THE BLOG
11/27/2012 09:16 am ET Updated Jan 27, 2013

How Would Your Colleagues Vote?

With the presidential election behind us, it's safe to say that we've all had a chance to experience the power of perceptions in the last few months. No matter what side of the political fence we live on, we tend to make judgments about the candidates based on how they deliver their messages -- their facial expressions, tone of voice, body language and demeanor. Think about the recent debates. Does a candidate seem to be exhibiting strength or is he being combative? Does he sound confident or flustered? Does he appear energetic or disengaged? As the fluctuating poll numbers demonstrate, perceptions can make a world of difference.

The same thing happens in the business world. But instead of losing an election because of negative perceptions, we might lose out on the promotion... the bonus... the new job. We all know colleagues who have killer resumes but still can't seem to get any career traction. On the surface, they are doing everything right, but they're simply stuck. The likely culprit? Less-than-glowing perceptions by their supervisors and co-workers -- or what I call professional blind spots, those unintentional behaviors or attitudes that can subtly derail promising careers.

Even the smartest and most talented professionals have blind spots. However, the people who are truly successful have learned how to identify and eliminate them. They pay close attention to the way they deliver messages and how those messages are received. They know how to "read" responses in others, ask for honest feedback, and modify their behaviors to make sure they are being perceived exactly as they intend. They have mastered the art of creating positive perceptions. Which equate to great reputations. Which translate into career success.

Take a moment to think about how your professional reputation would fare in an office election. If you were on the ballot for advancement as a corporate leader, how would your colleagues vote? Would you win by a landslide? Or do you really want to calculate the exact margin of error before you answer? Either way, I hope you'll see the value of learning more about this critical issue. I'd love to know what you think.