3 Artificial Intelligence Scenarios

A Short Story of Artificial Intelligence and Machine Learning for Project Portfolio

In this story, we explore Planisware's journey towards applying Artificial Intelligence (A.I.) to Project Portfolio.

What has changed today that makes Artificial Intelligence and Machine Learning viable technique in the Project Portfolio world?

And what are the main innovative use cases where A.I. can be deployed today?

  • From Bust to Boom:
    Artificial intelligence applied to Project Portfolio.
    Estimation, User Enablement: Why A.I. now makes sens in the PPM world?
  • Offer your Users a Smart Assistant
    Enable your users and reduce data issues with chatbots
  • Interact with your Projects on the Go
    Advanced interaction and project update anywhere
  • Leveraging Project Data with Machine Learning
    Train an Estimation Model for Forecast Estimation


From Bust to Boom: Artficial Intelligence applied to Project Portfolio

Pierre smiles as he gazes out the conference room window, over the Paris skyline at the Eiffel Tower. “Most of Planisware’s founders, and myself, we started our career working on A.I. (Artificial Intelligence) projects in the Eighties.” Now CEO of Planisware, his trim beard flecked with gray, Pierre Demonsant then witnessed the first “winter of A.I.,” when projects were begun but couldn’t deliver on their promises. “Those struggles are why I changed course to start working on Project Portfolio Management in the early Nineties.”

“Three years ago, I again started exploring A.I. ,” explains Pierre. “Today, we can access very large data sets with more computational power -- which means we have a chance now to successfully address more use cases,” he explains. “But I have learned prudence, and prefer to only talk about projects that we have delivered.”

Planisware started its journey toward integrating AI into its main products by exploring whether machine learning could help project leaders better assess, early on, estimates on project duration or cost.

Paul, a blond and slim figure in its thirties is managing Planisware Machine Learning effort in R&D recalls. “We started our work by asking our customers to share their database with us. “The idea was to investigate what would be the most suitable model to apply to the data, and then train the model afterwards.” Because Planisware customers’ databases often have millions of objects, the assumption sounded good.


Predictive Estimation - Portfolio Results

Predictive Estimation model applied to the Company Portfolio.

“But reality kicked in,” continues Paul. “Data quality was uneven. It was difficult to have good results across the board. In some areas we could get some results, but in others it was very difficult to get a reliable forecast,” Paul quietly tells us. “It came to us as a bit of a surprise, but in the end, it turned out that it was because we wanted to use the data in a multifaceted way -- not how you typically use it, because people tend to focus on specific metrics.”

“Even when users control their projects and follow corporate standards, data quality can suffer very quickly” explains Franck Lafitte in his low-pitched voice. Franck, Head of Planisware Consulting, is a tall man in its fifties, with an amused air about him. “Even with only 10 users, mistakes are made almost daily and can be difficult to spot. This issue gets compounded as the number of users gets higher.”

”There are so many mistakes that can be made daily,” laughs Franck. “Like re-using templates that don’t fit a project. Or costs or labor reported against the wrong task. You might have good data when it is used for computing key performance indicators.”

Franck pauses a bit before cautioning, “however implementation choices, like the level of granularity of cost management, could also limit how you can use that data for forecasting.”


Offer your Users a Smart Assistant

“To go any further, we had to find a way to help our customers with data quality,” says Paul. “So, what about using A.I. to help them? We started to think about an always-on A.I. presence next to the users as a great way to help ensure data quality.”

“That seemed worth trying,” notes Pierre. He explains that bots used to rely on a rigid set of rules and previously scripted or “canned” answers. But now they can develop answers to queries in a more natural, less structured fashion. “Advances in Natural Language processing are the key,” he says. “Processing normal human speech or text, computers can now understand context and make useful connections. Conversational bots - or chatbots - have become more accepted to interact with a service.”

“Chatbots are mostly just used in e-commerce today,” Paul says. “However, for things like user on-boarding, in addition to more standard features such as guided tour of screens or tool tips in context, chatbots can have their logic repurposed to be an always-on assistant and thus be of help with data quality.”

Specifically, as data quality depends a lot on users understanding the meaning of a specific process and pieces of data. And a chatbot can be a good companion and help remind users how to deal with specific process and what kind of data is needed from them.


Planisware Chatbot: Few Capabilities Demonstrated. Request and update information.


Interact with your Projects on the Go

Paul explains that, while working on the initial chatbot project to provide guidance to users, the product team soon had the idea of using the bot to update and engage with projects. “This scenario surfaced very quickly. However our language, when it intersects with the reality of projects, is very difficult for a machine to understand.

“It starts with project names that are frequently common words or ambiguous choices of words for actions to be completed. We had to develop a completely new approach to deal with truncated or misleading statements,” says Paul.

And enable the bot to engage in an interactive conversation – to respond, not just by doing as told, but by interpreting and asking for clarification.  To complicate matters, people have various ways of wording things and don’t use single, specific wording conventions, which could be misleading for a general purpose chatbot.

“We really had to keep the interaction fluid,” Paul adds. “Above all we had to adapt as closely as we could to the language of our users. We learned very early on that we had to preserve flexibility to let the bot adapt to local use of words.”


Leveraging Project Data with Machine Learning

“I was an early customer who Planisware asked about using data for machine learning. That was two years ago,” Jeff says. ”And after a few months, I went back to Planisware to try to understand what they got out of my input…..” Jeff tells his story to an eager audience in a packed room, at Planisware User Summit 2019. 

Jean-François Castells - or Jeff for his friend - is an IT Director with UCB in Belgium, and his enthusiasm for A.I. initiatives is infectious. The audience listens intently as Jeff tells how UCB, a major pharmaceutical company, always had a lot of data scientists, and this was the year that A.I. leveraging financial data pre-populated the budget. “A.I. is now mature enough to move forward, and we will have more and more data coming out of AI and machine learning”.

Paul, joining Jeff onstage explains how Planisware machine learning tools provides a simple mechanism to train a model with some data, and can then provide various kinds of estimates around the project activities: durations, resource consumption, costs, and more. Paul stresses that – although using advanced mathematics and learning technics -- the machine-learning feature itself remains very easy to use. “That’s something that took us some time to figure out how to do,” explains Paul.

An early result was that the Planisware Predictive Analysis model could quickly spot outliers and data quality issues in the database. “It’s like the defects are jumping at your face,” jokes Jeff in front of the audience. Users could then can quickly address those issues and start educating the user base with specific, targeted actions.

Planisware Enterprise / Predictive Estimation - Project Predictions

Spotting outliers: How reliable is the predictive model?

Another application of A.I. in Planisware is Predictive Estimation, which can discover drivers behind estimating trends and data points, in order to confirm and to gain fresh insight into what is really driving project activities. With understanding of the drivers comes the ability to predict project cost, duration, or resource levels, immediately upon project creation, to help project leaders build business cases.

Finally, with the drivers in place comes the ability to predict project cost, duration or resource level either at project creation, to help project leaders build their business case, or to provide estimate at completion once the project is under way.


Planisware Enterprise / Predictive Estimation - Drill Down Plot

Building and training the pedictive model 


“Usually the best ideas are not coming from us but from our user base. We are still early on and continue to explore the benefits of using Machine Learning with our customers. We provide cool tech,” concludes Pierre. “But then our customers show us what can really be done with it.”