Top 7 Takeaways from Gartner IT Symposiums 2025: Preparing for an AI-Driven Future

Artificial intelligence is reshaping every aspect of enterprise leadership, and CIOs and Enterprise Architects are standing righ at the center of that shift.

That was the clear message we heard across the keynotes, strategy tracks, and customer sessions at the 2025 Gartner IT Symposiums in Orlando and Barcelona. But while AI ambition has never been higher, the foundations required to scale it—governance, operating models, visibility, and skills—are still catching up.

If you missed the events, don’t worry. We’ve distilled the complex discussions into the top seven insights shaping the agenda for IT and transformation leaders in the year ahead.

Jump to:

1. CIOs Are Becoming Enterprise Change Agents
2. AI Ambition Is Outpacing AI Readiness
3. Trust, Accuracy, and Governance Are the New Battlegrounds
4. Productivity Is Not Financial Value
5. AI Is Transforming Decision-Making (But Only With Strong Data)
6. EA Is Moving From Documentation to Real-Time Insight
7. How to Harness Coherent Insight: EA’s Role in Proactive Governance and Value As Shown by Primark and Riot Games

1. CIOs Are Becoming Enterprise Change Agents

The CIO role is undergoing its largest shift in a decade. No longer just technology operators focused on service delivery, CIOs are evolving into enterprise transformation leaders who own business outcomes.

Gartner emphasized that today’s CIOs are under intense pressure. You are responsible for AI strategy, value realization, risk, compliance, and organizational readiness—all while keeping systems reliable with limited resources. Budgets and headcount remain nearly flat, but expectations are rising dramatically.

2. AI Ambition Is Outpacing AI Readiness

CIOs are scaling AI faster than their organizations can support it. While enterprise-level ambition is huge, the foundations required to deploy AI safely and effectively are often missing.

The readiness gap is real. Gartner data suggests that organizational AI readiness sits at around 50%, while human readiness is even lower at approximately 25%. This means a significant portion of the organization isn't prepared for the process, role, and responsibility changes AI introduces.

The operating model isn’t ready either. Organizations frequently lack mature AI governance structures, clearly defined roles for model oversight, or AI-enabled workflows and operating rhythms. Without established processes for validation, monitoring, and escalation, the technology is moving far ahead of the organization’s ability to absorb it.

Skills and people readiness are also major blockers. Teams report low confidence in interpreting AI outputs, creating a barrier to adoption even if the tech works perfectly. Despite these gaps, 45% of CIOs plan to deploy AI agents within the next 12 months. This structural mismatch creates risks like operational fragility, shadow AI, and compliance failures.

3. Trust, Accuracy, and Governance Are the New Battlegrounds

The real risk isn't just that AI is wrong—it’s that organizations cannot detect when it is wrong. Governance has become a board-level issue, yet AI accuracy remains a central blind spot.

With GenAI error rates hovering around 20% and 84% of organizations not tracking accuracy, most are deploying models without evaluating correctness. This means AI is entering business-critical workflows without validation, and leaders have no way of knowing whether their models are improving or degrading over time.

This uncertainty creates a massive trust gap. Current AI trust levels sit at just 14% in low-maturity organizations, compared to 57% in high-maturity ones.

Trust is not an “input” businesses can simply ask for; it is the result of governance, lineage visibility, and accuracy measurement. It is earned through deep lineage understanding and dependency mapping. IT leaders cannot expect to just talk people into higher levels of trust—it’s a significant shift in ways of working that needs to be proven with structure, visibility, and metrics.

4. Productivity Is Not Financial Value

CFOs report that while 74% of AI projects "save time," only 5-6% result in a revenue or profit increase. This is the "time savings" trap.

Time saved is not money saved unless organizations take deliberate, structural steps to capture it. Gartner calls this "productivity leakage." Most AI success stories today are operational rather than financial because organizations haven't done the heavy lifting required to cash in on that efficiency.

To achieve true ROI, companies must undertake enterprise change. This means redesigning processes, changing roles, renegotiating outsourced contracts, and rethinking operating models. The more of a process AI can disrupt—whether 10% or 70%—the greater the potential to shift from simple productivity to actual financial outcomes.

However, success isn't just about chasing immediate dollars. The best-performing organizations maintain a balanced AI portfolio that includes productivity gains, financial returns, and strategic bets. Leaders who succeed view AI not as a tool deployment, but as a transformation of how the business itself operates.

5. AI Is Transforming Decision-Making (But Only With Strong Data)

AI creates the most business value when it augments decisions, not just tasks. However, this value is only achievable for organizations with strong data quality, connected processes, and architectural visibility. Without these foundations, AI-assisted decisions become inconsistent, unreliable, and risky.

Gartner research showed that when data is incomplete or siloed, AI produces incorrect recommendations—even when the outputs look confident. This makes AI decision support high-risk without strict data governance and accuracy controls.

To recommend the right action, AI must understand the operational reality behind a KPI. It needs to know which processes influence the metric, which systems support those processes, and how data flows across the value chain. It also needs to identify where bottlenecks, dependencies, and risks exist. Without this visibility, AI cannot diagnose root causes or recommend meaningful interventions.

Decision-support use cases drive significantly more impact than simple “time-saving” initiatives, but they require clean, governed data and a clear linkage between KPIs, processes, and systems. These capabilities are often missing in organizations without modern EA practices and digital twin–level visibility.

Learn more about how EA provides AI with the structured and operational context it needs to deliver real value: Context Engineering: Why Your EA Practice Is Already the Secret to AI Success

6. EA Is Moving From Documentation to Real-Time Insight

Enterprise Architecture is no longer about producing static models or reference diagrams. It is shifting toward decision intelligence, shaping transformation, and accelerating outcomes.

EA teams must now connect strategy with execution rather than simply documenting the current state. Modern EA functions need to provide real-time insight, system visibility, change impact analysis, and risk modeling.

Gartner highlighted that organizations with connected, integrated architecture data manage change faster and with fewer failures. Companies using capability-led planning achieve 40% faster decision-making because the data links strategy directly to execution, removing bottlenecks caused by siloed information.

High-performing CIOs also achieve 1.24x higher execution ability when they practice dynamic reprioritization. This requires constant, integrated visibility into which systems support which business outcomes.

Integrated architecture data provides the foundation for this agility. It makes EA a core enabler of AI transformation because AI cannot scale without clarity over processes, systems, risks, and dependencies. Tools that enable a Digital Twin of the Organization (DTO) are becoming increasingly important, enabling CIOs and EAs to simulate scenarios, detect risks, and prioritize investments with confidence.

7. How to Harness Coherent Insight: EA’s Role in Proactive Governance and Value As Shown by Primark and Riot Games

Theory is good, but practical execution is better. During the symposiums, we saw powerful examples of how organizations are turning these concepts into reality by shifting the role of EA.

Primark shared how they are strengthening their architecture function by connecting business processes, applications, integrations, and risks. Their focus is on improving decision-making and delivering strategic change by moving from fragmented views to coherent architecture insight.

Riot Games outlined their journey from reactive operations to global architecture governance. By achieving better visibility into application portfolios, ownership, and dependencies, they shifted toward proactive planning. This allowed them to reduce risk and improve portfolio decisions across the enterprise.

Deep dive into Riot Games’ EA journey: Simplifying Technology Landscape and Aligning IT With Business Needs

In Ardoq’s on-stage session, "From Insight to Impact," James Tomkins demonstrated how organizations can move beyond dashboards to impactful action. He outlined a three-step loop using a Digital Twin of the Organization: spot an issue via metrics, diagnose the root cause via a process and system graph, and intervene to measure the outcome (including unintended consequences). This session showed that when architecture pairs with connected data and AI, it turns insight into enterprise change.

Watch James' full talk on-demand now:

The Path Forward

The message from Orlando and Barcelona was clear: AI is scaling fast, but success requires more than just new technology. It requires visibility, governance, and a structural shift in how we manage the enterprise.

CIOs and Enterprise Architects who can bridge the gap between ambition and readiness will define the future.

Is your organization ready to close the readiness gap? Book a demo to see how Ardoq helps you map, manage, and modernize your enterprise for the AI era.