As we put a bow on our direct coverage of ServiceNow Knowledge 26, we're still swirling from conversations on operational readiness, organizational visibility, workflow maturity, and the growing pressure enterprises face to make AI useful in practical, sustainable ways.
This does not mean the Knowledge conversation ends here. Trust us, there's still a lot to cover. Many of the themes discussed throughout the event will continue shaping enterprise strategy (and the articles on this page) long after the banners come down and the demo environments go quiet.
They will continue to influence how organizations approach AI readiness and workflows until the next major moment in the ServiceNow ecosystem arrives and pushes the conversation forward again. For the moment, though, the bigger question is: What should organizations do now?
Over the course of the event, plenty of ambitious ideas were discussed. So here are 10 practical moves enterprise teams can begin focusing on right now.
1. Build a Small AI Governance Group
This one almost seems too easy. Yet much of the chatter in the halls and boardrooms still centered on making this happen. Many organizations already have AI touching operational workflows in small ways through automation, summarization, search, recommendations, and internal knowledge experiences. But what often lags is governance.
A lightweight cross-functional group involving operations, security, service owners, and leadership can help organizations establish consistency early, before multiple departments begin moving independently. The goal is not to slow innovation down. The goal is to avoid multiple disconnected versions of AI strategy emerging across the enterprise at the same time.
2. Audit One Workflow That Everyone Complains About
Every organization has one process employees quietly dread. Vendor onboarding. Software requests. Access approvals. Equipment fulfillment. The specifics don't really matter. Just know that somewhere in the business, there is a workflow that has become synonymous with delays and confusion.
Start there. Map how work enters the system, how approvals move, where handoffs occur, and where requests disappear into email chains or spreadsheets. Most organizations discover operational friction much faster through workflow observation than through reporting dashboards alone.
3. Reevaluate Your CMDB for Operational Trust
CMDB conversations used to revolve around visibility. More and more, they're more focused on confidence.
AI-assisted operations, intelligent routing, and automated recommendations all rely on trustworthy operational relationships underneath the surface. If ownership is unclear or CI data has become stale over time, downstream workflows inherit those problems quickly.
A useful first step is to identify the most business-critical services in your environment and review the quality of the supporting operational relationships behind them. The gaps tend to reveal themselves quickly once teams start looking closely.
4. Review Your Last 10 Escalations End to End
Knowledge 26 placed a heavy emphasis on orchestration, visibility, and operational coordination. One of the easiest ways to uncover operational blind spots is to revisit recent escalations and trace how they unfolded from beginning to end. You can look for:
- delays in ownership assignment
- duplicated work between teams
- missing operational context
- manual updates between systems
This exercise tends to expose operational inefficiencies faster than theoretical process reviews because the friction has already happened in real environments.
5. Optimize Your Intake Before Expanding Automation
Many organizations still accept work requests through disconnected channels. At any point in a workflow, teams can submit requests through email, chat, spreadsheets, conversations, ticketing systems, and undocumented approval paths simultaneously.
Automation struggles in those environments because work enters inconsistently right from the beginning.
One of the most practical improvements enterprise teams can make right now is reducing the number of intake paths attached to high-volume workflows. Simpler intake creates cleaner routing, better reporting, and far less operational confusion downstream.
6. Treat Knowledge Quality Like Infrastructure
Several sessions (just from the ones I attended) at K26 reinforced how heavily AI experiences depend on more reliable enterprise knowledge. In other words, documentation does not stay isolated anymore. Instead, it spreads operational confusion once surfaced through AI-driven systems.
Most organizations already realize they have outdated documentation. To get ahead of this, choose one high-traffic knowledge area and review it aggressively:
- duplicate articles
- outdated screenshots
- conflicting instructions
- undocumented exceptions
- unclear ownership
The quality of internal knowledge directly shapes employee trust in automation and AI experiences.
7. Identify Processes Dependent on Tribal Knowledge
Almost every enterprise still has workflows powered by one person who “just knows how things work.” Those individuals are valuable, but they can also represent operational fragility.
Knowledge 26 highlighted how much modern operations depend on consistency, visibility, and repeatability. Hidden dependencies become much harder to sustain as automation expands across the organization.
To alleviate this, find one process currently dependent on institutional memory and begin documenting exceptions, decision reasoning, ownership, and more. You might be surprised by how much operational risk disappears once these workflows become visible.
8. Review Employee Workflows with Fresh Eyes
The HRSD and employee experience discussions at Knowledge 26 reflected a larger shift happening across enterprises. Internal operations increasingly shape how quickly organizations can adapt to broader transformation efforts.
Employees losing time to unclear processes, duplicate requests, or outdated systems creates downstream operational drag everywhere else. Instead, pick one internal employee journey and evaluate it honestly from beginning to end. Small improvements in employee operations often create outsized improvements in adoption and organizational agility.
9. Create a Shared Vocabulary Across Departments
One subtle issue surfaced repeatedly across Knowledge 26 sessions: many enterprise teams still define work differently from one another.
Priority levels vary. Escalation language varies. Service definitions vary. Approval expectations vary. Those inconsistencies become much more visible once automation and AI systems start interacting across departments.
A surprisingly effective exercise is bringing operational leaders together to standardize core workflow language across a handful of high-impact processes. Alignment at the vocabulary level often improves coordination more quickly than teams expect.
10. Pick One Operational Problem Worth Solving Well
After major industry events like Knowledge, organizations often leave feeling pressure to modernize everything simultaneously. That usually leads to stalled momentum.
The strongest operational teams tend to focus differently. They identify one meaningful problem, solve it thoroughly, learn from the process, and expand from there. That problem might involve:
- onboarding delays
- vulnerability identification
- service request sprawl
- asset visibility
- employee support
- approval bottlenecks
The important part is creating measurable operational improvement that teams can see, trust, and build on.
Knowledge 26 generated plenty of excitement, and the orgs that benefit most over the next several years will likely be the ones making thoughtful operational improvements long before they attempt massive reinvention efforts.
Now, back to our regularly scheduled programming.
