New yr, new conversations about AI. As 2026 begins, AI has moved from experimentation to execution, and expectations are rising simply as quick. Boards are investing, and clients are pushing for actual outcomes. The query is now not if organizations will spend money on AI, however how they’ll flip that funding into sturdy, long-term worth.
Over the previous yr, I’ve had numerous discussions with our Exactly management crew about what they’re seeing throughout industries, areas, and buyer environments. Whereas their views come from totally different disciplines, a transparent set of themes retains rising.
Under are a number of insights from myself and our management crew that replicate the place AI is headed, and what organizations like yours might want to prioritize as ambition offers strategy to execution.
AI Infrastructure is Accelerating – However Knowledge is The place AI Worth Compounds
The tempo of AI funding has been extraordinary. Firms are pouring billions into AI infrastructure to satisfy the capability calls for of the AI second. But it surely’s clear that the following chapter of AI gained’t be outlined by quicker fashions or larger investments – will probably be outlined by knowledge readiness. Accuracy, consistency, and context will decide whether or not AI delivers actual outcomes, and governance will decide whether or not organizations can belief what AI produces at scale.
Nevertheless, with the doorway of agentic AI, this problem is exponentially compounded. It’s now not about decision-making, alone. Agentic AI plans, causes, and acts based mostly on the information it’s given. From my perspective, that shift raises the bar considerably. With out a technique for Agentic-Prepared Knowledge, organizations danger amplifying incorrect data, knowledge bias, and poor outcomes pushed by inconsistent or poorly ruled knowledge. And at this time, many enterprises merely aren’t prepared.
As additional proof of this shift, in 2025 we started to see a number of high-profile acquisitions of knowledge corporations signaling a rising focus past infrastructure alone. In 2026, anticipate to see that consolidation speed up.
Contextual Knowledge Will Outline How Intelligently AI Operates at Scale
As AI methods develop extra succesful, the problem is now not simply processing data – it’s understanding the world wherein that data exists. Knowledge with out context limits how successfully AI can purpose, interpret, and act.
Throughout our management crew, there’s robust alignment across the position of contextual knowledge in shaping AI’s subsequent chapter. Context doesn’t simply enhance outputs; it helps AI methods make selections which are extra correct, explainable, and related to real-world situations.
Right here’s what a few of our Exactly leaders must say.
Tendü Yoğurtçu, PhD
Chief Expertise Officer
“As we transfer into 2026, geospatial knowledge will play an more and more important position in AI coaching, shaping how methods understand, interpret, and work together with the world round them. The present actuality is that enormous language fashions are educated on publicly out there knowledge, data that’s finite in quantity and infrequently restricted in accuracy and illustration. This rising “knowledge drought” dangers slowing innovation but in addition presents a strategic alternative to unlock worth via proprietary and curated knowledge.
Geospatial intelligence, together with satellite tv for pc imagery, GPS coordinates, and different location-based insights, introduces a brand new dimension of context. It helps fill data gaps the place knowledge is incomplete, providing a extra goal, full, and verifiable view of real-world situations. When mixed with a company’s personal proprietary knowledge, corresponding to buyer data, transaction patterns, or operational indicators, geospatial knowledge creates a strong basis for differentiated insights and lasting aggressive benefit.”
Andy Bell
Senior Vice President, International Knowledge Product Administration
“In 2026 we may see fast progress within the agentic AI workforce with adoption anticipated to develop 327% by 2027. Nevertheless, reaching the total advantages and efficiencies of those AI employees could possibly be hampered by a scarcity of knowledge readiness.
Presently, solely 12% of organizations report that their knowledge is of ample high quality and accessibility for AI. This may solely be heightened by agentic AI methods which function independently by planning, reasoning, and taking actions in direction of objectives with minimal human intervention.
As these methods depend on advanced processes, agentic-ready knowledge is vital to making sure correct outputs. Reaching true knowledge integrity requires contextual knowledge together with knowledge integration, knowledge governance, and knowledge enrichment.
Contextual knowledge gives an expanded perspective on knowledge, offering insights into locations, individuals, and behaviors. With out understanding the context behind your knowledge, will probably be troublesome to find out a nuanced and wealthy understanding of how agentic AI methods are reaching their outputs. It’s important to have an understanding of this to make sure that agentic AI methods are making totally knowledgeable, assured selections on behalf of what you are promoting.”
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Knowledge Integrity Turns into the Working System for AI Governance and Belief
As AI methods turn into extra autonomous and extra embedded in important enterprise selections, the query of belief strikes entrance and heart. In 2026, governance gained’t be one thing organizations layer on after deployment – will probably be constructed into how knowledge is structured, interpreted, and monitored from the beginning.
Knowledge integrity will function the working system for accountable AI. From semantic readability and explainability to compliance, auditability, and management over AI-generated knowledge, integrity will decide whether or not AI can scale safely and ship lasting worth.
As you concentrate on find out how to govern AI responsibly within the yr forward, right here’s what our management crew believes will matter most.
Dave Shuman
Chief Knowledge Officer
“In 2026, semantics will probably be crucial AI governance guardrail. Coaching AI is akin to managing well-intentioned interns. AI fashions could also be good and succesful, however like all agent – human or in any other case – they nonetheless require clear course, oversight, and constant analysis.
Including a semantic layer transforms advanced knowledge right into a business-friendly format that’s extra digestible, serving to AI interpret and translate knowledge into dependable output.
As AI conversations shift from implementation to purposeful motion in 2026, leaders will prioritize the individuals and sources wanted to construct the semantic layer, with a purpose to be sure that the enter knowledge straight aligns with the specified, measurable outputs.”
Jean-Paul Otte
Knowledge Technique Lead
“2026 is the yr when AI readiness frameworks will probably be reframed round knowledge integrity-first ideas. Organizations will transfer away from remoted AI pilots and in direction of repeatable, data-driven frameworks that guarantee AI is deployed responsibly and at scale.
Knowledge maturity assessments and AI governance applications will more and more revolve round verifying the provision, high quality, and trustworthiness of knowledge belongings earlier than any AI mannequin is developed or deployed. AI readiness would require a decentralized working mannequin regarding knowledge and metadata accountability.
The organizations that achieve 2026 will probably be those who embed integrity into each layer of their working mannequin, from position definitions and management frameworks to coaching and steady monitoring. In doing so, they won’t solely meet regulatory expectations however unlock AI that’s dependable, explainable, and able to delivering long-term worth.“
Turning AI’s Potential into Outcomes – With Trusted Knowledge
What strikes me most about these views isn’t how totally different they’re — it’s how carefully they align. Throughout roles, areas, and tasks, the message is constant: the way forward for AI will probably be constructed on trusted knowledge, grounded in context, and ruled with intention.
As we transfer into 2026, the organizations that succeed gained’t simply be those that undertake AI quickest. They’ll be those that make investments thoughtfully within the knowledge foundations that make AI – significantly agentic AI – dependable, explainable, and resilient over time.
That’s the place the following chapter of AI worth will probably be written – and it’s a problem I imagine many organizations are prepared to satisfy.
How will you strengthen your knowledge basis for AI in 2026? For help in constructing a sensible, tailor-made roadmap in your group, I encourage you to achieve out to our Knowledge Technique Consulting crew. They’ll present the skilled steering it’s essential responsibly scale and succeed together with your AI initiatives this yr and past.
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