DATA5000 — Artificial Intelligence Programming in Business Analytics
Workshop 12
From individual tools to integrated AI solutions
Our final workshop — bringing it all together
Your complete AI toolkit, the analytics stack, and an end-to-end case study with FreshCart
~45 minutes
Design an AI-driven solution for PulseFit using the complete framework you have learnt
~45 minutes
Key takeaways from DATA5000, career readiness, and your next steps
~15 minutes
By the end of this workshop, you will be able to design an end-to-end AI-driven analytics solution that integrates predictive, causal, generative, and agentic AI techniques to address a real business problem.
Over 11 weeks, you have not learnt 11 separate topics. You have built an integrated AI capability.
Weeks 1–4
AI fundamentals, predictive ML, causal ML, meta-learners
Week 5
Advanced neural architectures, transformers, deep learning
Weeks 6–8
GenAI, prompt engineering, AI-assisted coding
Weeks 10–11
Agent frameworks, evaluation, ethics
These are not competing approaches — they form a connected AI-driven analytics stack that modern organisations deploy together. Today, we integrate them into a single, coherent framework.
Remember the business analytics pyramid from Week 1? Here is what it looks like with your complete toolkit.
Full circle: In Week 1, this pyramid was abstract. Now you have concrete tools, techniques, and hands-on experience at every level.
A non-technical view of how the analytics stack is implemented in practice.
You are the orchestrator — not the coder who builds these layers, but the professional who designs the solution, interprets the outputs, and communicates insights to decision-makers.
An end-to-end AI-driven analytics solution
FreshCart is an online grocery delivery platform operating across Sydney and Melbourne, serving approximately 200,000 active customers. The company delivers fresh produce, pantry essentials, and household goods directly to customers' doors within 2-hour windows.
22%monthly churn rate
Customers are leaving at an unsustainable rate, but the leadership team does not know why.
31%late deliveries
Nearly one-third of orders arrive outside the promised delivery window.
18%weekly stockout rate
Popular items frequently run out, forcing substitutions that frustrate customers.
The CEO asks: "How can we use AI to reduce churn by 15% within the next quarter?"
"What happened?" — AI-Assisted Data Exploration (Weeks 6–8)
These are observations, not explanations. We need the next level.
"Why did it happen?" — Causal Machine Learning (Weeks 3–4)
We observed that churned customers had more late deliveries. But does late delivery cause churn, or do both result from a confounding factor (e.g., suburban customers have longer routes AND higher expectations)?
Business implication: Fix delivery reliability, especially for new customers.
"What will happen?" — ML Forecasting and Classification (Week 2)
Handles mixed data types, fast training on large datasets, interpretable with SHAP — ideal for business analytics where explainability matters.
Combines neural network flexibility with Prophet's interpretable trend/seasonality decomposition — accessible for business users to understand forecasts.
"What should we do?" — Agentic AI System (Weeks 10–11)
Now we design an agentic AI system that acts on the predictions and causal insights automatically.
Agent checks daily churn risk scores and demand forecasts
Cross-references risk factors with causal drivers (delivery issues, stockouts)
Selects personalised retention action based on customer segment and risk level
Sends targeted offers, adjusts delivery priority, or escalates to human team
Tracks outcomes, measures effectiveness, refines approach
All four analytics levels working together as one integrated system.
AI-assisted exploration reveals 22% churn, delivery problems, declining baskets
GenAI Coding
Causal ML confirms late delivery causes churn (+14pp), especially for new customers
EconML + SHAP
LightGBM identifies at-risk customers daily; NeuralProphet forecasts demand weekly
LightGBM + NeuralProphet
Agentic system executes personalised retention actions with human oversight
Agentic AI + Guardrails
Reduction in churn
(22% → 14%)
Reduction in stockouts
(18% → 8.6%)
Estimated annual
revenue retained
Testing your understanding of the FreshCart case study
Where you fit in organisations deploying AI-driven analytics.
Your ability to prototype with AI-assisted coding means you can build proof-of-concepts before involving technical teams — making you a more effective communicator and faster decision-maker.
The progression you have made in this course mirrors a real career trajectory.
Tool User
Run individual models, interpret outputs
AI Collaborator
Use GenAI to augment analysis and coding
System Designer
Design agentic workflows with evaluation
Solution Architect
Integrate everything into a complete business solution
At every stage, your value comes from asking the right questions, not from accepting AI outputs uncritically. The best business analysts are sceptical collaborators — they leverage AI's speed while applying human judgement on context, ethics, and business strategy.
Emerging trends shaping the next 3–5 years
Organisations are deploying teams of specialised AI agents — one for customer analysis, one for supply chain, one for financial reporting — that coordinate with each other. The business analyst becomes the agent orchestrator.
General-purpose LLMs are being fine-tuned for specific industries: FinanceGPT for banking, MedPaLM for healthcare, legal AI for compliance. Business analysts who understand their domain deeply will guide this specialisation.
Australia's voluntary AI Ethics Framework and the EU AI Act are establishing compliance requirements. Every AI-driven solution will need documentation of fairness, transparency, and accountability — skills you practised in Week 11.
The convergence of causal ML + GenAI + agents is creating "decision intelligence" platforms — systems that not only predict outcomes but explain why and recommend specific actions with confidence intervals.
What you can now articulate on your CV and in interviews.
On your CV: "Designed and prototyped AI-driven analytics solutions integrating predictive modelling, causal inference, generative AI, and agentic frameworks for business decision-making." — This is what differentiates you from someone who simply "used ChatGPT."
Part B
Design an AI-Driven Solution for PulseFit
~45 minutes — group activity
Link to notebookYour team has been hired as AI analytics consultants
PulseFit is an Australian fitness app with 85,000 paid subscribers ($29.99/month). The app provides personalised workout plans, nutrition tracking, progress analytics, and live virtual classes. After strong initial growth, the company is facing serious challenges.
35%cancel within 90 days
Over one-third of new subscribers cancel before the end of their third month.
2.1sessions per week (avg)
Active subscribers average only 2.1 workout sessions per week, well below the 4+ target.
67%use <20% of content
Most subscribers only engage with a small fraction of available workouts and features.
The CEO asks: "Design an AI-driven strategy to reduce 90-day churn to under 20% and increase average sessions to 3.5 per week."
In your groups, design a complete AI-driven solution using this framework. You have 30 minutes.
What data would you explore? What questions would you ask using AI-assisted coding? What patterns do you expect to find?
What causal questions need answering? What is the treatment? Outcome? Potential confounders? Which causal ML method would you use?
What would you predict? Which model(s) would you use and why? What features would be important? How would you evaluate performance?
What automated actions would the agentic system take? How would it personalise interventions by subscriber segment?
What could go wrong? What human oversight is needed? How would you monitor for bias? What evaluation framework would you use?
Summarise your solution for a non-technical CEO. What is the expected business impact? What resources are needed? What is the timeline?
Be specific — name actual tools (LightGBM, not "a model"). Reference causal thinking (confounders, treatment effects). Think about what the agent actually does, not just what it analyses. And always connect back to business value.
30 minutes — work in your groups
Each group presents their 3-minute CEO pitch
After all presentations, the class will vote on the most innovative solution and the best CEO pitch. Remember: creativity and business thinking matter as much as technical accuracy.
Part C: Course Wrap-Up
AI augments your analytical capability. Your value lies in judgement, context, and strategic thinking that AI cannot replicate. The human-AI partnership is where the best outcomes emerge.
The most dangerous business decisions come from acting on correlations as if they were causes. Always ask: "Is there a confounder?" Causal ML gives you the tools to answer rigorously.
The best AI solution is not the most complex one — it is the one that solves the business problem. Always start with the question: "What decision does this enable?"
Never accept AI outputs uncritically. Verify results, check for bias, question assumptions, and always consider what the model does not know. You are accountable for AI-informed decisions.
Fairness, transparency, accountability, and human oversight are not optional add-ons — they are foundational requirements of every AI system you design or manage.
You started this course as beginners in AI. You leave as AI-literate business analysts.
Most people can prompt an LLM. Very few can design a complete AI-driven analytics solution that integrates predictive modelling, causal inference, generative AI, and agentic frameworks — with proper governance. You can.
Best of luck in your careers.