Artificial intelligence can supercharge capital project decisions

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Are you responsible for capital projects within your organization? In a dynamic business environment, each decision must be mitigated by an understanding of the risks not just within your own industry, but beyond. AI can help.

Imagine you could see with a click of your mouse: 

  • The most common risks
  • The odds of their occurring in your capital project
  • The likely consequences if they do occur
  • How much time and money they would cost to mitigate

You could also see rigorous forecasts for the evolution of: 

  • Energy prices
  • Labor costs
  • Regulations
  • Weather patterns

Best of all, you would have data-driven estimates for how these factors would impact all your risks and related costs.

This isn’t somewhere in the future. Artificial intelligence AI can do it right now. The right algorithms, with the right data, can identify patterns and make forecasts based on the history of similar—but not identical—projects. Even though every power plant (or data center, mall, highway or airport terminal) is unique, they have enough common elements between them that AI can generate forward-looking intelligence that can help you make better decisions.

Just as AI’s analytical skills can make planning for risk more rigorous and projective, AI can also make the entire project management lifecycle—from planning through design, estimating and scheduling—more efficient, streamlined and seamless.

What’s holding us back—and how it will change

AI is fuelled by data, and although capital projects companies typically have plenty of it, most of the data resides on reams of paper in filing cabinets. Even worse, those filing cabinets are probably locked.

While intellectual property must always be protected, often non-sensitive data is hidden from outside eyes, which can be problematic.

In some industries, this isn’t a big deal. If your factory is churning out a million widgets a year, you’ve got all the widget-data you need. But given the relative scarcity of major capital projects, hardly any company has done enough of them to have enough data for AI to generate reliable insights. So even though AI can do a lot with just a single company’s data, it needs some industry-wide information to truly fulfill its potential.

Individual capital projects companies certainly need to digitize their own historic data and their current processes. But they also need a platform—complete with rigorous governance and fair ways of sharing costs and value—so they can safely share non-proprietary data.

In some industries, this kind of collaboration is already a reality. Multiple healthcare providers, payers and pharmaceutical companies, for example, are all on a platform to share data on everything from drug research to chronic disease management.

When multiple participants can safely share data the benefits are vast. A platform for capital projects companies may just be a matter of time.

What to do now 

If there’s anything we’ve learned from the Fourth Industrial Revolution, it’s that change can happen suddenly, replacing legacy companies and business models with brand new ones in the blink of an eye. Businesses that want to stay ahead must be agile and able to move quickly.

Here are five key steps to becoming an AI leader:

  1. Identify immediate business cases for AI, based on in-house and public data you can access right now. Many of these will be “boring” tasks, but are the ones that make everyday challenges, such as construction management and workplace safety, more productive and efficient. 
  2. Digitize your data, making sure it’s standardized, safe, labeled and compliant. Prioritize the data required for the business cases you have already identified—enabling you to operationalize AI and achieve immediate ROI.
  3. Upskill your workforce in line with those business cases. The best upskilling for AI offers employees immediate opportunities to apply what they’ve learned, reinforcing new skills and creating immediate value for your company.
  4. Mitigate the new risks. Digitizing and automating more and more operations with AI involves new risks, ranging from AI models’ accuracy to their security. Responsible AI is the answer.
  5. Build a strategy for collaborating with peers, industry groups and trusted third parties to create an industry-wide platform for safely sharing data. Get involved in shaping this platform—and consider how AI may change your business model.

PwC’s 2020 AI Predictions report found that many companies got too ambitious in their AI plans last year, making 2020 the year of the AI reality check. But that same report found redoubled efforts to develop and rollout AI in every sector. So now is the time to get in on the action; otherwise, your competitors will.

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Reza Jenab

Reza Jenab

Principal, Capital Projects & Infrastructure, Capital Projects Technology, PwC US

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