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Step into SAP's side as a Business AI Associate the morning a German manufacturer's line goes dark, and build the one-page Rescue Plan that diagnoses the real break, scopes a Joule-style copilot with honest guardrails, sizes the payback in euros, and earns a yes from the board. An independent ProoV case study, not affiliated with / endorsed by SAP SE; the customer is a fictional composite and every SAP figure is SAP-reported with public sources shown in-app.
Just completed
A graded ProoV project, completed step by step and marked against a published rubric.
Drop into a live supply-chain shock at a fictional German manufacturer running on SAP, just as it scales into India: a supplier has gone dark, a port is backing up, and the main line is days from idle. Record a quick employer-brand check-in, lock in your gut call for where the chain really breaks, watch the stakes count up, and take the trust pledge before any data arrives.
The loudest signal is usually a symptom. After a worked example, read a small ops dashboard and flag the one real bottleneck, the single-sourced critical component with about two weeks of cover feeding the most demand. Leave the dramatic port delay and the healthy alternate supplier alone; they are decoys. This produces Section 1 of your Rescue Plan, the one-line diagnosis.
Turn "a supplier failed" into a precise, buildable problem. Fill a structured builder with the pain, exactly who is hurt, a quantified cost in euros or idle days, and a measurable outcome (detection-to-mitigation from about six days to under one), then write one honest sentence on why this is the problem worth solving. This produces Section 2 of the brief.
A useful enterprise copilot surfaces the single best next action with a reason, grounded in live business data, with a human in control. Spec the rescue agent across four fields: the live SAP signals it reads, the one concrete next action it recommends, the outputs it drafts for human approval, and the one thing it must never do alone. This produces Section 3, the copilot spec.
Trust is a boundary, not cleverness. Apply the rule a beginner can use: act autonomously only when an action is low-value AND reversible AND high-confidence, and escalate everything else to a named human. Sort a list of agent actions into autonomous versus escalate, and catch the cheap-but-irreversible trap that must still escalate. This produces Section 4, the decide-vs-escalate boundary.
A board approves numbers, but the math stays light. Given the cost per idle day and the baseline, move the levers for days recovered and expedite freight avoided to land a defensible euros-saved figure on a live readout, then write one line justifying why it is honest and not overclaimed. This produces Section 5, the payback.
A fix is only worth much if it travels. The same copilot-surfaces-next-action engine supports SAP's real, publicly stated India push, but one piece must change. Decide which single element transfers unchanged (the decide-vs-escalate logic and next-action shape) and which must be re-localized (supplier network, lead-times, data residency), and say why. This extends Section 5 with the India read.
A pitch makes saying yes safe and easy. After contrasting a weak versus strong ask, assemble the five-beat exec memo (the break, the proof, the copilot, the guardrail that de-risks it, the ask) ending in one bounded ask: a scoped pilot with a timeframe, a named small resource, and a success number. Then write a 90-second elevator version. This produces Section 6, the ask.
Prove genuine understanding: explain in plain words what a Business-AI copilot is and why the guardrail matters, anchored to one real number. Record the after employer-brand reading at peak understanding, see your early prediction surfaced against the real bottleneck, and watch your six sections assemble into the downloadable Rescue Plan.
Review the assembled Rescue Plan one last time, see the transferable skills you built, then submit through the manual-submit gate. No surprise auto-submit; you confirm when the brief is ready.
You learn to separate the loud symptom from the actual constraint. On a live ops dashboard you flag the single-sourced component with the least slack feeding the most demand, while ignoring the dramatic port delay that self-resolves, then frame it as a precise problem with the pain, who is hurt, a cost in euros or idle days, and a measurable target.
You spec a Joule-style agent that surfaces the single best next action with its reason, grounded in live SAP signals and kept human-in-the-loop. Then you draw the trust boundary: act alone only when an action is low-value, reversible, and high-confidence, and escalate everything else to a named person, including the cheap-but-irreversible move that looks safe but is not.
You compute euros saved from two honest levers, days recovered times cost-per-day plus expedite avoided, stated with the days recovered and never overclaimed. You decide what carries unchanged into an India expansion versus what must be re-localized, then assemble a five-beat board memo that ends in one bounded ask: a scoped pilot with a timeframe, a named resource, and a success number.
The biggest value I gained from this project was learning how to turn a real business problem into a practical AI solution, rather than treating AI as just a technology. I learned to identify the root bottleneck, quantify its financial impact, design a copilot with clear human g…
Hands-on experience in framing high-stakes enterprise supply chain bottlenecks, establishing human-in-the-loop AI governance, and structuring defensible ROI models for executive decision-making.
Through this project, I gained practical experience scoping a Business-AI copilot with realistic guardrails and aligning it with SAP’s real-world Joule framework. I learned how to identify line bottlenecks and build a fundable business case with clear payback metrics.
