Product and service offerings follow a growth lifecycle:
Once you hit Flatline and Decline, it is very hard to bounce back. Stories of flatlining businesses that suddenly take off again are rare indeed. While there are ways to resuscitate a flatlining business, the ideal is to NEVER GET THERE IN THE FIRST PLACE and instead take the right actions to ensure continuous growth.
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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. 
"How would you feel if you couldn't use the product anymore?" According to growth hacking pioneer Sean Ellis, this is the question to ask to determine your level of product-market fit. In response, you are looking for at least 40% of your customer base who say they would be "very disappointed". This represents user passion.
Read any article on product/market fit and it will say "Talk to customers and focus on their problems. It took us 2 years but we found our product/market fit and sold for $X!". One thing? Simple! Talk to customers.
But there's a startling lack of practical guides on how to actually talk to customers to elicit and qualify pain points. And there are so many false pains that you can latch on to in customer conversation if you don't know what you're doing (which may explain why that article invariably says "it took us 2 years to find what customers wanted"). Let's look at the reasons why actually getting to a real customer pain point is so hard, and how to do it right. 
"Know your audience". "Understand the buyer." "Be customer-centric." The key to lead generation and growth has been in MBA 101 textbooks since the dawn of time.
These 101 textbooks also say to capture the relevant information about your target buyer in the forma of a persona. But the vast majority of teams don't actually create personas, or use them in the right way. 
Ten years ago, as long as you had a great product idea, growing a tech business was straightforward. But today the market has reached a level of noise that even the best products can't cut through. The average person sees 3,000 ads a day. Promotional channels from Google SEO to Facebook to Amazon are saturated and in constant flux. How do you cut through the noise?
Traditional data protection has been about securing data behind the corporate perimeter, locking down IT systems and endpoints with firewall and data loss prevention (DLP) technology. Now there is an increasing recognition that traditional data protection is not working. Faced with the new realities of cloud, shadow IT, BYOD, increasing collaboration with 3rd parties, and “last mile” endpoints like USB devices… no matter how well you secure data behind the perimeter, your data will eventually leak.
In their DLP magic quadrant analysis, Gartner said “At present, even with extensive DLP coverage across endpoints, networks and data repositories, there are still gaps and data flows where data can leak. The better answer is a data security strategy focused on securing the data itself, as opposed to trying to secure every system that comes in contact with sensitive data.” What Gartner is talking about is a revival of Digital Rights Management (DRM) technology, that embeds encryption directly in a company’s valuable data assets themselves – their sensitive files and e-mails – so that even if the data does leak beyond the perimeter, it’s still protected. Much like AI and Blockchain, Digital Rights Management technology is an extremely attractive concept that has had implementation challenges, but is now starting to overcome those implementation obstacles to go mainstream, most notably Microsoft Azure Information Protection (AIP). Here’s a look at what to expect. 
Product leaders are inundated with data. SaaS product and website analytics can slice and dice every aspect of the customer journey. I can see that 22% of my customers between the ages of 30 and 40 spent over 3.2 seconds looking at the new graphic on my website. I know that 16% of freemium users converted to paid in the last month since we added 3 new features.
Quantitative measures like this can point to areas of interest that require investigation and experimentation, but they won't tell you why these are of interest. Qualitative data, ie. talking to people, gives you the why. Qualitative data tells you what was motivating the user when they spent 3.2 seconds looking at your graphic, what problem they were trying to solve. You need both, quantitative and qualitative.  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.
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