Highlights from Gartner IT Symposium/Xpo 2025

Avoid the extremes of AI disbelief and hype. CFOs report 95% of GenAI initiatives are failing,and only one in five AI projects achieve measurable ROIl. The transformative impact of AI is clear, but overindexing on vendor claims or capability hype can quickly erode value. 

You need a dedicated AI leader. 91% of high-maturity organizations have already filled this role, leading to more sustainable AI projects, stronger trust in AI and a higher likelihood of meeting key performance outcomes.

AI value won’t come from productivity alone, it requires human readiness. By 2030, 75% of IT work will be performed by humans augmented by AI – underscoring the need for organizations to invest in upskilling, change management and strategic alignment. 

Align use cases to readiness to chart your path to value. CIOs should evaluate AI readiness in terms of cost, technological capabilities and vendors. To capture and sustain value, they should improve human readiness in terms of the workforce and organization.

Calculate hidden costs and prepare for digital nation-states. For every AI tool purchased, anticipate 10 hidden costs. Analyze and decide which costs you’ll fund to avoid owning a negative ROI business case. Every vendor is a sovereignty decision when AI touches all parts of your organization. Choose an AI vendor that matches your use cases and desired AI outcomes.

Capture AI value by building human readiness, developing critical skills and managing behavior byproducts. By 2036, AI solutions introduced to augment or autonomously deliver tasks, activities or jobs will create over 500 million net new human jobs. CIOs must go beyond training on new skills, build AI literacy programs with experiential learning and hire for a context engineering discipline.

The top trends for 2026 fall into three themes, the Architect, the Synthesist, and the Vanguard. 

The Architect: Trends in this category focus on building secure, scalable, and adaptive digital foundations that support rapid innovation and organizational resilience. Trends include AI-Native Development Platforms and AI Supercomputing Platforms.

The Synthesist: These trends highlight how to orchestrate diverse technologies, such as AI agents, specialized models, and integrated physical and digital systems, to unlock new sources of value and differentiation. Trends include Multiagent Systems, Domain-Specific Language Models and Physical AI.

The Vanguard: Trends in this area address the need to elevate trust, governance, and security, enabling organizations to protect their reputation, ensure compliance, and maintain stakeholder confidence. Trends include Preemptive Cybersecurity, Digital Provenance, and Geopatriation.

The pace of technological change in 2026 is unprecedented, requiring organizations to act quickly to stay competitive and resilient.

AI supercomputing, multiagent systems, and domain-specific language models are transforming industries by enabling faster innovation, automation, and more accurate decision-making.

Security and trust are critical, with trends like AI security platforms, preemptive cybersecurity, and digital provenance helping organizations protect assets and ensure compliance.

The shift toward confidential computing and geopatriation reflects growing concerns around data privacy, sovereignty, and regulatory alignment in a volatile global landscape.

Organizations that embrace these trends now will be best positioned to drive responsible innovation, achieve operational excellence, and build digital trust for long-term success.

Bridging the AI Optimism Gap: Executive leaders are enthusiastic about AI’s potential, but most workers feel their skills are underutilized and remain skeptical. Closing this gap is critical. To do so, leaders must address real barriers and support employees to realize AI’s true value.

Mastering Four Essential GenAI Skills: Success with generative AI depends on proficiency in use case identification, tech fluency, effective prompting, and output discernment. These foundational skills must be non-negotiable for every employee, ensuring reliable and ethical AI adoption.

Fostering Ongoing Engagement and Community: AI success is sparked through exploration, collaboration, and continuous learning—not training. Dedicate time for experimentation, reward breakthrough ideas, and form GenAI communities of practice to accelerate skill development and innovation.

Democratizing Access and Targeted Skill Growth: Provide all workers with access to GenAI tools and use variable skills assessments to identify and develop advanced talent. Set clear goals for GenAI skill development and empower employees to experiment and learn together.

 Treating Prompting as a Conversation: Effective prompting is an ongoing, interactive process—not a one-time task. Encourage iterative dialogue with GenAI tools to improve satisfaction and drive better outcomes.

Leadership is Multidimensional. Leaders must lead up to leadership, across peers, down to their own group as well as outside of the organization, and from within as an evolving leader.

Leveraging AI for leadership success is not cheating, it is simply using emerging technologies to strengthen influence and impact and champions a team-based approach. This is increasing your leadership EQ - emotional intelligence to better collaborate with your peers and leaders.

Co-leadership is Essential for Scaling IT Change. Today's technology leaders foster much of the change within the enterprise but will need to co-lead transformation with stakeholder leaders in all parts of the organization.

IT owns the technology of AI. HR owns the people that use that technology. The co-leadership by CIOs and CHROs is critical for AI success.

Engage Stakeholders Where They're At. CIOs must understand their stakeholders’ worldview. They should engage peers by first understanding their desired business outcomes, then use language that clearly connects the value of IT to the performance of each peer’s function.

A sense-maker is a leader who puts cybersecurity into a business context and gains board consensus on protection and cost. Every CIO can become a sense-maker by investing in their own leadership.

Simplify complexity by answering one question: "How much security is enough?"

Speak candidly by exposing gaps, but always bring solutions.

Start a fire but bring water. For example, 'our incident remediation time is 13 hours longer than our peers. We need to invest [x-amount] to support stakeholder defensibility.'

Treat peer deferral as a hidden cybersecurity risk. Invite questions by telling the board skepticism and curiosity are signs of strong board oversight.

Align cybersecurity with value by protecting what matters (revenue, cost and risk) and enabling what's next (the future of AI).

Reflect on your personal status as CIO. Ask the board how they feel about cybersecurity costs and levels of protection (low, just right, high).

Cybersecurity is at a crossroads in the boardroom: 90% of non-executive directors lack confidence in cybersecurity value. Yet, this skepticism in cybersecurity value is a resource for change.

Fewer cybersecurity breaches won't earn board trust. Business alignment will.

Create an AI strategy revolving around a vision that is fully aligned with business goals, market conditions and competitive pressure regarding the use of AI.

Make the AI strategy executable by setting priorities for a portfolio of concrete business-related AI initiatives, and by setting planning goals to build and mature an AI operating model.

Keep the AI strategy up to date by frequently aligning or realigning it with the business strategy and vice versa while continuously seeking synergies with complementary strategies such as digital/IT, and data and analytics (D&A) strategies.

Ensure that your AI strategy is aligned with your AI ambition and is pragmatic to implement, given your current technology architecture and organizational culture. 

Evolve your AI strategy and maturity across six key domains: Business Value, Organization, People & Culture, Data, Engineering, and Governance. 

Make AI a shared responsibility, instilling collaboration across the C-suite and various business and IT teams.

A definite transition has begun: from a scenario where cloud computing principles are influencing all aspects of workload deployment to a situation where artificial intelligence is enabling all aspects of cloud computing. 

The monetization of vendor AI investments will take multiple paths, with the most impactful being new AI-infused industry-specific services. 

Sovereignty will have huge implications for many enterprises outside the US, with many considering lessening their reliance on US providers

AI will place more of a spotlight on multicloud as models will be sourced from different environments different from where the data is and where they will ultimately run. 

AI workloads mostly use container technology as the underlying compute environment. However, most container deployments are woefully over-provisioned, often as much as 70%. So, optimizing the AI workloads will be vital; this includes incorporating a formal framework to manage costs.

Update your cloud strategy by weighing the impact that AI will have on your strategy. This will cause you to re-evaluate your plans for digital sovereignty, multicloud, security, cloud financial management, and sustainability. It will also force you to prepare for AI-infused industry-specific solutions.

Top performing technology executives have three things in common: the ability to be agile in their platforms and people; the ability to manage risk; and the ability to be tenacious, to see things through to the end, to get value, and to keep going.​

Year-over-year, CIOs and technology executives plan to increase their budget for AI and generative AI by 33% and 32% on average, even though overall IT budgets are mostly flat.

Pivots and reprioritization, not calendar-based planning, will dominate 2026.

Driven by the geopolitics of AI, CIOs and technology executives should recognize that data sovereignty risks are now viewed by many of their peers as a critical consideration in developing a global vendor portfolio. By 2030, at least 50% of countries will implement comprehensive digital sovereignty laws to protect domestic economies and culture, significantly impacting global data flows.

Through 2026, atrophy of critical-thinking skills, due to GenAI use, will push 50% of the global organizations to require “AI-free” skills assessments. “Treating AI like a wizard could set us back 10 years.” 

By 2027, 35% of countries will be locked into region-specific AI platforms using proprietary contextual data. “Employ model distillation as a tactic to reduce the impact of regional lock-in.”

By 2028, organizations that leverage multiagent AI for 80% of customer-facing business processes will dominate. “Design processes for AI agents, not humans.”

By 2028, 90% of B2B buying will be AI agent intermediated, pushing over $15 trillion of B2B spend through AI agent exchanges. “Make products machine-readable to unlock new markets.”

By the end of 2026, “death by AI” legal claims will exceed 2,000 due to insufficient AI risk guardrails. “Create contingency funds for damaging AI incidents.”