Buzzwords, Bots & Breakthroughs -Fictional summer short novel about AI

Emma wasn’t new to bold visions. As Chief Operating Officer of ACME Oy, a rapidly scaling B2B tech services firm, she had seen buzzwords come and go — digital transformation, agile revolutions, even a brief flirtation with NFTs. But nothing had spread through the company quite like AI.

It started innocently. A few LinkedIn posts from peers bragging about AI pilots. A keynote from the CEO of a rival firm boasting 40% operational efficiency gains thanks to a “proprietary GPT layer.”

Then came the consulting firms, delivering slide decks filled with infographics, maturity models, and five-year roadmaps promising cost savings so large they practically sparkled.

Emma wasn’t naive. She’d been around long enough to know that transformation isn’t born in keynote speeches. Still, she found herself swept up in the momentum. AI was everywhere. The board wanted answers. Sales leads wanted automation. HR wanted smarter onboarding. And her CEO wanted results, not in two years, but in this quarter.

She greenlit three consulting teams to draft AI strategies. The slide decks poured in, each one shinier than the last. Phrases like “hyperautomation,” “autonomous value chains,” and “LLM-enabled workflows” filled the air. But as Emma flipped through yet another deck over lukewarm office coffee, a realization sank in: no one was actually building anything.

The developers were frustrated. They were being asked to “AI-ified” workflows, yet given nothing more than vague objectives and a link to ChatGPT.

Marketing had started using AI to draft copy, but none of it matched brand tone.

Finance tried experimenting with invoice processing, but hit legal walls when someone pasted confidential numbers into a public AI tool.

And IT? They were drowning in half-baked shadow AI tools that security couldn’t control and procurement couldn’t track.

Worse still, Emma’s inbox was turning into a graveyard of good intentions. Every week brought a new “AI initiative” that died quietly in testing, or never made it past an enthusiastic kickoff. Everyone was excited about AI, but no one was delivering. It was as if the whole organization had confused talking about AI with doing AI.

Late one night, Emma sat alone in a glass-walled meeting room, surrounded by whiteboards filled with buzzwords. She opened her laptop and typed into her notes: We don’t have an AI strategy problem. We have an AI execution vacuum.

She needed something different — not more strategy, not more slides. She needed something that made doing the default.

The next spark came from an unexpected corner. While doom-scrolling LinkedIn at midnight, Emma stumbled on a Finnish tech company’s press release about slashing its internal ticket-handling cost from two euros to one cent—a 99.5 percent drop—after introducing AI agents that read, classified, and updated issues automatically. The best thing was that they wrote about the same problem of turning buzzwords to reality.

The article described developers who had grown tired of juggling niche tools and simply built an internal command center where AI agents could retrieve data, reason across systems, and then act—no more hopscotching between SaaS widgets, no more copy-pasting outputs back into ERP. In the process they saved almost one hundred staff hours every month and freed two full-time salaries for higher-value work.

Emma reread the post twice. The breakthrough wasn’t a moon-shot algorithm; it was a layer of boring, reliable plumbing that made doing AI easier than talking about it. A week later she arranged a quiet pilot inside ACME Oy’s own cloud tenancy—no glossy launch, just a small project to triage customer emails with the same agent model.

On the first Monday, the new system read every inbound ticket, matched each one to knowledge-base articles, and routed them in seconds. Support queues that once clogged by mid-morning were clear before coffee. By Friday, response times had halved, and support agents were spending their reclaimed hours solving real problems instead of shuffling emails.

Word spread. Sales asked whether the same approach could prioritize inbound leads. Marketing wondered if an agent could generate campaign variants without drifting off-brand. HR wanted a smarter chatbot that actually knew company policy instead of guessing. Emma realized the cultural shift taking root: employees were requesting AI not for its buzz, but because it made their days easier.

Three months later, ACME Oy launched its largest customer-facing release to date. Historically, launch days were controlled chaos—support lines jammed, servers strained, tempers frayed. This time, Emma watched the dashboards with calm delight.

An AI agent spotted a memory spike on a backend node and spun up extra containers before anyone paged an engineer. Another drafted polite, on-brand answers to the flood of common customer questions, leaving human reps to tackle edge cases. A sales agent sifted through hundreds of trial sign-ups and flagged twenty hot prospects for immediate follow-up.

The board no longer asked for slideware. They asked for live metrics, and the numbers spoke louder than any keynote: faster cycles, happier customers, lower costs. The phrase AI execution vacuum had become a private joke; the vacuum was gone, replaced by a steady hum of automated teamwork.

This tale is a work of fiction, a homage to cult classic The Phoenix Project that trades DevOps fire drills for today’s AI productivity chaos. In this imagined universe, organizations are drowning in lofty AI slide decks, half-baked chatbot pilots, and mounting executive pressure to “do something transformative.” Our narrative follows ACME Oy’s leadership as they stumble from PowerPoint promises to real-world impact—ultimately finding order in the very tool Cloud2 built for itself: the AI Control Room. Names, timelines, and results are dramatized, but the problems (and the solution’s principles) mirror the struggles many companies face as they move from AI hype to measurable value.

Read about AI Control Room