M&T Bank

Ryan Sattler

Enterprise Efficiency Shaper

Ryan Sattler is Director of HR Technology and Intelligence, leading HR systems and analytics. With 15 years of experience across HR, compensation, and strategy, he focuses on aligning enterprise technology, data governance, and workforce planning to improve decision-making and employee experience.

I have worked in HR for about 15 years, starting as an intern and moving through business partner, talent and compensation roles, running Workday deployments and spending time in a strategy role inside M&T’s consumer bank. Today, I lead HR Technology and Intelligence at M&T Bank, where I am responsible for Workday configuration and the analytics function that extracts data, turns it into usable information and governs it. That mix of generalist work, technology deployment and business strategy shapes how I approach a gap most organizations have not closed. There is a disconnect between deploying systems and making them usable at the employee level in a way that improves efficiency and decision-making. That gap becomes more visible as organizations introduce AI into environments that were never designed to support it or built around a human-centered experience.

The Two Failures Nobody Wants To Admit

The proliferation of AI and machine learning is real, but the issue is not the technology itself. It is the pace and the way it is entering organizations. These capabilities are arriving rapidly and from multiple directions, and most organizations are reacting instead of designing an enterprise-wide approach. The organizations that take an enterprise-wide approach to this are the ones that will see the real gains.

That shows up in two consistent failures. The first is automating broken processes. At its core, this is the problem of garbage in, garbage out. If a workflow is poorly designed, layering AI on top of it executes the dysfunction faster. You will not see the ROI, because there is no ROI to find in a flawed foundation.

The second is fragmentation. In most organizations, employees are already unsure where to go to get what they need. Do they need to go to Workday? Do they need to go to Concur? Do they need to go to an Archer risk platform? That confusion exists before AI is introduced. When a separate AI capability is added to each of those systems, the experience does not improve. The employee still has to decide where to go, and that decision is what limits efficiency, especially compared to what is possible when it is solved at the enterprise level.

“The Proliferation Of AI and Machine Learning is Real and Coming from all Directions. The issue is not the Technology, But Organizations Reacting Instead of Designing an Enterprise-Wide Response.”

Solving for the Employee, Not The System

Efficiency improves only when that decision is removed. The problem has to be addressed at the enterprise level, where a single interface can operate across platforms and allow the employee to focus on the task rather than the system. Without that, adding intelligence at the tool level only redistributes the same complexity.

At that point, HR technology is no longer an HR problem. It becomes an enterprise coordination problem across the employee lifecycle. One of the ways we approach this at M&T is through technology rationalization. This is a structured review of the applications in the HR ecosystem to understand what capabilities each one provides and whether those capabilities can be consolidated. Fewer tools reduce the number of integrations to manage, the number of third-party risk engagements and the administrative effort required to maintain them. The result is a simpler environment that maintains, and in some cases simplifies capability.

That work depends on partnership. It requires alignment with central technology teams and enterprise architects who can evaluate decisions against the broader direction of the organization. My role is to bring the human capital perspective into those conversations and ensure we are solving problems that span the employee lifecycle rather than focusing only on HR.

Where AI Actually Delivers

When the foundation is in place, the most meaningful application of AI is in talent-centered workforce planning. The opportunity is to look out over a horizon of 3 to 18 months and make decisions based on a clear understanding of workforce capabilities and the tasks and bodies of work that need to be executed.

That capability exists today, but not at scale. It requires more people than organizations are willing to staff. AI changes that constraint, but only if the underlying systems and data are structured correctly.

The Behavioral Constraint

Even in environments where these technologies are deployed, organizations are not consistently seeing returns. This is not a limitation of the tools. It is a behavioral issue.

The shift required is in how employees use these capabilities. They need to be treated as capacity freers, as a way to complete the same work faster or take on additional work without increasing headcount. What I see in organizations that struggle is a pattern of continuing to deploy new tools without investing in the change management required to drive adoption.

Supporting these deployments with training and creating internal forums where employees share use cases, what is working and where it breaks down helps build familiarity and makes usage part of how work gets done, not an optional layer.

Building in This Environment

For those building a career in this space, a few things matter. Agility is important because the work requires shifting context quickly while staying focused on the problem being solved. Resilience is necessary because the pace of change has been increasing and is not slowing down. Partnership is critical because no one individual has all the expertise required. Progress depends on coordinating across those capabilities to solve the business problem.