After seeing recent industry presentations on bots, machine learning and artificial intelligence (AI), I see the application of these technologies changing the practice of project management. The question is, is this future desirable or will we have a choice?
The project manager role
Much of the daily work of a project manager has not dramatically changed over the last 30 years. We may use different management methodologies, but we spend a great deal of time manually collecting and disseminating information between the various roles on a project. This effort directly results from the need to fill the information gaps caused by systems that can’t capture what is truly happening within the organization. In a recent PMI sponsored roundtable discussion, missing or incorrect data was highlighted as a significant issue. Today’s systems are totally dependent on human entry of information, where it can be nuanced or simply not entered.
The combination of artificial intelligence in the form of bots and cloud computing could radically change this situation. PM effectiveness would be dramatically enhanced and likely the need for some PM roles diminished. In the future, as data capture becomes richer and more automated, we may see new advisor services that arise from improved data quality and completeness. I foresee significant improvements in three key areas.
One of the black arts of project management is predicting the future, where we represent this future state as a new project plan. We draw upon our own domain and company experience to determine the steps, resources and time needed to accomplish the goal. Our success rate at predicting the future is not good. Our predictions are fraught with error due to the limits of our experience and that of the organization. If you’ve ever managed a project for something completely new to an organization, you are familiar with this situation.
Imagine if your scheduling bot generates a proposed project plan, based on the aggregated and anonymized experiences of similar sized companies doing the same type of project. Today, we use tools like Monte Carlo to simulate this information. The bot could incorporate real world data, potentially yielding better results.
Benchmarking of business data has been around for some time. These new cloud capabilities could see benchmarking expanded to include real-time project management data.
Another common challenge of project managers is that of resource constraints. Imagine a world where your resource pool is the world and it’s as easy to use as Amazon.
We are seeing the continued growth of the freelance nation trend in corporations. Currently, corporations use agencies to locate and recruit talent. Agencies may simply be a stopgap as bots become a more efficient clearinghouse of freelancer information. Staff augmentation agencies could become obsolete.
For example, your resourcing bot determines that you need a social media expert on your project on April 5th for two days of work. It searches data sources like LinkedIn and your public cloud calendar to find a list of suitable and available candidates. Three are West Coast of the U.S., one is in Paris and one is in Sydney. It then automatically reaches out to these candidates with offers. If multiple people accept, it automatically manages the negotiation. Once complete, the planning bot is informed, a virtual desktop with requisite software is provisioned, user login credentials are generated and the specific task information is sent to them. When the job is complete and rated as satisfactory, the bot coordinates with your accounts payable system to pay the freelancer. The planning bot automatically updates the plan and pushes the data to the BI dashboards.
Project feedback loops on work are awful. The largest challenge is incomplete data, which results from increasingly fragmented work days, limits of the worker’s memory and tools that rely on human input. It is also incomplete as it serves little benefit to the person entering the data.
Workers are overwhelmed with tasks arriving via multiple communication channels and no consolidated view.
Imagine a world where the timesheet is antiquated. Today, we have systems such as Microsoft Delve that know what content you’ve touched. We have IP-based communication systems that know what collaborations you’ve conducted. We have machine learning capabilities that can determine what you’ve discussed and the content of the documents you’ve edited. This week, we have facial recognition capabilities and other features that can track and interpret your movements. Given all of this, why is a timesheet necessary?
Professional athletes use this type of data in the competition setting to improve their performance, using the data feedback to spot areas of development. Combining this activity information could prove a boon to productivity.
I can see this working as a “Fitbit” type feedback loop that helps the worker be better at their job and allows them to get home on time. Doing so provides direct benefit to the employee and reduces the Big Brother feel of this data.
The personal bot acts as a personal assistant, reminding the worker of tasks mined from meeting notes and marking tasks as complete in real time. All the while, it is also keeping track of the time spent that enables to the worker to get a better picture of how they spent their time.
Brave new world
There are many challenges with the view I’ve presented above. Many of these challenges are the same faced when we automated and integrated procurement processes. It is also hard to deny that there is compelling opportunities to improve the worker lives as well. Bots, machine learning and artificial intelligence are reachable capabilities that should be incorporated in the PM toolbox as you plan your organization’s future work management needs.
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