AI IQ

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.

Unsupervised learning is a type of AI-based algorithm that relies on characteristics instead of labeled datasets that are used in supervised learning. A typical application is the ability to classify or cluster datasets based on their characteristics. For example, an unsupervised learning algorithm can easily classify fruit based on color, size, and shape. The algorithm does not know what a banana is, but it will create a common group for anything resembling a banana. In projects, three uses of unsupervised learning are for risks, task complexity, and change requests.

1) Risks. Using unsupervised learning to cluster risks might result in finding a common cause for a group of risks or developing a shared mitigation strategy. Clustering risks from several projects can also result in finding a risk on your project that was overlooked.

2) Task Complexity. For this application, tasks are grouped by complexity based on the task definitions. If there is an unusually high number of complex tasks, the project manager needs to evaluate the ability of assigned resources to complete them. Additional training or mentoring from an expert may be required. A review of the resource allocation plan may alleviate any concerns. A high level of complex tasks can also provide an incentive to validate the risk management plan.

3) Change requests. Grouping change requests from previous similar projects can result in being able to forecast expected changes on your project. This proactive approach allows more accurate estimates for budget and schedule. If all changes in your project require additional funding and a shift in the end date, that is good. However, the sheer number of changes or unexpected changes may still result in a deterioration of project performance.

In project management, unsupervised learning finds patterns that a project manager cannot detect. Finding these patterns allows proactive actions to be taken that keep the project on a trajectory for success.