๐ฅ๏ธComputerworldโขFreshcollected in 31m
Gartner: Only 28% IT AI Projects Hit ROI
๐กGartner: 72% IT AI fails ROIโlearn 3 success factors to fix yours
โก 30-Second TL;DR
What Changed
Only 28% AI use cases in I&O meet full ROI success
Why It Matters
IT departments risk wasting resources on AI pilots without proper integration and realistic planning, but adopting Gartner's factors can boost success rates significantly. This shifts focus from model sophistication to operational alignment.
What To Do Next
Score your AI use cases with Gartner's feasibility-risk-impact model before funding.
Who should care:Enterprise & Security Teams
๐ง Deep Insight
AI-generated analysis for this event.
๐ Enhanced Key Takeaways
- โขGartner identifies 'AI fatigue' as a significant contributor to project stagnation, where organizations struggle to move beyond pilot phases due to the high cost of maintaining custom-built models versus off-the-shelf solutions.
- โขData quality and governance remain the primary technical bottlenecks, with over 60% of failed projects citing 'data silos' and 'lack of clean, accessible training data' as the root cause for failing to meet ROI targets.
- โขThe shift toward 'Small Language Models' (SLMs) is emerging as a key strategy for I&O leaders to improve ROI, as these models offer lower inference costs and higher domain specificity for IT infrastructure tasks compared to massive general-purpose LLMs.
๐ฎ Future ImplicationsAI analysis grounded in cited sources
IT organizations will shift budget from custom AI development to AI-integrated SaaS platforms by 2027.
The high failure rate of bespoke AI projects is forcing leaders to prioritize vendor-managed AI features that guarantee ROI through pre-built integrations.
The 'AI Product Manager' role will become a standard requirement in I&O departments.
Treating AI as a product rather than a project requires specialized lifecycle management skills that current IT operations teams lack.
โณ Timeline
2023-05
Gartner introduces the 'AI TRiSM' (Trust, Risk, and Security Management) framework to address early AI adoption risks.
2024-02
Gartner publishes research highlighting that 80% of generative AI projects will be abandoned by 2026 due to poor data quality.
2025-09
Gartner releases updated guidance on 'AI Engineering' as a critical discipline for moving beyond experimental AI use cases.
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Original source: Computerworld โ