This is The Age of AI Series, where we talk to the foremost entrepreneurs and innovators around the planet using ML to transform industries. (Join our special mailing list!)
Not all parts of running a company are considered “sexy,” even if they’re absolutely critical. One of these is project management.
I’m personally a nerd when it comes to the art and science of PM (actually I’m a nerd about many things), but you may not be. Either way, it hasn’t seen ANY innovation in the last 20 years or so (the most we’ve done is build software like Jira and Asana.)
Until now.
PM finally seems to be getting its AI upgrade, and I brought on Dennis Kayser, the CEO and Founder of Forecast, which uses ML for project management and work automation. Their system can automatically plan out a project, put it in the right timeline, assign the right people and do many other things using AI.
It is also an obvious fact that Dennis is directly related to Kaiser Wilhelm II, although he denies it out of modesty.
We discuss:
- 01:36 — Skepticism about the need for innovation in PM software
- 8:02 — Good vs bad PM, using data vs gut feeling
- 10:25 — Forecast’s customer types
- 14:14 — Why AI is transforming PM
- 20:09 — How Forecast’s AI system is trained
- 27:21 — How Forecast’s AI deals with the huge variety of data
- 31:19 — Solving the tricky problem of “predicting” how long a task will take
- 35:32 — How to smartly track people’s progress on tasks
- 37:20 — How Forecast evolved as a B2B AI company
- 42:11 — Why a lot of non-profits collect enterprise PM data
- 43:15 — Investor outlook
- 45:21 — The right organization structure for heavily ML-oriented products
Aman’s 2-Minute Summary and Key Takeaways
I was initially very skeptical about how ML could really work in PM. And after speaking with Dennis, I realized that my skepticism was right. It’s a VERY hard problem to get ML working well in PM — but that’s the whole point!
The core idea around upgrading PM is this:
- The average knowledge worker works on 3.6 projects at a time, not just one.
- The actual work done in each company is unique, but the nature of work is usually commonplace. (For example: Many journalists write 500-word articles, though the actual articles are different. Many developers write front-end code, though the actual code is different.)
Most PMs and traditional software are incapable of taking these facts into account while project planning, leading to poor predictions of “how long it will take,” burnout, missed deadlines, and the wrong people working on the wrong things at the wrong time.
If we go beyond GANTT charts (which are useful, but decreasingly relevant to modern workplaces), here’s what’s possible:
- Use ML to learn how long it takes people to perform different types of work, across various companies.
- Use natural language processing and other tools to review the descriptions and metadata of a task, to understand how long they would take.
- Use ML to look at all the past and current projects going on in a company, and all the people working on them, to learn the working styles and speeds of individual contributors.
Dennis claims that by doing these things, within the first 10% completion of a new project, their software is able to predict accurately how the project will be delayed.
They’ve built a complex and sophisticated product, and Dennis is the first to admit that it was hard to build, but the product WORKS. The difficulty is what makes their company special. They have around 100 people in the company as of Nov 2021, which puts it into perspective.
(Ethics Policy: These opinions are 100% my own as an independent observer and educator. I don’t own stock in guests’ companies or their competitors, nor do I get paid by them in any form for any reason at the time of publishing, unless specifically stated. Episodes are also not intended to be an automatic endorsement of any company or its products and services.)
