| ISSUE #18 | JUNE 2026 The Invisible Infrastructure The network and connectivity architecture that makes culture-led AI possible — and the surprising number of properties getting it wrong. Your AI is only as intelligent as the infrastructure beneath it. This issue: what WorldVue’s data from 200+ properties reveals about the gap between what operators believe their networks can do and what their AI platforms actually need — and the traits that separate 3.7× ROI leaders from the rest. |
FROM THE EDITOR’S DESK — ROBERT GROSZ
In the last two issues, we built the case for culture-led AI adoption and handed you the measurement framework to prove it’s working. It’s more obvious than ever, technology change has taken root in our hospitality industry. Operators running their Staff Technology Enthusiasm Scores for the first time. Managers are auditing how many AI-generated alerts actually became guest interactions. A reader told us: “I showed my CFO the “Culture ROI Multiplier” math and got more technology budget than I’ve been asking for for three years.”
But alongside those wins, a different kind of message started arriving. Operators who had done everything right, chosen the right platforms, run the Superpower Briefings, identified their AI Ambassadors, built the feedback loops and were still seeing performance that fell short of the benchmarks we published. Proactive Service Rates stuck in the low teens. Guest personalization that worked beautifully in demo environments but felt sluggish on the floor. AI-generated alerts arriving twelve minutes after the moment had passed.
In most of those cases, the diagnosis was the same. Not the software. Not the staff. The foundation.
The network.
This issue is about the layer of technology that nobody puts in the vendor brochure but everything else depends on. We’re going to show you what WorldVue’s intelligence layer platform revealed when we analyzed network performance data across more than two hundred properties and we’re going to give you the clearest picture this industry has published of what separates a network that makes AI sing from one that silently suffocates it.
| “Your AI platform is the instrument. Your network is the hall it plays in. A Steinway in a closet doesn’t sound like Carnegie Hall. Infrastructure is not a footnote. It is the premise.”— Robert Grosz, President, WorldVue Connect LLC & Sparro Technologies LLC |
SECTION 01 — THE DIAGNOSIS
What Properties Told Us About the Infrastructure Gap
When WorldVue’s intelligence layer platform is deployed at a property, it does something most technology installations never do: it listens to the network itself. Not just whether traffic is flowing, but how it is flowing, where it is slowing, which devices are competing for bandwidth, and whether the latency profile of the infrastructure matches the real-time performance demands of the AI applications running on top of it.
Across the properties analyzed in the first half of 2026, what we found was not a technology crisis. It was a mismatch crisis. Properties had invested in AI platforms built for the 2020s and were running them on network infrastructure built for the 2010s. The software was ready. The foundation was not.
| 73%Network-AI Mismatch RateProperties with AI underperforming due to infrastructure gaps | 11 minAvg. Alert LatencyLaggard properties vs. under 90 sec for leaders | 58%Bandwidth ContentionAI workloads competing with guest entertainment traffic | 3.1×Performance GapLeader vs. laggard AI platform throughput on equivalent software |
Let us be specific about what “underperforming” means in practice, because it rarely looks like a flashing error message. It looks like this:
- A guest preference alert generated at 2:14 p.m. arrives on a housekeeper’s device at 2:26 p.m. The room has already been turned. The moment is gone.
- A dynamic pricing recommendation that requires sub-second PMS synchronization takes four seconds to propagate. In a high-demand compression period, that delay costs real revenue.
- A digital concierge interface that responds instantly in the vendor demo takes 3.8 seconds to load in the guest room because it is competing with in-room entertainment streaming on a shared VLAN.
- A predictive maintenance alert for a HVAC anomaly in Room 412 queues behind seventeen other device status pings and arrives after the guest has already called the front desk.
None of these are AI failures. They are infrastructure failures wearing an AI costume. As they look like software problems, operators go back to the vendor. The vendor updates the platform. Performance improves slightly. The real problem remains.
SECTION 02 — THE FIVE INFRASTRUCTURE FAILURE MODES
Why Good AI Performs Badly at Good Properties
Based on analysis across the WorldVue property dataset, five network failure modes account for the overwhelming majority of AI underperformance. Understanding which one is limiting your property is the difference between solving the right problem and spending money on the wrong upgrade.
Failure Mode 1: Flat Network Architecture
The most common infrastructure problem we encounter is also the most invisible: a network that treats all traffic as equal. In a flat network, your revenue management AI’s real-time rate queries compete for bandwidth with a guest streaming a 4K movie in Room 214. Your housekeeping optimization platform competes with the lobby digital signage. Your predictive maintenance sensors compete with a sales team on a video call in the boardroom.
Modern AI applications are not bandwidth hogs. But they are latency-sensitive. A 200-millisecond delay in a streaming application is imperceptible. A 200-millisecond delay in a real-time AI alert pipeline means the alert arrives after the context has changed. Flat networks cannot distinguish between these workloads. The result is AI that performs brilliantly during low-traffic periods and unreliably during the moments that matter most high-occupancy nights, check-in rushes, weekend compression periods.
| The Fix• Segment your network. AI and operational technology traffic belongs on dedicated VLANs with Quality of Service (QoS) rules that prioritize time-sensitive workloads over entertainment bandwidth. This is not a rip-and-replace project. For most properties, it is a configuration change to existing hardware. |
Failure Mode 2: Wireless Dead Zones in Operational Spaces
The front-of-house wireless infrastructure at most hotels was designed for guests. Back-of-house, housekeeping corridors, service elevators, laundry facilities, engineering workshops and loading docks were an afterthought if they were considered at all. The result is a property where the AI works perfectly everywhere guests never go, and unreliably everywhere staff spend their shifts.
When a housekeeping supervisor’s device loses connectivity as she moves through the linen corridor, the AI-generated room assignment update that arrived while she was offline may arrive two minutes after she has already committed her team to a different sequence. That two-minute gap is enough to turn a proactive service win into a reactive scramble. Multiply it across a full shift, and you have quantified the cost of back-of-house dead zones in human productivity terms.
WorldVue’s analysis found that 61% of properties with strong front-of-house wireless[CG1] coverage had significant coverage gaps in operational spaces. Staff who experience repeated connectivity drops develop workarounds that exclude the AI platform entirely. The technology gets blamed. The infrastructure problem persists.
| The Fix• Map your wireless coverage across operational spaces, not just guest areas. Conduct a walk-test with the actual devices your staff carry. Every dead zone is a point where AI-to-human intelligence transfer breaks down. Wi-Fi access points in back-of-house are among the highest-ROI infrastructure investments a property can make for AI performance. |
Failure Mode 3: Bandwidth Starvation at the WAN Edge
Hotels are bandwidth-intensive environments. A single occupied room may simultaneously be streaming entertainment, running a guest’s personal devices, processing a mobile key authentication, and sending IoT sensor data from smart room controls. Multiply that by 200 rooms at 85% occupancy and add the AI platform API calls, PMS synchronization traffic, cloud-based RMS queries, and video surveillance streams, and you have an aggregate bandwidth demand that surprises most operators when they see it calculated.
Many properties are running WAN circuits contracted three to five years ago, at capacity assumptions that predate AI platform deployment, smart room technology, and the explosion of guest streaming expectations. When the circuit saturates, everything slows, but AI platforms, which depend on continuous cloud connectivity for model updates, preference sync, and real-time data processing, degrade faster and more severely than most other applications.
WorldVue’s platform analysis found that 44% of properties experiencing AI performance complaints were operating at over 80% average WAN utilization during peak periods, with burst utilization regularly hitting 95-100%.
| The Fix• Commission a bandwidth utilization audit before your next AI platform investment. Understand your current peak utilization, your projected demand with AI workloads fully active, and your circuit headroom. A circuit upgrade is almost always less expensive than the revenue you are leaving on the table from AI systems that cannot process data fast enough to act on it. |
Failure Mode 4: Device Proliferation Without Network Intelligence
A modern hotel property runs more connected devices than most small enterprise campuses: room controllers, smart thermostats, occupancy sensors, POS terminals, security cameras, access control readers, staff communication devices, PMS workstations, kiosks, digital signage, and now AI platform endpoints. The device count at a 200-room full-service hotel routinely exceeds 800 connected endpoints, often considerably more at resort properties.
When these devices share a network without intelligent management, two problems compound. First, IP address conflicts and authentication bottlenecks create intermittent connectivity failures that are nearly impossible to diagnose without network intelligence tooling, they show up as sporadic AI platform errors that no vendor can reproduce. Second, legacy IoT devices with poor security configurations create attack surfaces that sophisticated cyber actors specifically target. The ransomware incidents that have plagued hotel PMS systems in the last three years frequently entered through an unmanaged IoT endpoint on an unsegmented network.
| The Fix• Implement a managed network platform that provides real-time device visibility, automatic segmentation of IoT endpoints, and anomaly detection. This is not optional infrastructure in 2026. It is the baseline security and performance architecture that AI-enabled hospitality requires. |
Failure Mode 5: The Integration Architecture Gap
This is the failure mode that no vendor talks about because no single vendor owns it. Modern hotel AI platforms depend on real-time, bidirectional data flow between multiple systems: PMS, POS, CRM, RMS, housekeeping management, maintenance platforms, loyalty programs, and the AI applications themselves. When these integrations are built on point-to-point API connections, each system talking directly to each other system in a fragmented web, the result is an architecture that is simultaneously brittle, bandwidth-inefficient, and nearly impossible to troubleshoot.
A property running six AI-adjacent platforms with point-to-point integrations may have up to thirty separate data connections operating simultaneously, each polling for updates on its own schedule. When one system’s update frequency conflicts with another’s, data arrives out of sequence. Guest profiles contain contradictions. Pricing recommendations are based on stale inventory data. Housekeeping assignments reference room status that is forty-five seconds behind reality. None of these are dramatic failures. They are the cumulative friction that prevents AI from performing at the benchmark levels we described in Issue #17.
| The Fix• Audit your current integration architecture before adding any new AI platform. Map every data connection, its frequency, its direction, and its latency. Properties that have moved to a hub-and-spoke integration model where a central data layer coordinates system communication rather than allowing systems to communicate directly with each other consistently report better AI performance with no change to their AI software. |
SECTION 03 — THE INFRASTRUCTURE DIAGNOSTIC
The 10-Question Network Readiness Assessment
Before your next AI investment, and before you blame your current AI investment for underperforming, run this assessment. It takes thirty minutes if you involve your IT manager or managed service provider. The questions are simple. Honest answers are the only requirement.
| # | Question | Green — Ready | Red — Address First |
| 1 | Is your operational technology traffic (AI platforms, PMS, IoT) on separate VLANs from guest entertainment traffic? | Yes — QoS rules in place | No — flat network architecture |
| 2 | Have you conducted a wireless coverage walk-test in back-of-house operational spaces within the last 12 months? | Yes — dead zones mapped & addressed | Never — or only guest areas tested |
| 3 | What is your average WAN utilization during peak occupancy periods? | Under 65% average, under 85% burst | Over 80% average or hitting 95%+ burst |
| 4 | Can you see, in real time, every device connected to your network and its traffic profile? | Yes — managed visibility in place | No — devices discovered only when problems occur |
| 5 | How long does it take for a guest preference update in your PMS to be visible in your AI personalization platform? | Under 90 seconds consistently | Unknown, or consistently over 3 minutes |
| 6 | Does your network automatically segment and isolate IoT devices from operational and guest networks? | Yes — auto-segmentation active | No — IoT shares network with core systems |
| 7 | When was your WAN circuit last right-sized against your actual device count and AI platform bandwidth requirements? | Within the last 18 months | Over 3 years ago, or never formally assessed |
| 8 | Do you have a documented integration map showing every data connection between your hotel technology systems? | Yes — current and maintained | No — integrations built organically over time |
| 9 | Has your property experienced unexplained AI platform performance degradation that no vendor could reproduce? | No — performance is consistent | Yes — this is a recurring pattern |
| 10 | Does your network infrastructure vendor proactively alert you to performance degradation before it affects operations? | Yes — proactive monitoring in place | No — issues are discovered when staff report problems |
| How to Score Your Results• 8–10 Green: Your infrastructure is positioned to support culture-led AI at benchmark performance levels. Focus your investment on software and culture, not foundation.• 5–7 Green: You have infrastructure gaps that are likely creating AI performance drag. Address the Red items before adding new AI platforms — you will get better ROI from fixing the foundation than from buying additional features.• 0–4 Green: Your infrastructure is the primary reason your AI is underperforming. No software upgrade will fix this. Start with the network. |
SECTION 04 — WHAT THE INTELLIGENCE LAYER REVEALS
The WorldVue Network Intelligence Platform: What Two Years of Data Shows
WorldVue’s intelligence layer platform was built on a premise that turned out to be more consequential than we initially understood: that a managed network is not just infrastructure, it is a source of operational intelligence. When you can see every device, every data flow, and every latency event across a property in real time, you stop reacting to problems and start anticipating them. That is, as our readers will recognize, exactly what we ask AI platforms to do for the guest experience. The network should do it for the technology stack.
Here is what the data across properties has shown us, and what it means for every operator building a culture-led AI strategy.
Finding 1: Network Investment ROI Compounds Differently Than Software ROI
When a hotel invests in a new AI platform, the return is visible and attributable; satisfaction scores move, ADR changes, ancillary revenue shifts. When a hotel invests in network infrastructure, the return is often invisible. Guest complaints don’t include “your VLAN segmentation was inadequate.” They include “the Wi-Fi felt slow” or simply “the service felt inconsistent.”
What the WorldVue platform has allowed us to do is connect those invisible network events to visible guest outcomes. Properties that upgraded from flat to segmented network architectures saw an average 34% improvement in AI platform response times. That improvement translated, on average, into a 9-point increase in the AI-Enabled Proactive Service Rate, the metric from Issue #17 that separates hotels that use AI from hotels that are changed by it.
The compounding effect matters here. Network improvements benefit every AI platform running on the infrastructure simultaneously. A software upgrade improves one platform’s performance. A network upgrade improves all of them.
Finding 2: The Relationship Between Network Reliability and Staff Trust
This finding surprised us more than any other in the dataset. Properties with higher network reliability scores, measured by uptime, latency consistency, and packet loss rates across operational spaces, had significantly higher Staff Technology Enthusiasm Scores, even when the AI software was identical.
The mechanism is intuitive in retrospect. Staff who experience a technology as reliable develop trust in it. Staff who experience the same technology as intermittent develop workarounds that exclude it. Once a workaround becomes habit, the AI platform stops influencing behavior regardless of how good its recommendations are. The network was not just a performance problem. It was a culture problem.
| 8.7Avg. STES — High Network ReliabilityProperties in top quartile for network consistency | 5.4Avg. STES — Low Network ReliabilityProperties in bottom quartile for network consistency | +3.3STES Gap from Network AloneSame AI software, different infrastructure | 61%Staff Workaround RateLow-reliability properties where staff bypass AI tools |
Finding 3: The Cybersecurity-AI Performance Connection
Properties that had experienced a cybersecurity incident in the previous 24 months showed AI platform performance degradation that persisted well beyond the incident recovery period. The explanation is structural: incident response typically results in the addition of security controls, additional traffic inspection, tighter authentication requirements, more aggressive packet filtering, that were retrofitted onto a network architecture not designed to absorb them without latency impact.
The lesson is not that security creates performance problems. It is that security retrofitted to an inadequate architecture creates performance problems. Properties that had implemented security by design, building inspection, segmentation, and authentication into the original network architecture, added security controls without performance degradation because the architecture had headroom to absorb them.
In 2026, with social engineering attacks on hotel PMS platforms having risen 300% since 2024, this is not an abstract consideration. A cybersecurity event at a property with inadequate network architecture will set back your AI culture adoption by 12 to 18 months. The network investment that prevents or contains that event is also the investment that protects your AI program.
| The Cybersecurity Infrastructure Baseline for AI-Enabled Properties• Zero-trust network access (ZTNA) for all AI platform connections: each system authenticates every request, not just at login• Automated IoT device segmentation: smart room devices, sensors, and kiosks isolated from operational and guest networks• Real-time anomaly detection: behavioral monitoring that flags unusual traffic patterns before they become incidents• Encrypted data transmission for all AI platform communications, including internal network traffic between systems• Immutable audit logging for all AI-platform data access and modification events |
SECTION 05 — THE 3.7× PROPERTIES
What Culture ROI Leaders Have in Common That Isn’t on Any Feature List
We promised this in Issue #17’s preview, and the analysis is more interesting than we expected. When we isolated the properties in our dataset that were achieving the 3.7× Culture ROI Multiplier or better, the leaders from the benchmark ladder, and asked what they had in common beyond their technology choices, four characteristics emerged with remarkable consistency.
None of them are software features. All of them are decisions.
Trait 1: They Treated Infrastructure as a Strategic Asset, Not a Maintenance Budget
At the 3.7× properties, network and connectivity infrastructure appeared in the capital budget as a planned investment with an expected return, not as a reactive repair line. Leadership at these properties could tell you the performance specifications of their current infrastructure, when it was last upgraded, and when the next upgrade was planned. The GM or Director of Technology could articulate why a specific bandwidth target mattered for AI performance.
At average and laggard properties, the conversation about infrastructure happened when something broke. At leader properties, it happened annually, proactively, in the same conversation as the AI platform roadmap.
Trait 2: They Had a Single Accountable Owner for the Technology Stack
This characteristic sounds obvious. It is not. At many properties, AI platform performance is owned by whoever bought the software. The network is owned by IT. The guest experience outcomes are owned by the GM. When AI underperforms, these ownership silos mean that every stakeholder can correctly explain why the problem belongs to someone else.
At 3.7× properties, there was a designated owner of the integrated technology stack, someone whose job included understanding how the network, the AI platforms, the PMS, and the guest experience outcomes connected to each other. This person could diagnose cross-layer problems because they understood all the layers. They were often, but not always, a technology director. In smaller properties, they were sometimes the GM themselves. What mattered was not the title but the accountability.
Trait 3: They Measured Infrastructure Performance the Way They Measured Guest Satisfaction
At 3.7× properties, network performance metrics appeared in the same operational reviews as GSS scores and RevPAR. Uptime percentages, latency profiles, AI platform response times, and device connectivity rates were reported to leadership with the same regularity as financial KPIs.
The reason this matters is not that network metrics are inherently interesting to hotel operators. It is that what gets measured gets managed. Properties that never see their network performance data cannot identify the slow degradation that precedes a performance crisis. The ones that watch it weekly catch problems early, address them inexpensively, and never experience the sudden AI performance collapse that sends other operators back to their vendors in frustration.
Trait 4: They Chose Connectivity Partners, Not Just Vendors
The 3.7× properties were not necessarily running the most expensive network infrastructure. Several were mid-scale properties with carefully managed technology budgets. What distinguished them was the relationship with their connectivity provider. They had a provider who understood their AI platform landscape, proactively identified infrastructure gaps before they caused problems, and participated in technology roadmap conversations rather than simply responding to service tickets.
The pattern here echoes what we documented in Issue #3 on vendor partnerships and Issue #9 on technology selection: the relationship architecture matters as much as the technology architecture. A managed connectivity partner who understands hospitality operations and AI platform requirements is a different proposition than a telecom provider who sells bandwidth by the gigabit and considers their job done when the circuit is live.
| What to Ask a Connectivity Partner Before You Sign• Can you show me the latency profile my AI platforms will need, and whether your network architecture can consistently deliver it?• How do you handle QoS for mixed workloads — AI platforms, guest entertainment, IoT, and POS running simultaneously?• What does proactive monitoring look like on your platform? How will I know about network degradation before my staff do?• Can you walk me through how your infrastructure has been upgraded at comparable properties to support AI deployment?• What is your involvement in our technology roadmap planning beyond the connectivity layer? |
SECTION 06 — THE INVESTMENT FRAMEWORK
Building the Infrastructure Case for Your Ownership Group
We want to give you the framework that has worked best when operators need to make the case for infrastructure investment to an ownership group that would rather hear about guest-facing amenities. The argument is simple and the math is compelling, but it requires connecting infrastructure to outcomes in language that resonates with owners, not technologists.
Frame It as AI Insurance, Not Technology Spend
Your ownership group has already approved AI platform investments, or is considering them. The infrastructure conversation is most productive when framed this way: “We have invested in AI capabilities. This investment ensures those capabilities perform at the levels that justify the investment.” An AI platform running on inadequate infrastructure delivers a fraction of its potential return. The infrastructure investment is what unlocks the return already committed to.
The math: if your AI platform investment is projected to deliver a 2.4× return on adequate infrastructure, and WorldVue data suggests that optimal infrastructure can lift that to 3.7×, the delta — 1.3× additional return — is the financial case for the infrastructure upgrade. For a $200,000 AI platform investment, a 1.3× performance improvement represents $260,000 in additional return over 18 months.
| Infrastructure Investment | AI Performance Uplift | 18-Month ROI Impact | Net Investment Case |
| VLAN segmentation & QoS | Avg. +34% AI response time | ~+0.4× Culture ROI Multiplier | Often under $15K at existing hardware |
| Back-of-house wireless coverage | Eliminates staff connectivity gaps | ~+0.3× (via STES improvement) | $20–$60K depending on property size |
| WAN circuit right-sizing | Eliminates peak saturation events | ~+0.5× (via consistency) | $15–$40K/year incremental circuit cost |
| Managed network platform | Full visibility & proactive monitoring | ~+0.3× (via issue prevention) | $1–$3/room/month typically |
| Integration architecture modernization | Eliminates data sync latency | ~+0.4× (via data freshness) | Varies; often a configuration project |
These are not additive in a simple linear sense, the properties achieving 3.7× did not simply stack every upgrade simultaneously. But the table illustrates the available levers and their relative impact. For most properties, VLAN segmentation and WAN right-sizing represent the highest ROI per dollar invested because they address the two most common failure modes with the lowest implementation complexity.
SECTION 07 — THE 60-DAY INFRASTRUCTURE ACTION PLAN
From Diagnostic to Deployment: Your Roadmap
We are going to close with a sixty-day plan that takes you from the ten-question diagnostic to a funded infrastructure roadmap. This is not a rip-and-replace proposal. It is a systematic approach to identifying the highest-impact gaps and addressing them in priority order.
| Weeks 1–2: Run the Diagnostic and Map the Gaps• Complete the 10-Question Network Readiness Assessment with your IT manager or managed services provider.• Request a network performance report from your current connectivity provider. Ask specifically for peak utilization data, latency profiles, and device count by network segment.• Walk every back-of-house operational space with a Wi-Fi signal strength meter or phone. Map dead zones.• Pull the last 30 days of AI platform performance logs. Identify timestamps of degradation events and cross-reference with network utilization data. The correlation will tell you what you need to know. |
| Weeks 3–4: Build the Business Case• Identify your top three infrastructure gaps from the diagnostic. Estimate the AI performance impact of each using the ROI framework from Section 06.• Calculate your current Culture ROI Multiplier using the Issue #17 framework. Estimate the Multiplier your infrastructure gaps are costing you.• Request proposals from your current connectivity provider and at least one alternative for the top-priority infrastructure improvements. Ask specifically for ROI projections framed in AI performance terms, not just network specifications.• Build the ownership presentation using the ‘AI Insurance’ framing: here is the AI investment we have made, here is what it is currently returning, here is what optimized infrastructure would return, here is the net investment case. |
| Weeks 5–8: Deploy Priority One and Measure• Implement the highest-impact, lowest-complexity improvement first — for most properties, VLAN segmentation and QoS rules.• Establish your baseline network performance metrics before the deployment. Measure the same metrics two weeks after.• Run a Staff Technology Enthusiasm Score pulse immediately before and four weeks after the infrastructure change. The STES improvement will often be the most persuasive data point for your next infrastructure investment.• Document the AI platform performance improvement with timestamps, response time data, and proactive service rate changes. This becomes your evidence base for Phase 2 infrastructure funding. |
FROM THE EDITOR’S DESK — CLOSING THOUGHTS
Fifty years in this industry teaches you which problems are actually the same problem wearing different clothes. The operator in 1985 who couldn’t understand why his early PMS wasn’t improving front desk efficiency, it turned out his staff had never been given a quiet fifteen minutes to learn it. The GM in 2002 who couldn’t understand why his new online booking engine wasn’t driving direct reservations, it turned out the dial-up modem in the back office couldn’t process the confirmation emails fast enough to compete with OTAs.
The pattern is always the same. Powerful technology. Inadequate foundation. Operator frustration directed at the software when the problem lives one layer down.
In 2026, the technology is AI. The inadequate foundation is the network. And the operators who will look back on this period with satisfaction are the ones who understood that culture-led AI is not just a software strategy or a people strategy. It is an infrastructure strategy too.
The 3.7× properties are not winning because they found a better AI vendor. They are winning because they built a property where every layer, the network, the integrations, the platforms, the people, the culture, is designed to move intelligence from the technology to the human and from the human to the guest in the moments that matter.
That is not a technology problem. It is a leadership decision.
The infrastructure is invisible by design. Your job is to make sure it is invisible because it is working perfectly, not because nobody thought to look.
| “The future is not technology replacing hospitality. It is technology making hospitality more human than it has ever been. But that future requires a foundation strong enough to carry the weight of everything we are building on top of it.”— Robert Grosz |
Robert Grosz
President, WorldVue Connect LLC & Sparro Technologies LLC
LinkedIn: linkedin.com/in/robert-g-9806552
Speaking inquiries: Ella Steele — esteele@worldvue.com
P.S. — Next month’s issue will tackle the question we receive more than any other: “How do we evaluate AI vendors when every one of them shows us the same impressive demo?” We will publish the buyer’s evaluation framework we believe every hospitality operator should use before signing any AI platform contract — including the questions that make bad vendors uncomfortable.
P.P.S. — If you ran the 10-Question Network Readiness Assessment and found more Red than Green, you are not alone — and you are not behind. You are simply now asking the right question. Email Ella at esteele@worldvue.com. A WorldVue infrastructure specialist will walk through your results with you at no cost.
[CG1]Should we expand this to say “wireless networks” or ”wireless coverage”