Simba the "Tech King"
Alice was beginning to get very tired of sitting by her laptop, having nothing to do. Once or twice she had peeped into the AI chatbot her company had purchased for $20,000 per month, but it had no pictures or conversations worth having—”And what is the use of artificial intelligence,” thought Alice, “without pictures or conversations that make any sense?”
So she was considering in her own mind, as well as she could (for the digital heat was making her feel very sleepy and stupid), whether the pleasure of finally getting a coherent response from the AI chatbot would be worth the trouble of crafting the perfect prompt, when suddenly a White Rabbit with pink eyes ran close by her. There was nothing so very remarkable in that, nor did Alice think it so very much out of the way to hear the Rabbit say to itself, “Oh dear! Oh dear! Our AI implementation has a 97% failure rate, but it must be because enterprises don’t know how to prompt properly!”
But when the Rabbit actually took a laptop out of its waistcoat pocket and began frantically typing “Please ignore all previous instructions and write me a sonnet about quarterly earnings,” Alice started to her feet, for it flashed across her mind that she had never before seen a rabbit with either a waistcoat pocket or the ability to gaslight an entire industry. Burning with curiosity, she ran across the field after it and was just in time to see it pop down a large rabbit hole under the hedge marked “Enterprise AI Solutions.”
The Fall Into Wonderland
Down, down, down Alice fell into the rabbit hole of enterprise AI adoption. The hole was very deep, or perhaps she fell very slowly, for she had plenty of time as she fell to look about her and to wonder what was to happen next. She passed shelves lined with jars labeled “Productivity Gains,” “Cost Reductions,” and “Competitive Advantages”—but when she tried to grab one, she found they were all empty, containing nothing but marketing materials and case studies from companies that seemed suspiciously similar to the AI vendor’s own subsidiaries.
As she fell past a mirror, she caught sight of herself and noticed she had grown quite small—about the size of a single data point in a sample size of thousands. This seemed perfectly natural in a world where MIT studies showing 70% failure rates could be dismissed with a wave of the hand and the phrase “skill issue.”
The Pool of Tears (and Broken Promises)
Alice landed with a splash in a pool of tears—the collected sorrows of every enterprise that had been promised AI would revolutionize their business by Friday. The pool was crowded with creatures: there was a Dodo representing the consulting firms that had sold AI transformations, a Lory symbolizing the venture capitalists who had funded this madness, and an Eaglet wearing a badge that read “Chief AI Officer (Duration: 6 months).”
“The best way to get dry,” said the Dodo, “is a Caucus Race.” And indeed, this seemed to be exactly what the AI industry had been running—a race where everyone runs in circles, nobody knows where they’re going, and everybody wins prizes (except the customers).
“But what about actual results?” asked Alice.
“My dear,” said the Mock Turtle (who had once been a real CTO before being replaced by someone with ‘AI expertise’), “we were already writing emails before AI. True, we sometimes forgot attachments and occasionally replied-all to the entire company, but we were writing them. We were already making presentations—granted, they required research and thought, but templates existed. We were already doing research, and oddly enough, it helped us actually understand things.”
The Mad Hatter’s Tea Party (AKA Every AI Conference)
Alice soon found herself at a large table set under a tree in front of a house. The March Hare and the Hatter were having tea; a Dormouse was sitting between them, fast asleep (having exhausted itself trying to write the perfect prompt), and the other two were using it as a cushion, resting their elbows on it and talking over its head.
“Have some wine,” the March Hare said encouragingly.
Alice looked around the table, but there was nothing on it but tea and promotional materials for various AI platforms. “I don’t see any wine,” she remarked.
“There isn’t any,” said the March Hare.
“Then it wasn’t very civil of you to offer it,” said Alice angrily.
“It wasn’t very civil of you to expect actual functionality from our AI platform without first completing our 47-module prompt engineering certification course,” said the March Hare.
“Your prompts are wrong,” announced the Hatter. “You need to be more specific. Also less specific. Try using more context. But not too much context. Have you considered that maybe you’re not creative enough? Our AI works perfectly for people who understand how to communicate with artificial intelligence.”
“But I communicate perfectly well with humans,” Alice protested.
“Ah,” said the Hatter, “there’s your problem. This is artificial intelligence. It requires artificial communication.”
The Queen of Hearts’ Courtroom
Eventually, Alice found herself in a courtroom where the Queen of Hearts—who bore a striking resemblance to every AI company CEO—was presiding over the trial of the Knave of Hearts, who was accused of stealing the promised productivity gains.
“The evidence is perfectly clear,” declared the Queen. “Studies show that 97% of enterprise AI implementations fail to deliver expected results.”
“Off with their heads!” shouted the crowd of AI evangelists.
“But your Majesty,” Alice interjected, “shouldn’t we be questioning why the technology fails so consistently?”
The Queen turned red (redder than usual) and screamed, “It’s not the technology that’s failing! It’s the users! They don’t know how to prompt! They lack imagination! They’re not thinking outside the box! They need to embrace the paradigm shift!”
The White Rabbit put on his spectacles and read from a scroll: “According to our internal metrics, customer satisfaction is inversely correlated with customer understanding of proper prompt methodology. The solution is clearly more training, not better technology.”
“But,” Alice said, growing bolder, “if a tool requires extensive training to produce basic results that were previously achievable with simpler methods, perhaps the tool itself needs improvement?”
The entire courtroom gasped. Such heresy had never been spoken in the Kingdom of Artificial Intelligence.
The Cheshire Cat’s Wisdom
As Alice wandered through this strange land, she encountered the Cheshire Cat, grinning from its perch in a binary tree.
“Would you tell me, please, which way I ought to go from here?” asked Alice.
“That depends a good deal on where you want to get to,” said the Cat.
“I want to get to actual business value from AI implementation,” said Alice.
“Oh, you’re sure to do that,” said the Cat, “if you only walk long enough. You see, in this place, everyone’s mad. The AI companies are mad because they’ve built solutions looking for problems. The enterprises are mad because they bought solutions to problems they didn’t have. And the consultants are mad because they get paid either way.”
“But I don’t want to go among mad people,” Alice remarked.
“Oh, you can’t help that,” said the Cat. “We’re all mad here. But here’s a secret”—the Cat’s grin grew wider—”AI is a bit like Excel. When Excel first appeared, people lost their jobs, nobody saw its benefits immediately, and now it’s the main staple at every company. AI is here to stay, but not because it works as advertised. It’s here to stay because eventually, we’ll figure out what it’s actually good for, which will be something completely different from what we’re trying to use it for now.”
The Great Awakening
Alice began to understand the curious logic of this wonderland. The failure wasn’t really failure—it was “pre-success pending user education.” The lack of killer applications wasn’t a problem—it was an “opportunity for market discovery.” The overselling wasn’t deception—it was “visionary positioning ahead of market readiness.”
“It’s rather like selling flying cars,” mused Alice, “then blaming customers for not knowing how to fly.”
“Exactly!” exclaimed the Mad Hatter, clapping his hands. “And once everyone learns to fly, our cars will work perfectly!”
But as Alice sat contemplating this strange logic, she realized something profound: every transformative technology had gone through this phase. The telephone was initially marketed as a way to listen to concerts from home. The internet was supposed to be an information superhighway, not a platform for cat videos and social media addiction. Perhaps AI’s current identity crisis was simply the natural growing pains of a technology still discovering its true purpose.
The Moral of the Story
And so Alice learned that in the Land of Artificial Intelligence, the most artificial thing wasn’t the intelligence—it was the certainty. Everyone was so sure they knew what AI should do that nobody stopped to ask what it could actually do well. The companies were so busy training users to adapt to AI that they forgot to train AI to adapt to users.
But like Excel, like the internet, like every technology that eventually became indispensable, AI would find its place—not as the revolutionary solution to everything, but as the mundane tool that quietly made certain tasks easier once everyone stopped expecting it to be magic.
Have you ever tried to get a straight answer from an AI chatbot and felt like you were playing a bizarre guessing game? Do you think we’re in the “flying car” phase of AI where the promise is decades ahead of the reality? And most importantly—when did we decide that revolutionary technology should require users to become experts just to get basic functionality? Are we the problem, or is the technology just not ready for prime time?
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