I believe one of the most significant 2026 ITSM risks relates to the increased use of artificial intelligence (AI) in IT service management (ITSM). With the proliferation of AI tools and their increasing ease of use, employees are leveraging AI to enhance the efficiency and effectiveness of themselves and their departments. However, the unauthorized use of AI by employees is also becoming an increasing issue for organizational security and integrity.
AI Security Risks in ITSM
Thanks to the rise of cybersecurity incidents seen in 2025 across multiple attack vectors, I see 2026 potentially as a watershed year when the disadvantages of using AI in a corporate (let alone ITSM) environment will take center stage. For me, the ITSM risks of data privacy leakage from using unauthorized AI tools, as previously happened to Samsung, are akin to dealing with “Shadow IT on steroids.”
Bias and Accuracy Risks from Uncontrolled AI Use
For ITSM departments, employees may readily use unauthorized generative AI (GenAI) tools to resolve tickets, improve automation, and refine processes. However, using AI in ITSM workflows without proper controls in place can lead to unchecked bias, for example, incident prioritization bias, where the model is poorly trained, resulting in incorrect ticket handling.
Worst still are AI hallucinations driving ticket resolutions, where the AI instructions sound technical and credible, but following them – whether as an IT team member or end-user – causes confusion or errors, such as linking to a non-existent knowledge article.
AI Governance: The Key to Safe ITSM AI Adoption
In 2026, it will be essential for organizations to establish internal AI governance across all organizational departments (if they haven’t already), with ITSM and security teams taking the lead. Security will drive AI governance, and ITSM teams will serve as the service provider and face of IT, responsible for approving change management and provisioning access to authorized software applications and services within the organization.
Data Quality and Governance for Reliable AI in ITSM
Managing the potential explosion in AI use in ITSM also requires consideration of the data that will be utilized by AI for decision-making purposes. If AI is allowed to run on poorly structured or incorrect data, then errors will occur, such as ticket misclassification, incorrect assignments, and improper escalations.
In 2026 ITSM risk terms, ITSM departments should ensure that data hygiene (i.e., accuracy, consistency, and reliability) is managed as a priority to preserve the integrity of the AI system. The controls required to improve data hygiene should include:
- Defining data governance and ownership
- Standardizing data inputs
- Automating data quality checks
- Security controls for scrubbing credentials and sensitive business information
- Managing bias where AI wrongly prioritizes outcomes
- Feedback loops to capture if AI-generated responses are accurate
- Monitoring and continual improvement using key performance indicators (KPIs) and dashboards for IT leadership.
GenAI Challenges and How ITSM Teams Can Overcome Them
Concern has already been highlighted by market experts who refer to GenAI as having entered into Gartner’s “Trough of Disillusionment” – reflecting that enterprises are now hitting issues of reliability and robustness.
However, experts still believe the long-term potential for GenAI remains strong. But they stress the need for rigorous evaluation and realistic expectations rather than hype. Likewise, AI agents, or Agentic AI, currently “at the peak of inflated expectations,” also face similar challenges of trust given their reliance on generative models.
However, despite these issues, implementing the necessary AI governance controls should unlock significant efficiency gains. Market analysts argue that even limited AI project success can justify investments, provided organizations adopt due diligence and combine AI technologies to mitigate weaknesses.
Beyond AI: Other Critical ITSM Risks in 2026
While AI represents a significant ITSM risk, ITSM leaders should not overlook other business risks associated with delivering services to their organizations. With an ever-increasing reliance on third-party SaaS vendors as part of the digital supply chain (in providing IT services across the organization), ITSM leaders should take stock in 2026 to ensure that sufficient governance is put in place to manage the full spectrum of ITSM risks involved in SaaS environments.
The distributed nature of SaaS procurement increases the size of the challenge by making it difficult to maintain centralized oversight of compliance obligations. For example, the ITSM risks associated with using SaaS vendors include:
- Data security gaps
- Vendor instability
- Non-compliance with regulations.
ITSM leaders might assume that vendors carry the burden of accountability, but they don’t. Without continuous monitoring and ranking of vendor risk, compliance gaps will emerge, putting your organization’s ability to deliver services at risk.
Please get in touch with me if you would like to discuss AI, and other ITSM risks such as governance.
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David Keen
David Keen began his career in financial management before transitioning into IT, where he specialised in service management. Over the past twenty-five years, he has led and contributed to a wide range of IT consultancy projects. His passion for aligning IT with business goals remains as strong as ever, especially amid the transformative impact of emerging technologies like AI.
