Two filters make this simple. First, does the consultant see the whole system, not just one slice of it? Second, do they feel trustworthy when you pressure test their answers? If something feels off, dig in. Ask what was promised, what was delivered, and what happens next. Do not get enchanted by polish or brand. If you paid for the full lawn, you should not accept half the lawn and a speech.

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AI adoption follows the same maturity curve organizations have seen with ERP. Early excitement inflates expectations until reality sets in around limitations and tradeoffs. This episode breaks down why starting with “we want AI” leads to poor outcomes, how hype and fear distort decisions, and why understanding what your existing systems can already do matters before adding agents, automation, or customization.

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This episode focuses on why AI agents must be designed with context and system awareness. We discuss how different operating environments require different control strategies, why rule-based agents depend on clear process definitions, and how a systems view helps prevent overcorrection and unintended downstream impacts.

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This episode explores how to determine what is too complex for a single AI agent and why task-based architecture matters, especially in heavy industry. We discuss breaking work into discrete, well-defined tasks, designing specialized agents for planning, execution, and coordination, and using strict context to reduce risk and hallucinations. The conversation also covers how to translate AI value to executive teams by tying agent workflows to clear operational outcomes like throughput and supply chain performance.

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This episode examines when AI agents add value and when they simply amplify existing problems. We discuss why unreliable processes and poor data lead to hallucinations, why over optimizing can eliminate the need for agents altogether, and how rule based workflows create the foundation for safe automation. The conversation emphasizes using AI to move operations from reactive to intentional while keeping human oversight in place.

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This mini episode breaks down what organizations actually mean when they ask for AI. We explain the differences between generative AI, machine learning, and predictive analytics, and why precision matters. Using a controlled autonomy example, the conversation shows how AI agents can monitor limits, detect drift, and make targeted adjustments without introducing unnecessary risk.

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This mini episode explores how end-to-end technology promises can widen the gap between leadership, IT, and operations. We discuss why outsourcing ownership to vendors or consultants often obscures accountability, misaligns goals, and leads to outcomes the business does not recognize or want. The focus is on keeping process ownership close to the business and understanding when simpler, modular solutions create better alignment than all-encompassing platforms.

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This mini episode explains how separating change management from project execution creates misalignment in ERP and AI initiatives. We explore why vague AI strategies, language gaps between IT and operations, and uncoordinated execution lead to surprise deployments, duplicated work, and eroded trust. The focus is on aligning delivery and change to prevent technology from amplifying organizational disconnects.

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This mini episode explores why domain expertise is critical when selecting partners for complex technology initiatives. We discuss how vendors can unintentionally add complexity, why industry-specific processes are often invisible to outsiders, and how weak fit gap analysis leads to late-stage surprises. The conversation highlights the importance of translating business requirements into technical solutions through deep process understanding, especially in asset-intensive and highly specialized industries.

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This mini episode explains why strategic alignment is not enough when complexity rises. We discuss tactical alignment between IT and operations, how interdependencies create disconnects, and why data platforms often expose the problem through inconsistent metrics. The focus is on planning methods that make work visible, surface process disparities, and help teams align at a granular level, so AI and technology initiatives can deliver real outcomes.

Watch the full episode here: https://youtube.com/live/KI-lnkDspKg 

Connect with me on LinkedIn https://www.linkedin.com/in/kylercheatham/ 

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