AI and Omnichannel Systems Supercharge Casino Revenue from TribalNet to G2E

TribalNet: Product evolution, AI, revenue generation, and G2E

Casino systems have outgrown the slot floor. With igaming, social, and sports betting in the mix, the player flow spans mobile, web, and on-premises devices. More channels mean more data, and the teams that turn that data into product decisions will win on revenue and speed.

At TribalNet in Reno, leaders from Light & Wonder, Aristocrat Interactive, Table Trac, and IGT shared how they're expanding system scope, operationalizing AI, and shipping features that directly lift time-on-device, carded play, and player value.

From floor-centric to omnichannel systems

"The customer flow isn't just in front of the machine," said Jon Wolfe of Light & Wonder. Systems now have to deliver a consistent experience across the casino, mobile on-prem, online, and sports betting.

As Ted Keenan of Aristocrat noted, that requires a casino management system that's bigger than slot accounting. It needs to unify data and expectations across all channels and help teams act on signals in near real-time.

AI is now both a product feature and an engineering tool

Across the panel, AI showed up in two places: inside the player experience and inside the product development lifecycle. Keenan called out using natural language to replace manual querying and using AI for requirements, test cases, and full lifecycle acceleration.

Wolfe highlighted using AI to build and deploy faster with more reliable outcomes. That's not hype-it's a throughput and quality play your roadmap can measure.

Specialist, on-prem AI for sensitive operations

Chad Hoehne of Table Trac emphasized specialist AI over generalist approaches. Their models learn the specifics of table-game, slot-floor, and yield management, then integrate your policies and local regulations.

Key detail for product and data teams: they're running AI on-prem to keep sensitive data in-house. If you operate in regulated environments, assume on-prem or private cloud options will be a feature requirement.

Table-games: yield management at scale

Hoehne sees a strong path for table-games management driven by AI. Think labor planning, pit optimization, and automating "teeny tasks" so one supervisor can oversee what used to take a team.

If you build for tables, your backlog should include staffing models, dealer performance insights, and automatic pit balancing tied to demand forecasts.

Personalization and bonusing that actually moves revenue

Jacob Lanning from IGT pointed to AI-driven bonusing that scales beyond host teams. At G2E, IGT is showing an updated Random Riches that lets operators configure one-to-one goals for point earnings; AI sets offers per player.

Light & Wonder's display manager can "own every pixel" on the slot screen, capturing each touch and in-game reaction. That feedback loop improves targeted messaging and bonus mechanics by game, cabinet, age, and demographic.

Payments and convenience: friction out, play time up

Lanning shared concrete lifts from cashless and account funding-VIPs who adopted saw about a 30% increase in value. That's the kind of result a product team can justify in a single planning cycle.

Convenience apps matter: drink ordering, machine reservations, quick tax forms on jackpot, and self-funded markers at the device. These keep players seated and shorten downtime between wagers.

Revenue generation playbook (what to build next)

• Unify the 360-degree player profile across slots, tables, mobile on-prem, online, and sports.
• Stand up real-time event streams to support micro-personalization and in-session offers.
• Productize AI for two tracks: player-facing (bonusing, recommendations, fraud) and builder-facing (requirements, tests, analytics).
• Offer on-prem or private deployment for regulated data; make data boundaries explicit.
• Add a feature store and model monitoring; treat AI like a product with SLAs and QA gates.
• Instrument UX on every screen touch; feed that data into offer testing and optimization.
• Integrate payments deeply: cashless, account funding, and instant markers tied to AML/KYC.
• Automate table yield management: staffing models, pit balancing, dealer performance.
• Fraud detection and slot-floor optimization using AI-driven anomaly and demand models.
• APIs first: make bonusing, wallets, loyalty, and offers callable across channels.

Guidance for product teams

• Define clear KPIs per feature: time-on-device, carded play rate, ARPU, offer acceptance, ops hours saved.
• Ship thin slices: start with one cabinet family or a single pit, then roll out by cohort.
• Build an experimentation culture: control groups, per-cabinet tests, demographic segmentation.
• Codify governance: model explainability, audit trails, and human review for high-risk actions.
• Close the loop with operations: hosts, pit bosses, and floor staff need tools, not reports.

G2E: what to look for and how to evaluate

Expect to see configurable, one-to-one bonusing and unified payment workflows on the floor. Use G2E to test integration depth, latency under load, admin UX, and the quality of analytics on offer performance.

If you're attending, bookmark the event site for updates and schedules: Global Gaming Expo (G2E).

Data and AI governance (don't skip this)

As AI moves closer to promotions and payments, tighten risk controls. Align your practices with established frameworks like the NIST AI Risk Management Framework and make deployment modes (on-prem vs. cloud) a product setting, not a custom project.

Quick 30-60-90 for your roadmap

• 30 days: define omnichannel data contracts, pick 2 high-ROI convenience features, and scope on-prem AI requirements.
• 60 days: ship a pilot of personalized bonusing to a single cohort; instrument every touch and event; stand up a basic feature store.
• 90 days: expand to two channels, add fraud and floor optimization models, and publish a governance checklist with audit logging.

Further learning

If your team is leveling up AI skills for product work, see curated training by role: AI courses by job.