Artificial Intelligence is reshaping how organizations make decisions, build products, and serve customers. Yet for every success story, there are many AI projects that fail compliance reviews, stall before launch, or create new risks because teams lacked clear, actionable best practices. This course, AI Technical Foundations: AI Best Practices, was created to bridge that gap. It equips business, IT, data, and security professionals with practical guidance to plan, build, and deploy AI features responsibly. The material avoids hype and focuses on real-world results that are achievable in everyday business environments.

Whether you are introducing a chatbot to improve customer response time, integrating predictive analytics into your operations, or exploring generative AI for reporting, this course provides a structured, realistic playbook for doing so safely, efficiently, and transparently.

The overall goal is to help you and your organization make AI work for you, by balancing innovation with governance, speed with accuracy, and automation with accountability.

Who This Course Is For

This course is designed for professionals who contribute to planning or implementing AI initiatives, including:

• Business and operations leaders who are responsible for project outcomes, budgets, and compliance.
• IT administrators and engineers who manage data, cloud infrastructure, or software integration.
• Security and risk professionals who establish privacy, monitoring, and compliance controls.
• Data and analytics teams who design, test, or support AI-driven solutions.

No prior machine learning experience is required. A general familiarity with software delivery and data concepts will help, but curiosity and a desire to use AI responsibly are the only true prerequisites.

Why This Course Matters

AI is no longer limited to research labs or specialized data teams. It has become a core capability that influences marketing, logistics, customer support, and product development. As adoption accelerates, many organizations struggle with the same predictable challenges:

• Using incomplete or biased data that produces unreliable outcomes.
• Deploying models without clear evaluation metrics or monitoring.
• Overlooking privacy and security requirements.
• Failing to maintain documentation that auditors or regulators expect.

These issues often result in lost trust, wasted effort, or non-compliance.

This course ensures that your team can move forward with confidence. It provides the essential guardrails that support safe experimentation and measurable success.

You will learn how to:

• Plan AI initiatives that include governance from the beginning.
• Evaluate data and model choices before investing major effort or budget.
• Monitor and document AI systems so they remain reliable and explainable.
• Communicate value and risk in a way that leadership understands.

Learning Objectives

By the end of this module, you will be able to:

• Describe a simple lifecycle for building and shipping AI features, mapping specific best practices to each phase. Why it matters: A clear lifecycle aligns technical and business teams and prevents wasted work.
• Apply practical data quality, privacy, and security guardrails before training or prompting. Why it matters: Strong data and privacy practices protect both users and your organization.
• Choose between common model options and prompting patterns and document your design decisions. Why it matters: Clear documentation helps with reproducibility and long-term maintainability.
• Design evaluation, red-team testing, and monitoring plans to detect risk and drift. Why it matters: Continuous evaluation ensures your AI remains safe and aligned with its intended purpose.
• Create a lightweight AI governance paper trail that satisfies auditors and stakeholders. Why it matters: Transparency builds trust and compliance readiness.
• Estimate and track AI operating cost versus quality tradeoffs and communicate ROI to leadership. Why it matters: Understanding tradeoffs helps prioritize investment and sustain long-term efficiency.