What IT teams need to know about AI workload changes

The artificial intelligence revolution has arrived in enterprise IT departments, bringing both unprecedented opportunities and unexpected complexities. Organizations worldwide are discovering that AI implementation creates a fundamental shift in how technology teams operate, demanding new skills while promising transformative efficiency gains.

IT departments now face a critical balancing act: supporting rapid AI adoption across their organizations while managing the increased demands on infrastructure, security, and governance. This dual pressure has created what many technology leaders describe as the AI paradox — tools designed to reduce workload often increase it substantially during the initial phases of deployment.

Understanding the true impact of AI on IT workload requires examining both immediate challenges and long-term benefits. As enterprises navigate this transition, the experiences of early adopters reveal crucial insights about resource allocation, skill development, and the evolving role of technology teams in an AI-driven workplace.

What is AI's impact on IT workload?

AI implementation fundamentally transforms IT operations, creating a complex landscape of challenges and opportunities that varies significantly across organizations. The initial deployment phase typically increases workload substantially as teams navigate new systems, integrate tools, and establish comprehensive governance frameworks. Research from the Upwork Research Institute reveals that 77% of employees report AI tools actually add to their workload initially, with 39% spending considerable time reviewing and moderating AI-generated content.

IT departments experience unique pressures during AI adoption: they must support organization-wide implementation while simultaneously managing heightened security requirements, compliance frameworks, and infrastructure demands. This creates what industry experts call the "implementation paradox" — while AI promises significant efficiency gains, it demands substantial upfront investment in time, training, and infrastructure development. Technology teams find themselves stretched between maintaining existing systems and building entirely new capabilities to support AI initiatives.

The long-term impact varies dramatically based on organizational size and maturity. Enterprise IT teams managing complex, distributed systems face different challenges than small business technology departments with limited resources. Large organizations often struggle with:

• Integration complexity: Connecting AI tools to legacy systems while maintaining data integrity
• Governance at scale: Establishing policies that work across diverse business units
• Shadow AI management: Controlling unauthorized AI tool usage, with 78% of knowledge workers bringing their own AI tools to work
• Performance optimization: Ensuring AI systems deliver value without overwhelming infrastructure

Small and medium businesses encounter distinct obstacles:

• Resource constraints: Limited budgets for AI infrastructure and training
• Skill gaps: Fewer specialized personnel to manage AI deployment
• Vendor dependence: Greater reliance on external providers for AI capabilities
• Rapid scaling challenges: Difficulty expanding successful pilots without dedicated resources

The paradox extends beyond mere workload considerations. While employees using AI report saving 30 minutes to four hours daily on routine tasks, IT teams must invest significantly more time in the foundational work that enables these efficiencies. This includes data pipeline creation, security protocol establishment, and continuous monitoring of AI system performance — responsibilities that didn't exist in traditional IT environments.

How AI Changes IT Team Responsibilities

The emergence of AI has transformed IT roles, moving beyond traditional support functions to a focus on strategic AI integration and oversight. This shift emphasizes the importance of data integrity and ethical AI practices, as teams transition from maintaining systems to enabling intelligent automation across business processes.

Traditional vs. AI-era IT Roles

• Strategic AI Enablement: IT teams are now pivotal in driving AI adoption, crafting governance structures that ensure ethical use and compliance. This involves setting frameworks to guide AI deployment and mitigate risks associated with new technologies.

• Data Integrity and Ethical Oversight: Ensuring the accuracy and fairness of AI outputs requires IT professionals to focus on data validation and ongoing system evaluations. This role is crucial for preventing biases and maintaining trust in AI-driven decisions.

• Intelligent Automation: As AI automates various tasks, IT teams facilitate the seamless integration of these systems into existing workflows. This requires collaboration with different departments to align AI capabilities with organizational goals, enhancing overall efficiency.

Emerging IT Responsibilities

With AI's rise, IT teams face new challenges:

• Evaluation and Security: IT professionals must conduct thorough assessments of AI tools to identify potential security risks and ensure alignment with enterprise policies. This includes safeguarding data against unauthorized access and breaches.

• Addressing Unauthorized AI Use: The influx of AI tools brought by employees, often without formal approval, necessitates IT intervention. Teams must identify and manage these tools to protect data integrity and organizational security.

• Policy Development and Governance: Establishing comprehensive AI usage policies is essential. IT teams play a key role in creating guidelines that define acceptable use and ensure data protection, safeguarding organizational interests.

• Performance and Bias Monitoring: IT teams are tasked with continuously evaluating AI systems to ensure optimal performance and address any biases in outputs. This involves implementing robust metrics to track effectiveness and accuracy.

• Training and Skill Development: To address skill gaps, IT departments coordinate training initiatives that focus on both technical competencies and responsible AI use. This ensures employees can effectively leverage AI technologies within ethical boundaries.

AI's impact on IT responsibilities highlights the need for strategic adaptation and proactive management. As these teams evolve, they are instrumental in guiding organizations toward innovative and efficient AI integration.

Key Challenges IT Teams Face During AI Implementation

Navigating the implementation of AI in enterprises introduces a myriad of challenges for IT teams. A prominent issue is the evolving skill set required to manage AI technologies. As AI systems become more integral, there’s a pressing need for IT professionals to expand their technical expertise and adapt to new roles that focus on AI oversight and optimization.

Security remains a critical concern, especially with the rise of sophisticated cyber threats. IT teams must enhance their cybersecurity strategies to counteract advanced phishing techniques and potential deepfake manipulations. This involves integrating cutting-edge security tools and continuously monitoring for vulnerabilities within AI systems.

The complexity of integrating AI technologies with existing infrastructure presents another hurdle. Ensuring that AI solutions seamlessly connect with legacy systems without disrupting ongoing operations requires meticulous coordination. IT teams need to establish robust frameworks that facilitate smooth transitions and maintain data integrity throughout the process.

Allocating resources effectively is also crucial. IT departments must judiciously balance the demands of AI projects with the maintenance of current systems. This includes strategic planning to align AI initiatives with organizational priorities while managing financial constraints and justifying investments.

Finally, managing relationships with AI vendors is essential to avoid dependencies and ensure flexibility. IT teams should establish clear criteria for evaluating AI solutions and implement metrics to assess their impact. This approach helps in maintaining control over AI deployments and achieving desired outcomes.

Training Requirements for IT Staff

As AI becomes integral to enterprise operations, IT teams must undergo targeted training to meet the demands of this technology. Developing both technical and soft skills is essential for navigating the complexities of AI systems and ensuring their successful implementation.

Technical Skills Development

• Foundations of Machine Learning: IT professionals should immerse themselves in the core concepts of machine learning to effectively support AI initiatives.

• Advanced Data Management: Expertise in organizing and preparing data for AI models is crucial for ensuring accurate outputs and system efficiency.

• Integration Architecture: Familiarity with modern integration techniques, including microservices, enhances the ability to seamlessly connect AI systems with organizational frameworks.

• Cloud Infrastructure Competency: Proficiency in leveraging cloud services is vital for scaling AI operations and optimizing resource utilization.

• AI-Specific Cybersecurity: Developing security strategies that address AI's unique risks ensures robust protection against emerging cyber threats.

Soft Skills Enhancement

• Effective Change Facilitation: IT teams must master managing transitions, ensuring smooth implementation and clear communication with stakeholders.

• Interdisciplinary Collaboration: Building skills to work collaboratively across departments helps align AI projects with strategic business goals.

• Ethical AI Implementation: Understanding the principles of ethical AI usage ensures responsible deployment in line with organizational standards.

• Strategic Business Insight: Enhancing analytical skills allows IT professionals to align AI solutions with business objectives for maximum impact.

• User Training and Support: Developing capabilities to educate and guide users on AI tools fosters widespread adoption and effective use.

Continuous Learning Priorities

• Commitment to Lifelong Learning: Staying informed about AI advancements is essential, with continuous education playing a key role in maintaining competitiveness.

• Optimization Techniques: Focusing on refining AI tool performance ensures systems operate at peak efficiency.

• Model Selection Awareness: Understanding various AI models and their applications aids in choosing the right tools for specific organizational needs.

• Compliance and Governance Mastery: Familiarity with regulatory landscapes and governance practices is crucial for maintaining compliance and ensuring ethical AI use.

Benefits of AI for IT Operations

AI integration in IT operations revolutionizes efficiency and empowers strategic growth. By automating routine tasks, AI liberates IT teams from repetitive activities, allowing them to concentrate on more complex projects. This transition enhances productivity and job satisfaction as teams engage in impactful work.

Strategic Insights and Proactive Maintenance

AI-driven analytics provide critical insights into system performance, enabling swift and informed decision-making. By identifying trends and potential issues early, AI facilitates proactive maintenance, reducing disruptions and optimizing system uptime.

Efficient Resource Allocation and Organizational Growth

AI optimizes resource allocation, allowing IT staff to focus on strategic initiatives. This efficiency supports growth by managing increasing workloads without necessitating additional headcount. Through AI, teams effectively scale operations while maintaining high service standards.

Streamlining Support and Boosting Security

AI chatbots enhance support desk operations by managing routine inquiries, freeing IT professionals to tackle more complex challenges. This boosts response times and user satisfaction. Additionally, AI strengthens security with advanced threat detection and automated responses, protecting organizational data from emerging threats.

Documentation Efficiency and Cost Management

AI aids in accurate documentation, reducing the administrative load on IT teams. By streamlining processes, AI contributes to long-term cost efficiency, fostering a financially sustainable operation. These benefits underscore AI's transformative role in enhancing IT operations and supporting organizational goals.

Managing the Transition Period

Effectively transitioning to AI requires a methodical approach to ensure seamless integration and maximize potential benefits. This involves evaluating infrastructure, piloting strategic initiatives, and fostering continuous improvement.

Phase 1: Assessment and Planning

Start by examining your IT infrastructure to identify any gaps that may affect AI deployment. Consider hardware capabilities, software integrations, and data management processes. Tailor your AI strategy to address specific organizational needs and align with broader business goals. Developing a clear roadmap with phased milestones helps manage expectations and resources efficiently.

Phase 2: Pilot Implementation

Select initial AI projects that offer measurable impact with manageable risk. This allows for experimentation and learning without extensive resource commitment. Establish clear success metrics to evaluate the performance of these pilot projects. Collecting insights from initial users helps refine approaches and troubleshoot issues. Documenting these experiences ensures that valuable knowledge informs future initiatives.

Phase 3: Scaling and Optimization

Once pilots demonstrate success, extend AI solutions across various functions to enhance overall efficiency. Adjust governance frameworks based on pilot learnings to ensure they remain effective as AI use scales. Cultivating a knowledge hub for AI fosters innovation and provides ongoing support for teams. Regular system evaluations and updates ensure AI tools continue to meet organizational demands and drive value.

Best Practices for IT Teams Adopting AI

Adopting AI successfully requires strategic planning and a focus on best practices that ensure smooth integration and long-term benefits. By starting small, IT teams can manage complexity and build confidence in AI technologies. Focusing on specific use cases allows organizations to demonstrate value quickly, ensuring stakeholder buy-in and providing a foundation for broader implementation.

Building a Strong Framework

• Prioritize Data Governance: Establish comprehensive data policies before deploying AI. Clear guidelines ensure data integrity and compliance with regulatory standards, reducing risk and enhancing trust.

• Collaborative Engagement: Engage diverse teams, including operations and compliance, to ensure AI aligns with all organizational facets. This approach fosters a cohesive strategy and unifies efforts across departments.

• Skill Development Investment: Dedicate resources to enhance team expertise in AI technologies. Continuous learning and adaptation to new tools are crucial for keeping pace with technological advancements.

Ensuring Robust Implementation

Effective oversight is key to successful AI deployment. Implement systems to monitor performance metrics, aligning AI operations with strategic goals. Transparent updates and discussions with employees about AI integration build a culture of openness and acceptance.

• Resilience Planning and Impact Assessment: Design flexible strategies to quickly address challenges. Evaluating outcomes against organizational objectives provides clarity on AI's contributions to success.

• Adaptive Tool Strategy and Encourage Experimentation: Explore diverse AI solutions to enhance adaptability. Nurturing a culture of innovation encourages creative problem-solving and continuous improvement while maintaining structured oversight.

The journey to AI transformation requires careful planning, strategic execution, and the right tools to support your IT team through every phase of implementation. While the challenges are real, the potential for enhanced productivity, streamlined operations, and strategic growth makes AI adoption an essential step for modern enterprises. We invite you to request a demo to explore how Glean and AI can transform your workplace and discover how we can help your organization navigate this critical transition successfully.