knowledge26 insights: moving enterprise AI into production and making it deliver.

Over the three days Randstad Digital spent at Knowledge 2026, AI was the throughline but not just in theory. The conversations and insights had moved from questions of whether to questions of how, what, when and why. How to build operational readiness and move AI into production. What infrastructure it actually requires. When to scale and what breaks if you do it too soon. 

Across conversations on the floor, in the sessions and in the hallways, it was clear that enterprises want to move AI out of controlled pilots and into production at scale. This was equally reflected in the features ServiceNow unveiled. Through case studies from organizations that saw real AI-driven results, we saw how ServiceNow has acted as the connective tissue for these efforts: linking systems, monitoring AI activity and keeping compliance intact.

Here’s what stood out to us, and what it means for organizations across every sector trying to make the most of their ServiceNow and enterprise AI investments. 

realizing AI-driven outcomes necessitates fixing your foundations.

Across several sessions, we heard the same fundamental truth about driving real results with AI: You can’t plug AI into broken processes and expect magic. This was evidenced by the case studies presented, where we also saw what happens when these foundations are in place: 

• A prominent global tech company had more than 2,000 applications spread across 100 countries. By standardizing on the ServiceNow platform first, they were able to build 1,200 digital workflows and reach 125,000 automations.
• A leading digital travel company collapsed 20 fragmented applications into one. With that foundation in place, AI took over supplier onboarding automatically and eliminated manual sorting across HR and finance — sustainably, at operational scale. Supplier relationships improved in the process.

We also saw examples of what it looks like when AI does the heavy lifting, with ServiceNow’s Otto, which executes tasks across Workday, SAP and Coupa rather than simply responding to queries. Paired with the AI Control Tower, which provides centralized visibility into what AI agents are doing and the controls to keep it auditable, it represents a meaningful shift in what enterprise AI can actually do day to day.

scaling AI in regulated environments

Banking and insurance sessions added a data point worth sitting with: only about 24% of banks are confident in how they currently monitor their AI. The same pressure points show up across other regulated industries. Scaling AI in these environments requires getting four things right before deployment:

• Clean, auditable data. Every company that demonstrated scaled results had solved data readiness first. Without it, governance frameworks can’t hold and audit trails don’t close.
• Operational integration. Fragmented systems limit what AI can actually do. The organizations that scaled had addressed this — building systems that talked to each other — before layering AI on top.
• Defined governance. Monitoring, compliance controls and audit trails need to be in place before AI agents are operating at scale, not built reactively afterward.
• Leadership alignment. The organizations that scaled had technology leadership and the CEO locked in on the same business goals from the start.

ServiceNow’s newest capabilities change what’s possible on that foundation.

With the right infrastructure in place, the question becomes what you can build on top of it so AI does the heavy lifting. Several Knowledge 2026 demonstrations and case studies made that concrete:

• An American multinational banking institution made the governance case concretely. They treat real-time monitoring as non-negotiable. Using the ServiceNow AI Control Tower, fully monitored AI agents resolved a payment failure automatically. Because the audit trail was clear, there were no compliance issues and no erosion of customer trust.
• A leading financial services company built a budget application in hours. They used ServiceNow’s native Build Agent while managing external integrations like Gemini Code Assist, and AI-generated code went through the same compliance review process applied to human developers. Speed came from having rigor and governance systematically built in.
• A leader in digital payments rebuilt their entire Dispute Case Management Platform on ServiceNow after years of a fragmented process slowing everything down. Once consolidated, AI could identify fraud patterns and process bulk tasks, accelerating recovery rates and reducing manual analyst workload in ways the previous architecture couldn’t support.
• A leading logistics company rolled out ServiceNow workflows and AI across their global supply chains at significant scale. The operational results were substantial, and the lesson that came through was the importance of complete alignment between technology leadership and the CEO on core business goals.

the real opportunity with AI is bigger than most organizations are aiming for.

The foundation and platform work matters, but it doesn’t automatically close the gap between investment and ROI. One tech leader challenged attendees to stop treating AI as a cost-reduction mechanism and start using it to tackle systemic business challenges. ServiceNow’s CEO framed the AI Control Tower in the same spirit. Not just to track data and fix blind spots, but as the infrastructure that lets organizations safely pursue genuinely ambitious AI initiatives. The ambition is achievable. The execution capability to support it is what most organizations are still building.

human judgment and discernment remain ever important

An ethical AI expert who closed out the conference framed the human side of this plainly: AI generates content and executes tasks, and human judgment determines whether the output is correct, appropriate or worth acting on. Their word for the year was “discernment,” i.e., the deliberate evaluation of what AI produces, applied before anything gets downstream.

The speakers also emphasized the importance of maintaining real relationships and having direct, goal-focused conversations before technology enters the room. Getting aligned on what you’re actually trying to accomplish is what makes the technology useful once it arrives.

where this leaves enterprise leaders.

Knowledge 2026 confirmed what the market has been building toward: production deployment is the work in front of most organizations now. While the sector changes the specifics, the fundamental requirements stay largely the same.

As a ServiceNow partner, Randstad Digital works with organizations across every industry on the foundations that determine whether your platform and AI actually perform: intake processes, governance structures and the integrations that let systems work together. Our SPARC framework — Skills, Platform, Automation, ROI, Cost — connects implementation to outcomes, because the technology only delivers value when it’s operating in the context of your organization, not alongside it.