In the world of project management, dealing with uncertainties is just part of the game.
But it can be costly. For instance, for every US$1 billion invested in the US, a staggering US$122 million is wasted due to poor project performance (KPMG). This staggering figure underscores the critical need for organisations to adopt effective project management practices to minimise risks and maximise project success.
Enter Artificial Intelligence (AI). We’ve now got a powerful new tool to tackle those risks head-on. An AI-powered risk management platform such as ours, is a sophisticated software solution that integrates artificial intelligence technologies to streamline project planning, execution, and monitoring, enhancing efficiency and decision-making for project managers and teams.
Its power lies in its adherence to four strategic steps of AI-driven risk mitigation, by automating data analysis, prioritising risks objectively, optimising resource allocation, and providing real-time insights, empowering project managers to navigate challenges with precision and foresight.
Case Study: NTT DATA Business Solutions
To understand how AI can aid project risk management, let’s dive into a real-life scenario. Our Galvia AI solution helped NTT DATA Business Solutions, a part of NTT Group and a trusted global innovator of IT and business services, improve project delivery, increase customer satisfaction, and achieve unprecedented growth. Read our Success Story in full here.
To combat persistent project failures hindering growth, NTT DATA Business Solutions sought an AI solution to enhance customer satisfaction, profitability, and project predictability, despite initial resistance from some team members.
We developed a secure, scalable AI platform leveraging machine learning and powerful data visualisations. After loading a decade’s worth of NTT DATA project data, the system was trained to predict and prevent failures, delivering insights via an intuitive dashboard interface. Following rigorous testing, the AI tool was gradually implemented across the organisation, with comprehensive training provided to project managers and teams, emphasising its role as a supportive copilot in project delivery, automating tasks and offering data-driven guidance.
Our AI platform follows four strategic steps, so lets take a look of them in more detail:
Step 1: Identify Risk
At the foundation of AI-driven risk management lies the process of identifying potential threats. Drawing from the wealth of data and insights, AI algorithms enable project managers to swiftly uncover risks that may impact project success and may not be evident to the human eye. In the case of NTT Data Business Solutions, our technology sifted through a decade worth of data to reveal nuanced risk patterns, ensuring a comprehensive understanding of potential pitfalls.
Step 2: Risk Analysis and Prioritisation
With risks identified and quantified, its time to figure out which ones are the most important. AI facilitates an objective analysis and prioritisation process. Through machine learning algorithms, and uncertainty quantification techniques, project managers can assess risk factors based on their probability of occurrence. This data-driven approach empowers decision-making, allowing resources to be allocated strategically based on the severity and likelihood of risks.
Step 3: Risk Mitigation Strategies
Now that we know what we’re up against, it’s time to come up with a game plan. AI serves as a catalyst in formulating proactive risk mitigation strategies. By simulating various future scenarios and evaluating the effectiveness of different approaches, project managers can optimise resource allocation and streamline contingency plans. NTT Data Business Solutions leveraged AI algorithms to anticipate potential outcomes, enabling them to implement targeted measures that preemptively address risks, safeguarding project progress.
Step 4: Real-Time Monitoring and Control
Even after you’ve started the project, you have still got to stay on our toes. In the dynamic landscape of project execution, real-time monitoring is indispensable. AI-powered systems provide project managers with actionable insights and early warnings, facilitating proactive risk management. Through continuous analysis of data and detection of anomalies, project managers can swiftly intervene to mitigate emerging risks, ensuring project timelines and objectives remain intact.
Conclusion:
With AI by our side, managing project risks has never been easier. Embracing AI within the framework of project risk management revolutionises the approach to navigating uncertainties. By adhering to the four strategic steps of risk identification, analysis, mitigation, and real-time monitoring, project managers can navigate challenges with confidence and resilience.
According to Donat Schiller, Regional Risk and QA Manager at NTT DATA Business Solutions: “The Galvia platform has allowed us to deliver vital innovation. By early and effectively managing and acting on project risks, we have more certainty that our projects stay on track, are delivered on time, and meet or exceed customer expectations.”
The case study of NTT Data Business Solutions exemplifies the transformative impact of AI in enhancing project outcomes and ensuring success in the face of adversity. Now project managers can understand what is happening, why its happening and and how to take action.
The solution empowered project managers to have complete knowledge to make optimal decisions with AI on hand to alert, prompt, and help focus attention when and where it is needed.
For more insights on game-changing AI for risk management join us for our “Managing Risk with AI” webinar with John Clancy, Galvia CEO and Donat Schiller, Regional Risk & QA Manager at NTT DATA Business Solutions on March 26th at 14:00 GMT. Register here.