Current status of AI in project management

Project management is complex, it will not be solved by AI in a big bang, but instead in several task level improvements. You will have AI drafting the project plan saving you 30% of the work, you suddenly realize that note taking, and distribution of action items is automatic now, and so on. In this way AI will transform much of the knowledge work as we know it today, task by task.

How will this happen? What can AI assist with? How to start?

I took a deep dive in the current understanding on how AI will transform project management.

Let’s start by looking which AI capabilities making it suitable for project management:

1. Data analysis: AI can analyze large amounts of data quickly, opening up for improved forecasting of timelines, budget, resource allocation, potential risks, and even project success probabilities. As long as the data is available.
2. Automation of repetitive tasks: AI is perfect for automating repetitive and time-consuming tasks such as data entry, schedule updates, and progress reporting. This will also help reducing human errors.
3. Create and summarize text: AI is great at text generation and can be used for creating draft project documentation, assisting with communication and understanding complex contracts. This might be the first thing to try.

Leveraging these capabilities will improve the efficiency, leading to less project management overhead with improved quality. Looking at the project triangle we usually say this is impossible: lower cost, shorter project time and higher quality. Great promises.

But there are some obstacles to overcome…

Challenges of AI in Project Management:

1. Resistance to Adoption: There might be resistance to embracing AI among project managers and members, it could be seen as a threat not an opportunity. Also, for companies to take the IT investment, up-skilling and adopting processes, will require giving it high priority.
2. Data Privacy and Security: With increasing AI reliance, ensuring the safety of project data processed by AI is crucial. If not solved, this will block roll out.
3. Risk of Over-Automation: There's a potential risk of over-relying on AI and overlooking human insight and judgement. This will be a dynamic challenge since the sweet spot between human or an AI will change as AI improves its capabilities.
4. Integration Issues: Seamlessly integrating AI tools with existing project management methods and systems can be technically challenging, especially giving access to company data.

Change mangers will have a busy time moving forward 🥳

How to get started?

First of all, start now. As Prof. Ethan Mollick suggests in a worklab podcast, take “the top 20 percent most creative people in your company, require they all use AI for a week, and give a million-dollar prize to whoever comes up with the best way to automate parts of their job while promising you’re not going to fire anyone as a result of this.”

That is, work from the ground up, put the tools in the hands of the users (project managers) and let them tinker, explore and share learnings.

You can start now without any risk and at an exceptionally low cost…

Microsoft Bing Chat Enterprise is a safe and solid way to get started, it gives you access to an AI-powered chat (GPT-4) with high Microsoft standards for data protection. This can be used for text-based tasks, such as drafting reports and streamlining communication.

Or set up a “AI-lab” with computers outside your IT infrastructure only allowing to use publicly available data. This will open for hundreds of tools. I would suggest keeping it simple and go for a ChatGPT Plus subscription, this gives access to plug-ins and the code-interpreter which can be used for working with data. Create business cases and go for a full-scale implementation if it looks good.

If you want to - go all in and fine tune your own language model.

When including your company data into the language model it opens a whole other level of benefits. For example, you could ask it to summarize the lesson learned from all similar projects into guidelines for the current project or create a project plan based on previous work and have it writing it according to the company language guidelines.

This is much more advanced and expensive compared to using pre-trained models, such as Google Bard, ChatGPT or Llama 2.

One way to explore including own data in the language models might be reaching out to service providers such as Sanalabs or Glean.

How to learn more

For learning more about how to leverage AI for project management check out:

• Ricardo Vargas offers insightful courses such as the AI-Driven Project Management Masterclass and additional content on LinkedIn.
• Peter Taylor has written multiple books on this topic.
• Michael Kaplan, explores these topics and shares his findings on LinkedIn.

Have you started using generative AI for your professional work yet? which tasks does it help with? I’m curious to hear what you have been up to 😃