ByteSized

How SMBs Can Leverage Predictive Analytics

This episode is all about making predictive analytics work for small and mid-sized businesses. We talk through how to get started without needing a full data science team, where to focus your efforts, and how to make sure your data is actually ready for smarter tools. You’ll hear practical advice on choosing the right vendors, organizing your data, and using AI to support the decisions that matter most to your business. If you’re looking for a way to get real value from your data, this conversation is a great place to start.
 
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Is AI Taking Jobs or Making Them?

Let’s talk about the big question everyone is asking. Is AI coming for your job? The short answer is yes, but not the way you think. In this episode, we get real about which jobs are getting replaced, why that is not all bad, and how a 500 billion dollar AI industry could open up even more opportunities. From boring journal entries to smart decision-making, we break down what is changing, what is staying human, and why loss can be the start of something way better.
 
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Predictive Waste in AI: Model Failures, Misused Metrics, and How to Fix Them

Predictive models are everywhere, but not all of them are useful. In this episode, we talk about the biggest reasons models break down, like bad data, overfitting, misaligned KPIs, and insights that just don’t land. We get into what went wrong, what to watch for, and how companies can keep things on track with real ownership, regular audits, and smart decisions. You’ll hear why Amazon’s data said Prime would fail and why Jeff Bezos did it anyway. Whether you're working with MES or SAP, this one is about getting value from your models without getting buried in dashboards.
 
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Dashboard Noise vs. Operational Intelligence

What separates a cluttered interface from clear decision-making? In this episode, we dive into the difference between dashboard noise and operational intelligence. It all started with a Tablo addiction, some stealthy dashboard requests, and one sales ops leader who had seen enough. Adam shares why too many metrics can blur your view and how the right ones create focus, speed, and real impact. This is the difference between data that distracts and data that drives.
 
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Predictive Analytics vs. Reality: Data Integrity, Trust, and the Limits of AI Decisioning

Why do predictive models fail to drive better decisions in the real world? In this episode, we explore the biggest barriers between AI-powered predictions and confident human decision-making—including fractured trust in data, lack of a clean single source of truth, and organizational readiness. From messy spreadsheets to misaligned patient forecasts in healthcare, we break down how bad inputs create conflicting outputs, and how even powerful models like IBM Watson Health stumbled on real-world complexity. Plus, we talk about the future of AI in high-stakes scenarios like autonomous vehicles and clinical care, and why the human-in-the-loop will always matter.
 
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Operational Intelligence: Building AI-Driven Strategy in Mid-Market Manufacturing

AI isn't here to replace your team. It's here to make them better. In this episode, we talk about how mid-sized manufacturers can use AI to boost efficiency without causing panic. From ERP tools to plant twins, we cover where to start, how to find real problems on the floor, and how to aim AI at the right targets. The key is simple. Talk to your people, learn what’s actually happening, and use that knowledge to shape your AI game plan.
 
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Operational AI in the Fortune 500

How far are major enterprises really going when it comes to handing over operational control to AI? This episode explores how Fortune 500 companies in complex manufacturing and healthcare are deploying AI to enhance safety, streamline production, and reduce risk—without removing the human element. From lifting multi-ton coils to preventing prescription errors, we examine the tension between speed and diligence, impact and oversight. It’s a deep dive into real-world AI implementation where quality assurance and strategic leadership matter more than ever.
 
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Why AI Keeps Failing Without a Systems-Level Strategy

Everyone is eager to plug in AI, but what really happens when it's done in isolation? This episode unpacks how surface-level adoption can create more problems than it solves, what it takes to build a strategy that actually scales, and how to turn cross-functional friction into impact. If you're thinking about AI beyond the buzzwords, you’ll want to hear this.
 
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Evaluating AI Solutions: Differentiating Strategic Platforms from Disposable Models

Not all AI vendors are built to last or built to scale. In this episode, we unpack how to assess enterprise AI tools for long-term impact versus short-term hype. From the stability of platform-based AI like IFS to the risks of newer, unproven vendors, we cover what it takes to make informed, data-driven decisions. Hear how to vet architecture, evaluate R&D maturity, and anticipate vendor viability in a rapidly evolving AI landscape.
 
Explore our blog insights: https://theconfluencial.com/blog
Try our ERP shortlist builder: https://theconfluencial.com/oldmanriver Connect with Kyler on LinkedIn: https://www.linkedin.com/in/kylercheatham
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Targeted AI Deployment: Aligning Strategy with Scalable Impact

AI isn’t magic, and it’s not going to fix your business just because you plugged it in. This episode breaks down how real impact comes from targeted AI deployments that are driven by strategy, not hype. We’re talking predictive maintenance that saves millions, IT help bots that actually help, and results you can scale—not guess at.
If you're still asking whether AI should focus on cost, revenue, or UX, you're asking the wrong question. Start with your why. Build from there.
 
Explore our blog insights: https://theconfluencial.com/blog
Try our ERP shortlist builder: https://theconfluencial.com/oldmanriver Connect with Kyler on LinkedIn: https://www.linkedin.com/in/kylercheatham
Follow The Confluencial on LinkedIn: https://www.linkedin.com/company/the-confluencial