AI IQ

     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.

An explanation of the basic functionality is required to understand how AI can be used in project management. While there is no clear definition of AI with boundaries and limits, describing what AI looks like and how it can be used is helpful. AI for project management has two main components: machine learning (ML) and natural language programming (NLP).

Machine Learning

ML is software that uses datasets to create a model, and based on that model, the algorithm produces a prediction or performs classification. Prediction is the probability that the answer or decision is correct. The results are valid for a single point in time but need to be updated with new data as the project progresses or the data changes. What can you predict? Anything where you have data such as overall project success, quality results, or the probability of achieving the budget.

For ML, the most common learning models are supervised learning, unsupervised learning, and reinforcement learning. These algorithms are incorporated into the software used by organizations to improve project performance.

Natural Language Processing

NLP converts words to data and is extremely useful for document analysis, sentiment analysis, translation, and using a virtual assistant. NLP reviews documents for accuracy and consistency. Sentiment analysis checks verbal or text input and identifies positive or negative feelings in the project team. There may be ethical issues if this is not implemented and used correctly. Virtual assistants determine the intent of a request and deliver a response. Commonly used virtual assistants are Siri, Alexa, or Google Assistant. NLP is also at the forefront of recent developments with large language models (LLMS) such as ChatGPT, Llama, and Bard. However, large language models use NLP and ML algorithms to achieve their amazing capability. The value of NLP is the ability to extract insights from large amounts of data, such as a database of historical projects.

Summary

ML and NLP are valuable AI components that can be combined when using LLMs. Further blogs will review how these algorithms work and how the project methodology is changing to take advantage of this opportunity.