The world of artificial intelligence is evolving at breakneck speed, and nowhere is this more conspicuous than in the emergence of AI agents. As organizations grapple with separating genuine innovation from marketing hype, we sat down with Ed Keisling, Chief AI Officer at Progress Software, to cut through the noise and understand what AI agents really mean for businesses today. Ed brings a unique perspective, having taken on his new role in February 2025 at a time when the industry is proclaiming this as “the year of agents.” His insights reveal both the tremendous potential and the current limitations of this transformative technology. As always, time is of the essence.

The creation of Chief AI Officer roles across the technology industry signals more than just a trend—it represents a fundamental shift in how businesses view artificial intelligence. As Ed explains, “AI needs to be a strategic pillar of a business to drive innovation and growth. It really reflects the pace at which technology is evolving, and having somebody that is accountable to follow all these latest updates and really look at it through the lens of new risks and opportunities.”

This observation resonates with the broader digital transformation patterns we’ve witnessed over the past decade. Just as Chief Digital Officers emerged to guide organizations through the digital transformation revolution, Chief AI Officers are now stepping up to navigate the AI transformation. The role isn’t merely about implementing technology—it’s about strategic thinking, risk assessment, and identifying genuine business opportunities in a rapidly changing landscape.

One of the most persistent challenges in the AI space is the confusion surrounding terminology. AI agents, in particular, have become an overloaded term that means different things to different people. Ed provides valuable clarity by positioning agents on a spectrum of AI capabilities.

“When generative AI came out, it was generally reactive,” Ed notes. “We would go to ChatGPT, provide a prompt, and it would generate a response based on its training patterns. Agents are moving along that spectrum in terms of capabilities—they have the ability to perceive their environment, access to audio, video, documents, and crucially, the ability to reason, plan, and learn from their actions.”

Traditional automation relies on strict rule-based systems—the digital equivalent of if-then-else logic. Chatbots, while more sophisticated, remain predominantly reactive. AI agents represent a step toward proactive, reasoning systems that can adapt to changing circumstances.

AI agents represent a step toward proactive, reasoning systems that can adapt to changing circumstances

The evolution doesn’t stop there. Ed introduces the concept of “agentic AI“—a broader paradigm where multiple agents collaborate, passing context between each other to accomplish complex tasks. This represents the holy grail of AI automation: systems that can dynamically adapt to real-time situations without constant human intervention.

Despite the exciting potential, Ed provides a sobering reality check about current capabilities. His observation about the Pareto principle in AI is particularly insightful: “AI is the ultimate manifestation of the 80/20 rule. You can very rapidly get to value with 20% of the work achieving 80% of the results, but actually getting it to work 100% of the time is still very, very difficult.”

This phenomenon explains why AI demonstrations look so compelling while real-world implementations often fall short of expectations. The gap between proof-of-concept and production-ready systems remains significant, requiring careful planning, clean data, and well-defined business processes. As always, I should add, “the more it changes, the more it stays the same,”as the French poet would have it.

While pure AI agents may still be evolving, Progress Software’s acquisition of Nuclia, an agentic RAG (Retrieval Augmented Generation) provider, demonstrates a more immediate and practical application of AI technology. Ed explains the fundamental problem RAG solves: “Large language models have been trained on the entirety of the Internet, giving them broad general knowledge, but they don’t have access to data stored behind firewalls or on personal computers.”

This limitation is critical for businesses. While public AI models are impressive, their real value emerges when they can access and reason about proprietary business data. RAG technology bridges this gap, allowing organizations to leverage AI’s reasoning capabilities while grounding responses in their specific knowledge base.

The practical implications are significant. As Ed points out, “Small to medium-sized businesses have lots of unstructured data—audio, video, log files, recordings, PDFs, charts—that represent proprietary business value, but they have no way of indexing or finding or correlating the data within it.” RAG technology makes this data accessible and actionable.

Ed’s experience at the AI4 conference provides valuable insights into the current state of the AI industry. His observation about AI washing is particularly relevant: “There was a lot of AI washing—companies that weren’t sure they understood the problem to be solved, with very thin wrappers around language models to solve point problems. It felt like a hammer looking for a nail.”

The key differentiator, according to Ed, lies in problem-solving focus rather than technology-first thinking. “AI allows you to solve old problems in a new way and to make seemingly impossible problems possible. You’re thinking about how to drive outcomes—making developers more productive, automating tedious workflows, providing better insights that weren’t possible before.”

This perspective aligns with successful technology adoption patterns throughout history. The most successful implementations focus on specific business outcomes rather than showcasing technological capabilities.

Progress Software’s ShareFile platform provides concrete examples of AI delivering measurable business value. The platform serves client-facing teams in regulated industries—doctor’s offices, law firms, and tax accountants—where document management is critical but time-consuming.

The AI implementations are practical and measurable: “We can create curated lists of documents appropriate based on your situation, and as you upload documents, we can figure out which document relates to which checklist item. We’ve measured this at being three and a half times faster.”

More importantly, the system addresses security concerns that many organizations face: “We have capabilities that scan for social security numbers, personal information, and credit card information that you didn’t want to upload. This single capability flags around 35,000 documents a week.”

These examples demonstrate AI’s sweet spot: automating routine tasks while enhancing security and accuracy. The value isn’t just in speed—it’s in freeing professionals to focus on high-value work instead of admin tasks.

One of the most contentious aspects of AI adoption concerns workforce impact. Ed’s perspective is refreshingly pragmatic: “This is a fundamental reshaping of how business is done—a new skill and opportunity for people to grow, learn, and reinvent themselves. There aren’t experts who have been doing this for five or ten years. If you have the headspace and desire, you can become that expert.”

This view positions AI as an enabler rather than a replacement. The technology’s real power lies in eliminating organizational silos and enabling individuals to accomplish more with the right tools and training. “One person is now going to be capable of doing multiple things with the right prompts, giving them opportunities to do more and drive more value for the organization.”

The message is clear: organisations with infinite backlogs of valuable work don’t need to fear AI displacement. Instead, they should focus on upskilling their workforce to leverage these new capabilities effectively.

Ed’s recommendations for AI adoption focus on practical, incremental approaches rather than transformative leaps. “There’s enormous green space for individuals to become fully enabled with AI. The majority of people using AI today are using it in a Google-like fashion, but they haven’t taken time to understand how to correctly prompt agents or use advanced capabilities.”

The most successful implementations start with individual productivity tools—document summarization, email assistance, and internal search capabilities—before advancing to more complex agentic systems. This approach allows organizations to build AI literacy while demonstrating concrete value.

Our conversation with Ed Keisling reveals that AI agents represent both enormous potential and significant current limitations. While the vision of fully autonomous AI systems remains largely aspirational, practical applications of AI technology are already delivering measurable business value.

The key insight for business leaders is the importance of realistic expectations coupled with strategic preparation. AI agents are not yet ready to replace human workers, but they are already transforming how work gets done. Organisations that focus on practical applications, invest in workforce development, and maintain healthy skepticism about vendor promises will be best positioned to benefit from this technological evolution.

As Ed concludes, “You have to put your personal opinions and biases aside and accept and lean into it. At least you can be part of the process and conversation and understand where the edges are.” This balanced approach—embracing the technology while maintaining critical oversight—represents the most reasonable path forward in the age of AI agents.

The future of AI agents is being written today, not in grand demonstrations of artificial general intelligence, but in the practical applications that solve real business problems, one automated workflow at a time.

Ed Keisling is the newly appointed Chief AI Officer at Progress Software Corporation, bringing over three decades of technology leadership experience. He previously served as Senior Vice President of Engineering for Infrastructure Management at Progress, was an executive team member at Vecna Technologies overseeing Engineering, IT, DevOps, Support, Program Management and Analytics, and spent over 17 years in senior engineering roles at Pegasystems. Specializing in complex system architectures, cloud computing, and large-scale infrastructure management, Keisling also mentors through the UNH Pathways Program and MIT’s Undergraduate Practice Opportunities Program (UPOP). In his new CAIO role, he leads Progress’s AI strategy and product portfolio transformation, reporting directly to CEO Yogesh Gupta.

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