Popularized by Amazon, a Single-Threaded Owner (STO) is a leader who is 100% dedicated and accountable to a new initiative such as inventing a new product, launching a new line of business, or executing a digital transformation. The Single-Threaded Owner is responsible for turning strategy into real results.
SaaS businesses are all the rage! Even companies that have success with traditional products are re-thinking their business as a SaaS offering, to reap the many benefits:
These benefits are so attractive that every and any product and business model is being re-imagined as SaaS, leading to some great ideas (e.g. Spotify, Coursera, bacon-of-the-month dropped off at your doorstep!). But not every business lends itself intuitively to a SaaS model. Especially if you have years of legacy technology and processes established with an existing customer base, the transition won't happen overnight. There are key questions to answer in each facet of the business.
Yes the PMP exam does have many "trick questions". Here is my cheat sheet of tricks, gotchas and pitfalls collected about these questions. You have to be very careful about the wording and cut through the noise in the question to find what the really important answer is. This is meant to simulate project management in the real-world, where day-to-day you have to cut through the noise and make decisions based on what's truly important. There is also lots of terminology, acronyms and references to names of theories and principles that you need to know, and sometimes these terms are not even in the PMBOK itself.
In many industries, sales is used to ‘selling’ the vision of the product in advance of it being built, and customers assume vapourware by default. No one bats an eye because we’re accustomed to the idea that engineering will always be able to fulfill whatever we’re selling, given enough time and money.
New technologies like machine learning and blockchain offer a world of possibilities, but many of these possibilities may not actually be able to be implemented in practice, even with a huge budget. It's easy to promise "The product will automatically predict X with high accuracy." where X could be anything from detecting a security breach to predicting stock prices to finding the perfect outfit for you wear. But even if the prototype is already 70% accurate, it may never get to 80%, or whatever you need it to be to be commercially viable.
AI/ML has the potential to deliver incredible value to customers. A single breakthrough can make a product company's growth soar. A fine-tuned model can completely transform an enterprise's operations.
The mechanics of delivering such a program are very similar to traditional projects on the face of it, so it is tempting to think of the implementation as being the easy part. But because of the non-deterministic nature of AI/ML, all the pitfalls of ordinary software development programs - misaligned objectives, underestimation, lack of process, skimping on QA, ignoring risks, etc. - are amplified x10. To deliver successfully, you need to ratchet up the program diligence.