During the gold rush, contrary to popular belief, most fortune seekers didn’t end up getting rich beyond their wildest dreams.  They bought the shovels and pickaxes, worked long days in the dirt, and ended up empty-handed because, even though they were working as hard as everyone else, they were focusing their tools and efforts in the wrong areas.  See where we’re going with this?

AI is having its own ‘gold rush’ moment right now.  Analysts estimate that worldwide AI spending will surpass $2 trillion by 2026 as organizations race to embed AI into products, infrastructure, and day‑to‑day operations.  Corporate investment is already massive—global private and corporate AI investment topped roughly a quarter of a trillion dollars in 2024 alone, and surveys suggest that a large majority of businesses now use AI in at least one function.  Yet, much like the prospectors of old, that investment won’t turn into “striking gold” if the underlying data isn’t accurate or dependable.  In that scenario, AI isn’t a breakthrough; it’s just a more expensive way to dig in the wrong place

According to Gartner, poor data quality costs organizations an average of $12.9 million every year in wasted effort, lost revenue, and operational headaches.  If your business data is inconsistent, scattered, siloed, or lacks clear governance, AI will not be the magic fix it’s being billed as—it will take your existing problems and make them worse, for more money.

In this post, you’ll:

• See the real symptoms that your data is not ready for AI.
• Reset your expectations around what AI is actually good at.
• Understand how Microsoft’s stack and Cosmos work together.
• Walk through a practical checklist for “AI‑ready” data your executives will understand.

Here’s the blunt truth: AI cannot fix bad data.  It just makes the mess bigger.  The companies that come out ahead with AI will be the ones that get serious about data governance, not just playing around with prompts.

Why This Matters: Three Clear Signs You’re Not Ready to Use AI with Business Central

Before you start talking about Copilot, AI, or fancy models, ask yourself the critical question: Is our data even ready for this?

These are the warning signs Cosmos CRO Anthony Bonaduce sees when a company’s data just isn’t ready for AI.

1. Garbage in, garbage out

When the underlying data is wrong, incomplete, or inconsistent, AI cannot provide meaningful value.

Many executives hope AI will swoop in and clean things up or magically figure it out.  But here’s the reality: AI just learns from whatever history you give it, good or bad.  What this means: if your forecasts, margins, or inventory numbers are shaky now, AI will confidently double down on those same shaky numbers.

2. For many mid-market companies, critical data is scattered everywhere—ERP, CRM, spreadsheets, and even those shadow systems IT barely knows exist. If AI can’t see across all those silos or has to dig through patchy integrations, it’s going to miss the bigger picture and inevitably miss key context.

If you’re not breaking down data silos or ensuring you have a unified single source of truth, your so-called AI-powered view of the customer, supply chain, or cash flow is still just a narrow slice of the real story.

3. No clear ownership of data

If no one owns the definition and quality of your key metrics, everything else is a symptom.

Who actually decides what counts as a customer, a margin, or an on-time delivery?  Who signs off when those definitions change?  Without clear ownership, AI is just another user of ungoverned data—not the trusted advisor you want it to be.

If you’re evaluating AI tooling, you might get wowed by flashy demos, but in real meetings, you’re still stuck arguing over which numbers are actually right.  And then there’s the silent symptom nobody wants to admit: in the current business environment, many executives are chasing AI just for the sake of saying they’re doing AI.  There’s no clear goal, just pressure to be seen doing something with AI.  When you have leadership that’s focused on getting AI implemented without having a clear understanding of what business outcomes they’re trying to drive, you end up with AI-powered flashy dashboards but uninformed decision-making.

The Hype vs Reality Gap in Mid‑Market AI

So where’s the biggest gap between all the AI hype and what’s actually happening in the mid-market?

According to Anthony, it all starts with a simple misconception: people think AI is good at everything.  Spoiler alert: it’s not.

AI is excellent at some very specific things—pattern recognition, summarization, basic list‑style reports—and terrible at others, like designing complex financial statements or understanding unwritten business rules.

A few patterns show up repeatedly:

• Overestimating what AI can actually do. Many leaders assume AI will handle all their reporting and analytics needs.  In reality, Cosmos sees the opposite: AI is still pretty green when it comes to core finance and operational reporting, especially when things get complicated.
• Wanting AI without a real problem to solve. Saying ‘We need AI’ is not a strategy.  Without a clear business problem (like speeding up consolidation, improving forecast accuracy, or standardizing KPIs), AI projects just drift aimlessly and burn spend.
• Ignoring the risk stack. When asked which risk worries executives most—financial, compliance, or reputation—the honest answer is “all of the above.” If AI makes a bad pricing call, misses a compliance rule, or exposes a shaky metric to the board, the fallout is the same: trust erodes.

Gartner predicts that through 2026, 60% of AI projects will be abandoned or fail to realize the full benefits of AI due to insufficient governance of data and models.

The companies that see real AI ROI will be the ones that set proper expectations and use AI for what it’s genuinely good at—on top of data they’ve already cleaned and governed.

Microsoft Gives You the Foundation, Not the Finish Line

If you’re running Dynamics 365 Business Central, Power Platform, or Microsoft 365, you already live in one of the strongest data ecosystems on the market.

But here’s the trap: just because you’re on Microsoft doesn’t mean your data is automatically trustworthy.

Here’s how Cosmos sees it.

• Dataverse is another rich data source that Cosmos can integrate, right alongside Business Central. It standardizes how many Dynamics apps store their data.  What this means: it’s a great foundation, but it doesn’t automatically create executive‑ready, governed metrics.
• Fabric and OneLake are powerful building blocks for modern analytics and unified storage, but Cosmos delivers value independently—no Fabric rollout required. For many mid-market teams, Fabric comes with premium licensing and months of custom development to turn “tools” into business-ready reporting, making it more of a long-term destination than an immediate solution.
• Purview brings cataloging, classification, and governance tools to the table. It’s useful for understanding and controlling your data, but it won’t clean it up or design your reporting logic for you.

Customers come to Cosmos with a Microsoft-first stack, thinking the hard part is already done.

In reality, we still find:

• Siloed data across ERP, CRM, and spreadsheets.
• Conflicting definitions of core metrics between departments.
• Raw tables that are technically accessible but not usable by business users.

A lot of businesses have got the plumbing in place, but they’re still missing the business-trust layer that makes AI and reporting actually reliable.

Cosmos isn’t here to duplicate Microsoft’s work.  Our job is to normalize and operationalize Microsoft data so business users can actually run the company on it—and set the stage for successful AI use cases.

What “AI‑Ready” Data Looks Like

We’re going to cut through the jargon.  AI-ready data isn’t magic.  It’s just a handful of concrete things that make executives actually trust AI-driven decisions.

Here’s a checklist you can use.

1. Clear, shared definitions of your core metrics

Everyone—from finance to operations to sales—uses the same definitions for revenue, margin, on‑time, backlog, and churn.  If you ask three different teams for gross margin, you get the same answer—not three different spreadsheets.

2. A single, normalized reporting layer

Instead of aiming AI and reporting tools at raw ERP tables, you’ve got a normalized data warehouse that business users can actually understand and use.

This is one of the big things Cosmos does: it cleans up and reshapes raw Business Central data, as well as other datasets, into a model that non-technical users can actually work with

3. Governed access and visible lineage

You can answer the question, ‘Where did this number come from?’ right away.

Sensitive data is access-controlled, and you can trace key metrics all the way back through your systems and changes.

4. Repeatable reports

Your reporting logic—joins, business rules, calculations—is captured once in governed models, not reinvented from scratch every time you build a report.

Cosmos makes this reusable logic available through familiar tools like Excel and Power BI, so business users can self-serve without breaking consistency.

5. A real data strategy and realistic expectations

You know exactly where AI could help, because you’ve already cleaned up your data and reporting processes.

Cosmos sees a common pattern in successful customers: they invest in data ownership and governance first, and they treat AI as an accelerator, not a silver bullet.

Bridging the Gap: From Raw Data to AI Readiness

So how does Cosmos actually help?

To a non‑technical CEO, Cosmos can be described simply: it’s the first and only reporting and analytics solution born in the cloud and built specifically for Business Central online.  It enables business users to take ownership of their data and reporting processes—without living in raw ERP tables.

Under the hood, that translates into three practical capabilities.

• A normalized, business-friendly data warehouse. Cosmos takes Business Central and Dataverse data and organizes it into a warehouse that’s easy for business users to navigate and report on.  What this means: instead of sending AI or analysts into a maze of raw tables, you point them at a curated, trusted model.
• An Excel-first experience and simpler Power BI. Cosmos gives you a business-user-friendly Excel interface and makes it much easier to build and maintain Power BI reports. What this means: your finance and operations teams can own reporting directly, while still fitting in with your BI strategy.
• Ownership for business users, not just IT. Since Cosmos is cloud-native and built for Business Central, it’s easy enough for business users to take real ownership of their data and reporting.  What this means: IT is no longer the bottleneck for every new report or AI experiment.

Cosmos doesn’t promise that AI will do everything you need.

What Cosmos does is help you build the one thing AI absolutely needs: clean, consistent, governed data your team already trusts.

Customer Proof: Getting Data Right First

Real companies are already living this shift—from messy data and manual reporting to governed analytics that make future AI worth doing.

ATTA Elevators: Multi‑entity growth without chaos

ATTA Elevators operates more than 15 entities, with plans to grow to 30 or 40 as the business expands.  Consolidation used to be a bottleneck, with manual processes and reporting risk increasing as they scaled.

With Cosmos, ATTA uses build‑once templates and rapid refresh to deliver consistent financial reports across all entities.  The result: they’ve already avoided at least one full‑time headcount and can scale further without losing control.  When they decide to use AI for forecasting or performance analysis, they’ll do it on top of a normalized, multi‑entity data model that leadership trusts.

Elevated Industrial Solutions: Clean first, AI later

Elevated Industrial Solutions spans multiple entities and acquisitions.  Their takeaway was clear: they needed to clean and standardize data while making acquisitions easier, and preparing for their future technology needs.   That’s a step that needs to happen before AI or advanced analytics can deliver real value.

Cosmos helped them centralize visibility, standardize definitions, and onboard acquisitions more smoothly, improving decisions even before full system transitions.  AI is now a future accelerator for a governed data layer, rather than a bandage solution for conflicting spreadsheets.

These stories aren’t about AI features; they’re about doing the unglamorous, unflashy work necessary to make AI as valuable as possible for your business.

Reset Expectations, Then Act

If there’s one belief Cosmos’ CRO strongly disagrees with, it’s this: that AI will do everything you need it to.

In five years, the companies that see real AI ROI won’t be the ones chasing every shiny new feature.  They’ll be the ones that:

• Set proper expectations about what AI is actually good at.
• Built a clear, owned data strategy before launching pilots.
• Used AI exactly where it adds value—and only once the data layer was ready.

When your business aims to solve the data problem first instead of counting on AI to solve it for you, you set the stage for the kind of insight and agility that AI is promising.  Without first addressing data governance, you’re just another gold rush prospector with a shiny new shovel and no map—treating “AI strategy” as a box to be checked and fielding tough questions from board members about why your AI projects keep stalling out.

Author Bio:

Anthony Bonaduce

Chief Revenue Officer

Anthony Bonaduce brings nearly a decade of sales leadership expertise to the Microsoft Channel, where he has built lasting partnerships and driven significant growth for data analytics solutions. As co-founder of Cosmos Data Technologies, Anthony combines his passion for relationship building with deep industry knowledge gained through executive roles at Jet Reports (now insightsoftware) and Rand Group.

Anthony’s business philosophy centers on exceptional customer service and transparent communication—principles he credits as the foundation of his success. His proudest achievement includes expanding Jet’s partner channel to unprecedented levels, earning him the trust of countless Microsoft partners and clients.

He holds a bachelor’s degree in Business Administration with concentrations in Finance and Marketing from the University of Oregon. When he’s not helping clients unlock their data potential, Anthony enjoys cheering on the Oregon Ducks, playing golf, and spending time with family and friends