Shift from Living in the Project’s Past to Predicting its Future
This article is part of a series of blogs on Artificial Intelligence in Project Business. See the rest of the articles in the series at the end of this blog.
While many industries are starting to embrace the benefits of AI, project-based companies, or Project Businesses, are still struggling to make that transition from living in the past to predicting the future. Project Businesses have an abundance of data but lack a data infrastructure that is “AI ready.”
Because Project Businesses operate in a disparate landscape of applications to manage their core business processes, their data is not stored and managed in any standardized way. As a result, there is little to no integration between point solutions, and Project Businesses are not operating on a single source of truth.
Therein lies the problem.
It’s Impossible to Predict Project Outcomes Based on Unreliable Data
Although Project Businesses are still behind the technological curve, many are attempting to predict how their projects will turn out. Earned Value Analysis (EVA) is a tool for doing that.
For example, let’s look at project budgets. Once actual costs are incurred on a project task and they are coming in over budget, it’s reasonable to assume that the over budget trend for that particular task will continue. The Cost Performance Index can give you some indication of that trend. Of course, project managers can also look at other project performance data such as delays, resource conflicts and capacity overloading to make some assumptions about future performance.
However, the problem is that most project managers do not have access to all this data, especially not in real time. How is it possible to make assessments, decisions and predictions based on incomplete and delayed data? It’s almost impossible to predict project outcomes based on this unreliable data.
In addition, project managers cannot take all this data into account simultaneously and compare it with hundreds of other similar projects to make a prediction. This is where AI comes into play and what AI can do very well.
We can train the AI by feeding it all these metrics from hundreds or thousands of past projects along with the project outcomes (i.e. how a project finished regarding planned completion time and budget). The AI takes this data and learns by testing algorithms using these metrics as variables.
Once the algorithm has been trained, we can deploy it on current projects. As the same standardized metrics from current projects come in, the AI can start to make reliable predictions on the outcomes.
Keep in mind, project data does not have to be from the same company. Utilizing data from a community of companies can significantly improve the AI’s accuracy. This is what a SaaS Project Business Automation platform can provide.
It pools together project data from many companies that can be normalized and anonymized on a PBA SaaS platform. As the data comes in, the AI learns and increases accuracy over time, incorporating different factors from across industries, locations and more.
Of course, joining this community is voluntary. While companies can choose to work from their own pool of project data, joining the community allows instant access to the AI, rather than having to train the AI with your own data, which could take years.
From Laggards to Leaders in AI
Project Businesses have an opportunity to come up to par or even leap-frog other industries by adopting a Project Business Automation approach. PBA is a way to systemize and integrate all processes and data for Project Businesses and can produce the necessary real-time financial and operational metrics that AI can use to make accurate and valuable predictions. This PBA approach presents an opportunity for Project Businesses to take a giant leap ahead into the AI forefront.
CEO of ADEACA - Daniel founded ADEACA in 2007 to address the needs of project-driven organizations who were lacking a holistic solution for project and financial management. As one of the principal architects behind Microsoft Dynamics AX, Daniel was in a unique position to recognize the best way to fulfill this need was inside the ERP. ADEACA was born. Prior to founding ADEACA, Daniel was a principal architect and product director of Axapta at Damgaard Data, which was acquired by Microsoft in 2002 and later became Dynamics AX and now called Microsoft Dynamics 365 Operations. His experience at Damgaard equipped him with the skills to create user-centric software, an art form not practiced by the large, generic ERP companies.
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