Gartner study reveals challenges in achieving ROI from AI in IT operations

At a glance:

• Gartner study finds only 28% of AI use cases in IT infrastructure and operations fully succeed and meet ROI expectations
• Unrealistic expectations and skills gaps are common reasons for AI project failure
• Embedding AI into existing systems, executive support, and realistic business cases are key success factors

Gartner study highlights AI challenges in IT operations

A recent Gartner study reveals that only 28% of AI use cases in infrastructure and operations (I&O) fully succeed and meet ROI expectations, while 20% fail outright. The study, which surveyed 783 I&O leaders, sheds light on the challenges organizations face when implementing AI in their IT operations.

Reasons for AI project failure

According to Melanie Freeze, a director of research at Gartner, AI projects often fail due to unrealistic expectations of what AI tools can do and skills gaps during the pilot phase. Many IT departments experiment with AI without a clear plan, treating them as side projects rather than aligning them with business needs.

Success factors for AI in IT operations

Gartner has identified three key success factors for AI in I&O:

1. Embedding AI into existing systems and processes to boost adoption and create visible impact
2. Receiving full support from top executives to remove roadblocks and ensure focused investment
3. Creating realistic business cases to align AI with operational needs

Prioritizing AI use cases

To achieve ROI from AI, I&O leaders should prioritize and determine funding for AI use cases by managing them as products, avoiding duplication, and tracking their collective impact on I&O and business outcomes. Collaborating with stakeholders across the organization can help assess each use case for feasibility, risk, cost, and expected business impact.

AI success in specific areas

The study found that the majority of AI successes in I&O occur in IT service management (ITSM) and cloud operations, where markets are mature and have proven business value. Ensuring that AI wins are shared broadly within the organization and maintaining a cohesive, centrally led AI strategy are crucial for success.

Importance of a business case

Starting an AI project without a plan grounded in a business case is never a good idea, Freeze emphasized. Understanding the organization's needs, ambitions, and problems that current tools cannot solve is essential for success. Failed AI projects can have major implications for business outcomes, affecting an entire organization's ability to provide secure, reliable, and available infrastructure.

Funding AI initiatives

Once priorities are clear, I&O leaders can determine which use cases deserve funding and at what level. As AI infrastructure spending continues to rise, CEOs and CFOs need to play a more active role in setting funding criteria and approving major investments.