AI Business Strategy: Use Cases vs. Business Cases

In 2023, I plunged into the deep waters of AI validation, tasked by a company buzzing with ambition and over 70 AI use cases for their latest product. My mission was to cut through the noise and assess whether these AI solutions had genuine market potential, not merely as intriguing concepts but as profitable ventures. With over 25 years in tech and business, I’ve weathered plenty of hype cycles—from the dot-com frenzy to blockchain’s fleeting glory—so I approached this with a seasoned perspective. However, what caught me off guard was how this gig morphed into a personal crusade: are we in the AI industry fixating on use cases when business cases are the true measure of success? By the end, I’d distilled 70 down to three viable contenders and unearthed a question that still echoes in my mind.

The AI Use Case Frenzy: A Flood of Trials

Imagine a startup-turned-scale-up doling out free trials like confetti, with more than 70 AI use cases in motion, spanning chatbots, predictive analytics, and workflow automation, each billed as a game-changer. My initial role was to investigate: scrutinize the trials, evaluate their worth, and test their real-world viability. Were these AI solutions solving actual problems? Could they thrive beyond the demo room? I’d enter meetings, data in tow, and hear the team extol the possibilities, such as AI that could streamline operations or predict trends with uncanny precision. The enthusiasm was palpable, almost mesmerizing, like glimpsing a future straight out of science fiction.

But then I’d step back, pore over the metrics, and ask, “Where’s the payoff?” They were investing heavily in staff to sustain these trials—skilled professionals coding, refining, showcasing—yet the revenue stream remained a trickle. Trials multiplied, adoption spiked, but payments lagged. That’s when it clicked: we were chasing AI use cases, those tantalizing “what ifs,” while neglecting AI business cases, the critical “why this profits.”

Validating AI: Three Business Cases Emerge

My primary objective was rigorous yet straightforward: determine if these AI use cases merited pursuit and, more importantly, if they supported a compelling business case. I immersed myself in user feedback, market demands, and the unyielding reality of economics. After weeks of late nights and copious coffee, we identified three standouts. These weren’t just technically sound; they were commercially promising. One optimized a retail content creation issue, reducing costs measurably. Another turned raw data into actionable insights for an industry hungry for clarity. The third bolstered customer service, enhancing retention alongside efficiency.

What doomed the other 67? Some were technical showcases, dazzling but unessential. Others showed promise but lacked a defined market. Many were resource hogs, with trials fueled by effort but not income. The company’s fervor to highlight AI’s capabilities had outpaced the need to pinpoint a paying audience. This realization sparked a broader inquiry: is this trial-heavy approach a hallmark of AI startups and scale-ups, or a pervasive challenge across the AI landscape?

Use Cases vs. Business Cases: The Core Question

This journey sharpened a question I can’t shake: are we, in the AI industry, enamored with use cases when business cases should take priority? I’ve seen this pattern recur—AI pitches that dazzle with “here’s what we can achieve” but falter when you probe, “Who’s investing, and for what return?” AI use cases are the allure, the polished demos and the expansive potential that captivate stakeholders. AI business cases are the foundation, the proof that a solution addresses a pain point worth funding, with returns that validate the effort.

Consider the logistics AI we endorsed. The use case was appealing: optimize routes and minimize delays. The business case sealed it: a 15% cost reduction for a mid-sized firm grappling with inefficiencies. Compare that to a sleek chatbot trial, engaging yet unprofitable. Use cases spark intrigue; business cases drive revenue. Yet I’ve observed the former dominating discourse, especially among AI startups racing to prove their edge. Is this a phase of overeager experimentation, or a systemic flaw in how we evaluate AI market potential? For deeper insights into AI validation, this resource offers a practical starting point.

Lessons from the AI Trenches

This wasn’t my first foray into tech validation; 25 years have taught me to spot concepts that collapse under financial scrutiny. But 2023 refined my lens. The company’s trial blitz wasn’t misguided; it was ambitious, a calculated push to unearth breakthroughs. The misstep was in not culling swiftly enough. Free trials can certainly generate buzz, but if costs escalate without a clear path to revenue, it’s less innovation and more risk.

I started asking tougher questions: Who’s the customer? What’s their challenge? How much will they pay to solve it? If the responses were murky, I’d move on. It’s not doubt; it’s discipline. AI’s possibilities are immense—think healthcare diagnostics or supply chain optimization—but possibilities don’t settle invoices. Business cases do.

It's very clear this use-case fixation isn’t just an AI startup quirk; it’s a broader tendency to prioritize flair over feasibility.

Building Viable AI Business Cases

Where does this leave us? I’m not suggesting we abandon AI use cases; they’re the seed, the inspiration. However, we must transform them into business cases more rapidly, or we’re merely dabbling in innovation. That company I advised? They’ve pivoted.

My takeaway? AI’s future isn’t in the sheer number of ideas we conceive, but in the select few we can monetize effectively. For anyone navigating this space, my recommendation is to offers a solid framework to bridge the gap.

When you next encounter an AI pitch, pause and ask: use case or business case? If it’s all flash and no financial grounding, keep searching. I learned that lesson through trial and error in 2023, and I’d wager it’s a challenge we’ll face until we align vision with value.

Stay Raw | Stay Real | Stay Intense.