How to Harness Individual AI Wins and Build Organizational AI Infrastructure

Most mid-size B2B companies have individuals in various departments doing impressive things with AI — and, unfortunately, organizational infrastructure that hasn’t caught up. Closing that gap requires an intentional, strategic sequence: audit what's already happening, document how work gets done, and build from existing tools before adding new tools.

Mike Kaput, Chief Content Officer at Marketing AI Institute and SmarterX, shared the framework with me in a recent episode of Weidert Group's podcast, The ChangeOver | Industrial Marketing & Sales Growth Solutions for Today's Dynamic Landscape.

Watch episode 39 of The ChangeOver, then subscribe on Apple, YouTube, Spotify, Weidert.com, or your favorite podcast app. You'll be notified when part two of our conversation drops.

The AI Adoption Gap is Real

You’ve probably seen this. Someone on your team has figured out how to cut a four-hour task down to one using AI. Maybe it’s a content manager who built a research-to-writing process. Maybe it’s a sales rep who drafts personalized follow-ups in a third of the time. Whatever it is, they’re ahead … but the rest of the organization doesn’t even know about it.

That gap between what individuals can do with AI and what organizations have actually built around it is the defining challenge for mid-size B2B companies right now. Mike Kaput, Chief Content Officer at SmarterX and co-author of Marketing Artificial Intelligence, has spent the past decade helping companies navigate it. SmarterX’s 2026 State of AI for Business Report, drawn from surveys of 2,100 professionals across functions and industries, puts hard numbers on a dynamic most midsize industrial leaders feel but can’t quite articulate.

The good news? You don't need a perfect plan to build real AI infrastructure. But you do need a smart sequence.

Why Individual AI Adoption Outpaces Organizational AI Adoption

The 2026 State of AI for Business report found that more than half of individual professionals say they’re in the integration or transformation phase of AI adoption.

But only 25% of organizations can say the same. Nearly half (47%) are still in the piloting phase.

This points to the structural challenge of scaling AI adoption across the business.

“One individual or a couple of scrappy people on a team that are really interested in learning this stuff on nights and weekends are unlocking actual superpowers for themselves. But duplicating that kind of adoption at an organizational level is very, very hard.” — Mike Kaput, Chief Content Officer, SmarterX

The folks finding those AI wins are self-selecting: they’re curious, they make time on their own terms, and they iterate without needing committee approval. Replicating that across a team of 20 or 200 requires different machinery: governance, training, shared tools, documented workflows. Most mid-size companies haven’t even begun building any of that.

Why the Middle Market Is the Most Stuck

Companies with $50 million to $500 million in revenue have the lowest AI scaling rates of any segment, at just 14 to 16 percent. Small firms hit 32 percent. Large enterprises reach 30 percent.

As Mike put it, SMBs are contending with “the worst of both worlds.” Small companies can move fast because a single leader can drive decisions from the top. Large enterprises have the resources to spin up dedicated AI councils and governance infrastructure. Mid-size companies have neither.

“If you're a small, scrappy firm, the CEO can own everything or at least drive top-down innovation and adoption. Enterprise is definitely moving in the right direction, probably at a slower rate, but they have more resources. Whereas, SMBs, it totally depends on the people you’ve got, the leadership you’ve got, the business you’re in." — Mike Kaput, SmarterX

For industrial manufacturers, distributors, and professional services firms in that revenue band, this isn’t solely a technology problem, it’s a leadership and infrastructure problem. But it’s solvable.

How Should an SMB Leader Build AI Infrastructure?

Start with the work you already have access to, not with the tools you wish you had.

Mike shared a framework comprising three sequential tiers:

Tier 1: Existing approved technology

Every major software vendor is embedding AI into their core platforms. Your CRM, your marketing automation, your project management tool likely all have AI features you probably haven’t used. Start there — not because it’s the best AI available, but because it’s approved, procured, and requires no new legal review to access. The point is to start generating learning and momentum. Mike’s advice here is direct: if the alternative is doing nothing, make the compromise and start with what you have in front of you.

Tier 2: Frontier models

ChatGPT, Claude, and Gemini are meaningfully more capable than embedded AI features for most knowledge work. At this stage, every person in the organization who does knowledge work should have access to at least one. Mike’s recommendation is to identify which of these you can get approved and widely adopted quickly, and treat it as a near-term priority, not a nice-to-have.

Tier 3: Specialized third-party AI tools

Domain-specific platforms — for content, sales, research, operations — have a place, and chances are your area-specific leaders have their own wish lists. But the specialized tools are often the third step, not the first.

Mike recommends respecting the sequence over trying to do all three at once. Organizations that jump the tracks without establishing foundational literacy through the work of the first two tiers are setting themselves up to struggle with AI adoption.

“Our Data Isn’t Ready” Is No Excuse

The most common objection Mike hears from leaders is the belief that they can’t really move forward on AI because thier data isn’t ready.

Usually partly true, but also largely beside the point. Here’s why:

“Tomorrow, you start to learn, as a leader, how to integrate AI into strategy, decision-making, analysis, synthesis … that doesn’t require proprietary information. You would be shocked how far you can get using AI as a thought partner. That doesn’t require a single thing except your time and effort.” — Mike Kaput, SmarterX

Data readiness certainly matters for specific use cases, such as integrating AI with your CRM, building automated pipelines, training models to accurately describe your products based on proprietary information. But tons of high-value, low-risk AI applications require little more than your own knowledge, an AI chat interface, and some practice.

Workflow mapping. Strategic analysis. Content development. Research synthesis. Meeting prep. You can build real organizational capability here, today, without waiting for a data governance project to conclude.

Why Tools Don’t Solve the Root Cause of the AI Adoption Gap

Mike described scenarios where companies spent millions on AI tools, only to watch teams use them like search engines. The better tool won’t change behavior unless the team understands what AI is actually capable of. And that calls for education, training, and change management.

AI literacy can’t be a one-time onboarding session. It’s an ongoing investment in helping broader teams understand what these tools can do — reasoning, synthesis, drafting, analysis, research — and figure out how to unlock that value in their specific roles. As models mature and capabilities advance, education and training become even more crucial, and leaders play a key role.

The 2026 report found a telling shift: 58% of professionals said they want training on integrating AI into their existing workflows, while only 15% prioritize training on prompting.

Much of the workforce is quickly moving past “how do I structure a quality prompt?” and toward “how do I integrate AI into the work I do?” Most organizational training hasn’t caught up.

The Step Too Many Companies Skip: Workflow Documentation

The most underrated piece of advice in this conversation seems obvious, but is often overlooked. Before you can standardize or systematize the work, and certainly before you can delegate work to an AI agent, you need to know — in detail — how that work actually gets done.

Mike recommends picking one process you run at least every week, probably something you’re doing on autopilot right now, and document it. Need help thinking through it? Use AI to interview you through the steps and note the sequence. Your goal isn’t to automate the whole thing in one fell swoop, it’s to understand it well enough to identify where AI capabilities slot in, and which tools deliver those capabilities.

"It’s not about saying, ‘can ChatGPT do this?’ That’s like asking, can a computer do this? Is it image generation? Is it reasoning models? Is it agentic research? You’re mapping capabilities onto parts of your existing work.” — Mike Kaput, SmarterX

This documentation step costs little and delivers big value. It unlocks everything that comes after — better AI use, cleaner workflows, and a real foundation for automation. We would argue it helps your humans better understand these processes too, which can be equally valuable.

Ready to Start? Do These Three Things

If you’re a sales or marketing leader at a mid-size industrial company and you’re ready to break down silos and start building infrastructure, Mike’s got specific guidance for you:

Step one: Audit before you act 

Find out what your teams are already using, what AI is already approved (often embedded in platforms you use), and where work is being duplicated across departments. Confirm what every team is using AI for at a high level, and what tools and licenses they have access and approval for today.

Step two: Map use cases by value 

Document the work teams do, identify where AI can be applied, and stack rank the opportunities by value and feasibility. Don’t try to solve everything at once. Get the list narrow and prioritized.

Step three: One first win

Pick a single use case that’s low-risk, available to test right now, and likely to succeed. Spend the time to make it happen, and use it to build momentum.

The sequence builds on itself. The first win frees up time. Put that time toward the next use case and grow your AI infrastructure from there.

If you've been watching the AI conversation from the sidelines, absorbing the hype, feeling the pressure, unsure where to start, that feeling isn't a sign you’re too far behind to catch up. It shows you’re paying attention. The gap between where your organization is and where it needs to be is real, but it’s not as wide as it looks from your seat.

Pick one process. Document it. Test something small. Protect the time it saves you. Then do it again.

That’s not a shortcut, it’s the path. Every organization that’s successfully scaled AI started there.

Listen to the full conversation with Mike Kaput on Episode 39 of The ChangeOver Podcast, and subscribe so you don’t miss Part 2, where we get into what AI-assisted content and sales workflows can look like in practice.