If we combine the best of humans and algorithms, we can reach new levels of project delivery performance, writes James Lea
Today is a busy day. I start by responding to emails and checking my diary, attending video calls, checking the progress of actions and agreeing new ones.
I then get together with my PMO to review progress updates. We update the schedule and extend our plans into the next phase of work. A team member asks me to review their documents. Then I’m reviewing and refreshing the risk and opportunity register. I compile and submit progress, governance and senior stakeholder reports.
In parallel I am resolving commercial questions. Will we make the revenue expected on this project? What should the contract look like for the next phase of work? And so on.
And then a substantial risk matures into an issue (such as a pandemic) and everything else pales into insignificance, yet cannot be forgotten. All the plates must be kept spinning.
Does this – the life of a project manager – sound familiar to you?
There is no doubt life can be frenetic. That is part of the excitement and reward of our profession. However, I often think: there must be a better way. Can we find more time to breathe, reflect and guide without having to steer the ship every day? Can we find a way to be less busy, and thereby free ourselves to focus on the big questions and challenges, to maximise our chances of success?
I think we can. To achieve this goal, I would like to pose a grand challenge to the project delivery community – one which may be uncomfortable, but which we must embrace. We can and should replace a significant part of what we do with algorithms: with artificial intelligence (AI).
Why should we consider such a radical step?
Over the last few years, the pace of technological change has been unrelenting. Algorithms permeate society. Unimaginable quantities of data are generated through our daily interactions with online systems. With such pervasive technology, attitudes are changing.
It is not just about efficiency gains. We owe it to clients, industry and society to deliver better and more predictable projects. But it’s not a simple picture. We must decide how much of our role to supplant with AI.
There are some things a computer can do better than a person. It has perfect recall, is indefatigable, relentless and consistent. It can network and recognise patterns, speech and images. It can construct complex visualisations and has unsurpassed numerical capabilities. But there are things people do far better: empathise, reason, imagine, create and more. We have emotions and form complex relationships.
So why not combine the best of both? Let the computer take the strain of repetitive activities. Let it become the autopilot that liberates us to focus on strategic activities.
Consider what it means to design and operate a project
We scope, design and deliver projects by building models. Those models manifest in our visions, requirements, plans, estimates, schedules, progress records, reports, outputs and benefits. Our models reflect the past in the future. Through our plans, communications and progress reports, we are describing those models and their evolution to our clients. We are telling stories with data.
Some models are descriptive: a statement of what has happened or how something is. Others are predictive: what we expect to happen. The most powerful automation occurs when we build prescriptive models that not only predict future possibilities, but also how to make them happen.
To achieve this, we need the best possible models. We must codify our knowledge and expertise accurately, then provide the human oversight that ensures the models function as we expect and benefit from our coaching.
The structured descriptions we need to do this are challenging to build. There are two possible approaches:
- Specify our models using logic, science and hard-won personal knowledge.
- Employ process mining and build our models automatically. Using software tools we can inspect the digital ‘exhaust plume’ of our engagement with online systems to build descriptions of processes and activities as they are carried out.
We are at a tipping point
Having built models, we can employ robotic process automation to accelerate workflows, allowing one system to automatically drive another. Data analytics is another burgeoning sector, providing enormous insights into our models of the world that drive new ways of thinking.
A schedule is another type of model: a forecast of what needs to happen to achieve an outcome, plus a record of actions to date. The idea that a schedule describes a single path is being replaced with ensembles of paths. Using ensemble modelling techniques, we can calculate the probabilities of various outcomes by modelling the likelihood of each potential pathway and its range of efforts and durations.
A computer now has a clear advantage over a person: it can evaluate countless scenarios rapidly and make optimal recommendations. Ensemble models can be informed by thousands of historical schedules, exploiting hard-won knowledge to improve future outcomes. This is learning from experience enabled by machine.
Our models are not only growing in power and diversity, but they are linking up. The information they contain is no longer sitting in silos. We are at a tipping point where we can combine them freely.
When we join our models up we can generate powerful human-like chains of reasoning. Process mining can be used to identify where process bottlenecks occur. Then we can pose those scenarios as machine-learning problems to find out why these bottlenecks occur. This is applied AI in action – a variety of algorithms being brought to bear on a group of problems to yield human-like insights, but at scale.
Imagine the potential, then, if we could share project and process data across companies and sectors for the greater good? Data trusts may be the way forward here.
A note of caution
I believe if we combine the best of humans and algorithms, we can reach new levels of project delivery performance. The whole can be greater than the sum of its parts. But when we replace something, it can only be a part of the whole. We must retain control and a full understanding. We are the designers.
I believe we can indeed begin to meet the challenge and take this opportunity to build a better future.
You can read more on future trends in project management in APM’s Projecting the Future challenge papers. Paper one covers: The fourth industrial revolution: data, automation and artificial intelligence.
This article is an edited extract from a piece in the summer 2020 edition of Project journal, APM’s quarterly membership publication. Read more at apm.org.uk/project/
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