By 2030, CIOs expect every IT workflow to be touched by artificial intelligence — with 75 percent of tasks handled by humans augmented with AI and the remaining 25 percent completed autonomously by AI systems. Yet, according to Gartner, most organisations are nowhere near ready for this shift.
Speaking at the Gartner IT Symposium/Xpo 2025, analysts outlined a blunt message for enterprises: technology is moving fast, but people are not. Unless businesses invest equally in AI readiness and human readiness, they will fail to realise value from their AI programmes.
AI Will Reshape IT — but Humans Are the Bottleneck
Gartner analysts argue that while AI capabilities have accelerated dramatically, workforce capability has not kept pace. Many companies have invested heavily in models, infrastructure, and pilot projects, but lack the talent maturity to use AI effectively.
“AI readiness ensures the technology can find value,” said Gartner’s Galliopi Demetriou. “Human readiness determines whether your workforce can actually capture that value.”
Key human barriers highlighted:
Overreliance on AI leading to skill atrophy
Lack of foundational understanding of AI
Limited preparedness for AI–human collaboration workflows
Insufficient training on interpretability, oversight, and governance
Gartner warns that organisations must redesign roles, rethink hiring strategies, and reposition talent to prepare for AI becoming embedded across every IT workflow.
AI’s Job Impact: Workforce Transformation, Not Workforce Reduction
Despite concerns about job losses, Gartner maintains that AI’s impact on employment will remain balanced through 2026 — and become net-positive after 2027.
Gartner advises CIOs to shift strategies from hiring for skills to building skills internally:
Reduce hiring for low-complexity, easily automated roles
Reposition existing talent into new revenue-generating areas
Focus on skills that AI amplifies, not replaces
Introduce periodic skill testing to avoid overreliance on AI tools
According to analysts, AI will diminish the importance of tasks like summarisation and translation, while elevating skills related to reasoning, leadership, communication, and problem-solving.
Organisations Are Breaking Even or Losing Money on AI
A major challenge revealed by Gartner’s 2025 data:
74% of CIOs say their AI investments are breaking even or losing money.
The reason? Hidden and underestimated costs.
For every AI tool purchased, Gartner estimates up to 10 additional cost categories, including:
Data quality and integration
Governance and compliance
AI accuracy systems
New security and risk frameworks
Change management and training
Vendor-specific lock-in expenses
Organisations must treat AI like a multi-layer investment, not a single-tool deployment.
Technical Readiness: Not All AI Capabilities Are Enterprise-Ready
Gartner categorises current AI capabilities into two buckets:
AI That Is Ready Today
Search and information retrieval
Summarisation
Basic content generation
AI That Is Not Fully Ready
Autonomous AI agents
High-accuracy automated decision-making
Complex multi-agent systems
To avoid fragile outcomes, CIOs must evaluate AI capabilities through a readiness lens — and build internal processes for accuracy verification, oversight, and risk controls.
Vendor Strategy Is Becoming a Sovereignty Decision
The choice of AI vendor is no longer a simple procurement process — it is now a decision about data sovereignty, AI control, and long-term dependency.
Gartner outlines the strengths of each ecosystem:
Hyperscalers — best for large-scale transformation requiring reliability and global reach
Industry-specific startups — strongest for specialised agents and domain-focused use cases
Research-driven AI companies — ideal for cutting-edge innovation but not yet enterprise-scale
Every AI adoption decision, Gartner warns, is ultimately a sovereignty decision about who controls your data and how dependent you become on external systems.
The Path Forward: Humans + AI, Not Humans vs. AI
Gartner’s core message is simple:
AI value emerges only when human capability catches up.
Enterprises must simultaneously develop:
AI readiness — selecting the right use cases, understanding costs, and ensuring technical maturity.
Human readiness — upskilling, role redesign, transparent governance, and preparing talent for AI-augmented work.
When both move together, AI becomes transformational instead of experimental.
