A Conversation with Precisely CEO Josh Rogers
AI has moved from experimentation to a top priority in the boardroom. Yet for many organizations, meaningful return on investment (ROI) remains frustratingly out of reach. As organizations shift their focus toward Agentic AI—systems that can autonomously make decisions and take action—a harder truth is emerging. The data that AI depends on isn’t ready. It is often fragmented, outdated, and difficult to trust at scale.
In this Q&A, Precisely CEO Josh Rogers shares his perspectives on why businesses have struggled to move AI from pilot programs to enterprise implementation, what has fundamentally changed as organizations move toward Agentic AI, and why data integrity has become a strategic imperative. He also outlines the importance of attaining Agentic-Ready Data and how forward-thinking organizations are positioning themselves to turn AI into a competitive advantage.
McKinsey has reported that while AI adoption is widespread, most initiatives fail to deliver meaningful bottom-line impact.1 From your perspective, what’s holding organizations back from scaling AI into real business value?
AI investment has moved incredibly fast, but most organizations have struggled to move from experimentation into full production. Some have achieved smaller productivity gains, which is useful, but incremental gains will not keep pace with the level of investment being made, nor will they fundamentally change how the business operates at the scale AI is meant to deliver.
The bigger issue is data. In our 2026 State of Data Integrity and AI Readiness report, of the more than 500 senior data and analytics leaders surveyed, 43% cite data readiness as the most significant barrier to AI alignment with business objectives.2 AI systems reflect the data they are given. And for most companies, that data is fragmented, incomplete, outdated, inconsistent, or poorly governed. When AI is fed data that lacks accuracy, consistency, and context, the output does not translate into sustainable business value and, in fact, can create significant risk.
What fundamentally changes when organizations move from AI experimentation to Agentic AI?
Agentic AI represents a shift from assistance to action. These systems don’t just analyze information, they make and act on decisions, in real time.
This changes everything. When AI starts influencing or making decisions, at enterprise scale, there is no room for uncertainty. You need confidence that the data the AI system is using meets the highest quality and governance standards. That’s the moment many organizations realize their existing data foundation simply is not built for what comes next.
You’ve introduced the concept of the “Agentic AI Data Integrity Gap.” What is it, and why should executive teams care?
The Agentic AI Data Integrity Gap is the widening gap between what Agentic AI requires and the reality of enterprise data today. Most organizations still operate with data that is hard to access, fragmented across hybrid environments and legacy systems, lacks context, reflects a backward-looking view or is outdated, and is expensive to manage manually. This creates blind spots, limits scale, and makes it difficult for systems to make accurate, autonomous decisions.
Executives need to understand this challenge because it is not a technical issue, it’s a strategic one. If this gap is not addressed, AI investment will stall, trust will erode, and it will impair the organization’s ability to scale. This will result in failed projects and an inability to realize the ROI that AI can bring when implemented in alignment with the right processes and high-quality, trusted, governed data.
How should leaders be rethinking their data strategy in light of Agentic AI?
Leaders need to move beyond thinking about data as an IT-owned asset and start treating it as a core operational foundation for the business. The question is no longer whether data is accurate, but whether it is ready to support real-time, autonomous decision-making.
Organizations that succeed will be the ones that align their data strategy directly to how they want the business to operate in an Agentic AI world. This means building a strategy based on the core elements of data integrity—accuracy, consistency, and context. What they need is Agentic-Ready Data, purpose-built to support autonomous systems at enterprise scale.
EBOOKAchieving the Agentic AI Advantage
Closing the data integrity gap with agentic-ready data.
What does “Agentic-Ready Data” actually mean in practice, not in theory?
Agentic-Ready Data is the highest quality data that is integrated across systems and enriched with the context AI needs to operate confidently. It is continuously updated and well governed so decisions are explainable, traceable, and compliant, supporting AI, automation, and analytics initiatives across the enterprise.
Just as important, it is manageable. It does not rely on heavy manual processes or specialized skills to maintain. When data reaches that state, organizations can trust their AI systems to operate at enterprise scale and deliver real business value.
Why is data integrity such a critical foundation for Agentic AI and what makes Precisely uniquely positioned here?
Agentic AI raises the bar for trust. When AI systems are making decisions, the integrity of the underlying data becomes non-negotiable. Leaders need to know where the data comes from, how it’s being shaped, and whether it’s fit for purpose in that moment. Without that foundation, organizations either limit what AI can do or expose themselves to unacceptable risk.
Precisely is uniquely positioned because data integrity is not something we added recently in response to AI. It is our core expertise. For more nearly 60 years , we’ve helped over 12,000 customers, including some of the world’s leading enterprises integrate, enrich, govern, and operationalize data across complex, hybrid environments.
That expertise, combined with our software, data, and data strategy consulting services, puts us in a unique position to help organizations close the Agentic AI Data Integrity Gap in a way that no other company can.
You talk about maximizing context and usage while minimizing effort. Why do those matter so much when it comes to execution?
As organizations move toward Agentic AI, success depends less on model sophistication and more on whether data integrity can be executed at scale to maximize context, maximize usage, and minimize effort. A unified data foundation, enriched with authoritative third-party and location intelligence and supported by sustained data quality gives a more complete view by eliminating blind spots and allowing AI systems to reason with confidence.
It’s just as important that trusted data can be used wherever decisions are made. When data integrity logic is defined once and applied consistently across complex hybrid environments, organizations can deliver the right data, in the right context, at the right time. Built-in governance also ensures AI operates responsibly at scale, turning governance from a constraint into an enabler of innovation.
Finally, data integrity must scale without becoming a cost or complexity problem. Automation, AI-driven guidance, and interoperable, modular services reduce manual effort and accelerate time to value. This allows organizations to start where they are today and scale at their own pace.
That’s the role the Precisely Data Integrity Suite plays. It brings these capabilities together as interoperable SaaS services on a common foundation, enabling Agentic-Ready Data across environments while reducing manual effort through AI-driven automation. And through our Data Link partner network, customers can easily integrate complementary third-party datasets alongside Precisely data, unlocking richer insights and better outcomes with far less effort.
What do you see data-leading organizations doing today that others are not?
The organizations that are getting it right are aligning data initiatives directly to business outcomes—not just analytics or reporting.
They are investing in foundations that support real-time decision-making. They’re treating governance as an enabler, not a constraint. When governance matures, agency follows. When you build clarity and trust into your processes, you unlock the ability to act decisively, dramatically, and with results that last.
And most importantly, they recognize that data integrity is not a one-time project. It is an ongoing capability that must evolve as the business evolves.
If you could leave executive teams with one takeaway as they think about Agentic AI investments in 2026 and beyond, what would it be?
AI will not deliver meaningful impact without a data foundation built for autonomy.
The organizations that win will be the ones that invest now in Agentic-Ready Data, treating it like a strategic imperative, not a nice to have. The data integrity gap is a risk today and will quickly become a liability for those who fail to address it.
This is not about chasing the next AI tool. It is about building the trust, context, and operational readiness required to turn AI into a durable advantage for the business.
Sources:
1. Quantum Black AI by McKinsey
2. 2026 State of Data Integrity and AI Readiness
