Bizzuka, Inc.

Bizzuka, Inc. We teach organizations how to implement our proven AI framework so they can scale and compete within their industries.

Bizzuka provides professionals with practical AI training designed for real-world impact. Our programs help business leaders, consultants, educators, and entrepreneurs integrate AI into strategy, operations, and innovation. What We Offer:

- AI Certification & Training – Gain recognized credentials and hands-on experience.

- AI Strategy for Business Leaders – Learn how to implement AI for busines

s growth.

- AI Skills Development – Build expertise that keeps you competitive. Our expert-led courses bridge the gap between theory and application, providing actionable knowledge that delivers results.

08/28/2026

45% of executives believe their AI investment is paying off. Only 27% of their managers say the same.

That's exactly why so many get bought, rolled out, and quietly abandoned within a few months. It's rarely the software's fault. It's usually a sign nobody stopped to check where the team actually stood before the purchase happened.

Check the comments for the link.

08/27/2026

Getting employees to use AI is one challenge. Getting them to actually want to learn it is a different one entirely.

On the Built To Scale Podcast, John Munsell joined host Shmuel Herschberg to talk about the part of AI adoption most rollouts underestimate: the psychological and emotional resistance that kicks in when you ask people to reimagine workflows they've been running comfortably for years.

The instinct most organizations have is to address this with more training content. More videos, sessions, and material. But that approach tends to deepen the resistance because it asks people to invest time upfront with no visible return.

John's approach is built around reversing that sequence. At each level of our training, employees produce something directly applicable to their own job before the level ends. Something that reduces the time they spend on actual work they do every week.

When someone can see that 1.5 hours of training will save them 1.4 hours every single week going forward, the math becomes obvious. They stop watching at normal speed and start watching at 2x. They're no longer asking why they have to do this and instead asking when the next level opens.

Check out the complete discussion in the comments.

08/26/2026

Two years from now, some companies will have teams operating at a level of AI proficiency that their competitors can't catch up to. The difference between those two groups is mostly being decided right now.

On the Built To Scale Podcast, John Munsell joined host Shmuel Herschberg to talk about the two things organizations consistently treat as afterthoughts: governance and training. Most companies handle governance by finding an AI use policy template online, swapping out a few words, and considering it done. Most handle training by pointing employees at Coursera or YouTube and moving on.

John's point is direct: neither of those is governance, and neither is training.
The number that illustrates why this matters is one he's tracked across a wide range of organizations. Without a structured approach, the average employee takes 19 to 24 months to reach a level of AI skill where they're generating real value for the business. With structured training and proper oversight in place, that same employee gets there in four to six weeks.

That's not a marginal improvement. That's a 22-month head start on every organization still running on templates and YouTube playlists.

If your company is still figuring out and , this episode is the right place to start.

Check out the complete discussion in the comments.

08/25/2026

If your team is using AI every day but results haven't moved much, the problem probably isn't the tools.

John Munsell shared a telling example on the Built To Scale Podcast with host Shmuel Herschberg. Ramp, a well-known fintech company, hit a 99.9% rate and then realized they still weren't getting the productivity gains they expected. The reason was simple once you saw it: almost every employee was operating at the same two basic skill levels. They'd been given entry-level onboarding and nothing more.

John walks through seven levels of AI skill development in this episode. The early levels cover basic prompting and question refinement. The middle levels get into prompt structure, team-wide prompt sharing, and reducing duplicated effort. The higher levels involve building knowledge documents, creating prompt databases, and deciding who governs all of that as the organization's AI use grows more complex.

That governance question is where things get complicated fast. If multiple employees each build their own version of a product knowledge document, you end up with conflicting sources of information spread across tools that don't talk to each other. The goal is one central source of truth that flows into every AI tool the team uses. Most companies aren't close to that yet.

This conversation is worth the time if you're managing AI rollout and wondering why the numbers aren't reflecting the effort.

Check out the complete discussion in the comments.

08/24/2026

Quick gut check for any leader tracking this quarter:

Your dashboard might be showing green checkmarks while your team is quietly double-checking every single thing the AI produces because they don't trust it yet.

Usage numbers and are not the same thing. One tells you who logged in. The other tells you whether it's actually working.

We broke down the 4 metrics that separate the two in the comments below. 👇

08/21/2026

In a recent conversation with Mike Schiano on In the Queue, John broke down exactly what consulting firms like McKinsey, Ernst Young, and BCG are actually counting when they report "86% AI adoption" numbers. It comes down to one AI-embedded application somewhere in the organization. That's the entire definition.

John's definition looks different. Real adoption means 80% or more of computer-dependent staff using AI daily, with their own hands, building their own tools across the business. By that standard, most businesses he works with are sitting at around 20% adoption, regardless of what the reports say.

The bigger issue is that nearly all of that 20% is self-taught, which means everyone learned it differently.

That fragmentation doesn't appear in any benchmark report. But it shows up in productivity, in consistency, and in competitive positioning.

John and Mike went deeper on what real adoption looks like. Check out the complete discussion in the comments.

08/20/2026

Here's a question worth asking your leadership team this week: who actually owns AI in your organization?

Most companies answer "IT", and it makes sense at first glance. But John Munsell recently sat down with Shmuel Herschberg on the Built To Scale Podcast and walked through exactly why that answer tends to create more problems than it solves.

When IT owns AI, the default response is restriction. Governance becomes a bottleneck. And meanwhile, employees are using personal accounts, installing tools on laptops without authorization, and potentially exposing confidential data to public training models.

John's approach separates governance from IT entirely, building a three-tier structure that scales with the organization's capabilities; from a sandbox for early experimentation all the way up to an AI Council that sets binding policy.

He also described a tabletop breach simulation his firm runs with organizations. Six people, a simulated crisis, and cascading problems that hit legal, finance, PR, and the executive team simultaneously. Every time, the room realizes they were not prepared.

If you're still sorting out who owns AI in your org, this conversation is worth the time.

Check out the complete discussion in the comments.

08/19/2026

When a CEO tells John Munsell that their company uses AI, his first question is: what does that actually mean?

Most of the time, the honest answer is that a few employees figured it out on their own, everyone developed different habits, and nobody can tell who's doing it well. What John calls , employees using personal AI tools because the company restricted access, is operating in the background at almost every organization he works with.

To find out where a company actually stands, we build a heat map of AI proficiency across the workforce. Ten skill levels, plotted by employee volume. And without fail, the map lights up on the left. Levels 1 and 2. Even in teams that have been experimenting with AI for years.

The reason is straightforward. When employees have real jobs to do, it takes 19 to 24 months to reach mid-level proficiency on their own. That distance is widening faster than most leadership teams realize.

John sat down with Shmuel Herschberg on The Built To Scale Podcast to walk through how this problem shows up, why it matters, and what closing it actually requires.

Check out the complete discussion in the comments.

08/18/2026

When John Munsell joined Shmuel Herschberg on The Built To Scale Podcast, he challenged a number that gets repeated in almost every AI conversation: the claim that 89–95% of companies have adopted AI.

Here's the issue: the research behind those numbers defines adoption as having one application somewhere in the organization that contains AI, like a chatbot or Copilot license. That's it.

John defines it differently. Real adoption starts when you look at every employee who uses a computer more than 30% of their day and ask two questions: Are they actually using AI? And how proficient are they?

The answer, across organizations we've assessed, is sobering. More than 90% of employees are operating at Level 2 or below on a 10-level proficiency scale.

The conversation goes deeper than the number, though. John walks through the four workforce types every AI governance team needs, why skipping proficiency levels creates risk even for high performers, and what it actually looks like to build an AI-capable workforce from the inside out.

Check out the complete discussion in the comments.

08/17/2026

If your only metric when assessing is "how many people have access to the tool," you're measuring a purchase.

Two employees can have identical access and be operating on completely different planets in terms of what they can actually build with it.

We put together a breakdown of what to measure instead, and where most companies get this wrong. 👇

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