AI and Google: A Dance of Simplicity and Complexity

If you've been swamped by the flood of AI news, don't feel bad if you've cast Google as that nerdy high school friend who suddenly morphed into a football star before you could so much as utter 'cloud infrastructure'.

Just like Salesforce played 'hard to get' at its NYC road show last year, Google has been more inclined to chat about generative AI than its bread and butter – cloud infrastructure and platform providing. Not that we're complaining - their recent AI showcases were like watching a magic show.

AI Over Here, AI Over There, AI Everywhere!

Enter the stage: Google's Gemini large language model (LLM), the star of the show. Promising to boost productivity across the platform, it swanned into the spotlight with a flourish. Its performance? Heavily peppered with demos, of course. But some might say the dishes tasted slightly unrealistic, considering many businesses store their data in pantries other than Google's ecosystem.

Some demos, like the e-commerce one where the presenter called the vendor to complete an online transaction, couldn’t hide the question: 'Do we really need AI for this?' But hey, who doesn't love a chance to show off a new shiny AI-powered sales bot?

Artificial Intelligence: A Not So Simple Affair

Herein lie the complications of implementing generative AI. It might look easy when presented on a shiny auditorium stage, but mixing advanced technology into an organization's big bowl of operation batter is more than a little challenging. It's a bit like trying to put together a 10,000 piece jigsaw puzzle blindfolded.

Technological revolution isn't a walk in the park, unless the park in question is Jurassic Park, then it's a whole different matter. Past switches from older tech to newer have often been served with a side dish of hype, but often result in a dessert of disenchantment. Just think about the delayed acceptance of mobile, cloud, containerization, and marketing automation - tasty in theory, much stickier in practice. AI might be the most daunting ingredient yet to incorporate into the business recipe.

Hurdles in the Relay Race of Technological Adoption

Kitchen full of outdated tech appliances? Favorite recipe for disaster. And dealing with corporate inertia, internal disputes, political red tape and well, refusing to change, is a bit like cooking with expired ingredients. Not ideal. Companies that are ‘yet to embark on the deep dive into the digital sea’ are likely to struggle to understand AI, according to Vineet Jain, CEO at Egnyte.

Big players like Google may make it seem like whipping up an AI implementation is a simple task, similar to making toast. But, as the wise chefs say, it's all about the quality of your ingredients. Or in AI's case, the quality of input data.

Behind The Scene: A Grapple with Data Governance

Google's new arsenal of AI tools is all about helping data engineers juggle data flow and preparation for model training. It’s like hiring a sous chef to do the chopping in order to expedite cooking. But companies still fiddling with their tech lasagna might feel the heat even with Google's cutting-edge utensils.

Furthermore, the journey with AI doesn't end at successful implementation. Can anyone tell Andy Thurai, an analyst at Constellation Research, where he can park his list of concerns? We’re talking governance, liability, security, ethical considerations, privacy and, lastly, compliance.

Conclusion: Are We Ready for Google's Dance?

For companies not ready to cha-cha-cha with AI, or who were looking for another dance partner, Google Cloud's intense focus on AI could seem intimidating. Maybe big companies just need to loosen up, take a deep breath (maybe a sip of good whiskey), and embrace the rhythm of emergent technologies. Even if those rhythms seem quite a bit off the beaten track.

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