AI Delivery Recommender combined delivery analytics with simple AI‑driven recommendations to help teams see delivery risk early and understand what actions could reduce slippage. Their solution pairs a Power BI dashboard with a lightweight recommendation model to translate delivery signals into practical guidance.

Action & Narrative Generator focused on converting early delivery risk signals into clear, prioritised management actions and executive‑ready narratives. Their work defines structured prompts that transform activity and capacity metrics into severity‑scored action lists and dashboard‑aligned narratives, ensuring early warnings lead directl...

Project:Hack27 is a community hackathon bringing teams together to design, pitch and judge practical solutions across six defined challenges, with a focus on innovation, clear value and real‑world impact.

SIGNAL delivered SIGNAL (Schedule Intelligence and Guidance for Noticing At‑Risk Lines), a production‑ready data enhancement and alerting engine that turns raw schedule and capacity data into early‑warning signals. The solution enriches datasets with drift metrics, risk scores, behaviour archetypes and plain‑English recommendations, read...

Delivery Confidence Radar built an early‑warning Delivery Confidence Radar that integrates activity and capacity data to surface instability, pressure and likely slippage before dates move. The solution emphasises explainable signals and a clear ‘what’s at risk, why, and where to intervene’ structure suitable for planners and senior lead...

Delivery Confidence Assistant built a data‑driven Delivery Confidence Assistant that scores forecast‑credibility risk and translates complex delivery signals into clear, actionable insights. The solution combines transparent rules, lightweight modelling and Power BI‑ready outputs to show which tasks are likely to slip, why programmes and w...

Delivery Risk Classifier explored predictive classification to identify tasks likely to complete late, focusing on understanding the limits of snapshot‑based delivery data and how modelling could still provide early warning signals. The team combined exploratory modelling with clear recommendations on how delivery datasets need to evolve to su...

Early Slip Predictor focused on identifying early indicators of delivery slippage by analysing capacity pressure and task behaviour across work centres. Using simple machine‑learning techniques and clear capacity metrics, the team demonstrated how likely future slip can be predicted early and translated into understandable risk signals.

Assumption Drift Canvas focused on collaboratively mapping how critical assumptions emerge, drift and impact delivery confidence across projects. Using a shared visual workspace, the team structured the logic linking assumptions, confidence, external signals and portfolio‑level assurance to support earlier, clearer decision‑making.

Assumption Assurance Pitch presented a concept‑led solution for managing critical assumption drift, focusing on clearly communicating the problem, proposed approach and value to portfolio leaders. Through a structured pitch and supporting materials, the team outlined how assumptions could be surfaced, monitored and reviewed to provide earlier...