AI IQ

In my previous blog, I stated three reasons why AI will not be able to replace project managers.  That should not make anyone complacent.  AI will change how projects are managed, and in this blog, I explain three areas where AI will replace the project manager functions and deliver improved project performance.

1. Administrative tasks.  Tools already exist to perform functions such as organizing a meeting and creating a project status report.  Known as Robotic Process Automation (RPA), this clever software captures a standard sequence of events familiar to project managers but adds the ability to handle more complexity.  When searching for a meeting time, RPA checks calendar availability for all participants books the time, and sends out meeting invitations to reserve the time.  
2. Decision making.  The first step for better decision-making is to build a knowledge repository.  This requires a well-conceived data strategy and comprehensive data capture on the selected issues.  For example, once the actions are captured that successfully resolve an issue, that action can be used again when faced with similar conditions.  Imagine capturing issues and risk responses for every project in the organization over ten years.  That becomes a valuable resource for decision-making that can be successfully utilized to avoid the bias of a human project manager making a decision. AI can review and analyze vast amounts of data faster and more effectively than humans.
3. Team building.  Pundits continue to suggest that AI can never handle the people side of managing projects without providing evidence to support their position.  One survey revealed that a majority of employees would rather work for a robot than their current manager.  How does a robot build a strong team?  AI tools capture sentiment analysis to better understand the performance and feelings of project team members.  This analysis is used for actions such as providing additional communication, clarification, or customized individual communication.  Next, based on task performance, an AI software agent provides unemotional, unbiased feedback to project team members on an individual basis.  Each team member now has a private coach to enhance their work habits, skills, and personal development. 

An AI-based project agent will eventually be much better than a project manager in these areas.  I will not predict when this will happen since it depends on a comprehensive change management process as AI-based tools are deployed in the project methodology.  Achieving the result also requires collaboration between a project manager and AI tools to make this vision a reality.

Fear of new technology is often based on the belief that it results in a loss of jobs. As technology such as artificial intelligence (AI) becomes more prevalent in updating project methodologies, project managers ask the same question. Will we still need project managers?  Using AI in projects is growing because it improves project performance and increases project success rates. Below is my opinion on why AI will not be able to replace the project manager role.     

1. Managing data.  AI, especially machine learning algorithms, requires structured and relevant data.  From my experience working with organizations, they have a lot of project data, but it is unstructured, not easily accessible, and often misses important data points.  Project managers need to define a project data strategy, provide constant updates for data used as input to AI tools, and ensure the data being collected is the most relevant to the project type or organization. This is not a function that an IT person or a business specialist can provide. A project manager knows project management language and concepts such as the critical path and earned value.
2. Interpreting results and taking appropriate action.  AI is based on math, not myth. Project managers need to interpret machine learning output and determine what actions are required.  AI algorithms produce a prediction or perform classification.  Prediction is unlikely to be a 100% probability, and a classification result may include pointless outliers. A project manager with knowledge of statistics can determine the proper evaluation and next steps toward a decision.
3. Collaborating.  Studies show that when people collaborate with AI tools, the results are better than either could achieve on their own.  As shown in reasons 1 and 2 above, project managers have a critical role in optimizing this technology's effectiveness.

The knowledge required to be a great project manager will change, and the role will be slightly different.  As mundane tasks, such as creating a project status report and organizing a team meeting, are automated, there will be other more interesting and challenging tasks for project managers to perform that will improve project performance.

My next blog outlines how AI-based tools can replace project managers.

Fear of new technology is often based on the belief that it results in a loss of jobs. As technology such as artificial intelligence (AI) becomes more prevalent in updating project methodologies, project managers ask the same question. Will we still need project managers?  Using AI in projects is growing because it improves project performance and increases project success rates. Below is my opinion on why AI will not be able to replace the project manager role.     

1. Managing data.  AI, especially machine learning algorithms, requires structured and relevant data.  From my experience working with organizations, they have a lot of project data, but it is unstructured, not easily accessible, and often misses important data points.  Project managers need to define a project data strategy, provide constant updates for data used as input to AI tools, and ensure the data being collected is the most relevant to the project type or organization. This is not a function that an IT person or a business specialist can provide. A project manager knows project management language and concepts such as the critical path and earned value.
2. Interpreting results and taking appropriate action.  AI is based on math, not myth. Project managers need to interpret machine learning output and determine what actions are required.  AI algorithms produce a prediction or perform classification.  Prediction is unlikely to be a 100% probability, and a classification result may include pointless outliers. A project manager with knowledge of statistics can determine the proper evaluation and next steps toward a decision.
3. Collaborating.  Studies show that when people collaborate with AI tools, the results are better than either could achieve on their own.  As shown in reasons 1 and 2 above, project managers have a critical role in optimizing this technology's effectiveness.

The knowledge required to be a great project manager will change, and the role will be slightly different.  As mundane tasks, such as creating a project status report and organizing a team meeting, are automated, there will be other more interesting and challenging tasks for project managers to perform that will improve project performance.

My next blog outlines the areas where AI-based tools will replace project managers.

     There are two options for obtaining AI software: build your own or procure them from a vendor. Developing AI tools should be based on a well-conceived strategy. The first step is to understand existing project problems, such as the inability to achieve budget goals, constant change requests, or the inability to identify and manage risks. The next step is to create the objectives for the new AI-based process. The organization must consider a change that disrupts the project methodology instead of simply automating existing tasks and roles. It should also be evident that applying AI to the project methodology requires an effective change management process.

            There are advantages and disadvantages to building or buying AI tools for project management. Building tools internally brings increased knowledge to the organization, maintains a higher level of data security, and allows a faster, more flexible response to feedback. Buying tools allows the organization to take advantage of vendor experience in the market, avoids the search for highly skilled AI resources, and set more attention on solving the project problem.

Build Advantages                                            

• Increase internal knowledge            
• Maintain data security
• Provide flexibility for changes
• Offers instant feedback and adjustments

Buy Advantages

• Capitalize on vendor experience
• Utilize industry solution
• Reduce resource acquisition concerns
• Focus on data, not algorithms

Additional considerations in either scenario include responsibility for managing data, providing support for the new AI-based process, proper interpretation of results, and the strategy for testing and validation. There are numerous vendor offerings that use AI as a core algorithm in their software. Some organizations take advantage of this opportunity, while others use internal resources to create their own machine learning and natural language processing (NLP) algorithms to apply to their project methodology.

Project managers must embrace new technology, especially when it can improve project performance. Large language models (LLMs) such as ChatGPT, Bard, and llama can be excellent tools if used properly. As with most technology, we must learn how to effectively interact with this software application to optimize the results. Humans are inconsistent when asking questions.

“What is the weather today?”

“What is the temperature?”

“What is it like outside?”

“Is it going to rain or be sunny today?”

The words, phrases, and sentences people say are known as utterances. The LLM evaluates them to determine an intent. For the questions above, the intent is to get a report on the weather. How we ask questions determines the results obtained, and there are tips for using this process effectively. A new area of knowledge is being developed called prompt engineering, which is the ability to make a request to an LLM and obtain the best response.

Prompt Engineering Techniques

1. Chain prompting. This technique is based on LLMs being a conversational technology that remembers previous questions and answers. Based on this characteristic, after you ask a question and receive a response, you can modify your next question. Using this method, you can seek a change to the first response or use feedback to develop a better series of questions.

Project Scenario

Q1. What is the greatest risk to my project?                   

LLM answer 1. The project schedule.

Q2. Why is this such a big risk?                                     

LLM answer 2. There are resource issues where allocated resources have insufficient

experience to complete the tasks on time.

Q3. What is the best way to mitigate this risk?                

LLM answer 3. Perform an assessment for critical path tasks comparing task complexity to

resource capability.

Q4. Will there be residual issues if this risk occurs?        

LLM answer 4. If your schedule is late, there is the potential for additional risks that affect

product quality.

2. Persona replication. This has the potential to be the most exciting and the most dangerous feature of LLMs. By loading content from a specific individual, the LLM can assume the characteristics of the person and respond in that persona. For example, once you load a series of texts by a famous scientist, you ask the LLM to respond based on the manner and knowledge of that person.

Project Scenario

In an agile project, customer feedback is an important factor for iterations. The project manager can load information about the customer (with their permission) to acquire feedback when the customer is unavailable. The process is to load personal background information, experience, organizational responsibilities, emails, messages, and previous decisions. When a sprint is completed, the project manager asks the LLM to respond in the voice of the customer (VoC).

3. Chunking. Sometimes, you need a long response, and the best approach is to break it into smaller segments. For example, you want the LLM to write a movie screenplay. Rather than providing the basic plot and characters and then letting it create an entire movie script, it makes more sense to ask for the first few scenes. Based on the initial response, you can modify the parameters before you ask for the next series of scenes. Chunking is the process of accomplishing your request using a step-by-step approach to provide a better result.

Project Scenario

Instead of asking for an entire project plan, the project manager provides the project type and objective then requests a plan for the first stage, such as design. After reviewing the results, the following request is for details on the implementation stage. Similarly, rather than asking for an entire project management plan, the project manager asks for a sequence of components such as a risk plan, resource plan, and communication plan.

4. Response customization. Additional features known as temperature and frequency penalties allow you to alter the randomness of a response and the number of repetitive words or phrases.

Conclusion

LLMs offer a myriad of capability that has not yet been fully exploited. For example, a project manager can create a status report or an important message and ask the LLM to modify it to eliminate bias or improve clarity. Learning how to collaborate with this technology using prompt engineering techniques improves the project results and the performance of project managers.