How to effectively learn AI Prompting, with the 'AI for Vice Presidents of Operations (Prompt Course)'?

Cut cycle times and costs with AI-built for Vice Presidents of Operations

This prompt course gives Vice Presidents of Operations a practical, end-to-end approach for using AI and ChatGPT to improve decisions, reduce waste, and standardize execution across plants, warehouses, and service operations. It consolidates core operational disciplines into a cohesive learning path so you can move from quick wins to durable, scalable practices that fit your governance, data, and performance goals.

Course overview

The course assembles a set of focused modules covering efficiency analysis, inventory, process documentation, supply chain, demand forecasting, employee productivity, risk, vendors, facility layout, cost reduction, market trends, sustainability, technology integration, crisis planning, and customer feedback. Each topic provides a structured way to apply AI to real operational questions, turning scattered inputs into executive-ready outputs and team-ready action steps. The tone is practical, evidence-based, and outcome-driven.

What you will learn

• How to convert operational goals into AI workflows that surface bottlenecks, quantify impact, and recommend feasible actions under real constraints.
• Ways to aggregate data from ERP, WMS, MES, EAM, CRM, and spreadsheets into consistent, repeatable analyses that support planning, scheduling, procurement, and service levels.
• Methods for translating KPIs into structured prompts so AI helps you monitor performance, flag exceptions, and produce concise briefings for executives and line leaders.
• Approaches for building scenario analysis and sensitivity checks that compare alternatives before committing resources.
• A standardized approach to process documentation so teams can maintain SOPs, training materials, and change logs with less effort and better version control.
• Playbooks for vendor and risk reviews that bring consistency to scorecards, contracts, and mitigation plans.
• Frameworks for evaluating facility layouts, capacity options, and cost trade-offs without heavy software or long study cycles.
• Ways to synthesize market signals and customer feedback so operations roadmaps stay aligned with demand patterns and service expectations.
• Guidance on sustainability and technology adoption so efficiency gains link to compliance, ESG targets, and systems already in place.

How the modules connect

Each module builds artifacts you reuse elsewhere. Efficiency analysis feeds process documentation and standard work. Forecasting informs inventory, supply chain, and vendor plans. Vendor evaluation links to risk registers and cost initiatives. Facility layout informs productivity and safety programs. Customer feedback loops into demand signals and improvement backlogs. Technology integration supports automation and reporting that make the earlier wins stick. The result is a single, coherent operating approach rather than isolated experiments.

Effective use of prompts in an operations context

• Start with the decision: Define the decision type (policy, budget, schedule, sourcing) and the timeframe. State any constraints, such as service levels, cash limits, labor availability, or compliance rules.
• Provide compact context: Share the essential facts: scope, sites, product families or services, key KPIs, recent changes, and critical assumptions.
• Specify the output format: Request structured outputs (for example, bullet lists, tables, or a clear executive summary) that you can plug into dashboards, presentations, or SOPs.
• Ask for options with criteria: Request multiple feasible options with pros, cons, risks, required resources, and an implementation checklist.
• Incorporate sensitivity checks: Indicate variables that may move (lead times, yield, demand variance) and ask for the effect on cost, service, and capacity.
• Set quality bars: Define what "good" looks like: ties to KPIs, use of provided data, clarity, and compliance with your internal standards.
• Calibrate for the audience: Request an executive-level brief and a separate, action-oriented version for managers or frontline teams.
• Version and share: Label prompt versions and keep a simple log so teams reuse what works instead of starting from scratch each time.
• Validate and iterate: Pilot in one site or product line, measure results, and refine the workflow before broader rollout.
• Protect data: Use approved tools and channels, remove sensitive fields, and follow vendor and contractual requirements.

Inside each module

Every topic follows a consistent structure: objective, recommended inputs, steps to run the analysis, guidance on reviewing outputs, and integration tips for dashboards or SOPs. You also get checklists and review criteria so teams know when an output is ready for decision or deployment. This keeps the course practical and adaptable across different industries and system landscapes.

Value for executive leaders

• Faster decision cycles: Turn weekly bottlenecks, supplier exceptions, and forecast updates into hours, not days.
• Better use of staff time: Shift analysts and managers from manual consolidation to higher-value problem solving.
• Consistent governance: Standardize how analyses are requested, produced, and reviewed, improving auditability and knowledge transfer across sites.
• Stronger cross-functional alignment: Finance, procurement, logistics, operations, and customer teams use common inputs and outputs to reach decisions faster.
• Measurable impact: Tie outputs to KPIs such as service level, forecast accuracy, inventory turns, cost-per-order, OEE, and on-time, in-full deliveries.

Data readiness and tooling

You will learn how to work with the data you have, whether it's clean system exports or mixed spreadsheets. The course explains light-weight ways to standardize column names, units, time windows, and identifiers so prompts perform consistently. It also covers how to connect outputs to your existing dashboards, ticketing systems, and document repositories, and how to identify cases where a simple automation or API will improve repeatability.

Governance, risk, and compliance

• Confidentiality and IP: Practical methods to limit sensitive data exposure and stay aligned with company policy.
• Human-in-the-loop: Clear checkpoints for review, sign-off, and audit trails so AI augments rather than replaces critical judgment.
• Bias and fairness: Guidance on vendor and workforce assessments that reduce subjective bias and improve transparency.
• Operational integrity: Guardrails that keep prompts consistent with quality systems and safety standards.

How this course pays off

• Sustained savings: Target recurring drivers of waste and expedite fees with repeatable analyses rather than one-off efforts.
• Greater resilience: Build structured ways to spot risks, rehearse contingencies, and coordinate responses with vendors and internal teams.
• Scalable training: New managers can produce executive-ready analyses by following the same prompt workflows and checklists.
• Cleaner process documentation: SOPs, change logs, and training packs stay current with less manual rework.
• Customer-centric operations: Feedback and market signals drive your improvement backlogs and capacity plans.

KPIs to monitor during and after the course

• Decision lead time for updates, approvals, and exception handling.
• Forecast accuracy and bias at product family or service level.
• Inventory turns, stockouts, and expedite spend.
• Supplier performance and issue resolution time.
• OEE, changeover time, and unplanned downtime.
• Cost-per-order or per unit, and contribution margin impact.
• Time to create or update SOPs and training materials.
• Employee productivity and time redirected to improvement work.
• Energy usage, waste, and related emissions metrics where relevant.

Who benefits

VPs of Operations, Directors, plant and distribution leaders, operations excellence teams, procurement, supply chain planners, service and facilities managers, and data-minded analysts who support them. The course scales from single-site operations to multi-site networks and from manufacturing to distribution and service environments.

Time and prerequisites

Plan for short study blocks and focused practice. No coding is required. Familiarity with your operations data and KPIs is helpful. The course provides checklists, templates, and review criteria so you can apply lessons while you learn.

Limitations and how the course addresses them

• Data quality varies: You will learn small steps that make a big difference, such as consistent time windows and unit normalization.
• AI can be inconsistent: The course promotes explicit objectives, structured formats, and review criteria to reduce variability.
• Change management is essential: Guidance is provided on stakeholder briefings, pilot plans, and rollout rhythms to help teams adopt new workflows.

Why this course stands out

Instead of treating AI as a collection of tricks, this course treats it as an operations discipline. The modules work together, the outputs fit your governance and data realities, and the practices scale from individual analyses to full operational routines. You gain practical ways to reduce cycle time, strengthen service, and build a durable improvement engine.

Ready to get started?

If you lead operations and want consistent, measurable results from AI without extra noise, this course gives you a clear path. Start with the overview module, pick one priority area for a quick win, then extend the same approach across the remaining modules. Your teams get clarity, your reports get cleaner, and your decisions get faster.