Over the past year, AI moved from an innovation topic to an operating priority, but the conversation has turned since then because the market moved faster than most operating models could absorb. Customers were no longer asking whether AI mattered, or whether the technology was capable enough. Where can AI remove friction, improve productivity, create new value? How do we turn this into something practical, secure, and measurable? And why, after months of pilots, proofs of concept, and internal pressure to “do something with AI,” is the business impact still inconsistent? For many teams, the answer is complicated by bringing the pilot to production. Once AI moves beyond a controlled test, it has to work with real data, real users, access controls, costs, and business processes. Governed, monitored, secured, integrated, and most importantly, owned; the complexity of this phase slows many initiatives down.
This is the turn that led Island Networks to launch the Path to AI Roadshow. We wanted to move the conversation past general AI interest and into the practical work required to make AI useful at scale. Set across Philadelphia, Raleigh, and Dublin, the focus was on the foundations that determine whether AI becomes a business capability or stays trapped in experimentation: platform readiness, data strategy, automation, governance, security, and a clear operating model.
The goal: To show what has to be true underneath the technology for organizations to move from pilot to production with confidence.
Each stop had its own dynamic, but the conversations kept coming back to the same set of issues.
Philadelphia had the most mature discussion, many of the attendees were farther along in their AI journeys. Conversations moved quickly past theory with less time spent on where AI could be applied, and into execution, more time spent on the outcomes achieved and how we got there. Attendees were already running initiatives, questions were about control, scale, and how to stop costs and complexity from increasing as usage grows.
Raleigh was an executive style, smaller group, which changed the nature of the discussion. The time on the course before the session meant people had already started comparing what they were working on and where they were getting stuck. By the time everyone was back in the room, it felt less like a room of attendees and more like a group working through similar problems from different angles.
Dublin leaned more narrative in how the content was delivered, this stop was our most varied in terms of implementation, but the discussion quickly moved into the same territory. There was strong engagement throughout, and it was clear that organizations are under the same pressure to show progress, even if they are at different stages in their adoption.
What came through early across all three stops is that the barrier to value is rarely the model itself, but what surrounds it. Most teams know how to get a mode working in isolation, the challenge is making that model part of a system the business can rely on. The duty of working with data that is often fragmented/not ready for this type of use, deploying across environments that were not designed to behave consistently, and doing it in a way that can be secured, monitored, and governance without slowing everything down falls to those same teams. It also raises practical questions that do not always get addressed early enough.
Operational questions that determine whether an AI initiative creates value or introduces risk:
- Who owns this once it is live?
- How is usage controlled?
- How do you know if it is still performing as expected?
- What happens when something changes in the data or the model output drifts over time?
The strongest engagement came when the discussion moved into real projects. The Island Networks project path and our customer example consistently drew the most attention because they showed what happens beyond the initial build. What needed to be standardized, what had to change, and how decisions were made once the project had to operate as part of the wider environment.
1. Pilot to production is a real problem
IT professionals today are not lacking ideas but struggling AI into production and keep it there. The discussion kept coming back to ownership, cost, governance, and operational discipline. When that shift happened, from ‘what can AI do” to why does it fail in production”, the conversation changed completely.
2. Real world examples matter more than strategy
The Island Networks project path and customer story is what drove the most engagement. The room responded to what actually happened. What broke, what had to change, how things were standardized and scaled, moving the conversation from interest to relevance.
3. Peer discussion was as valuable as the content
One of the strongest signals across all three stops was how much everyone wanted to compare notes. Questions to our flagship customer speaker we consistent and direct. One half of the room wanted to understand how the other half were structuring projects, where they struggled, and what they would do differently. Peer benchmarking, not just a vendor perspective.
The Path to AI Roadshow could not have been what it was without our dedicated and industry leading vendor partners like, NetApp, Red Hat, Cato, and Cisco. At Island Networks we value the solutions and organizations that make up our Partner Portfolio, and work with them closely to provide our customers with the right solutions and services for their business. What matters most is not the individual perspective of any one partner, but the way each layer connected into a broader picture. AI in production is not solved in isolation, it relies on how data, platform, and security come together in a way that is consistent across environments.
Regionally, we noticed some differences in our delegates AI journeys. Philadelphia showed more progress in lifecycle and execution. Dublin leaned more toward framing the problem and building a path forward. Raleigh sat somewhere in between, but benefitted from the smaller, more focused group dynamic. You’d think core challenges would vary too, but really, they were the same everywhere
- Getting from the pilot to production
- Controlling cost and risk
- Aligning IT execution with business outcomes
- Building something that can scale.
As with all technical blockers, they remain the same regardless of geography.
Engagement was strong across all three stops, with rooms filled over 50 in Philadelphia and Raleigh, and a more segmented but highly engaged group in Raleigh. More importantly, conversations continued beyond the sessions. Opportunities were brought to light that were previously hidden behind well-deserved hesitation.
The biggest take away is straightforward, AI is not stalled because organizations lack ambition. They lack a clear, repeatable path to production. Once the foundations are in place, platform, automation, governance; the path becomes clearer, the gap begins to close.
The roadshow was a checkpoint, not a conclusion. The focus is now on turning that momentum into execution. Helping organizations identify a production ready use case, close the gaps in their platform and operating model, and build something that scales.
The conversation has moved on from what is possible, to what actually works.