“The main challenge isn’t technology. It’s culture.” If there was ever a credo for casino operators to heed, that’s it. The quote is the subhead of the July-August 2019 edition of Harvard Business Review (HBR). The issue’s main feature is “Building the AI-Powered Organization.” There was probably no one at the magazine, or at the three authors’ firm of McKinsey & Co., that aimed this piece on Artificial Intelligence at the gaming world, but they hit the target at our weakest and most-vulnerable spot.
“We’ve surveyed thousands of executives about how their companies use and organize for AI and advanced analytics, and our data shows that only 8% of firms engage in core practices that support widespread adoption.” That’s the bad news, but they do offer hope: “we’ve also seen that leaders, who at the outset take steps to break down those barriers, can effectively capture AI’s opportunities.
Of course, there is the argument that our casinos have been doing just fine without AI and machine-learning analytics. Experience and instinct are pretty good guidelines. Aren’t they? Why do we need a change? The article says that firms “often relied on gut instinct and on input from senior managers, who also were operating on their instincts to make decisions.”
It takes miles of shoe leather and countless guest contacts to earn a good “casino gut instinct. But there’s simply too much to comprehend in today’s $80 billion gaming industry. We should always take the advice of our executive hosts when it comes to the wants and needs of our top VIP guests. It’s doubtful a machine will ever be as good. That just leaves the remaining 95% of our customer base without much guidance. Which mid-range guests just became at risk? When did a guest’s visitation period change? Which machine just reached the tipping point of popularity? Is time on device slipping? The amount of data and time required to answer such questions in a timely and actionable way is beyond our capacity. But not for today’s AI algorithms.
We all probably have a horror story where an initial computer suggestion was ridiculous. “Organizations must shed the mindset that an idea needs to be fully baked or a business tool must have every bell and whistle before it’s deployed.” There’s an urban myth making the rounds in slot departments where the “analysts upstairs” recommended that a bank of machines should be moved from Zone 2 to Zone 5 to improve their profitability. When the geeks later questioned why the slot director hadn’t taken the “corporate” advice and moved the machines within 30 days, he/she responded that the machines in question were 11-feet tall and the ceiling in Zone 5 was barely nine feet. ‘Should I have used a chainsaw? (Machine height and ceiling height are currently missing in most slot databases). For many of us, that story made us feel a bit better about job security, along with having a good laugh at the bar with our old-school colleagues.
HBR counters, “A test-and-learn mentality will reframe mistakes as a source of discoveries, reducing the fear of failure. Getting early user feedback and incorporating it into the next version will allow firms to correct minor issues before they become costly problems. Development will speed up, enabling small AI teams to create minimum viable products in a matter of weeks rather than months.” (That’s also known as machine learning. Shouldn’t ceiling and machine heights be in our databases?)
It might be easy to think that talk of AI, advanced analytics and machine learning are great discussion points, by not relevant to today’s real-world business. Especially those that depend on quirky casinos guests or equally individualistic consumers like female clothing shoppers. Think again. The June 17, 2019 issue of “Bloomberg Businessweek” has a relevant article about “Stitch Fix.” They are an online retailer and styling service that now uses algorithms to remove bias in their clothing recommendations to customers. “It takes guts to tell a fortysomething woman that she should wear a romper.” CEO Katrina Lake adds, “When our algorithm is recommending to me a romper or jumpsuit for a 40- or 50-year-old, I totally trust it. Human bias would have counseled against the item.” Using AI, this firm, which had been projected to lose $2.3 million this year, saw an increase of $250 million and should hit revenues of $1.58 billion by year end.
There are several new firms now targeting casinos with artificial intelligence tools. Kiran Brahmandam, the CEO of Gaming Analytics.AI says, “Casinos are is the ideal environment for AI solutions. We have mountains of data, both current and historic, that can provide previously hidden strategies for improving operations and profitability. But it wasn’t until recently that we’ve been able to develop tools that can access this data in a productive and actionable way.” His firm is one of the first to employ AI to solve complex slot and marketing questions such as identifying “at-risk guests,” making realistic budget projections, and featuring the ability to ask simple questions like, “what games should I replace/convert today.”
Data Robot is another prominent AI firm now targeting gaming. In the past, they’ve made their mark in industry and sports analysis for pro teams (think “Money Ball” on steroids). Andrew Engel is their GM for Sports and Gaming. He says, “The real advantage today with AI is from a Marketing perspective. Casinos want to understand what their customers desire today, but more importantly, what they’ll want in the future.” This topic of predictive analytics (a subset of AI) has been gaining traction with casino operators for the last few years. In terms of culture, Engel said, “the most success comes when you merge what data scientists present with what the business users experience. Then find the most common solutions.” He adds, “Not only do marketers need to gain a better understanding of AI, but at the same time, your data scientists need to learn to speak in a manner that everyone can comprehend.” Perhaps his most salient advice was, “start small and take the easy wins.”
Several other firms have been successfully using AI in casino applications. eConnect and VSBLTY are working to improve surveillance and monitoring of table games. You can also check out Bally BI, IKAST, nQube Data Science and Optimove for their AI casino-specific products.
So how can our “slow-to-adopt-technology” casino culture change to use these new tools effectively? Getting rid of silos is a great place to start. Just about every business book recommends this strategy, but it is critical in transitioning to an AI culture. HBR says, “Siloed processes can inhibit the broad adoption of AI. Organizations that assign budgets by function or business unit may struggle to assemble interdisciplinary agile teams”
The bunker-like protectionism common between many casino departments can quickly kill progress. Having IT head your AI implementation with no input from Marketing or Casino Ops is a formula for failure. HBR says, “Fully integrating AI is a long journey. Creating a joint task force to oversee it will ensure that the three functions (Business, IT, Analytics) collaborate and share accountability, regardless of how roles and responsibilities are divided.”
The “Harvard Business Review” article by authors Tim Fountaine, Brian McCarthy and Tamim Saleh of McKinsey & Company runs about 9 or 10 pages and this short overview misses many of the key points that can help you move forward with AI, Big Data, Analytics and/or Machine Learning. In general, an HBR subscription is valuable year round, but this current AI Culture piece is a must-read for casino executives at every level. You can order it online (www.hbr.org) or find it at the newsstand for $19.95.
