by Andrew Cardno
When thinking about generative artificial intelligence (AI), it might seem like it is happening too fast. The hard reality is that it has already happened and organizations must deal with it, or be left behind.
Casino operators are not deciding if artificial intelligence will run parts of a casino. They are deciding when, how well, and how quickly. That is the competitive reality now. The ways technology has been evaluated for 20 years – long RFP cycles, tidy proofs of concept, waiting for “proven” solutions – do not map to a world where platforms reinvent themselves several times a year. Generative AI is not a bolt-on feature; it is a shift in how work gets done, who or what does it, and how the results are governed. The challenge is not adoption; it is transformation.
Generative AI Isn’t Yesterday’s AI
Traditional AI has been seen in models that optimized the casino floor, predicted demand, or scored marketing lists – valuable, but narrow. Generative AI is different. It reads, writes, reasons, and orchestrates tasks across systems. It behaves less like a calculator in the back office and more like a digital colleague who can summarize surveillance notes, draft a host’s outreach, check a comp against policy, schedule follow-ups, and show its work. The value moves from isolated improvements to end-to-end reinvention of operations, guest service, compliance, and even the org chart.
This difference also changes time horizons. For instance, features might once have been planned a year out. Now, no one can state with certainty what these systems will do a year from today. That uncertainty isn’t a flaw – it’s a signal to organize differently: shorter cycles, faster feedback, and roadmaps that flex.
From Adoption Curve to Transformation Curve
Geoffrey Moore’s adoption curve – innovators, early majority, late majority, laggards – still applies, but the economics flip. In stable times, waiting for maturity was prudent. In a generative era, waiting is expensive because early movers do not just buy tools; they shape them. When operators engage early, they imprint the property’s voice, tribal values, and regulatory realities on systems that will become “standard.” Waiting means inheriting patterns designed by someone else.
That does not mean betting the property on unproven tech. It means accepting ambiguity and structuring work to manage it: rolling roadmaps instead of rigid plans, small cross-functional teams instead of silo projects, measurement over assumptions, and a willingness to keep only what proves itself in live operations.
Leading Without Betting the Property
Leading early looks practical, not flashy. It starts with real workflows (e.g., hosts, slots, cage/credit, marketing, compliance) that produce measurable outcomes. Set baselines, define what “good” looks like, and pilot in production with guardrails. Frontline staff should be involved so the tools reflect the property’s voice, not vendor jargon. Expect small misses, fix them fast, and move forward. The cadence feels less like “install a system” and more like “train a colleague.”
Infrastructure Becomes a Strategic Catalyst
Infrastructure teams shift from “keeping the lights on” to enabling safe, rapid change. Foundation models, vector databases, orchestration layers, and GPU options evolve quickly; advantage comes not from perfect prediction, but early results. Priorities include clean, governed data; role-based access for humans and agents; retrieval pipelines so systems can cite their sources; and logging that explains who did what, when, and why. Vendor relationships are recast. In a fast cycle, operators do not merely purchase software; they co-design workflows and expect the roadmap to bend toward property needs with knowledge transfer that leaves teams stronger each quarter.
Talent will be tight. Operators will blend trusted partners with internal upskilling. The key is capability that compounds: every deployment makes people and patterns better.
Agents Enter the Org Chart
The visible shift is this: AI agents can now be placed inside operations to perform and coordinate work. A guest service agent might triage VIP requests, draft host messages in the property’s voice, book amenities, and honor comp policy. A marketing agent might segment audiences, draft offers, check regulatory constraints, schedule campaigns, run A/B tests, and write recaps. An operations agent might monitor the floor, flag anomalies, generate technician task lists, and file work orders. A risk and compliance agent might assemble case files, summarize alerts, and maintain a clear audit trail.
These agents do not tire, do not play politics, and apply policy consistently. They are also “Spock-like” – calm, direct, and focused on evidence. That can be refreshing, and at times, challenging. Real change management is required: setting expectations, clarifying roles, and helping teams see AI as support, not threat. When agents coach performance or propose changes, employees need the context to understand why and the authority to accept or override.
Governance Means Operators Stay in Control
Control is a design choice. Leaders decide which actions agents may take autonomously, which require human approval, and which remain strictly human. Policy libraries – comp rules, AML/KYC procedures, privacy consents, self-exclusion lists – are embedded so that agents check before acting. Every action and rationale is logged, making decisions explainable to regulators, executives, and employees. Prompts and workflows are treated like code: versioned, reviewed, and reversible. Bias and drift are tested continuously, not once. With that structure, AI does not replace human judgment; it amplifies it and makes it auditable.
A Tribal Lens on AI
For tribal operators, sovereignty, culture, and community impact shape every choice. Data location and processing are not just IT questions; they are questions of jurisdiction and self-determination. Leaders decide what lives on sovereign infrastructure, what runs in private cloud, and how contracts respect tribal IP. Automation is paired with reskilling, moving people from repetitive tasks to higher-value guest engagement and analytics roles. Partners are preferred who understand that success is measured not only in throughput and revenue, but in how well the system reflects values and serves the community over time.
The First 100 Days
Start by naming a leader with decision authority and a working group that spans the right functions. Choose two or three high-value, lower-risk workflows – assembling compliance packets, drafting and scheduling host outreach with approvals in the loop, generating slot performance summaries, and technician task lists. Stand up the platform basics: secure data access; retrieval so agents can cite sources; identity and permissions for agents; and thorough logging. Write guardrails in plain language – what the agent can do, what it must never do, and when it hands back to a human. Implement with real staff, on real timelines, against real baselines. The goal is not a slick demo – the goal is a repeatable win that matters every day.
Simple mantras for the early stage should be:
• Make it real. Implement on live work with clear leadership and measurable outcomes.
• Make it safe. Put guardrails and logging in place before the first task runs.
The Next Year: Building a Rolling Roadmap
Replace the annual plan with a living roadmap that updates quarterly. Expand into adjacent workflows when pilots hit their targets; perhaps from host outreach to the full VIP journey, from floor alerts to predictive maintenance, from case assembly to continuous monitoring. Unify how systems talk about guests, games, rooms, and spend so agents don’t need translators at every handoff. Standardize agent “roles” – researcher, planner, operator, reviewer – so new use cases stand up faster with consistent controls. Treat ROI as a flywheel: the lift and savings from early wins fund deeper transformation. Communicate constantly. Show employees where time is being given back and how roles are growing. Show leadership the metrics. Show regulators the controls.
Where ROI Shows Up
Value will be seen in numerous areas. Revenue lifts arrive through faster, more relevant outreach; comping that is both generous and compliant; recommendations that reflect real-time demand; and a floor that adapts before guests feel friction. Speed goes up and cost goes down when reporting takes minutes, not hours; swivel-chair tasks disappear; handoffs stop breaking; and rework from errors declines. Risk tightens when case files assemble themselves, policies are checked consistently, and the system’s recommendations can be explained clearly.
Keep the measurement simple. Establish baselines, run pilots long enough to be meaningful, and compare. Keep what works. Cut what doesn’t. Scale winners.
Culture Is the Multiplier
Technology will change again next quarter. Culture is what makes those changes accretive instead of exhausting. Strong cultures experiment small and often, share what works and what does not without blame, invite frontline insight (because hosts, attendants, cage staff, and surveillance see frictions first), and balance optimism with rigor by red-teaming systems. When employees see AI removing drudgery and elevating their craft, adoption stops being a memo and becomes a movement.
Strategic Move
Casinos operate in community-anchored, highly regulated, and relentlessly competitive environments. Generative AI can feel chaotic, but so did the web and the smartphone until they became table stakes. The difference now is speed. Waiting for the dust to settle is itself costly. Casino operators do not have to “boil the ocean,” but they must choose their place on the curve, select real work, govern it carefully, and invest in people and platform agility. Doing so answers the only question that matters – not whether AI will run meaningful parts of a casino, but how well it will run because leadership led the change.
Andrew Cardno is Co-Founder and Chief Technology Officer of Quick Custom Intelligence (QCI). He can be reached by calling (858) 299-5715 or email [email protected].
