I write extensively about the need for change in how we deliver projects. Delivery productivity remains stagnant and as a profession we continue to pursue iterative rather than transformational change. But we see this starting to change.
Organisations such as Costain have employed a head of AI with a strapline “Transforming AI in Costain so everyone can use it for everything they do and everywhere they do it”. In 2020, Laing O’Rourke have created a data team that will “be part of a movement to create a legacy and evolution in a sector that is ready for a new era”. Petrofac are rolling out new data driven insights and are at the early stages of a transformation. United Utilities and Thames Water are also experimenting with the use of advanced data analytics. We have seen a definite increase in momentum in 2020.
But I also see some of the larger organisations struggling with implementation because of internal inertia. They have to gain senior level buy-in, which requires cross-functional agreement, training needs analysis, software analysis, meetings with the IT department etc. This process can take years, during which the technology has moved on. Agility is crucial.
In the middle of a pandemic, cash for new initiatives can be difficult to get hold of. Sir Robert McAlpine are tackling this by up-skilling their project delivery workforce in project data analytics. They are conducting fast fail experiments from within projects, iterating new solutions in a matter of days and helping to develop a data driven culture. Teams are empowered to develop new ideas and work with the community to crowd source solutions. A rapid and cost effective implementation strategy, supported by a backlog of user stories within an overarching data strategy. They really get it.
I’m undecided on the future of project management consultancies; it is more nuanced. Some have a business model centred on selling people on a day rate; automation will ultimately lead to self destruction so is it in their own interest to delay deployment? Others trade on the basis of cross fertilising knowledge between clients and projects, but pooling data will reduce this advantage. Smaller companies are emerging that thrive on disruption and offer different commercial models. The bigger companies will need to adapt or become increasingly obsolete. Disruption is a certainty.
Early wins will be associated with the deployment of standard Microsoft tools such as PowerBI, Power Automate and AI. We have seen early adopters of PowerBI pausing to reflect as they have amassed thousands of different dashboards. Project managers are faced with a barrage of data, but they don’t have the time and often the skills needed to properly interpret it. Dashboards need to be dynamic, presenting information on areas of focus, lead indicators and to identify when intervention may be required. Projects such as the A14, a £1.5bn road construction project, made some great inroads into this area with project board meetings being entirely driven by PowerBI. But they agree that they are only scratching the surface. Apps such as from Meeting Quality on team performance can provide game changing insights.
COVID-19 is a catalyst for organisations to think differently, often as a means of long term survival. Some organisations are battening the hatches and stopping all discretionary spend to preserve cash, but others are starting to spin up an emergent data analytics capability. As organisations begin to understand their use cases better they will deploy apps and IOT solutions to capture more data to drive better insights. Data pipelines will improve and data volumes will increase. They will also forge strategic relationships with the leading minds in the subject, which will identify areas of focus. The lead that this creates may become unassailable.
I see automation solutions from UI Path and Microsoft moving at such a phenomenal rate, with the recent introduction of hyperautomation, that smaller vendors will never be able to compete. The same applies for AI capabilities. Microsoft have their Azure machine learning studio and AI builder, the latter is a low code solution. These capabilities are becoming increasingly accessible. But the challenge for organisations is more in how they are deployed. The balance between quick wins and forging the foundations for game changing improvements when data volumes reach critical mass. Prepackaged platforms such as Procure will need to compete with DIY solutions developed using a Microsoft backbone.
There are also niches where new entrants will solve unique problems. Sharktower is an AI driven solution for managing projects that helps to reimagine project management. Nodes and Links are developing solutions that run through thousands of scenarios to optimise. nPlan are developing solutions to assess schedule variance risk. Scopemaster have a capability for software requirements analysis. There are hundreds of others.
Capabilities are beginning to emerge on forecasting too at a project and WBS level. I’m not sure how this will pan out. The tools are cool but it is possible to recreate them using AI if you can get hold of the data.
My own view on the future is one of democratisation, combined with high-end niche applications. A future where we work together to develop adaptive dashboards, automation code and AI libraries for the benefit of all. Why invest hundreds of thousands in developing the fundamentals when we can share them and move a lot quicker together? Organisations can then focus on specific niches that will give them an edge. This could be predictive analysis of change or compensation events, where decisions are made earlier to reduce risk and increase margins.
AI Enabled project delivery.
Some project delivery organisations will differentiate themselves by being ‘AI enabled’. Data driven decision making, re-using their and the client’s hard won experience codified in data. Digital PMOs that integrate data from across the business and provide forensic and predictive insights. They will differentiate themselves on the ability to identify opportunities for cost and schedule reduction, whilst delivering with greater certainty. They will look very different. I have seen some SMEs with a vision to move towards ‘the automated project’. I don’t think we’ll ever take the human out of the loop because we need to manage interfaces and people, but I don’t think many of us yet understand how far we’ll be able to get towards this objective.
Very few organisations will possess the volume of data required to deliver the full potential offered by advanced data analytics and AI. Although organisations such as Endeavour Programme and Oxford Global Projects have demonstrated that as few as 40 projects can provide a suitable training dataset for a cost estimation model, it is insufficient to be able to extract actionable insights on courses of action. e.g. Risk emergence, triggers for benefits shortfalls or commercial disputes. In order to solve these broader challenges we need a highly connected and validated data set. We have been working with the Open Data Institute to develop data trusts within a number of different sectors; the construction data trust is a good example. By 2025 these data trusts will be commonplace.
Clients will begin to contract for project delivery data so that the learning from one project can be deployed to the next. But this poses problems for suppliers; how will they protect their IP and secret sauce that gives them an operational advantage? How do they ensure that they don’t suffer reputational damage when their mistakes become more visible? But with bids being won or lost on fine margins, data analytics provides an opportunity to open up the competition and turbocharge innovation led change.
Clients will have a significantly higher level of access to data. Experts will be able to forensically analyse it to get under the hood of project delivery professionalism. From benchmarking through to understanding the effectiveness of risk management. Project delivery organisations who bury mistakes will be found out; there will be an increased focus on collaborative models. Contracting strategies will also evolve, recognising the characteristics of complex adaptive systems and predisposition of work packages to variance. Dev Amratia’s blog on the future of contracting is a good read.
The project management that I grew up with will become increasingly obsolete. Reporting and document control will become increasingly automated. Risk and schedule management will be driven by evidence and insight. Project controls will be fundamentally transformed. There will be an increased emphasis on understanding the predisposition of projects to variance; this is where margins will be made. But this requires a radically different skill set to the ones we have today.
We set up our project data analytics foundation degree apprenticeship to address this specific issue. We aren’t convinced that organisations will need a centralised team of data scientists. They will need to be up-skilled in this new technology, balancing domain expertise with a knowledge of deploying the science and tools. The average age of our apprentices is 30. It is these people who will be the engine of change. Not just delivering project data analytics capabilities, but helping to develop a culture from the inside out. A culture that values data as a strategic asset.
There are a wide range of research topics to be explored. But I’m not convinced that these should be driven by academia alone. We need to have a vision and a roadmap for a 2025 and 2030 future, identify the jigsaw pieces then use research to underpin the delivery of the vision. It needs to be tightly coupled to the challenges that we are facing rather than the generic ‘how can AI improve project management’ assignments that I get asked to participate in on a frequent basis. There is such a phenomenal opportunity in this space.
This isn’t a tweak. It will be hugely disruptive. With a community of >6,000, hackathons with as many as 250 people signed up and a growing cadre of apprentices, this isn’t going away. The business case for disruptive change continues to escalate.
This isn’t an easy problem to solve. It requires a cross sector approach, working collaboratively for the greater good. No single organisation has the solution and by working in stovepipes we become inefficient. Hence, why I am collaborating with UCL and other organisations to develop a Project Data Analytics Task Force. More details in the coming weeks.
Martin Paver is the CEO and Founder of Projecting Success, a consultancy that specialises in leveraging project data to transform project delivery; from high end strategic consultancy through to apprentice training. He has led a $1bn megaproject and a multi $billion portfolio office. He is the founder of the Project Data Analytics community, comprising ~6,000 members who share a passion for leveraging the exhaust plume of project data. He regularly blogs and presents at international conferences, helping to ignite the professional imagination and inspire change. He is helping to lead the charge for disruptive change.