What do these three things have in common: Artificial Intelligence (AI), self-driving cars and project management? The answer: It takes project management to create a self-driving car. It takes a project that uses AI to create a product. The members of the project team need to be well-versed or experts in AI, including the project manager.
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The team needs AI to ensure that its projects are focused on value add tasks. AI should be doing routine tasks, analyzing complex data and making rational decisions. However, the project team still needs to oversee these decisions to ensure that the AI tool arrives at logical decisions. Remember, AI is a tool, not a silver bullet.
If I asked the average person if he or she would benefit from AI, the answer would most likely be yes. However, if you ask that same question about an autonomous vehicle, the answer would not be so clear.
AI Includes Three Main Categories: Narrow, General, and Super
AI is defined as computer systems that can function like human intelligence. It has three main categories: narrow, general, and super. The most common types of AI are narrow in scope, for example motion detectors and virtual reality receptors. Online shopping agents and voice assistants that can help you make selection fall into the general category of AI. Facial recognition and drone delivery programs that can anticipate actions fall into the super category.
Most Autonomous Vehicles Have Basic Rudimentary Functions
Most autonomous vehicles do not have super learning capabilities. In other words, they have basic rudimentary functions, but higher-level learning precludes advancement. So what ethical considerations need to be made for an autonomous vehicle? For example, if a car runs over a bug, is that ethically acceptable? If a car runs over a beloved pet, is that ethically acceptable? If a car runs over a pedestrian, is that ethically acceptable?
For many, liability is a key factor when discussing the ethics of an activity. According to Amitai and Oren Etzioni in Science and Technology, Summer 2017, “a significant part of the challenge posed by AI-equipped machines can be addressed by the kind of ethical choices made by human beings for the new millennia.”
Currently, transportation rules dictate that when there is a vehicular accident the responsibility usually lies with the driver. However, in an autonomous vehicle, does the responsibility fall to the owner of the vehicle? Responsibility can range from “both novel and known forms of damage, harm and injury.”
Faisal Riaz and Muaz A. Niazi, writing in PLOS ONE stated that “defining responsibility is key and can include developing meaningful distinctions, examining their ramifications, and refining the underlying concepts that together inform the idea of responsibility.”
States Are Moving toward Some Level of Acceptance of Autonomous Vehicle Technology
As Lindsey Brock and Lindsay Tropnas concluded in their Survey of the Regulations of Autonomous Vehicles “there is a clear trend that states are moving toward some level of acceptance of autonomous vehicle technology and that the future and the current liability schemes will either adapt to fit the emerging technologies or new standards should develop through legal precedent or legislative action.”
This trend must include increasingly sophisticated technology to help autonomous vehicles teach themselves. In his book Robots, John M. Jordan writes, “vehicles need both AI and deep learning to quickly teach itself [sic] safe driving techniques from simulations, millions of miles on the road videos, real world testing and input from human assistants such as engineers and professionals.”
Jordan also notes that as sophisticated robots are able to make “value judgments, professional aspirations, and ethical assumptions, there is also a rapid progression of technology that will allow computers to raise questions that involve law, belief, economics, education, public safety, and human identity.”
So, as society moves toward adoption of autonomous vehicles, there is a growing need to quash public anxiety by teaching the benefits of AI to the intelligence of autonomous vehicles. This can lead to machines that are not only capable, but intelligent and ethical.
Surprisingly, there are articles written as far back as the late 1980s and 1990s on AI and project management. Artificial Intelligence has been around for many years, but the technology has changed over the years and it has gone by different names.
While some would consider logistics integral to project management, others would not. It depends on the industry that is practicing project management. For example, project management on U.S. government contracts is done cradle to grave – that is, writing a request for proposal (RFP) that covers everything from manufacture and maintenance of the equipment procured to its disposal after it is no longer needed. Other organizations conduct project management from planning to delivery. But no matter what, clients want their projects to be strategic, delivered on time, and within budget.
Historically, project management efforts have not been good at delivering on time and within budget. AI can help, but, as always, it is not a silver bullet. It is a tool that project managers need to use to become better at their job and to be more valuable to their company.
The Project Management Institute (PMI) in its 2019 Pulse of the Profession In-Depth Report: AI @Work: New Projects, New Thinking noted that project managers would have to have a “high Project Management Technology Quotient (PMTQ)” to be efficient with the new AI technologies.
PMI defines PMTQ as “a way of evaluating an organization’s ability to manage and integrate technology — based on the needs of the organization or the project at hand — to turn AI strategy into reality.” Since the landscape for AI changes almost daily, the astute project manager will make sure that the project stays up-to-date and advocates for the tools the company needs for its projects.
A PMI survey found that early adopters of AI deliver completed projects 61% on time. In addition, they meet business benefits 69% on time, and 64% of projects met or exceeded ROI (return on investment) estimates. Those that did not accept AI as willingly found that their projects were on time about 47% of the time, only 53% met business benefits and 52% made their ROI.
Today, AI is being used within project management, but may not always be viewed as such. Tools like Trello, Slack, Jira and Flock are used as separate tools for company project managers. However, some project management tools have actually been incorporated into project managers’ tool suite. Companies may consider buying a consolidated tool suite instead of having diverse tools for their project managers. Chatbots that are included with some of tools can schedule meetings, answer questions or even send out meeting reminders. These were once activities done by project managers or by others on the project team.
Study Finds that Managers Spend 54 Percent of Their Times on Administrative Tasks
Accenture conducted a study which found that managers said they spent 54% of their time on administrative project tasks. Company leadership understands that if project managers are freed from many of these administrative duties, they are then able to do more value-added work. The technologies cited above relief managers of some routine administrative duties.
Some machine learning tools can do scheduling and develop budgets with much more accuracy than when humans perform the same activity. With more analysis of project data, tools will be able to help project managers and other officials make better decisions to drive project success.
Because so many projects today are complex, project managers need AI assistance to review data and find trends. Eventually, project managers and all team members will have an Alexa, Siri or Google assistant on their desk. They will learn the user’s strengths and shortcomings. These assistants will also be linked to all the various AI tools. With all the various parts of a complex project, the project manager or manager will be able to see the risks more clearly and take steps to eliminate them.
About the Authors
Dr. Kandis Wyatt, PMP, is a full-time professor of Transportation and Logistics Management in the School of Business. Professionally, Dr. Wyatt has implemented process development practices, designed and created instruction, and developed procedures and programs for civilian employees. Dr. Wyatt’s teaching philosophy includes emphasizing the importance of being an information facilitator and content guider to help students apply real life experiences to foundational principles. Online teaching is more than teaching to the test, it is creating an online learning community. The traditional role of the instructor has changed from “the sage on the stage” to the “guide on the side.” Dr. Wyatt’s teaching style includes creating an environment that emphasizes diverse talents and ways of learning, prompt feedback, and active learning.
Wanda Curlee is a full-time professor in the School of Management. She is a certified project manager and has led projects in various industries, including for the U.S. Government and various Fortune 500 companies. She has co-authored books on complexity theory and project management, program management and virtual project management office. She is currently researching the area of artificial intelligence and is writing a book on neuroscience and leadership.
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