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by Garrett Datz, SC&H Capital

Around 80% of AI projects fail, and in my experience, it’s rarely because the technology itself doesn’t work. The real issue usually lies in how that technology is introduced, managed, and supported inside an organization.

Most failures stem from a gap between expectation and execution. Companies buy into the promise of AI — faster processes, smarter decisions, leaner teams — but skip the foundational work needed to make that promise real. They want transformation without strategy, tools without training, and results without meaningful engagement.

I’ve seen firsthand how AI can drive measurable value, from improving workflow efficiency to accelerating insight. However, I’ve also seen initiatives falter when there’s no clear plan, no alignment across teams, or when AI is introduced into the business without proper context or readiness.

In this article, I’ll walk through the most common reasons AI projects fall short, what successful teams do differently, and how your organization can take a more grounded, practical approach to building AI capabilities that last.

Most AI Projects Fail Before They Even Begin

One of the biggest misconceptions I run into is that AI is a plug-and-play solution — something you can install, turn on, and expect immediate transformation. But that idea sets the wrong tone from the start.

Too often, I see companies treat AI like a shortcut. They want to reduce headcount, skip repetitive tasks, or bypass legacy systems without first preparing the necessary groundwork. But AI isn’t a magic wand. It’s a capability, and like any capability, it needs to be developed thoughtfully. That means investing in your systems, your people, and your processes if you want AI to deliver long-term value.

The biggest red flag? When leadership expects AI to drive results but stays removed from the implementation process. I’ve seen plenty of situations where teams roll out tools without a strategy, avoid hands-on engagement, and delegate everything, only to wonder why the impact never materializes.

If you want AI to work, you have to start with the fundamentals. Define the business problem you’re solving. Align your teams around realistic goals. Clean and structure your data. Create responsible usage guidelines. If those elements aren’t in place, don’t expect meaningful outcomes.

Need help building a foundation that actually works? This guide to creating an AI policy for businesses is a great place to start.

AI Can’t Fix Your Data Problems

There’s no getting around it — AI needs data. And more importantly, it needs clean, consistent data.

I’ve seen numerous promising projects fall apart due to messy data. Records were incomplete, field names changed month to month, and systems weren’t aligned. One client wanted to analyze phone usage data to identify trends. The concept made perfect sense. However, their billing data arrived in multiple formats and with inconsistent field mappings. Nothing was standardized. What could’ve been an automation win quickly turned into a time-consuming cleanup exercise.

AI doesn’t stop to ask clarifying questions. It simply takes what it’s given and produces an answer, regardless of the reliability of the input. If your data isn’t reliable, layering AI on top won’t solve the problem. It’ll just surface those gaps more quickly.

Before jumping into AI, take the time to assess your data environment. Look at how your systems connect, where your data lives, and whether it’s being validated in real time. If you’re not confident in that foundation, you’re not ready for AI.

Our Data Strategy & Analytics team can help you assess where things stand and build the structure you need to move forward with confidence.

You’re Not Replacing People, You’re Redirecting Them

Most AI tools don’t replace people. They reassign where those people are needed. When AI handles routine or repetitive tasks, it often frees up your team to focus on more strategic work.

Take the legal industry, for example. Law firms are beginning to use AI-powered tools for things like document prep, eDiscovery, and legal research. These tools don’t replace paralegals or attorneys. What they do is reduce the time spent combing through case law or contracts, allowing teams to shift their focus to client strategy and outcomes.

The goal with AI isn’t to shrink your team. It’s to make them more effective. When you implement it thoughtfully, AI becomes a force multiplier. It allows your people to do what they do best, only faster, with fewer bottlenecks.

3 Practical AI Uses for Businesses to Start Today

If you want proof that AI works, don’t look to the headlines or high-budget pilot programs. Look at what people are using every day to make their jobs a little easier, a little faster, and a lot more efficient.

The best AI projects address a specific, solvable problem, and they work because they’re tied to real operational needs. Here are a few examples I’ve seen succeed:

  • Meeting recaps and note-taking: Tools like Teams Premium now generate surprisingly accurate summaries and action items. But here’s the key: success depends on how the meeting is run. I’ve started structuring my approach to meetings, clearly outlining key takeaways and repeating action items, so the AI can pick them up and produce better results. The tool is helpful, but it’s even more effective when you know how to work with it.
  • Document scanning and pattern detection: One of my clients receives massive monthly pricing PDFs from a vendor. Reviewing those files manually in Excel used to take hours. Now, we’ve implemented an AI workflow that automatically detects changes and flags anything out of the ordinary. What used to be a tedious task now runs in the background.
  • Mining internal knowledge: With our co-managed IT clients, we’ve used AI to scan years of past support tickets and internal documentation. It helps us identify patterns, respond more efficiently, and solve issues faster, without relying on someone to dig through folders and emails.

These aren’t big, headline-grabbing use cases. But they save time, reduce noise, and build trust in what the technology can do when it’s thoughtfully applied. And that trust is critical if you want to do more down the line.

How Mid-Market Companies Can Actually Win with AI

If you’re a mid-sized business, you probably don’t need a large-scale AI deployment right now. And in most cases, your infrastructure, data, or workflows aren’t set up for one anyway, and that’s okay.

What you do need is a realistic entry point. One that aligns with the systems and people you already have in place, and that allows you to move forward without overextending your team or your budget.

Don’t chase a $150K dream project before you’re ready. Start with what’s working. Identify what’s not. Then build from there.

Here’s the framework I use with clients:

  • Identify a real business problem: “We want to use AI” isn’t a strategy. Instead, look for pain points — recurring bottlenecks, repetitive tasks, or areas where you’re constantly playing catch-up. That’s where AI can make a meaningful difference.
  • Evaluate your data readiness: Ask the tough questions. Is your data clean? Is it centralized? Can you trust it? If the answer is “sometimes,” start there before layering on any automation.
  • Choose tools that match your maturity: You likely don’t need to build a custom model from scratch. In many cases, AI features in Microsoft 365, Google Workspace, or your CRM offer plenty of value — if you take the time to implement and use them properly.
  • Build internal literacy: The more your team understands what AI can (and can’t) do, the more value they’ll get from it. Provide training, create usage guidelines, and encourage experimentation with clear boundaries.

The best way to start with AI? Think smaller. Then grow. You don’t need a flawless roadmap or a moonshot vision. What you need is momentum, anchored in your business reality.

Innovate Faster with the Right Partner

AI can absolutely move your business forward, but only if you start with the right foundation. At SC&H, we work with mid-market companies every day to take AI from buzzword to capability. Whether you’re exploring it for the first time or trying to scale existing efforts, we help you:

  • Assess your current tech and data environment
  • Identify realistic, high-impact opportunities
  • Implement responsibly with a focus on governance, security, and long-term value

AI isn’t a product you install. It’s a capability you develop. And like any capability, it requires the right mix of planning, structure, and support to succeed.

About the author

Garrett Datz is a Principal in SC&H's Technology Advisory Practice, where he helps clients manage and improve their IT function and business outcomes. He specializes in technology infrastructure, cloud solutions, strategic IT planning, and cybersecurity and has worked with clients across various industries and verticals with extensive experience that spans the nonprofit, hospitality, manufacturing, retail, and professional services sectors.

Garrett has more than 25 years of experience leading companies through transformation and modernization initiatives, especially as the information technology footprint for businesses continues to rapidly evolve. He firmly believes that a robust foundational approach is key to building the proper IT function for business success. Recognizing that all businesses must develop efficiency without adding staff, effectively and securely enable remote work, and reduce IT overhead, Garret has successfully structured and executed strategic plans for his clients aligned with these objectives.

This article originally appeared on SC&H Group’s website, and is reprinted with permission.