Pannysylvania Magazine

ALPHARETTA, Ga. – October 1, 2026 – Artificial intelligence has quickly moved from experimentation to a larger business question: How can organizations put AI to work in ways that produce measurable results?

For global business and technology executive Eric Kalin, answering that question requires more than selecting the right technology. As companies begin applying AI to established workflows and operations, he believes leadership, data readiness, security, ownership, and coordination between teams will become increasingly important.

“The technology is moving very quickly, but organizations still have to solve the fundamentals,” Kalin said. “What problem are we trying to solve? Who owns the outcome? Do we have the right data? How will we measure whether this is actually making the business better? Those questions need to come before the technology decision.”

Kalin has spent more than 25 years working across technology, consulting, sales, operations, data, cloud infrastructure, and business transformation. His career has included leadership roles with IBM, Accenture, Amazon Web Services, Microsoft Azure, and Oracle. That experience has given him a close view of how large organizations approach major changes in technology and business operations.

Starting With the Business Problem

One risk Kalin sees in periods of rapid technology adoption is the temptation to start with a new capability rather than a specific need.

AI can potentially support a wide range of functions, but Kalin believes organizations should first identify the business outcome they want to improve. That might involve reducing the time required for a process, helping employees make better decisions, improving a customer experience, or finding useful information more efficiently.

The more clearly the problem is defined, the easier it becomes to determine whether AI is an appropriate solution.

“Being able to use AI is not the same as having a reason to use it,” Kalin said. “A strong use case should have a clear connection to the customer, the employee, the operation, or the financial performance of the business.”

That distinction becomes more important as organizations move beyond small pilot projects. An experiment may demonstrate that a technology works. Scaling it requires leaders to determine how it fits into existing systems, responsibilities, security requirements, and workflows.

Data Readiness Remains Essential

The rapid growth of generative and agentic AI has also placed greater attention on the information behind these systems.

Kalin sees data readiness as one of the foundational questions organizations should address before attempting to scale AI. Companies may have large amounts of information while still struggling with inconsistent data, isolated systems, unclear access rules, or questions about accuracy.

AI does not automatically solve those underlying issues.

“Companies have spent years building data across different systems and parts of the organization,” Kalin said. “Before asking what AI can do with that data, leaders need to understand what they have, where it sits, who should have access to it, and whether they can trust it.”

Security must be part of the same discussion. As AI becomes more closely connected to business processes, organizations need clear rules for how information is accessed, used, shared, and protected.

AI Needs Clear Ownership

Kalin also points to ownership as a leadership issue that can determine whether an initiative advances beyond the experimental stage.

An AI project may involve technology teams, business leaders, security professionals, data specialists, sales teams, operations, and other functions. Without clear accountability, decisions can slow down, or objectives can become unclear.

Kalin has spent much of his career working across those organizational boundaries. He believes bringing different groups together around a shared outcome is especially important when implementing technology that affects multiple parts of a company.

“Cross-functional work succeeds when people understand both the shared objective and their individual responsibility,” he said. “Collaboration cannot just mean adding more people to a meeting. Someone has to own the outcome, and every team involved needs to understand the role it plays.”

Measuring What AI Actually Changes

As enterprise AI matures, Kalin expects the conversation to shift increasingly toward measurable outcomes.

Early adoption has often focused on what AI can do. The next stage will require companies to determine what it actually changes.

That measurement will vary by organization and use case. A successful project might save employees time, reduce repetitive work, improve response times, create new revenue opportunities, strengthen customer relationships, or improve the quality of business decisions.

Kalin believes leaders should determine those measures before scaling an initiative rather than attempting to define success afterward.

“The goal should not simply be to say that your organization is using AI,” Kalin said. “The goal is to understand what became better because you used it.”

For leaders, that may be the larger challenge of enterprise AI adoption. The technology will continue to evolve, but connecting new capabilities to real problems, reliable data, accountable teams, secure processes, and measurable results remains a leadership responsibility.

As businesses move from AI experimentation toward broader adoption, Kalin believes organizations that maintain that discipline will be better positioned to separate useful innovation from technology adopted simply because it is new.

About Eric Kalin

Eric Kalin is a global business leader and technology executive with more than 25 years of experience across technology, consulting, cloud, data, sales, operations, and business transformation. His career has included leadership experience with Oracle, Microsoft Azure, Amazon Web Services, Accenture, and IBM. Based in Alpharetta, Georgia, Kalin connects technology strategy with practical business outcomes, builds cross-functional teams, and helps organizations navigate periods of significant change.

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