New year, new conversations about AI. As 2026 begins, AI has moved from experimentation to execution, and expectations are rising just as fast. Boards are investing, and customers are pushing for real outcomes. The question is no longer if organizations will invest in AI, but how they’ll turn that investment into durable, long-term value.
Over the past year, I’ve had countless discussions with our Precisely leadership team about what they’re seeing across industries, regions, and customer environments. While their perspectives come from different disciplines, a clear set of themes keeps emerging.
Below are several insights from myself and our leadership team that reflect where AI is headed, and what organizations like yours will need to prioritize as ambition gives way to execution.
AI Infrastructure is Accelerating – But Data is Where AI Value Compounds
The pace of AI investment has been extraordinary. Companies are pouring billions into AI infrastructure to meet the capacity demands of the AI moment. But it’s clear that the next chapter of AI won’t be defined by faster models or bigger investments – it will be defined by data readiness. Accuracy, consistency, and context will determine whether AI delivers real outcomes, and governance will determine whether organizations can trust what AI produces at scale.
However, with the entrance of agentic AI, this challenge is exponentially compounded. It is no longer about decision-making, alone. Agentic AI plans, reasons, and acts based on the data it is given. From my perspective, that shift raises the bar significantly. Without a strategy for Agentic-Ready Data, organizations risk amplifying incorrect information, data bias, and poor outcomes driven by inconsistent or poorly governed data. And today, many enterprises simply aren’t ready.
As further proof of this shift, in 2025 we began to see several high-profile acquisitions of data companies signaling a growing focus beyond infrastructure alone. In 2026, expect to see that consolidation accelerate.
Contextual Data Will Define How Intelligently AI Operates at Scale
As AI systems grow more capable, the challenge is no longer just processing information – it’s understanding the world in which that information exists. Data without context limits how effectively AI can reason, interpret, and act.
Across our leadership team, there’s strong alignment around the role of contextual data in shaping AI’s next chapter. Context doesn’t just improve outputs; it helps AI systems make decisions that are more accurate, explainable, and relevant to real-world conditions.
Here’s what some of our Precisely leaders have to say.
Tendü Yoğurtçu, PhD
Chief Technology Officer
“As we move into 2026, geospatial data will play an increasingly critical role in AI training, shaping how systems perceive, interpret, and interact with the world around them. The current reality is that large language models are trained on publicly available data, information that is finite in volume and often limited in accuracy and representation. This emerging “data drought” risks slowing innovation but also presents a strategic opportunity to unlock value through proprietary and curated data.
Geospatial intelligence, including satellite imagery, GPS coordinates, and other location-based insights, introduces a new dimension of context. It helps fill information gaps where data is incomplete, offering a more objective, complete, and verifiable view of real-world conditions. When combined with an organization’s own proprietary data, such as customer information, transaction patterns, or operational signals, geospatial data creates a powerful foundation for differentiated insights and lasting competitive advantage.”
Andy Bell
Senior Vice President, Global Data Product Management
“In 2026 we could see rapid growth in the agentic AI workforce with adoption expected to grow 327% by 2027. However, achieving the full benefits and efficiencies of these AI workers could be hampered by a lack of data readiness.
Currently, only 12% of organizations report that their data is of sufficient quality and accessibility for AI. This will only be heightened by agentic AI systems which operate independently by planning, reasoning, and taking actions towards goals with minimal human intervention.
As these systems rely on complex processes, agentic-ready data is key to ensuring accurate outputs. Achieving true data integrity requires contextual data along with data integration, data governance, and data enrichment.
Contextual data offers an expanded perspective on data, providing insights into places, people, and behaviors. Without understanding the context behind your data, it will be difficult to determine a nuanced and rich understanding of how agentic AI systems are reaching their outputs. It is critical to have an understanding of this to ensure that agentic AI systems are making fully informed, confident decisions on behalf of your business.”
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Data Integrity Becomes the Operating System for AI Governance and Trust
As AI systems become more autonomous and more embedded in critical business decisions, the question of trust moves front and center. In 2026, governance won’t be something organizations layer on after deployment – it will be built into how data is structured, interpreted, and monitored from the start.
Data integrity will serve as the operating system for responsible AI. From semantic clarity and explainability to compliance, auditability, and control over AI-generated data, integrity will determine whether AI can scale safely and deliver lasting value.
As you think about how to govern AI responsibly in the year ahead, here’s what our leadership team believes will matter most.
Dave Shuman
Chief Data Officer
“In 2026, semantics will be the most important AI governance guardrail. Training AI is akin to managing well-intentioned interns. AI models may be smart and capable, but like any agent – human or otherwise – they still require clear direction, oversight, and consistent evaluation.
Adding a semantic layer transforms complex data into a business-friendly format that’s more digestible, helping AI interpret and translate data into reliable output.
As AI conversations shift from implementation to purposeful action in 2026, leaders will prioritize the people and resources needed to build the semantic layer, in order to ensure that the input data directly aligns with the desired, measurable outputs.”
Jean-Paul Otte
Data Strategy Lead
“2026 is the year when AI readiness frameworks will be reframed around data integrity-first principles. Organizations will move away from isolated AI pilots and towards repeatable, data-driven frameworks that ensure AI is deployed responsibly and at scale.
Data maturity assessments and AI governance programs will increasingly revolve around verifying the availability, quality, and trustworthiness of data assets before any AI model is developed or deployed. AI readiness will require a decentralized operating model concerning data and metadata accountability.
The organizations that succeed in 2026 will be those that embed integrity into every layer of their operating model, from role definitions and control frameworks to training and continuous monitoring. In doing so, they will not only meet regulatory expectations but unlock AI that is reliable, explainable, and capable of delivering long-term value.“
Turning AI’s Potential into Results – With Trusted Data
What strikes me most about these perspectives isn’t how different they are — it’s how closely they align. Across roles, regions, and responsibilities, the message is consistent: the future of AI will be built on trusted data, grounded in context, and governed with intention.
As we move into 2026, the organizations that succeed won’t just be the ones that adopt AI fastest. They’ll be the ones that invest thoughtfully in the data foundations that make AI – particularly agentic AI – reliable, explainable, and resilient over time.
That’s where the next chapter of AI value will be written – and it’s a challenge I believe many organizations are ready to meet.
How will you strengthen your data foundation for AI in 2026? For assistance in building a practical, tailored roadmap for your organization, I encourage you to reach out to our Data Strategy Consulting team. They’ll provide the expert guidance you need to responsibly scale and succeed with your AI initiatives this year and beyond.
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