EVA29, organised by Steve Wake, is scheduled for thu 2 November 2023 at Armourers’ Hall, Moorgate, London. To book your place visit https://www.stevewake.com/eva Lessons from Applied AI What can we learn from applied AI in project and programme delivery? Attend the conference to find out!

On 16 March 2023 James Lea presented an APM real world webinar - Designing and Delivering with Data – transforming project delivery with data science. James asked the question: how can we deliver our complex projects and programmes with certainty and confidence? Project data science signposts the way. We live in a universe of data; like the James Web Space Telescope we are making new discoveries every day. But how can we best observe the project cosmos, and how can we make use of our discoveries to predict the future?

Project Science Ltd presented a challenge at Project:Hack 18. The challenge, tackled by a team of data scientists and analysts, is how can we auto-generate work breakdown structures? For more information visit, Project:Hack18 APM.

In this event, organised by the APM governance SIG, James will join a panel of subject matter experts to assess the problems with projects ‘in the large’. We will explore how we can transform project success through data-enabled investment decisions. The event forms part of the series “Effective governance, unlocking data and project success”. We will assesses the problem at large with project success, the concept of reference class forecasting to inform investment decisions and the principle that ‘all projects are data designed and data enabled’.

On 7 December 2022 I presented my talk “How do you know your project is on track? The role of data” to the APM Midlands branch in Worcester. Building data-designed and enabled projects: Thanks to Temi and the APM Midlands branch for organising a very interesting and enjoyable event.

A question we get asked quite often is: how do we estimate agile projects? And our reaction is nearly always: what’s different about agile? We are of course, being tongue-in-cheek! Agile is different, but not so different that all the techniques in waterfall, iterative, hybrid project management should be thrown out the window. You can use comparative and parametric estimating, benchmarking, and so on. In fact, you should use these, because without an idea of how much work you’re taking on, how can you be confident you have about the right size team for the job?

We presented three challenges at the Project:Hack 17. Optimising Delivery through Collaboration Insights narrative WBS Automation - Planning Crossrail 2 narrative WBS Automation - Understanding WBS diversity narrative

Project Science is leading the Visions workstream within the Project Data Analytics Task Force. We’ve set ourselves the challenge of a ten-fold improvement in project delivery outcomes by 2025. Just one in two hundred projects achieves its original time, cost and quality goals. Imagine the savings if we can lift that to one in twenty. Workstreams To work toward this goal, we’ve developed a set of workstreams, including: WBS Automation NextGen PMO Human Performance Benefits Management These workstreams have devised some challenges for the upcoming Project:Hack 17 - see you there!

EVA26, organised by Steve Wake, took place on Wednesday 16th and Thursday 17th March 2022 at Armourers’ Hall, Moorgate, London. Our founder, James Lea, presented “The psychology of estimating and planning - working with your team.” For a video interview with James and a summary of the key points, visit https://youtu.be/qmE-huseZR4?t=7318 Emotions, Plans and Predictions Last time you gave an estimate, were you: Under pressure? Distracted? Worried your numbers will be used against you?

What does quantum computing mean to project delivery? How might this nascent technology change the way we do business? Observations Let’s start with an observation: we have no lack of computing power today. There are few project problems that are constrained by the available processing power. Powerful CPUs and GPUs (such as those used for bitcoin mining) power our machine-learning and natural language systems. They drive the recommendation engines used in online commerce and social media sites.

We are awash in a sea of data - how can we make sense of it all, and turn it to our advantage? In particular, how can we exploit data to deliver complex change? Machine learning / AI / algorithmic bias - these are all 21st century terms. But what do they mean, and how should we adapt and evolve? How can modern business exploit the exponential growth in data science to deliver projects and business change more effectively?

Challenge All projects - what to focus on, when will it be done? What we did Define milestones For each milestone, build consensus on scope and date Iteratively improve predictions using data-driven models Outcome Benefits Clarity Delivery on time

Your customers demand high quality product; this is a short case study on how we use data to inform document reviews. Challenge High volume of documents to review Volatile and uncertain: some reviews quick, others slow What we did Collect data Analyse the correlations; generate insights Improve processes; set clearer expectations to team Outcome Benefits Better quality work into review Greater certainty On-time delivery

Challenge Diagnosing and fixing software faults on critical path Work volatile and difficult to plan What we did Get the data Analyse - generate insights on rework and waste Gain team buy-in: workshop to find solutions Enter improvement loop; exploit quick wins Outcome Benefits Predictable delivery Project back on track

Challenge All projects - what to focus on, when will it be done? What we did Define milestones For each milestone, build consensus on scope and date Iteratively improve predictions using data-driven models Outcome Benefits Clarity Delivery on time