That is a process that requires a continuous feedback loop to develop a valuable product and build a viable business model. Continuous innovation is a mindset where products and services are designed and delivered to tune them around the customers' problem and not the technical solution of its founders. On FourWeekMBA I had Ash Maurya explain why continuous innovation matters so much. Let' start from the key principles! One of the biases that that many entrepreneurs fall run into is this premature love of the solution.

In my roles as a customer success and business development executive covering Artificial Intelligence & Machine Learning (AIML) at leading tech companies, I've spoken with executives, data scientists and IT managers across startups, Fortune 500 and Global 1000 companies about their AIML needs. After discussing what is AIML, platform features or API services easiest to use for non-specialist, companies get stuck on an equally important component of enterprise AIML, governance of operations. Companies get caught up in the hype led by consultants and industry media outlets that promote AIML led digital transformation is happening across every industry, in companies of all sizes with millions of models being deployed to production weekly. AIML software vendors promise adoption of their solution enables instant production readiness enabling their customers to, "Build and deploy a machine learning model in 9 minutes," with limited or no expertise. The reality is not quite as advertised but I'll help you on your journey by discussing why deploying ML in production can be difficult, provide a way to assess your return on investment (ROI) with AIML, how to create a comprehensive ML platform and provide a framework for assessing your organization's AIML maturity to better determine the capabilities you need to acquire to improve your org's proficiency. There are many definitions for Machine Learning Operations (MLOps) and governance but to keep things simple, I'll define governance and MLOps as the best practices and policies for businesses to run AIML successfully.

Machine learning creates room for continuous business model innovation. One recent summer, Charles Weinstein, CEO of New York City-based accounting firm EisnerAmper, had an epiphany: machine learning could either destroy his business or remake it. A 35-year veteran of the industry, Weinstein sensed that the practice of accounting--issuing financial statements three months after the quarter closes--while still necessary, was losing relevance in the real-time, data-driven economy. So he organized a three-day partner meeting to consider how machine learning capabilities in particular might remake the traditional accounting firm for the digital era, enabling it to help its clients look into the future rather than simply reporting on the past. Weinstein invited a partner in charge of global innovation at a Big Four accounting firm (not a direct competitor) to talk about the moves his firm was making.

Thanks to the first round of digital disruption, e-commerce and the platform economy have become a way of life all over the world. Order on Amazon Prime with a single click and get free shipping. Compare insurance policies on The Zebra or places to stay on Booking.com. Whether it's saving gas and time by skipping the mad dash around town for supplies or finding the best deal on a hotel, the first wave of digital disruption brought consumers personalized offers, savings, and convenience beaucoup. For the companies who moved into the e-commerce and platform economy early on during this first wave, this revolution captured significant value as they monetized direct access to their customers and served as third-party aggregators in a wealth of ecosystems.

Weekends are time when things slow down a little. Your weekend shouldn't be two more regular work days. Take time to refresh yourself. Take time for something different. Take time for some of that reading you can't find time for during the week.