Getting Q1 right matters more than ever. By the time the year begins, strategies are already set, budgets are allocated, and expectations are high. Early decisions shape not just first-quarter results, but how much flexibility teams have as competitive pressure increases over the year.
The challenge is not simply moving fast. It is knowing where to focus first and which foundational choices will either accelerate progress or slow it down later. To help organizations think through those early moves, IDC analysts shared practical guidance based on what they are seeing across client conversations and the market.
Together, their perspectives highlight where leaders should focus in Q1 to avoid common pitfalls and build momentum that lasts beyond the first quarter.
Getting your data AI-ready in Q1
It has become a common refrain – getting data governance right is key to a successful AI strategy! This conventional wisdom is very true, but it is not a new problem. For as long as I have been involved in IT, both as an analyst and as a CIO, companies have struggled with wrangling the various data sets across the applications running at the organization.
The information corpus at most organizations can be split into three broad categories:
- Structured data, usually in relational databases that support transactional information or the organization’s performance context.
- Unstructured data found in documents, images, and voice. The transformer algorithms that create LLMs bring some order to this category which provides the knowledge context.
- Streaming or telemetry data which might include sensors on a factory floor or clickstream data from a website. This category provides situational context.
Efforts to organize, govern and utilize the data must link all three categories of information. To achieve the tremendous potential of agentic AI, a company must be able to link the knowledge to the situational and performance context. This requires advanced tools for semantic graphing and knowledge mapping with a strong commitment from the organization to elevate comprehensive data management to a strategic priority.
IDC does advise companies that they don’t have to get this all done before they undertake agentic efforts. Rather, it is important to have the tools, organization, and policies in place and then synchronize the data domains with the agentic priorities. For example, if the company wants to focus on marketing, then the information relevant to that function should be prioritized for governance. It is easy to acknowledge that data is critical to AI success, but realization requires a comprehensive approach to data across all categories.
Infrastructure decisions now reach the board
In the world of AI that is racing from experimentation to production, IT Infrastructure has become a board level conversation. CIOs must ensure that IT infrastructure investments are a strategic imperative, with the right amount and type of attention given to ensure successful outcomes of their organization’s AI transformation initiatives. Some examples of how IDC sees infrastructure investments taking shape:
- By 2027, 80% of organizations running AI workloads will leverage ultrahigh bandwidth/low-latency fabrics for infrastructure resource pooling, resulting in faster time to insights.
- By 2028, 40% of enterprises will adopt an IT architecture that brings accelerated computing, AI stacks, and vector databases closer to dedicated storage to improve efficiency and speed AI insights.
- By 2029, 30% of enterprise datacenter infrastructure will be used to combine an organization’s current and historical data with an integrated set of AI processes, creating an “enterprise brain.”
These are strategic decisions that cannot be taken lightly by the C-suite. They require careful planning to ensure the investments yield measurable ROI. To succeed in the AI era CIOs and IT Decision Makers must:
- Take a strategic, future-ready approach to infrastructure planning and investment.
- Prioritize partnerships with vendors that offer platforms with robust management, automation, and interoperability features.
- Invest in IT teams to manage and optimize complex, high-performance environments.
- Stay informed about the latest advancements and best practices.
- Adapt infrastructure strategies to future regulatory, security, and business requirements.
AI readiness in Q1 starts with the full stack
One of the strongest signals I have taken away from my conversations with IT Buyers and IT Suppliers is that AI readiness is no longer about “having GPUs.” It’s about orchestrating the full stack. Enterprises across the globe are moving quickly from experimentation to execution, but the leaders are those aligning accelerated compute, power and cooling, data pipelines, security, and operations as a single system. The advice I’d offer heading into Q1 is this: treat AI infrastructure as a strategic platform decision, not a series of tactical purchases. Organizations that integrate infrastructure, cloud, and operational readiness early will move faster from pilots to production — and avoid costly redesigns later in the year.
Scaling agent adoption without undermining the foundation
2026 is the year to focus on execution. Most CIOs are starting to achieve significant value from some of their GenAI investments, but it’s unevenly distributed. Most still struggle to achieve meaningful gains from even half their efforts. With the C-Suite now looking to agents to simplify and more effectively target the best opportunities, CIOs must ensure that rapid innovation churn doesn’t stop the laying a foundation for scalable incorporation of AI into the business.
Brace for an agent surge, but don’t sacrifice modernization efforts
- Don’t delay or skimp on application, data management, and datacenter modernization efforts.
- Get ahead of new, agent triggered, security updates in data governance and identity access management (IAM)
- Don’t over rotate towards simple cost management for AI, with agent adoption the focus must be on measuring and tracking value
Turning early AI gains into sustained impact
Early productivity gains from AI in applications are becoming increasingly common, but many organizations find themselves stuck, receiving isolated improvements and incremental gains, rather than compounding value fueling enterprise-wide momentum. Most significant bottlenecks revolve around organizations’ ability to operationalize their AI. Breaking through this plateau requires focus on a few key objectives:
- Treat AI adoption as an operating model transformation, not a tooling upgrade
- Revisit KPIs and value metrics to ensure they reflect AI-driven improvements in speed, quality, and decision effectiveness
- Establish clear ownership and accountability for AI outcomes across business, IT, and operations
- Prioritize AI initiatives that focus on end-to-end process redesign rather than isolated task improvement.
Why Q1 is the moment to get it right
Across data, infrastructure, and operating models, a consistent theme emerges. Organizations that build momentum early are those that focus on foundational decisions first and act with clarity rather than assumptions.
Q1 sets the pace for the year. Leaders who use this period to align priorities, make strategic investments, and operationalize AI with intention give their teams the confidence and flexibility to adapt as the market evolves.
If you want to validate your Q1 priorities and pressure-test your go-to-market strategy against real market signals, explore IDC’s Q1 GTM validation resources and see where your strategy is aligned and where it may need adjustment.