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.
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.
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.
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.
Studying for the PMI-ACP agile PMP exam? Here is a cheat sheet to review before the exam, to help answer all the trick questions.
From research and prototyping to commercial development, AI/ML requires making decisions and trade-off's. This is where the PM needs to provide loads of context to the R&D team: what are the customer problem(s) are that we are trying to solve? What parameters and trade-off's would be acceptable to a customer? Have we considered the cost trade-offs of operating in practice?
As a CIO, if your enterprise relies on solutions that look like something out of the 1990s, it's often because that's exactly what they are. These applications - ERPs and home-grown core operations systems met an immediate business need at the time, then layer upon layer was built on top and entrenched into the foundational processes of the business. Now you are at a catch-22: pressure to modernize to meet the growing digital needs of the enterprise, while at the same time not risking the legacy software that is vital to day-to-day operations.
How do you prioritize legacy transformation as part of a digital transformation roadmap?
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.