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Business Applications of AI
Where value is real, where it is theatre, and how to build the business case.
In this module
0 of 6 activities complete
Reading
0% readThe value map by function
In finance, forecasting and anomaly detection. In operations, demand planning and predictive maintenance. In sales, prioritisation and next-best-action. In HR, screening support and attrition risk. In service, deflection and summarisation.
Selecting the first use case
Score candidates on value, data readiness, decision frequency and reversibility. Start with a high-frequency, reversible decision on data you already own. Never start with the most strategically important, least reversible decision.
The business case
Quantify the baseline, the expected lift, the cost of the error, and the run cost of the model. A model with no baseline cannot be evaluated and will be defunded within a year.
Real-world case studies
Case study · Retail
A grocery chain reduces waste and stock-outs at the same time
Situation
A grocery chain of two hundred stores ordered fresh produce on store-manager judgement, producing both spoilage and empty shelves in the same week.
Challenge
Demand varied by store, day, weather and local events, and no individual manager could hold all of that in view while running a shop.
How data was used
Two years of item-level sales, waste records, promotional calendars, local weather and public holiday data were assembled per store.
How AI was applied
A demand forecasting model produced item-level order recommendations per store per day, with managers retaining override authority.
Business outcome
Waste and stock-outs both fell, and managers reported spending less time on ordering and more on the shop floor.
Lesson for the learner
Recommend rather than dictate. Keeping the expert in the loop improved both adoption and the model, because overrides became training signal.
Case study · Human resources
A professional services firm makes screening consistent and defensible
Situation
A firm receiving several thousand graduate applications a season found that screening outcomes varied noticeably between reviewers.
Challenge
Unstructured review invited inconsistency, and the firm needed both efficiency and a decision trail it could defend to candidates and regulators.
How data was used
Historic applications were reviewed to identify which recorded attributes genuinely predicted success in role, and proxies for protected characteristics were deliberately excluded.
How AI was applied
A structured scoring assistant summarised each application against published criteria and flagged missing information, with every shortlisting decision made by a human reviewer.
Business outcome
Screening time fell, inter-reviewer consistency improved, and every rejection carried a documented, criteria-based rationale.
Lesson for the learner
In decisions affecting people, use AI to standardise and document the evidence, and keep the decision itself with an accountable human.
Business Applications of AI, executive briefing
17:25 · Video briefing
Key takeaways
- Value concentrates in high-frequency, reversible decisions
- Data readiness matters more than model sophistication
- Without a measured baseline no AI initiative can prove its worth
Score three use cases
Apply the selection framework.
- Score each of your three Module 1 candidates on value, data readiness, frequency and reversibility
- Rank them and select one
- Write the baseline metric you would measure it against
Module 7 quiz
1 questions · 70% to pass
01Which first AI use case is most likely to succeed?Applied
Your notes
Lesson discussion
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