR and the EU AI Act, which applies in phases and classifies AI systems by risk level. Sector rules such as NIS2 and DORA may also apply. In the United States, state laws such as the California Consumer Privacy Act (CCPA/CPRA) are relevant. The exact scope depends on your industry, your data and the regions you operate in, so legal review is essential.

How can organizations protect personal data when using AI?

Key measures include:

Data minimization, so only necessary data is processed
Anonymization or pseudonymization before data reaches a model
Strict role-based access controls
Encryption in transit and at rest
Clear retention and deletion policies
Contractual guarantees that your data is not used to train third-party models

Should AI run as SaaS or on-premises?

It depends on your risk profile. SaaS offers faster deployment, lower maintenance and easier scaling. On-premises or private deployments give you more control over data location and access, which can matter for public sector bodies and regulated industries. Many organizations choose a hybrid model based on data sensitivity.

How can organizations keep AI decisions transparent and explainable?

Start by documenting what each AI system does, what data it uses and where humans stay in the loop. Keep audit logs of AI-driven actions and use models or tools that can show why a result was produced. Regular testing for errors and bias, plus clear escalation paths for contested outcomes, also help build trust internally and with regulators.