Over the years, we’ve worked with a number of client boards who’ve come to us with a particularly intractable problem: they couldn’t trust the data they were receiving about their projects. In such a scenario, the usual ground-rules of project management and decision-making don’t apply – for the simple reason that if you aren’t sure whether you’re getting good data, then you can’t be confident in any of the decisions you make. Put simply, you’re flying blind. Which is neither a pleasant experience nor a reliable way to reach your destination.
Clients facing this situation usually ask us to bring greater certainty by applying more discipline and rigor to their data and decision-making. Clearly, the best time to do this is at the start of a project, not half-way through. But whatever the stage at which we’re brought in, these situations always serve to underline the critical importance of getting the basics right around project data and performance reporting: having the right people report the right data at the right time about the right things, to enable the right analysis and timely decisions.
That’s why the topic of this blog – project execution strategy & planning – is an appropriate way to wrap up my seven-part series of posts on the Project Excellence System. So far, I’ve looked at the system’s objectives, capabilities and benefits; examined integrated project technology; described how project controls and governance can capitalize on the flood of data; discussed how performance insight and reporting tools and methodologies are evolving in response to the expanding volumes of data for decision-making on projects; drilled down into how to help maximize project team and organizational effectiveness; and stepped up to the enterprise level to look at capital portfolio management & governance .
Looking across the field of project execution strategy & planning, it’s clear that I’ve already touched on several aspects of this capability earlier in the series. So I’m going to focus on an area of it that I believe eclipses the others in terms of impact and innovation: performance reporting. As well as being increasingly pivotal to successful project delivery, this aspect also embodies two themes that have recurred throughout the series. First, the need to identify and harness the opportunities opened by digitization. And second, the importance of looking not just to mitigate downside risk, but also to identify and exploit upside opportunity.
I’ll add another theme to those two: the increasingly vital role that artificial intelligence (AI) is set to play in project execution strategy & planning. PwC’s 2017 Global Digital IQ Survey: AI found that leaders in capital project-intensive industries like power and utilities expect to be investing a lot more in AI in three years' time than they do today. And our experience on capital projects of all types – whether successful or troubled – consistently underlines that project executives are yearning for better data and intelligence to enable better performance reporting and thereby better decisions. This is where AI comes into its own.
So, what’s new about AI? Intelligent decision support systems have been around for years, and there’s a grey area where these morph into fully-fledged AI. To my mind, the leap to AI comes when software uses algorithms iteratively not only to identify patterns and produce insights from a mass of data, but also to learn how to refine and improve the value and impact of its own outputs over time. For a glimpse of the future, I would point to a prototype AI solution that PwC is trialing with a number of clients running major capital projects in the US, Australia and the UK.
The solution is an integrated modular system for analysis, understanding and prediction on capital projects, and it provides three vital benefits:
The system’s AI engine is an effective driver of all three elements, not least foresight. Importantly, the solution enables companies managing projects to gain far deeper intelligence and future visibility from their existing data without going to the expense of a major systems implementation. It does this by ingesting the massive amounts of data generated from capital project portfolios, and applying AI algorithms iteratively to predict areas of risk and opportunity that the human mind would be incapable of identifying. And it does this at high speed to accelerate not just the quality of decision-making, but also its timeliness.
What’s more, the solution can provide insights that go way beyond anything available from analyzing traditional sensor or financial data. In the UK, for example, we’ve used it to conduct sentiment analysis for a client running a massive portfolio of large, time-critical capital projects. This involved trawling through and analyzing a mass of structured and unstructured data ranging from daily logs to progress reports to weather data, and using techniques like regression, cluster analysis and natural language processing to help figure out whether each project was actually in a good state or not. The resulting insights proved helpful to the client – and are continuing to shape and inform how it manages its portfolio.
This is just the start of the AI revolution in capital projects. Over the next few years we expect to see an accelerating flow of industry-focused AI-powered solutions come onto the market. Their common focus will be on using AI not just to provide a faster and more accurate picture of project performance, but also to enable project companies to know what the future looks like so they can change it. And – as we’ve shown with our innovations to date – PwC aims to stay ahead of the curve.
I hope you’ve found this series of blogs on the capabilities in the Project Excellence System interesting, informative and thought-provoking. If you’d like to discuss any of the views of concepts raised in these blogs, please don’t hesitate to get in touch. But most of all, thanks for reading.