AI for Market Analysis & Business Strategy

AI. Machine learning. Data analytics. You’ve heard the buzzwords—but do you actually know what they mean or how they fit together? This lecture breaks down these core concepts in plain language, so you can stop nodding along and start using them to make better strategic decisions.

You’ll learn:

• The difference between AI, machine learning, and data analytics—and why it matters

• How supervised learning, unsupervised learning, and natural language processing work

• What these technologies actually do with your data

• Where human judgment fits into the AI-driven decision-making process

With so many AI tools on the market, how do you know which ones are actually worth your time? This lecture takes you beyond the buzz and into the platforms that are actively reshaping how real teams do market analysis—from dashboards to decision-making.

You’ll learn:

• What leading tools like Tableau, Power BI, Crayon, and Brandwatch actually do

• How AI features like sentiment tracking and natural language queries enhance insights

• What to consider when choosing and integrating a platform

• Common reasons tools go unused—and how to avoid them

Classic strategy models still matter—but AI is changing how we use them. In today’s fast-moving environment, frameworks like SWOT, PESTEL, and Porter’s Five Forces can’t rely on quarterly reports and instinct alone. This lecture shows how AI tools bring speed, depth, and real-time intelligence to these time-tested approaches.

You’ll learn:

• How AI populates SWOT quadrants with dynamic, real-world data

• Ways AI enhances PESTEL scanning—from policy shifts to climate signals

• How companies like Unilever and Audi use AI to anticipate external change

• What AI adds to each of Porter’s Five Forces—from rivalry to substitution

Want to know what your customers will do next—before they even know it themselves? Predictive analytics turns historical data into forward-looking insights that can shape everything from product launches to pricing, staffing, and messaging. It’s one of the most powerful ways AI is reshaping market strategy—and it’s more accessible than ever.

You’ll learn:

• How predictive analytics works using AI and machine learning

• The difference between forecasting outcomes and forecasting behaviors

• What leading indicators are—and why they matter

• Real-world examples from Starbucks, Walmart, Delta, and healthcare

• Best practices for building reliable, actionable predictive models

What does it actually look like when a global brand uses AI to reinvent its strategy? In this real-world case study, we unpack how Yum! Brands used AI to overhaul its marketing—from personalized offers to real-time A/B testing—and what made their transformation succeed where others stall.

You’ll learn:

• Why Yum! shifted from traditional mass marketing to AI-powered personalization

• How cross-functional teams, not just new tools, drove adoption

• What tech stack and infrastructure supported their AI rollout

• The measurable impact AI had on loyalty, engagement, and revenue

• Lessons you can apply to your own organization’s strategic roadmap

AI can drive results—but it can also raise red flags. From biased data to opaque algorithms to privacy violations that trigger public backlash, ethical missteps in AI aren’t just theoretical—they’re already happening in the real world. This lecture is your guide to using AI responsibly in market analysis and strategy, so you can build trust and avoid costly mistakes.

You’ll learn:

• How bias enters AI systems and what it means for your customers

• What privacy laws like GDPR and CCPA mean for data collection and use

• Why transparency matters—and how to improve it with tools like model cards

• What accountability looks like in an AI-driven organization

• Steps smart companies are taking to build responsible, compliant AI strategies

It’s one thing to explore what AI can do—it’s another to actually build a plan around it. Whether you're launching a new initiative or overhauling your go-to-market strategy, integrating AI isn’t about plug-and-play tech. It’s about smart planning, the right team, and knowing what success looks like from day one.

You’ll learn:

• How to align AI use cases with your business goals

• What makes an AI roadmap realistic—and effective

• Who needs to be involved across strategy, tech, legal, and change management

• How to phase your AI investments for pilot, scale, and sustain

• How to set and measure KPIs that show real strategic impact

What happens when AI stops being a tool—and becomes a teammate? That’s the future we’re heading toward, where strategic planning is powered by live data, predictive models, and AI agents that work alongside you in real time. In this forward-looking session, we explore what’s coming next for AI in market strategy—and how you can get ready.

You’ll learn:

• Which emerging AI technologies are redefining market analysis

• How tools like GPT-4, multimodal models, and agentic AI are already being used

• What “continuous planning” looks like in a live-data environment

• How roles, skills, and decision-making will evolve in AI-assisted teams

• Practical steps to future-proof your strategy and stay adaptable

Before you move forward, let’s take a moment to step back. In this final lecture, we’ll connect the dots across everything you’ve learned—so you can move from understanding AI to actually leading with it.

We’ll review the most important concepts from the course and talk about how to translate insight into action, long after the course ends.

You’ll learn:

• The key takeaways from each major section of the course

• How to stay sharp in a fast-changing AI landscape

• Tips for applying AI to real-world strategy—starting small and scaling smart

• Where to find ongoing resources, tools, and communities

• How to build a strategy that adapts and improves over time