If our eternal rate of change doesn’t match the rate of change in our environment, we’re in big trouble. The way to avoid this is innovating more.
When new ideas arrive, it might be easy to see that they will eventually win. But when should you adopt these ideas? It’s a hard question to answer.
Is technology about to disrupt your market? Chunka Mui and Paul B. Carroll think so, and they have some good suggestions about how to respond.
There’s a difference between having a great idea, and creating value with that idea. Creativity, entrepreneurship and innovation are all words that are used to describe the value creation part of the equation. So do they mean the same thing?
Most organisations have more than enough ideas, but many struggle to choose the best ideas to pursue. Here are ten methods you can use to improve your idea selection.
Do people love change or hate it? Are big firms better at innovation or are small ones? The answer to these questions is “both” – and we need to develop some skills for dealing with that.
Wouldn’t it be great if you could do customer service like Zappos? Or design like Apple? Or innovation like 3M? Who wouldn’t want to be like those firms? Well, it’s not so simple. Barry Dalton wrote an excellent post called You Can’t Be Zappos (and why would you want to be?) addressing exactly this issue. […]
The emergence of the poll aggregators, especially Nate Silver, was one of the stories of the election for me. While these methods are a huge advance, there are still ways that they can fail. The key cause might be political innovation.
The Bing It One challenge would have been a great tool in 1998. Unfortunately, now that Google dominates search, an improved algorithm isn’t enough to get people to switch. This is the Attacker’s Dilemma: unless you bring a major performance improvement, there is no point in directly attacking a strong incumbent in their area of strength.
How can you learn what you need to know to become an expert? It requires deep knowledge of a field, but it also requires broad knowledge of related fields. Our current technologies support deep, but not broad. We need to figure out a way to find t-shaped knowledge.