As a project manager, you are always on the lookout for ways to improve your project management skills and streamline your workflow. One way to do this is by incorporating AI technology into your project management process. AI can help you save time, reduce errors, and improve decision-making capabilities.
The benefits of using AI in project management are widely acknowledged. However, many project management professionals are still hesitant to adopt these technologies due to a lack of clear communication between technical experts and business users. In this article, we will address this communication gap and explore ways in which project managers can start using AI to their advantage.
At Sharktower, we talk to senior project professionals all the time, and we’re regularly asked the same questions. Things like:
“What tools can provide working examples?”
“Our current project data is not good, how can we get any value for data solutions?”
“How difficult are they to implement, how can we create a common understanding of AI?”
“What skills do PMs need to develop in order to remain relevant?”
In this bite-sized blog, we’ll explain that you don’t need advanced programming skills to get into machine learning – you just need the curiosity to give things a try. There are steps you can take today to familiarise yourself with the techniques and begin to see the benefits.
Ready? Let’s go.
1. Remove repetitive tasks (so you can focus more on delivering value and outcomes)
Mundane manual tasks can take up 30-50% of your time. Scraping data, capturing next actions, copying and pasting, or simply chasing for updates to find out what’s going on.
But you can easily create simple rules-based automation of repetitive tasks using free and low-cost tools like Zapier, UiPath and Microsoft Power Apps. By creating simple rules-based automation, project managers can free up their time and focus more on delivering value and outcomes. For example, Chat GPT can be used to automatically generate tasks from email and Slack conversations, collect data from various sources and aggregate it into a single file (more about that later).
Examples of simple automations you can perform with basic project management apps:
Each of these is essentially basic Robotic Process Automation (RPA). You will inevitably have multiple applications in your organisation, so it makes sense to use agnostic tools and experiment with triggers from one system to another, rather than trying to rely on one single vendor.
While not technically RPA, another technique to familiarise yourself with is data visualisation. Power BI, for instance, uses integration and automation to connect multiple data sources, and model and visualise your data.
2. Experiment with advanced techniques to improve predictability
There are many areas in project planning that have to be estimated or predicted, and – as with any estimate – there is a risk of getting it wrong.
When estimating how long a project will take, for instance, there’s always an element of guesswork, especially for new change or innovation projects. The risk lies in under or over-estimating project completion times. To help mitigate the risk, you probably take the estimates for how long each project task will take and come up with a best and worst-case scenario for when the project will complete.
By using a range of possible values like this, instead of a single guess, you can create a more realistic picture of what might happen, but you still can’t predict how likely each scenario is. Techniques like Monte Carlo analysis can help you do exactly this. This is a type of computational algorithm and you can experiment with it yourself by using resources like RiskAMP’s Monte Carlo Simulation Engine for Microsoft Excel.
A conditional risk model from one of RiskAMP’s sample spreadsheets
What Monte Carlo simulation does is analyse all the potential scenarios and give you probabilities on when the project will complete. For example, 2% chance of completing the project in 12 months; 15% chance of completion within 13 months, 95% chance of completion within 15 months, etc.
Like any forecasting model, the simulation will only be as good as the estimates you make. It’s important to remember that the simulation only represents probabilities and not certainty. Nevertheless, Monte Carlo simulation can be a valuable tool when forecasting an unknown future.
3. Apply Machine Learning to model project complexity
Machine Learning techniques are excellent at finding patterns and outliners in data, often previously unseen. Using pattern recognition and historical data, along with network analysis of connect dependencies, AI is proven to provide better scheduling and is able to quickly model the impact of different scenarios.
However, projects are complex systems to model, and even the smallest of projects has an endless network of cause-and-effect impacts. In particular, behavioural aspects of different stakeholders are difficult to model but critical to project success.
CAUTION: Many projects have typically suffered from poor historical data which is heavily biased, subjective and therefore unreliable for training robust models. This shouldn’t stop us using the data we have today to gain new insights and indicators, but we need to work on improving project data capture and quality.
4. Streamlining Project Management with ChatGPT
Chat GPT is a remarkable technology that has been making waves in the field of language modelling. As a large language model developed by OpenAI, Chat GPT (which is still free to use at the time of writing this article) has been praised for its impressive ability to generate human-like responses to a wide range of queries, making it an invaluable tool for various applications, including project management.
Chat GPT can assist project managers in various aspects of their work, including:
- Creating project plans: ChatGPT can create outline or detailed project plans which PMs can then customise.
- Information gathering and research: Chat GPT can help project managers by automating the process of gathering and researching information. It can quickly scan through vast amounts of data and provide relevant insights, allowing project managers to make informed decisions.
- Text summarisation: Chat GPT can assist project managers in summarising lengthy reports, documents, and emails. This can save time and improve productivity by allowing project managers to quickly review key information.
- Professional development and training: Chat GPT can assist project managers in developing their professional skills and knowledge. By providing access to relevant training materials and resources, it can help project managers stay up-to-date with the latest developments in their field.
- Meeting minutes: Chat GPT can help project managers by automating the process of creating meeting minutes. By summarizing key points and action items, it can save time and improve productivity.
Experiment with ChatGPT for things like project outlines and requirements. The more specific your search terms, the more relevant the responses will be.
While Chat GPT can streamline communication and handle repetitive tasks, it cannot replace human judgment and decision-making skills. Therefore, project managers should see Chat GPT as a tool to enhance their work and team productivity, rather than a substitute for their role.
Bonus tip!
Allocate time to learn and experiment, then expand.
If you’re new to automation, don’t expect to get everything working perfectly from the get-go. Allocating time to learn and experiment is crucial to ensure that the automation tools you use are set up correctly for your needs.
By taking the time to learn and experiment, you can achieve significant time savings and efficiency gains with automation. As you become more comfortable with the technology, you can expand your use of it and take on more advanced tasks.
If you’re thinking of getting started with AI in your project delivery, request a demo and we’ll be happy to discuss ways you can adopt a more data-driven approach.