The 2026 Readiness Imperative: How to close the year with a data-first strategy 

2026 will reward the ready. Those who win the next round of transformation will have unified data architectures, agile systems that scale, and people empowered to turn intelligence into advantage.  

While boardrooms debate how to scale AI, many teams are discovering the same truth: there’s no shortcut around data readiness. Poor data quality, siloed systems, and underprepared teams quietly erode the value of even the most advanced AI pilots. As you close 2025 and plan for the year ahead, readiness—not ambition—must define your transformation strategy. 

Why year-end planning must start with data readiness 

Every organization knows the pain of disconnected systems: customer data spread across multiple CRMs, inconsistent financial models, and spreadsheet workarounds that silo information, dilute insight, and slow execution.  

As the year winds down, it’s the right moment to take stock of what’s working, what’s breaking, and where to reinvest. 

Because without clean, connected, and contextual data, even well-funded transformation initiatives will stall. Organizations that strengthen their data foundation enter the new year with the clarity and agility to act—not just react—as opportunities emerge. 

Six moves leaders should take now  

Year-end planning is the ideal moment to prepare your foundation for 2026. Each of these moves helps your enterprise strengthen one of the core layers of data readiness—the structure that every AI and digital transformation depends on. 

1. Audit your data foundation 

Before setting 2026 priorities or chasing new AI use cases, commission a quick, executive-level review of how well your existing data systems, processes, and governance align with organizational goals. 

This can take the form of a data strategy alignment session or a readiness audit led by the CIO or CDO with input from finance and operations.  

Outcome: A clear, shared understanding of your current data maturity and where strategic investment will have the highest impact. 

2. Define your data governance ownership model  

Before scaling analytics or expanding AI pilots in 2026, executives should clarify ownership across data quality, compliance, and access. 

Establish or reaffirm a cross-functional data governance council, including IT, legal, finance, and operations, to align on standards, escalation paths, and accountability. 

Outcome: A governance framework that drives trust, consistency, and sustainable innovation.  

3. Run a data architecture health check 

Before closing out the year, partner with IT to quickly evaluate whether your current architecture can support enterprise-wide scalability. Review your cloud infrastructure, data integrations, and pipelines to uncover any bottlenecks or redundancies that could slow innovation in 2026.  

Outcome: A clear view of where modernization is needed—ensuring your data environment is stable, connected, and ready to scale with next year’s strategy. 

“I always say ‘accuracy before automation.” Said Sean Bonadeo, Partner and Practice Leader, Strategy, Technology & Transformation at Highspring. “Data, when clean and well-structured, becomes a currency of transformation—fueling insight, innovation, and strategic growth. But when you don’t establish this as a foundation, it turns into a liability. It drains resources and undermines the very initiatives it was meant to empower.” 

4. Conduct a year-end security resilience review.  

Before building any new data transformation initiatives, work with your CIO/CISO to evaluate how well your current security framework protects critical data assets. Confirm that safeguards—like access controls, encryption, and monitoring—are embedded within systems, not layered on after the fact.  

Outcome: A strengthened, defense-in-depth posture that ensures your organization’s data remains protected, compliant, and resilient as you scale into 2026. 

5. Target one high-value, low-effort quick win 

Before setting 2026 goals, identify one business area where data can be transformed into real-time decision support—for example, forecasting demand, monitoring workforce trends, or optimizing customer engagement. Partner with analytics leaders to ensure insights are accessible, visualized, and tied directly to operational decisions. 

Outcome: A proof point for how intelligence drives measurable results—helping your organization shift from reactive reporting to predictive, data-informed decision-making going into the new year. 

6. Align your 2026 talent strategy to your data readiness goals. 

Close out the year by partnering with HR and business leaders to evaluate whether your current workforce strategy supports the organization’s data and AI ambitions. Identify where you’ll need new roles, partnerships, or reallocation of talent to execute your data strategy effectively. 

Outcome: A forward-looking talent plan that ensures the right people—and skills—are in place to turn your data and AI strategy into measurable business outcomes in 2026. 

These keys mark the entry point to data readiness for any transformation—AI or otherwise. Once they’re underway, you can evolve from reactive to ready, and from ready to scalable. 

Readiness isn’t a one-time milestone; it’s a continuous process 

Highspring meets organizations where they are—helping them advance from foundation to scale with structure and speed. 

“The race to innovate is on, but the finish line will be crossed by those who are best prepared,” said Johnathan Tate (see his article Data Readiness: The Unseen Foundation to AI Success), Data Practice Leader at Highspring. “The future doesn’t belong to the most AI-enabled organizations; it belongs to the most data-ready. It is time to move beyond the hype and focus on the foundational work that will actually deliver a tangible return on investment and create a sustainable competitive advantage.”

Foundation: Establish stability  

Focus: clarity, ownership, and readiness. 

Actions: conduct readiness assessments, define data strategy, identify talent and system gaps, and deploy project-based teams to stabilize core systems. 

Outcome: teams move from reactive firefighting to strategic alignment. 

Alignment: Execute with structure  

Focus: structure and accountability. 

Actions: stand up governance models, modernize architecture, and activate cross-functional data and AI strategies. 

Outcome: business and IT alignment, measurable improvement in data quality, and predictable outcomes across initiatives. 

Scale: Automate and sustain 

Focus: acceleration and sustainability. 

Actions: deploy accelerators, integrate AI into core systems, and evolve teams for continuous improvement. 

Outcome: an AI-ready organization capable of scaling innovation responsibly. 

Use this quarter to prepare your data, systems, and teams—so 2026 starts ready, not reactive.  

The coming year will reveal which enterprises have laid the groundwork to deliver—and which are still pursuing transformation faster than their foundations can support.  

At Highspring, we help organizations build that foundation through integrated strategy, technology, and talent solutions that make transformation scale. Because the future won’t wait—and in 2026, readiness will decide who wins. 

Ready to see where your data really stands? 

Start with a data readiness diagnostic to baseline your maturity and pinpoint the areas that will drive the greatest impact in 2026.