I’ll admit I’m scared. I’m scared because artificial intelligence (AI) is predicted to take over and change the world in profound and perverse ways. In fact, in March, you may have seen 1,100 or more notable AI leaders signed a letter requesting the leading AI companies, like the developers of Chat GPT-4, “to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4”. It’s true, these improvements must be carefully curated, guided, and perhaps even regulated, to ensure they don’t unleash more evil than good.
But that’s a topic for another column. I’m worried for more practical reasons that you might recognize.
Like every good leader, I’m worried about all the ways new technology could negatively impact my business. And the negative impact is going to be harder to fix than a drop in revenue or loss of customers. The most worrisome component of all these changes has shown themselves time and time again to be one of the hardest challenges to lick and this era will be no different.
AI will no doubt change the way we all do our jobs. The difficulty, of course, will be to get people to change and embrace their new capabilities. We all know change is hard and we also know change is scary. Eventually, we get there because inevitably, we’re not given a choice. We humans are famous for changing with conviction when our backs are against the wall. Therein lies the bigger challenge of change that I, as a leader, am thinking about much more than the change itself: timing.
For businesses like ours, the timing of changes is nearly always the “killer app.” John Chambers, the former CEO of one of the largest technology companies in the world, Cisco Systems, once famously said, “it’s just as bad for business to be early as it is to be late.” I saw that firsthand during the beginning days of a technology company I co-founded in the early 2000s. We had great technology that eventually proved very valuable to our customers, but we were two years early and it cost us nearly everything.
Furthermore, the aspect of timing for all of us in parking is an even greater challenge because the use of technology is the means to the end, it’s not the end. The end, of course, is our ability to enable people to quickly park their cars with minimal friction. The technology is supposed to help people reserve, find, and pay for parking. That’s why we all exist. The specific danger lies in the huge expense, the plethora of choices and the challenge of changing it if it doesn’t work. Which brings me back to timing.
Our specific challenge of timing is deciding when to begin the huge investment in AI and how to convince our board, the leaders, and the employees that the timing is right to make changes to a business that is humming along on all eight cylinders.
A parking operator’s specific challenge of timing, in addition to choosing the right time to make huge bets, is choosing partners they can trust to help them enable the latest in technology to improve the experience for their customers. All must occur without breaking the bank and/or making the experience worse with an incorrect choice.
Operators manage many different facilities with diverse tech stacks. That gives them the unique opportunity to use a myriad of technology and a variety of situations as their testing ground. We all know there is no silver bullet to solving parking challenges because all facilities are different. Operators can use these differences to try experiments in distinctive locations and varied combinations to “smoke test” solutions from several vendors. I’ve seen many times that the variety of technology across numerous garages has presented operators with challenges. I’m suggesting the heterogenous nature of your operations may be a positive, because it presents you the ability to try new technology in one place without betting the farm across your entire portfolio.
At the last PIE Conference, I sat through many sessions where experimentation and “pilot projects” were part of the recipe for success for countless operators. They tried new things in small doses and learned what worked and what didn’t.
And so, while the AI experts sort out how to deploy newer and smarter capabilities, those of us coping with parking cars can take a more pedestrian approach to implementing new technology, including AI. We just need to run an experiment (or three) before jumping in the deep end. That’s what we’re doing. Care to join us?
