AI Strategy for Business: Identify and Evaluate Use Cases

Name: AI Strategy for Business: Identify and Evaluate Use Cases
Rating: 4.4 (326 reviews)

Identify, qualify & land real AI opportunities: feasibility, risk analysis, and deployment roadmap for business people

Role Play

Created byData Universe

Last updated 4/2026

English

English [Auto],Italian [Auto],

What you'll learn

Identify real AI opportunities by analyzing pain points like costs, time, and errors across marketing, sales, operations, and customer support.
Evaluate the technical feasibility and business value of AI use cases using frameworks for financial impact, data complexity, and risk management
Translate business problems into technical requirements by defining data inputs and outputs to bridge the gap between business and AI teams.
Design a strategic roadmap from pilot to deployment, establishing business KPIs and utilizing professional templates to prioritize high-value cases.
Distinguish between AI hype and reality to understand why many AI ideas fail and how to ensure your projects deliver actual business value
Categorize different AI solutions such as prediction, classification, and GenAI to select the right tool for specific business problems.
Perform a comprehensive risk analysis for AI projects, covering critical aspects like bias, privacy, compliance, and reputational impact.
Apply learned frameworks to real-world scenarios in finance, marketing, and operations through a practical hands-on workshop

Course content

11 sections • 25 lectures • 1h 44m total length

What we mean by AI in business3:06
Artificial intelligence uses intelligent systems to predict outcomes, automate decisions, and generate content to solve real problems, bridging business needs with data teams.
From hype to reality: why many AI ideas lead nowhere4:09
Explore why AI initiatives fail, due to vague problems, poor data, and unclear value, and follow a structured idea-to-use-case path that delivers real business impact.

Types of AI solutions: prediction, classification, recommendation, and GenAI5:15
discover the four main AI solutions in business: prediction, classification, recommendation, and generative AI, and learn how each creates opportunities by solving specific problems.
The 4 key elements: data, model, product, and business value6:10
Identify and align data, model, product, and business value to turn AI ideas into viable use cases, ensuring quality data, appropriate models, usable products, and measurable value.
Practical examples: scoring, recommenders, and text assistants3:12
Explore concrete AI use cases in business: customer scoring to prioritize leads, recommendation engines for personalized learning, and text assistants that speed and standardize support.

Identifying pain points: time, cost, errors, and customer experience4:10
Identify ai opportunities by mapping pain points—time, cost, errors, and customer experience—and gather team input to turn frustrations into ai-enabled improvements across processes.
Guiding questions to generate AI ideas by functional area4:28
Discover guiding questions to spark AI ideas across marketing, sales, operations, customer support, finance, HR, and more.
Typical AI ideas in marketing, sales, operations, and support3:32
Explore typical ai ideas across marketing, sales, operations, and support, such as predictive lead scoring, dynamic content personalization, campaign performance prediction, opportunity scoring, churn prediction, and ai-assisted proposals.
Mini exercise: turn problems into initial AI ideas3:48
Turn business problems into initial ai ideas by crafting predictive models for cart abandonment, analyzing reasons, routing tickets, ai assisted responses, and demand forecasting for stock.

Evaluation framework: potential value, feasibility, and risks3:43
Apply a simple three-part framework to evaluate AI use cases by potential value, feasibility, and risks, prioritizing high-value, feasible, low-risk ideas for immediate action.
Estimating value: impact on revenue, savings, and experience4:27
Quantify ai value across revenue impact, cost savings, and experience improvements with concrete, data-driven estimates. Use real numbers to project outcomes like higher conversion, lower costs, and better service.
Estimating feasibility: data, complexity, and technical dependencies4:17
Assess feasibility of use cases by evaluating data availability, accessibility, and quality; weigh complexity and technical dependencies to plan phased, low-risk implementations and manage risks.
Analyzing risks: bias, privacy, compliance, and reputation4:27
Identify and mitigate four AI risk categories: bias, privacy, compliance, and reputation, through testing, governance, and diverse data, then score value and feasibility to guide use cases.
Strong vs weak use cases: examples and filtering criteria4:32
Contrast strong and weak use cases with concrete examples, assessing value, feasibility, and risk to craft specific, measurable, and scoped AI opportunities.

How to describe the business problem clearly3:45
Bridge business and technical teams by clearly describing the current context, specific objective, constraints, and success metrics for an AI use case.
Defining inputs and outputs: data, decisions, and result usage3:41
Define inputs and outputs for AI by detailing concrete data such as purchase history, clickstream, and demographics, and specifying predictions, classifications, or recommendations and their use in workflow.
Key questions to ask data/IT without being technical3:27
Ask a structured set of business-focused questions to assess data quality, feasibility, accuracy, risks, timelines, resources, integration, pilots, and ongoing maintenance for artificial intelligence projects, without technical details.
Use case template explained step by step4:01
Discover a simple seven-section use case template that captures critical information—from name and owner to risks and feasibility—to align business, stakeholders, and technical teams on AI initiatives.

From pilot to deployment: typical phases of an AI project3:36
Follow the typical AI project phases from concept to deployment, including proof of concept, pilot, deployment, and monitoring, with emphasis on testing feasibility, real-world use, and continuous optimization.
Success indicators: business KPIs to measure results3:48
Define business KPIs upfront to measure AI success beyond model accuracy, linking revenue, efficiency, quality, and experience metrics to baselines and dollars through ongoing tracking.
Final checklist: from idea to prioritized use case3:02
Apply the practical AI use cases checklist to translate a pain point into prioritized ideas, assess value, feasibility, risks, and stakeholder alignment, and plan phased deployment with success metrics.

Scenario 1 - Marketing and Sales5:58
Explore five AI-driven ideas for a mid-sized e-commerce business and evaluate them via value, feasibility, and risk, highlighting lead scoring and product recommendations as strong pilots.
Scenario 2 - Operations and Customer Support5:52
Evaluate five AI ideas for a 10,000-customer SaaS support team facing 500 daily tickets and 24-hour response times. Prioritize routing, a chatbot for common questions, and AI drafting with oversight.
Scenario 3 - Finance and Internal Management7:49
Evaluate five AI use cases for finance and internal management in a mid-sized manufacturer, focusing on value, feasibility, and risk; start with cash flow forecasting.

Requirements

No Technical Background Required: You do not need to know how to code or have a background in data science; we focus on the bridge between business and tech.
Basic Business Knowledge: A general understanding of how business departments (Marketing, Sales, Operations, or Finance) function is a plus
Problem-Solving Mindset: A desire to identify inefficiencies (pain points) such as high costs, errors, or slow processes within an organization
Interest in Strategic AI: Curiosity about how AI can be applied to real-world scenarios rather than just theoretical concepts.

Description

This course gives business professionals, managers, and consultants a practical framework to identify, evaluate, and land real AI use cases in their organizations. No technical background required.

Most AI initiatives fail not because of bad technology, but because of poor strategic alignment. You will learn to spot genuine AI opportunities by analyzing pain points across marketing, sales, operations, and customer support, and to separate hype from real business value before investing resources.

The course covers AI fundamentals in a business context: prediction, classification, recommendation, and Generative AI. You will learn to balance the four key elements of any successful AI project: data, model, product, and business value, and to categorize solutions based on your organization's specific needs.

You will also assess technical feasibility, estimate financial impact, and perform risk analysis covering bias, privacy, and compliance concerns. And you will learn to communicate requirements clearly to AI teams by defining inputs, outputs, and decisions without writing a single line of code.

The course closes with a strategic roadmap from pilot to deployment, including business KPIs, evaluation frameworks, and professional templates to present and defend your AI use case internally. Through practical workshops covering real industry scenarios in finance, marketing, and operations, you will gain the confidence to lead AI initiatives that deliver measurable results.

Who this course is for:

Aspiring AI Professionals: Anyone looking to transition into the AI field by learning how to identify and evaluate real-world business opportunities.
General Interest Learners: Students or professionals curious about AI fundamentals who want a practical framework to measure feasibility and risk.General Interest Learners: Students or professionals curious about AI fundamentals who want a practical framework to measure feasibility and risk.
Business Leaders and Managers: Professionals in Marketing, Sales, Operations, or Finance who want to solve departmental pain points using AI.
Project and Product Managers: Individuals responsible for the roadmap and deployment of technology projects from pilot to production.
Data and IT Teams: Technical profiles who need to improve their communication with business stakeholders and better understand business value.
Consultants and Entrepreneurs: People building their own AI solutions or advising clients on how to move from a hype-driven idea to a solid use case

AI Strategy for Business: Identify and Evaluate Use Cases

What you'll learn

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Course content

Introduction - From Idea to Use Case2 lectures • 7min

Quick AI Fundamentals for Business3 lectures • 15min

Spotting Opportunities - From Pain Point to AI Idea4 lectures • 16min

Evaluating an AI Use Case5 lectures • 21min

Practice Test I - AI Strategy: Foundations and Use Case Evaluation0

Landing the Use Case - Talking to the AI Team4 lectures • 15min

Roadmap and Next Steps3 lectures • 10min

Practical Workshop - Identifying Use Cases3 lectures • 20min

Practice Test II - AI Strategy: Communication, Roadmap and Real-World Scenarios0

Role Play: Defend Your Use Case1 lecture0

Requirements

Description

Who this course is for: