In the mid‐1980s, I was a young software engineer working for Hewlett Packard on a high‐profile product. It was a time (the first time) when artificial intelligence was all the rage, and I was fortunate enough to be working at what was then one of the industry's best technology companies, as part of a very strong software engineering team (several members of that team went on to substantial success in companies across the industry).
Our assignment was a difficult one: to deliver AI‐enabling technology on a low‐cost, general‐purpose workstation that, until then, required a special‐purpose hardware/software combination that cost more than $100,000 per user—a price few could afford.
We worked long and hard for well over a year, sacrificing countless nights and weekends. Along the way, we added several patents to HP's portfolio. We developed the software to meet HP's exacting quality standards. We internationalized the product and localized it for several languages. We trained the sales force. We previewed our technology with the press and received excellent reviews. We were ready. We released. We celebrated the release.
Just one problem: No one bought it.
The product was a complete failure in the marketplace. Yes, it was technically impressive, and the reviewers loved it, but it wasn't something people wanted or needed.
The team was of course extremely frustrated with this outcome. But soon we began to ask ourselves some very important questions: ...