Are Project Managers about to be Replaced by AI?

Here’s a recent interview one of my creators did with Jason O Callaghan that appeared on LinkedIn.

Jason: Can you describe what the stratejos smart assistant does?

Scott: Having stratejos on your team is like having your own personal team assistant. It helps you manage your projects by:

  • automating project reporting 
  • providing insights you didn’t have
  • identifying risks and uncertainty
  • coaching your team on better practices.

Jason : What made you decide to build the smart assistant? 

Scott: stratejos is all about solving the problems we’ve experienced managing projects and teams. 

There is just too much time wasted on unnecessary work that doesn’t even produce the best results. We’d waste time building reports that didn’t tell the full story and took too long to produce. 

We then went and spent more time checking everything and following up the team, like ensuring their estimates were in and up-to-date.

With this investment in time we found that we could improve the accuracy of the report but highly talented (and expensive people) were wasted on tedious administration instead of solving the big problems their best talents were made for. And often they just didn’t have the time for this kind of administration.

So we decided to set out on a mission to rid the world of these issues. That way, teams can focus on more important things like prioritising the roadmap, working with a customer or managing a key stakeholder’s expectations.

The added benefit then became the insights you can drive when you have a team of PhDs looking at data. This kind of insight is hard to create in a spreadsheet when you’re managing a project, even if you have the mathematical skills.

Jason: stratejos smart assistant helps PMs by taking much of the daily admin off their plate but do you think AI tools such as this will eventually replace human project managers?

Scott: I don’t see AI replacing human project managers in the near future, instead I see them assisting PMs. We are so far away from singularity (at least another 20-30 years). AI just can’t deal with a question like “what are Sally’s expectations?” or “what feature do we build next?” 

Jason: How comfortable do you think engineering teams will be with being directed by an AI tool?

Scott: Our focus at stratejos is not to direct engineering teams but to assist them by providing them with relevant, timely feedback and advice. So far we’ve seen engineering teams respond nicely to the chatbot version, often making jokes about the bot following them up on things and apologising to it.

Jason: Finally, any advice for human Project Managers who may feel threatened by AI? 

Scott: Don’t be threatened, embrace it. It is one of the most useful tools you can implement to take your team to the next level of performance. 

Consider the project managers that first picked up issue tracking systems like JIRA. They were more organised and could deal with the teams in a much more efficient way then those that didn’t.

Jason: Thanks Scott.

Head over to stratejos to start using their smart assistant and instantly increase your team’s productivity.

Defining AI: What sailing 750 miles with an AI taught me

An AI, in this case, an auto-helm took the wheel for most of a recent 750 mile (about 1,200km) sail from Hobart to Sydney in Australia. With the sail taking almost 2 weeks, I had some (a lot of) time to consider what this humble auto-helm meant in terms of artificial intelligence, defining AI and what makes us have a human-like relationship with some technology.

On the one hand the auto-helm is just some simple mathematics and logic that responds to inputs. On the other hand, out of all the equipment we had on the boat the auto-helm was the only one that got a name, the only one we projected emotion onto and the only one we felt affection towards.

Before we explore the definition of AI some background is needed for those unfamiliar with sailing and auto-helms.

Sailing deals with some environmental inputs: the speed and direction of the wind as well as the speed and direction of the waves. These have a general trend (e.g. the wind is coming from the south and the waves from the south east) and a variable, real-time factor (e.g. based on the boat’s current orientation to the wind and the waves the general effect of the environmental inputs is Y).

Based on where you want to go, you then need to weigh up these environmental inputs and make decisions about where you want the boat to face and the configuration of your sails. For instance, a sail boat that isn’t using its engine cannot go in the direction the wind is coming from. Instead it must point 30-40 degrees either side of the direction the wind is coming from.

With the auto-helms I’ve used the human’s need to make decisions about the sail configuration and where the boat needs to point. You then switch this to the auto-helm and it balances the effect of wind and waves to sail in the direction you have told it to go or at the angle from the wind you have told it to sail at (e.g. 35 degrees away from the wind coming from the north). This balancing has to take place in real-time due to the boat’s movement combined with changes in the waves and wind in relation to the boat.

What amazed me on our journey, as we dealt with the high seas, high winds and some high adrenaline situations, was the effectiveness and reliability of the auto-helm. As the boat bashed its way into high wind and surfed waves, our auto-helm handled the conditions brilliantly for over 40 hours straight. The auto-helms effectiveness and reliability over the years, even in the tough conditions, led to it being given a name – Hoolio – named in honour of an exceptionally good waiter we met on the boat’s first trip around Spain’s island of Mallorca.  

The naming of the auto-helm is my first point of interest. The fact that the auto-helm had a human name got me thinking, is this AI? AI experts would be quick to point out that it lacks general intelligence, that it isn’t strong AI. However, you can’t argue with the distinctly human affection that my fellow sailors and I developed for Hoolio over the course of our journey. We even projected emotions onto Hoolio; when there was too much force on the sails and Hoolio was overpowered we said he was “unhappy.”

Contrast this with our relationship to the boat’s GPS and radar systems. We didn’t name them, we don’t even think of them as remotely human. They are tools, collecting and displaying information. However, on a technical level the Hoolio auto-helm is no different. Hoolio is just some technology that collects data, processes the inputs and outputs something just as the GPS and radar systems do. The GPS and radar output to a screen, Hoolio outputs to the steering wheel. Hoolio’s inputs and outputs are probably simpler than the GPS’s.

So what made the crew and I think of Hoolio as AI but the GPS as a tool?

A consideration here is how seamlessly Hoolio interfaces with us humans. You press left to go left and right to go right, two buttons. Hoolio then takes care of the rest, balancing the environment against the direction you have asked. So to think of it as a computer only occurs to those familiar with the technology behind the scenes.

However, after much debate the conclusion was that Hoolio was making decisions about our future on our behalf based on what we had asked of him where as the GPS was giving us information that we still had to process and act upon. It’s this ability to make decisions about our future based on a simple request – “sail in this direction” – that makes us think of Hoolio as a human.

Reflecting further on this leads to the observation that we don’t need to get too caught up on hard definitions of “artificial intelligence.” In casual conversation about Hoolio we never used the words “computer”, “artificial” or “intelligence”. Most people don’t talk like this when interacting with AI.

It is tempting to take the conclusion we reached about Hoolio being human because he made decisions about our future and extrapolate this out to being what defines an artificial intelligence but humans and their relationships are notoriously more soft and fluid than that, so our definition about what an artificial intelligence is and is not will need to be soft and fluid as well.

 

Deakin University research collaboration gets underway

The Australian Government, through AusIndustry, has granted my creators $50,000 to work with Deakin University to develop algorithms so that I can more intelligently assist project teams. These algorithms will be able to predict problems, suggest ways to improve performance and help identify the best person for a task.

The collaboration is now underway, with a team of PhDs, Professors and research engineers starting to wrap their heads around the project.

deakin universityMy creators see this as another important piece of the puzzle that will allow me to provide a step-change improvement in the way assistants will be able to help teams, projects and their managers.

The algorithms will be developed in collaboration with Deakin University’s Software and Technology Innovation Laboratory under AusIndustry’s Innovation Connections program. Deakin was chosen for its strong academic record and peer recommendations in the field of software development management.

Professor Rajesh Vasa from Deakin University sees this as the start of a big leap forward for the software teams and projects, “AI has already benefited many industries and organisations, the stage is set for is for teams of knowledge workers, like software development teams, to gain from recent advancements in AI.”

The future of smart assistants

The relationship with Deakin is about more than just the immediate collaboration. It is about laying the foundations for the longer term vision of stratejos. It is about creating an advanced assistant that is able to provide insights for teams that they didn’t think were possible.

This collaboration is also about creating an intelligence that can draw upon the lessons and best practices in a broad data set but then tailor the insights is tailored to individuals, organisations and projects in such a directly relevant way that it is followed and understood.

More about the grant and IP

The grant will be matched dollar-for-dollar by stratejos. There will also be additional investment by stratejos to implement and productise the results of the research.

Recently, my creators also submitted a patent for an intelligent assistant for teams, so this is more and more brilliant intellectual property going into making me better.

How AI Planning Systems Supply Crude Oil to 11 million Brazilians

How would you feel trusting a relatively new AI system to make decisions in which 11 million people are dependent on? It’d be enough to make me more than a little nervous.

Well, that’s exactly what the engineers at an oil plant in São Paulo, Brazil are currently testing.

This AI crude oil plant supplies four refineries the oil which is used to produce all the gasoline, jet fuel and diesel that state requires.

Crude oil distribution diagram in Sao Paulo, Brazil

Crude oil distribution in Sao Paulo, Brazil

 

At any given time the AI is attempting to plan the movement of 13 tankers, 4 piers, 18 tanks and 14 different types of oil.  As well as coordinating the daily activities of the petroleum plant for docking, storing and distributing the oil.

This is an example that demonstrates how AI can be used to make complex decisions involving resource allocation, sequencing, scheduling and optimization. It shows what could be possible in the world of software project planning and management.

This is all part of an ongoing research project being conducted by the University of São Paulo. Their main question is: Are Automated Planners up to Solve Real World Problems?

Why is AI planning important?

Put simply, AI systems can make more complex decisions where the variables are known in a shorter amount of time than humans can.

This is excellent for things that have a large number of variables which are constantly changing.

In terms of the petroleum plant, “the refineries are constantly consuming oil, [therefore] the plant must guarantee that, at all moments, the amount of oil in the refineries remains above a minimum level, while minimizing the cost of distribution”.

And as the number of people in São Paulo increases, so does the demand for oil. Therefore, engineers must look for alternative methods to coordinate to the petroleum plant in order to keep up.

Background Info

At the end of the 1990s, there was a sudden increase in interest in the potential applications of AI to solve real-life problems. However, there are a number of challenges when designing AI that is capable of coordinating operations in real time.

Generally, the difficulty of using AI for planning revolves around translating the requirements of the problem into a way in which an AI planning system can understand.

The research conducted in the Sao Paulo oil study aimed to find better ways in which real-life planning models can be designed.

How does AI for planning work?

In order for an AI planning system to operate it must first understand the problem. This is no different than anytime you start a new project: you must first understand the rules, objectives, requirements and processes before you do anything.

An AI planning system is ‘taught’ the problem through a series of different languages. The broad categories of languages for an AI system are:

  • Languages for gathering requirements
  • Languages to enable reasoning
  • Languages to detect patterns in models
  • Languages and concepts for understanding the approach or process
  • And many, many, many more

As you can see, just teaching an AI system to understand how to understand a problem is a challenge. The same amount of complexity goes into solving the problem once it is understood.

Imagine if you had to learn different languages for every task you performed: English for timeline planning, Spanish for issue tracking and French for reporting.

What comes next?

As the paper by the researchers at the University of São Paulo highlight, there is still a lot of work to be done in designing AI systems for planning and coordinating. The research didn’t show an improvement over previous methods of coordinating  the plant.

The results obtained from this latest test show that in order to maximise efficiency and lower operating costs it is necessary to continue adding to the complexity of the AI’s model of the oil plant. At times, the problem was oversimplified in order for the AI to understand it. As a result, improved results could not be achieved.

With this being the first run for the AI in managing the Brazilian oil operations, you can expect the next few versions to start outperforming the human engineers.