Ed Keisling, Chief AI Officer, Progress Software – Interview Series

Ed Keisling, Chief AI Officer, Progress Software, brings over 30 years of technology leadership to his role, where he oversees and advances the company’s AI strategy and integrates AI across its product portfolio.

Previously, Keisling served as Senior Vice President of Engineering for Infrastructure Management at Progress, where he led innovation efforts and improved operational efficiency. He has also held executive engineering roles at Vecna Technologies, where he directed DevOps, IT, analytics, and support, and spent more than 17 years in senior engineering leadership positions at Pegasystems

Progress Software is a leading provider of infrastructure and application development tools, with a growing focus on embedding artificial intelligence across its platform ecosystem. The company’s AI efforts span infrastructure monitoring, content automation, semantic search, and responsible generative AI. By integrating AI into products like Sitefinity, Flowmon, and Semaphore, Progress helps businesses accelerate development, streamline operations, and improve decision-making with context-aware, intelligent automation.

You’ve had a decades-long career leading engineering and infrastructure teams across major organizations like Pegasystems, Vecna Technologies and now Progress Software. What originally drew you to the intersection of software engineering and enterprise transformation—and how did that path evolve into your current AI leadership role?

Throughout my career, I’ve been fascinated by different management approaches to getting things done. They all have their benefits and drawbacks, but I’ve always thought that the best process would be one where you put the information in the hands of the people closest to the work and then give them the autonomy they need to make the decisions they need in real time. This has been one of the pillars of how I’ve managed both large and small organizations.

This approach applies to all functional areas, but I’m particularly drawn to Engineering where the developers just want to put their heads down and crank out their work and have minimal interruptions.

AI is one of the best levers to enable this ideal. It’s true democratization of knowledge, enabling people to really take ownership of their ideas and work and drive outcomes in a more systematic way. I’m passionate about enabling the team at Progress to take advantage of AI to make their work easier, and I’m equally driven to try to figure out how we can leverage this within our Product suite to drive even greater value for our customers.

Before becoming CAIO, you led engineering for critical Progress products like WhatsUp Gold, Flowmon and Chef. What lessons from managing infrastructure and operational scale inform your approach to embedding AI across an enterprise product suite?

The key to any successful AI implementation is high-quality data. More comprehensive data allows you to drive better insights and actions for your customers and allows us as a vendor to tailor better experiences for them. We have an ongoing initiative at Progress to tie the monitoring capabilities of our Loadmaster, Flowmon and WhatsUp Gold products together to drive more holistic insights of the customer’s environment.

We think there’s an interesting opportunity with Progress Chef to be able to make changes in the customer environment, based on the events. AI truly allows us to accelerate this work, not only in our ability to bring new capabilities to market at speed, but also by surfacing actionable items for the operations team today and for AI agents in the future.

At Progress, you’ve emphasized creating “safe AI learning spaces” that allow experimentation without fear. How do you strike a balance between fostering innovation and maintaining responsible AI governance in such environments?

On the development side, we have robust pipelines and coding standards that have been refined in some cases over decades, and we have a human in the loop to review all changes. It’s important we use this opportunity to raise, not lower, our standards in terms of code quality and completeness, and in some cases, AI allows us to further shift left here with improved security and code reviews.

We have regular AI hackathons within the Products to come up with new product ideas and ways to help automate or streamline our products. We have a formal process that products must move through if they want to advance any of these ideas to production, where we explicitly vet the feature for ethical and compliance risks, among a host of other things, with leaders from across the organization.

Many AI projects in enterprise settings never make it past the proof-of-concept phase. From your experience, what are the most common barriers to AI adoption—and how are you helping Progress’ clients overcome them?

There’s a gap between hype and reality in terms of what can be practically implemented in an enterprise environment regarding AI. The guardrails here are similar to any emerging technology; you need to be pragmatic about understanding the risk, scope and benefits— and then design small experiments that you can safely deploy to give you the experience and data you need to iterate further.

So this is the approach we are taking at Progress right now, identifying high-value, lower risk, human-in-the-loop solutions that customers can experience to build that confidence and then build from there.  Trust is critical here because we want to safely prove our value proposition, as our software is central to many of our customers’ businesses.

You’ve led initiatives across analytics, DevOps and cloud architecture. How do these disciplines integrate with today’s AI deployments, and where do you see the most exciting convergence happening?

The biggest advancement here is the holy grail of real-time event monitoring and actions that are now possible because of AI. The challenge with any data set is deriving the actionable items or trends, particularly in today’s environment, where you can have multiple systems feeding information. It’s very difficult and time-consuming to filter those messages down to figure out what you need to do next. We’re entering a new world where the system will be able to dynamically provide those insights for you, and you’ll simply be able to ask in natural language for the information you need.

You advocate for grounding AI in business logic rather than hype. Can you share an example of how aligning AI development with measurable business outcomes has led to real success—either at Progress or in your previous roles?

At Progress, we prioritize business value over AI buzzwords by focusing on measurable outcomes that directly impact our customers’ bottom line.

One example is our approach with the Progress Sitefinity AI integration. Rather than adding AI features for the sake of having them, we identified specific pain points our customers were facing and developed targeted AI capabilities to address those challenges, including intelligent content optimization, AI-driven performance analysis and automated media search. With the latest AI capabilities in Sitefinity 15.3, marketers, developers and content teams can deliver digital experiences that will enable them to compete and win in an AI-driven world.

Another example is our Progress Flowmon platform. Powered by AI, Flowmon acts as an always-on cybersecurity analyst, able to distill and summarize only the most important events and findings, providing a contextualized understanding of incidents and key insights into the dynamics of any security situation in complex hybrid environments. With Flowmon network monitoring, security teams can focus on genuine threats instead of sifting through noise, improving their response times and mitigating the risk of missing critical incidents in complex hybrid environments.

With Progress serving hundreds of thousands of global customers, how do you ensure your AI strategy scales responsibly across such a large and diverse ecosystem of developers and enterprises?

We want customers to have access to the best technology to make them successful and allow them to derive the most value from our products. Progress has multiple products, each leveraging AI to bring innovative solutions to the market. One of our advantages is that we can take those learnings, standardize them and make them available to other products in the portfolio to further accelerate their capabilities. At their core, concepts like semantics, document processing and summarization, observability and anomaly detection can be leveraged across multiple products.

You’ve contributed to mentorship programs at MIT and the University of New Hampshire. What skills or mindsets do you believe the next generation of AI leaders must develop to build both ethical and impactful solutions?

Something I try to instill in the students I mentor is the concept and value of “learning how to learn.” Can they take a complex project they have completed, assimilate the learnings and then abstract them out to apply them to a problem space they have never seen before? The more you can do this, the more it becomes a superpower where you build the confidence that no matter what problem you may encounter, you know you have a success toolkit to break it down and to be successful. This skill will allow students to navigate shifts in technology.

But beyond that, we want the students to be curious, to have a scientific approach, and to find work that they are passionate about. If you truly care about your work and take pride in it, the ethical and impactful aspects will often follow naturally—provided your organization establishes proper guardrails to guide this process.
 
Progress has been incorporating AI into its products for years. What recent advancements or shifts in the AI landscape do you believe have finally made scalable AI implementation more feasible than ever?

Obviously, the technology has improved dramatically, and it’s become much more accessible. You no longer must build or train models to drive value from an LLM or have a team of data scientists to optimize algorithms.

But practically, I think this starts with awareness—OpenAI/ChatGPT put AI in the spotlight and made it accessible to everyone. This widespread accessibility builds trust with people in terms of what is possible and the types of problems  AI can solve. Inevitably, this leads to more confidence for those building solutions that can solve real problems, as well as for the end users of those solutions.

Looking ahead, how do you envision the role of a Chief AI Officer evolving as AI becomes more deeply embedded into organizational DNA? Will it remain a distinct role, or become a core competency expected of all C-level leaders?

I believe that AI will need to become a core competency of all employees, not just leaders of the organization, for organizations to thrive. This is something we are driving at Progress and are working hard to enable our teams so they can participate in this transformation. In fact, I think that’s part of the secret sauce—along with great products, customers and partners—that will truly drive adoption and innovation. I believe organizations that figure this out will be the new leaders in their industry. My job is to help ensure that this happens and that we are aligned with the right industry trends that will make us successful.

Thank you for the great interview, readers who wish to learn more should visit Progress Software.