Stop pretending you’ve “Applied AI”

If I hear one more “innovation leader” ask how generating a PRD from 440 real meetings in minutes is any different than just dumping prompts into GPT, I might actually lose my mind!

Are you serious? If you still can’t see the canyon-wide gap between clicking “chat” in some LLM playground and building a business system that turns first-hand, proprietary customer data into high-impact strategic insight, then frankly, you’re not in the game. “AI” alone isn’t a solution. If your entire product comes down to a GPT-wrapper, you are completely missing the point (and you’re on borrowed time).

This is what being a laggard looks like: pretending that piping business problems into a foundational model is “applied AI”. Real value in customer research doesn’t come from using an LLM. It comes from designing actual, working workflows, where your own proprietary signals, context, and decision-making get built into the stack. Anything less, and you’re irrelevant.

Let me be crystal clear, in B2B SaaS, if your customer research engine is just “upload stuff to GPT and wait for magic,” you’re already dead in the water. Everyone’s playing with the same models. If you aren’t investing in getting your data in, structuring it, segmenting it, and driving actual outputs into your unique workflows, you’re not applying AI, you’re stuck in the demo phase while the competition is shipping.

So let’s break down what actually matters in making AI real for your business, step by step.

Getting Stuff In

Most teams are butchering “AI-powered” customer insight from the very first step. Manual downloads? Endless clicking around? Copy/paste therapy? That’s not a workflow; that’s medieval.

Manual Mess and Data Dropout

Still “driving innovation” by herding transcripts into ChatGPT one by one like it’s 2023? You’re wasting hours, missing chunks of insight, and your team hates this busywork. Every time you download files, switch tools, or juggle prompts, you bleed efficiency and drop valuable data. It’s not just slow, it’s a guaranteed way to miss the signals that actually matter.

Scattered Data, Zero Control

And even if you brute-force your way past manual chaos, you still face the next monster, your data isn’t just in Zoom calls. It’s in emails, tickets, Slack, CRM notes, and a dozen other hiding spots. If you aren’t pulling it all together automatically, you end up with a half-baked, fragmented picture that nobody actually trusts. Manual hacks don’t scale. At all.

Partial, Broken Analysis

Most of your customer conversations aren’t even getting ingested. You capture a tiny fraction...and then call it “analysis.” Missed meetings, unrecorded calls, lost context, if only half your data makes it into the AI, don’t act surprised when your insights are wrong, incomplete, or worse, lead you down the wrong path.

Security (Or Complete Lack Thereof)

If you’re dropping anything sensitive into public AI playgrounds, you’ve already failed enterprise security 101. The risk of exposing proprietary or customer info isn’t hypothetical, it’s only a matter of time before things get ugly. This should be a career-ending mistake.

But even if you dodge the security meltdown, most teams hit the next brick wall: scale.

This Does. Not. Scale.

Anyone can piece together a scrappy demo for a single team and claim “AI success.” But scaling this chaos across a 1,000-person SaaS org? Good luck. Manual workflows break immediately. Every early GPT “competitor” that ignored this died for a reason: you can’t fake scale with spreadsheets and copy-paste.

Zero Integration—Zero Leverage

And here’s the final nail, if your “AI workflow” involves transferring insights by hand from ChatGPT to your CRM, you have NO workflow. Insights float around, unlinked, unsearchable, and totally useless for actual revenue or renewal strategy. No integration = no leverage. End of story.

If this is how you “get data in,” you’re nowhere. You’re not even at the starting line.

Getting Stuff Ordered

If you haven’t solved getting data in (and integrated) stop here. But for those still with us, the next hurdle is even uglier: actually organising the mess.

This is the black hole most so-called “AI-powered” companies stumble into, making order out of chaos.

First off, unless you’ve invested months into prompt and model knowledge, you’re blindfolding your team and hoping they find the exit. Most people don’t know what to ask or how to ask it, so you get garbage in, garbage out. Good luck “scaling” that. If you’re pretending your whole business can prompt its way to insight nirvana, you’re about to get crushed.

Then there’s the automation fantasy. Spoiler alert, most solutions don’t have it. Data just piles up, nothing is grouped, tagged, or organised. You’re left sifting through an endless swamp of raw transcript fragments and “insights,” hoping someone manually ties it all together. But nobody actually does, so priorities melt and critical feedback rots in the backlog.

Standardising and segmenting? One customer calls it “auto-renewal pain,” another says “can’t manage subscriptions” now they’re in two separate buckets and product can’t see the pattern. Or worse, the tool lumps everything into one incoherent topic and you miss urgent risks buried deep. You can’t prioritise what you can’t even find.

And what about data consistency? If you’re merging years of old sales notes with product interviews from last week, and your team all uses their own jargon, you’re fighting uphill, because your system isn’t smart enough to connect the dots or clean up the chaos.

Let’s not forget, generic tools like ChatGPT are unpredictable. Run the same ask a few times, get a scattershot of different “answers.” No repeatability, no audit trail, no way to trust the structure. And as you’d expect, all this chaos means when it’s time to actually get value out, things go from bad to hopeless.

Getting Stuff Out

Let’s talk about extraction, that vital last mile. Most teams are slogging through the mud, trapped in manual, brain-numbing cycles of prompting, review, and hand-sifting insights. You want speed and clarity, but all you get is a bottleneck, the “AI” promised to move you faster, and instead you’re still chopping wood with a butter knife.

“Automated” tools? Usually a joke. They spit out summaries so bland and context-free, zero action, zero relevance, and absolutely no real recommendations. Try plugging those generic blurbs back into an actual business workflow or a CRM and watch them disappear into the void. Good luck mapping findings when all you have are half-baked tags, missing context, and no way to pinpoint what actually matters for your product, your roadmap, or your revenue targets.

And privacy? Let’s be honest, the LLM black box means your outputs are often too risky or unreliable to share. Teams freeze up, outputs get siloed, and now you’re back to using email and Slack threads - that is, if compliance hasn’t already red-flagged your whole brilliant “automation” effort.

Half the time, nobody even trusts what comes out. “key conclusions” are buried under mountains of noise. Teams can’t even tell what’s a signal and what’s random chatter. Actionable feedback goes unrecognized, and big opportunities drown in LLM-generated guff.

Consistency? Forget it. ChatGPT (or pick-your-LLM) is a slot machine, ask the same question, get ten different answers depending on which prompt fairy happens to be in charge. Even smart people on your team know that “AI summaries” mean unpredictable, non-repeatable results. You can’t run a business pipeline on hallucinations and hope.

Want structured, strategic insights? Tough luck. LLMs are glorified note-takers, capturing surface-level data at best, no depth, no rigor. You’re stuck with transcripts and soft summaries instead of strategy-driving, workflow-ready outputs. Anyone pretending otherwise is either selling snake oil or hasn’t left the proof-of-concept phase.

If you want actual decision-making power? Stop shipping the raw outputs of generic LLMs and start demanding workflows (and outputs) that force true accountability, depth, and repeatability. Otherwise, you’re not applying AI, you’re just spinning the roulette wheel and hoping for the best.

Stop calling it “AI” when all you’re doing is running transcripts through ChatGPT and praying for magic. Real “applied AI” is building the pipes, the structure, and the outputs that make your organisation faster, smarter, and accountable, at scale, on your own data, running in your real workflows. Anything less is just a demo. Ship work, not wishful thinking or get left behind.