You're Not Ready For AI. Please, Stop Buying These Myths.

AI is everywhere right now. Some of it’s good! But a lot of it’s hype. And I’ve already seen billions wasted on projects that never had a shot at working ... and the headlines support this view, such as Gartner, CIO Dive, and more.

Why?

Because the data foundations were garbage and companies bought into myths instead of fixing the basics.

Let me explain four all-too-common AI lies I see organizations buying into, then jump into the path forward.

Here are the four I see most often.

Myth #1: AI makes data easier to use

No, it doesn’t. It just accelerates whatever process you already have … good or bad.

For years, the playbook rewarded “tech first.” New system? Buy it. Bolt it on. Squeeze out value. Move on. That habit worked when innovation meant adding efficiency - think early CRM rollouts.

But do you remember when your company bought a shiny new CRM? Did it magically fix sales process? Of course not. It just made the bad processes faster and at a larger scale. Same with ERP, MAP, and the list goes on.

AI doesn’t reward motion. It rewards structure. With a weak foundation, AI doesn’t give you clarity, it gives you chaos - faster.

That’s why I tell clients to pause before chasing “the next big thing.” Because if you haven’t fixed the way your business understands its own data, AI will simply automate your confusion.

It’s the same with AI.

Here’s the reality: most companies don’t even know how much data they’re generating every day. They already have more data than they know what to do with. And half of what they do know about, they aren’t using effectively.

AI doesn’t solve that. It just pours gas on the fire. And because AI is still in its infancy, the risk of things going sideways is even higher.

Related: start with your data architecture

Myth #2: Data should be characterized by platform

pulls out soapbox

This is a serious problem and most everyone falls into it at some point.

Data is platform-agnostic.

I am now going to put this in all caps because it’s that important and foundational to all things data.

DATA IS PLATFORM-AGNOSTIC.

What your customers, teams, and systems are generating everyday is data. Platforms and technologies are simply where this data is housed, but the data itself can go from anywhere to anywhere with the right methods in place.

Too often, IT Teams start solutioning with platforms or packages they're buying. 

Companies (including IT!) are sick of buying stuff - they just want to use what they already have and get better value on these investments.

Here’s why this is a mistake:

• It immediately constrains your data by putting it into technology “boxes” of what it can/can’t do

• Assumptions are made about the technologies that may not be true

• This fails to identify what’s truly relevant and reusable (two of the four data Rs)

Possibly worst of all, though, this approach leads to more and more data bloat - because you’re not truly attacking the underlying problem. You’re only solving for a symptom.

Related: don't make this data mindset mistake

And this leads to…

Myth #3: Data bloat isn’t a business problem

I hear this one all the time. Leaders think data bloat is IT’s problem. Wrong.

Here’s the reality: every hour your people spend digging for the right number is an hour they’re not selling, not serving, not innovating. That’s not an IT problem — that’s a business problem.

The stress is real. Executives think, “We’ve been collecting this data for years, we should be able to answer this simple question.”

But the answers don’t come easily. That gap erodes trust, slows decisions, and costs the business money every single day.

Myth #4: Fixing data challenges is hugely expensive

Sure, you can spend millions. And I’ve seen plenty of enterprises do exactly that - with very little ROI to show for it. You’ve seen the headlines around Generative AI failures already, such as that MIT report about 95% of pilots failing (nuanced take: don’t read as much into that, their findings aren’t nearly as conclusive as the headline makes it seem and it’s not a holistic survey).

I’ve also seen mid-market firms solve their biggest issues with a clear framework and a fraction of the cost.

The difference? They didn’t start by buying a “solution.” They started by understanding their data.

Here’s the trap:

• Start with a shiny tool.

• Buy or revamp tech to fit that tool.

• Deploy at scale.

• Spend millions.

And then realize the gaps are still there.

The better path is this: segment your data into the eight buckets (customer, sales, quote, purchase, invoice, service, marketing, communications), then run them through the Four Rs test (Relevant, Reliable, Revealing, Reusable). That’s how you strip out the noise and see what’s actually useful.

This works for a $5M SaaS startup and a $10B manufacturer alike. The scale is different, but the challenge is the same.

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The ROI of Getting This Right

Once you nail down your data strategy, everything flows from it:

• Technology has a clear role to play.

• Teams align around a shared starting point.

• Bloat gets removed.

• Noise leaves the company.

• Decisions get made faster and with more confidence.

And the payoff is measurable.

If every employee makes 5% more correct decisions and spends 5% less time hunting for data, what’s the impact on your business?

• Fewer wasted tech licenses (IT loves this one).

• Leaders acting on accurate insights when it matters most.

• Faster sales cycles, better service responses, tighter forecasting.

These aren’t hypotheticals. I’ve seen it. One client grew revenue by 74%. Another cut M&A integration time, a primary way they were growing, in half.

That’s what happens when you fix your data foundations first.

The Foundations You Can't Skip

If AI readiness = data maturity, three foundations get skipped, which is why many pilots stumble and lead to nowhere.

Definitions

Ask ten people in your company to define “customer.” More than likely, you’ll get ten different answers. What industries? What products? How do they pay? Context shifts the definition.

Until you agree on what its data actually means, automation won’t make it useful.

Customer is one of eight data categories I like to say every company has, and yet you’ll find nobody’s working on common ground for the one category everyone should … much less Purchase, Sales, Marketing, or any other category.

Information architecture is the structure of how data relates, flows, and evolves, and is the single-most overlooked prerequisite for AI success.

You can’t predict what you can’t define. And then, you need…

Trust

Companies collect data faster than they can validate it, and they create more data than they realize. AvePoint research shows 41% of organizations manage at least 500 petabytes of data.

You create hundreds of CRM fields, dozens of integrations, and yet teams still can’t answer basic questions with confidence or fill out the 3-4 fields they’re required to and then move on to something else.

This goes for any team - IT, Sales, Support, Finance - and for any system, by the way. Your ERP, invoicing, and any other system have the exact same challenge.

Let me ask you this: if you opened a sales opportunity in your CRM, how many fields would you say are truly useful right now?

On a recent webinar I did, most answered fewer than 10.

That’s not a data shortage. That’s a data credibility problem.

If teams don’t trust today’s data, AI won’t stick. Build trust first.

Activation

Even when data is structured, many companies stop short of activation.

They store it. They report on it. But they don’t connect it to the decisions that matter.

I like to say most organizations are information rich and knowledge poor. You need clear activation paths such as data that’s governed, flowing, and accessible to the right systems at the right time.

Even mature teams falter here. A warehouse isn’t readiness. It’s storage. Activation turns data into information that feeds insight and decisions.

Without activation, AI is just an expensive analytics engine.

Your Takeaway

Don’t buy the AI lie. If your data is a mess, AI won’t save you.

Start with the fundamentals. Get your buckets right. Test them against the Four Rs. Align your strategy to the business outcomes that actually matter.

Then … and only then … are you ready to bring AI into the mix.

You can get there - and you will! And if you want this faster, we’re ready to help.