You aren’t going to stop it. The trend is going to keep moving - Ginni Rometty
For many years, IBM is the leader in the field of cognitive computing with IBM Watson. According to IBM, the purpose of cognitive computing is to enhance and scale human expertise, rather than an attempt to replicate human intelligence. IBM prefers to call it augmented intelligence (AI) instead of artificial intelligence.
The underlying thought is that cognitive computing functions in an assisting, subordinate relationship to humans. This is an interesting point of view and positioning of the technology, because many experts believe that cognitive computing has the potential to advance in a superior relationship to humans.
There are a number of technologies that are related to cognitive computing like machine learning, text-to-speech recognition, natural language processing, image detection, sentiment analysis and others. All of these technologies have the intention to improve human productivity and decision making.
Cognitive computing will have a material impact on the way we manage technology driven-change projects. It is a fantastic opportunity to bring the role of the project manager to the next level. The emerging technology shall operate as an assistant and expert in many project management disciplines. It will change the execution of tasks and shift the focus of the project manager to more creative and analytical activities. It will provide better information to make decisions.
Here are five examples of where I think cognitive computing will have a material impact on project management in the future.
Methods, tools and best practices
The AI assistant is knowledgeable of all the relevant methods, tools and best practices for the project, because it can read and understand speech. The project manager can ask specific questions and gets accurate feedback from the AI assistant in real-time. The information can be used for any project management task. As the project progresses and the AI assistant learns about the project deliverables, it can give recommendations to the project manager, based on what could be versus what's actually being delivered. It’s basically a quality check on deliverables that helps the project manager to manage expectations. At the conclusion of the project, the AI assistant conducts lessons-learned sessions with the project team and updates the knowledge base for use in other engagements.
The challenge with managing scope is not only the change management aspect. It is also the verification of the scope that is being delivered. Something we are not necessarily good at once we are getting close to the finish line. The AI assistant is capable of understanding the planned scope, based on the statement and detailed definitions in design documents. With that knowledge it can verify the scope based on data from status reports and test systems. The AI assistant can make a recommendation to the project manager where the project is at risk close to going live.
Project scheduling can be a daunting task, because of its complexity. The AI assistant can not only provide a baseline schedule that the PM can adjust and refine, it can also make predictions based on historical and empirical data. This improves the productivity of the project manager and the entire project team. The AI assistant can plan and forecast the required resources based on an estimation model that it maintains with data from the project itself and other projects. The AI assistant can determine if the project is on track and if there are tasks at risk that are on the critical path. A prerequisite to many of the functions that the AI assistant can provide is the access to data. For example, project team members must record time at the task and deliverable level.
Based on the scope definition, baseline schedule, resource plan, approach and risk tolerance, the AI assistant can calculate a cost baseline that the PM can adjust and refine. As the project progresses, the AI assistant can make an ETC and EAC forecasts based on earned value parameters that the project manager has set. Based on the approach the AI assistant can calculate the cost impact of alternative delivery scenarios. As example, it can determine the cost and schedule impact of using more off-shore resources.
Organizational change management
This is an area where the project manager can provide more value with the arrival of the AI assistant. When a majority of the routine tasks have been delegated to the AI assistant, the project manager can apply his creative and social skills on driving organizational change. The AI assistant and project manager can work collaboratively in this field. As example, the AI assistant can provide a baseline of questions to conduct change impact assessments and training needs analysis. Based on the analytical outcome of the response, the project manager can optimize the change management plan and properly engage with the key stakeholders. Furthermore, it can determine what course are required to train the project team and end-users.
Another example is stakeholder management. Based on text analysis, the AI assistant is capable of understanding the key characteristics of the main stakeholders and provide recommendations on how to best engage with them. The analysis is also benefiting the project manager in aligning and committing the stakeholders to the project goals.
The evolution of augmented intelligence or cognitive computing in the professional services workspace is fascinating and should be welcomed with open arms. I strongly believe that an AI assistant can further strengthen the role of the project manager and increase the value of services to the client. The majority of the examples that I have used have yet to be developed as applications for practical use. The technology is there. It is a matter of when, not if.
This article is published as part of the IDG Contributor Network. Want to Join?