We may be reaching the point where fraud and fakes are becoming so prevalent that the costs and pain are overwhelming enough that we’re ready to take the effort to try new solutions. Blockchain technology might finally have a mass market use here. But we’re not quite there. Except for crypto most of the work has been in infrastructure. Consumer use cases are few. This might be a natural evolution. The progression from infrastructure to platforms to applications is a movie we’ve seen before. And consumers likely don’t see safety and security issues as the most fun to deal with. At a certain point though, the answer to “where do you want to take the pain” turns into “ok, we have to deal with this now.”

The challenge remains getting adoption when there’s serious effort. After all, isn’t it annoying enough to have to identify how many traffic lights are in a picture just to get to your own account somewhere? Well, dealing with today’s crop of blockchain identity solutions is a bit more of a hurdle. At least for now. You may be thinking, “But don’t we already have all manner of fraud solutions, from credit checks to heuristic rules and now even AI trust scores?” Yes we do. And how is all that working for us?

There are a fair number of articles on whether to choose a platform or a point solution. But what if you’re choosing to build one or the other? Some startup work I’ve been doing lately has wrestled with this challenge. So I thought I’d share some of the considerations.

Lessons come at us from many directions. Some – at least for me – have been most valuable when applying something to one area that came from a completely different area. Here’s a handful of thoughts I’ve found that cross over from aviation into business.

So why not force return to office? There’s clearly benefits to the traditional model, right? Well, it should be obvious at this point there’s a great many benefits for remote work and hybrid options – at least for roles where it’s possible – for both employees and employers. (See Remote Work Statistics & Trends.) Potentially reduced costs on both sides, better work/life balance for employees, wider range of talent selection, etc. (However, note that cost reduction might be less than originally thought; stipends for remote gear, travel costs for team meetings that might not otherwise have been necessary, etc. So cost issues will as always be highly situational.)

Welcome to Part 2 in this series. When I started Part 1, I had no idea how quickly a whole lot of thoughts would flow regarding the cross-over utility of some ideas from sometimes time-pressured or higher-stakes fields into general product management.

Train How You Fight

Another great line from a fire instructor, “You can never train too hard for a job that can kill you.” This stresses the importance of realistic training that prepares firefighters for real-life scenarios. And train we do. Beyond initial training, we have weekly sessions, special classes, and more. In aviation there’s recurrent medical and flight checks, etc. Now, in corporate, there are hopefully less things that will kill you than in emergency response situations. But there are systems and equipment breakdowns. There are high-stakes situations. Are you prepared? Have you practiced scenarios? Or is the book up there on the shelf getting dusty? The problem with risk mitigation is you don’t make money from it. It’s value preserving, not value accretive. It’s a cost item, not a revenue item. Backup systems, security efforts are necessary, but they are on the cost side. Regardless, the only way to be really ready is training. Some say – with some validity – that’s what insurance is for; to buy away risk. And yet, is that responsible? Is it ethical? Maybe. It depends on your situation. You have to assess that. It’s not all gloom and doom though. There’s items on the plus side here. What about customer training / onboarding? Or sales? Whether it’s to mitigate bad outcomes or drive towards good ones, are you constantly learning? The better the training, the better the game is likely to go. Train how you fight.

This article started from chats with a colleague about how I managed doing volunteer emergency work at night or weekend adventure sports and then went into the office the next day to deal with important, but less extreme matters. It turned into lessons I’ve learned, (or think I’ve learned anyway), from that part of my life and how they affect my perspective on working in more corporate environments. She thought I should share them. I quickly ended up with more than I’d thought so this will turn into a few posts perhaps. Here we go…

Introduction

Artificial Intelligence is yet another tool that is an overnight success, decades in the making. The Fear Of Missing Out (FOMO) seems huge since ChatGPT brought AI from what Gartner’s Hype Cycle calls the Trough of Disillusionment to what might be the beginnings of the Slope of Enlightenment. But what do you really need to do with it, if anything? Do you need to build anything strategically core to your customer offerings? Use it as a tool to enhance existing offerings? Or use something more operational; from process management to marketing. One challenge right now is people seem to want to just sprinkle some AI on things to make sure the buzzword is in a product checklist or annual report.

According to Statista’s “AI Trends & Predictions Roadmap to 2025,” AI market value will increase from US$244 billion in 2025 to US$827 billion by 2030. You can find predications of various magnitudes from other sources. But what does it mean? Chances are it means your competitors are deploying AI. Whether it’s for something useful with solid ROI or not is the question. If your competition is deploying things, you’d better at least be doing research.

So where do you start? Too many posts I’m seeing talk about AI Strategy go right to implementation. That’s AI at tactical level. There’s value in that. And it’s possible to think of general capabilities as strategic. But being strategic means starting with your business drivers. Your AI strategy, (just like all the others), should be based on the value you’re trying to create. At the very highest level, as always… there’s basically two goals: Increase Revenue. Or Decrease Costs. Unless you’re actually building and selling the AI tools themselves, chances are the value you’re creating comes from elsewhere. In which case AI is “just” a tool; another means to an end. Being skilled at it may be a strategic capability, but it’s not a business strategy in and of itself.