I hate having to have this conversation!
A CEO calls. They want AI. They’ve read the headlines. A competitor just announced something with “AI-powered” in the name, and the board is asking questions. They’re ready to invest.
I have but one question: “What specific business problem are you trying to solve?”
The silence is always the same.
It’s not the technology. I need you to hear that clearly, because most of what you’re reading right now tells you the opposite.
The technology works. It’s powerful. AI can draft content, qualify leads, automate workflows, predict customer behavior, and handle routine tasks that used to eat your team’s week. These aren’t theoretical capabilities — I watch them work every day inside HubSpot implementations.
So why did 42% of companies abandon their AI initiatives last year, up from 17% the year before?
Because they skipped the most important step. They bought the technology before they understood what it could actually do for their business, in their context, with their data.
And that’s where the wheels come off.
After 35 years of helping businesses adopt new technology — including HubSpot implementations — I can tell you the failure pattern is remarkably consistent.
It starts with a demo. The vendor shows something impressive. The CEO gets excited. The team gets amandate. Someone buys the tool.
Then reality shows up.
The CRM data is a mess, so the AI can’t learn from it. Nobody mapped which processes should actually change, so the tool sits next to the old workflow instead of replacing it. There’s no verification process for AI outputs, so the first time it produces something inaccurate — and it will — the team loses confidence and stops using it.
Three months later, you’ve got an expensive subscription nobody touches and a team that’s now more skeptical of AI than before you started.
Most CEOs seem to have two misconceptions that torpedo their projects before they begin.
The first: AI is a product you buy. It’s not. It’s a capability you implement. The difference matters enormously. You don’t buy HubSpot’s Breeze AI agents and expect them to workout of the box any more than you’d buy a CRM and expect it to manage your contacts without setup, training, and clean data. AI features are powerful, but they require architecture — the right data flowing to the right tools, configured for your specific business context, with humans reviewing outputs until the system earns trust.
The second: AI should replace people. This is the misconception that creates the most expensive failures. If your ROI model assumes you’ll fire three people and replace them with an AI tool, you’re going to be disappointed. AI makes your people faster, smarter, and more effective. It handles the repetitive work so your team can focus on the judgment calls, the relationship building, the creative problem-solving that actually moves your business forward.
The companies getting real results from AI aren’t the ones that replaced their teams. They’re the ones that armed their teams with better tools and taught them how to use them.
I get it. Half of CEOs believe their job stability depends on getting AI right. Boards are asking about AI strategy. Competitors are making announcements. There’s enormous pressure to demonstrate progress.
But here’s what I’ve learned after three decades of helping companies adopt new technology: the pressure to move fast is where the most expensive mistakes get made.
Fifty percent of companies admit to launching AI initiatives before being thoroughly prepared. That tracks with what I see in the field. They’re not investing in AI — they’re performing AI. There’s a difference.
The smart move isn’t faster. It’s more deliberate. It’s asking the hard questions before you write the check.
At 30dps, we’ve built our entire practice around a principle that sounds obvious but apparently isn’t: diagnose before you prescribe.
We call it the Growth Architecture Framework™, and it has three phases. First, we Diagnose — we look at your current technology stack, your data quality, your team’s readiness, and your actual business objectives. Not what AI could do in theory, but what it should do for you, right now.
Then we Design the implementation — specific workflows, data requirements, verification processes, and training plans. Everything mapped before anything gets built.
Then we Develop & Adapt — building in phases, measuring results, and adjusting based on what the data actually tells us, not what the sales deck promised.
It’s not flashy. It doesn’t make for a great keynote slide. But it’s why our implementations don’t end up in the 42% that get abandoned.
Whether you work with us or someone else, ask these questions before you sign anything. If your vendor can’t answer them clearly, you’re not ready to begin.
“What specific business problem does this solve, and how will we measure whether it’s working?” If the answer is vague — “improve efficiency” or “transform operations” — you don’t have a plan. You have a hope.
“What does our data need to look like before AI can effectively work with it?” Most AI implementations fail because the data isn’t ready. If nobody’s talking about data quality, they’re skipping the foundation.
“What happens when the AI gets it wrong?” Because it will. AI outputs are drafts, not finished products. You need verification processes, and those processes need to be designed before you go live — not built in a panic after the first mistake.
“What will my team need to learn, and how long will that take?” The technology is the easy part. Adoption is where projects die. If there’s no training plan, there’s no implementation plan.
AI isn’t magic. But it’s not “just hype” either. It’s a genuinely powerful set of tools that can transform how your business operates — if you implement it with the same rigor you’d bring to any major business initiative.
The companies winning with AI right now aren’t the ones that moved fastest. They’re the ones that got the foundations right. Clean data. Clear objectives. Realistic expectations. Human oversight. Proper training.
Forty-two percent of AI initiatives got abandoned last year. That’s not a technology problem. That’s a planning problem. And planning problems have planning solutions.
The gap between AI expectations and AI results is where projects die. Close that gap before you begin, and you’ll save yourself from becoming another cautionary tale.
Not sure if your business is ready for AI?
We offer a free AI Readiness Assessment that takes about 15 minutes and gives you a clear picture of where you stand — your data quality, your team’s readiness, and which AI features would actually move the needle for your business. No pitch. Just clarity.