Six Thinking Hats

As we put the finishing touches on this year’s (sold out!) DTS program, I wanted to share some of the ideas we have on how to have a productive and useful event.

Several months ago I came across the De Bono Group’s Six Thinking Hats, a system designed to help facilitate discussions among groups. The underlying concept is that humans think in several distinct ways, and approaching a topic with this in mind can help structure conversation. Participants are encouraged to think about the topic at hand through these different lenses by wearing different colored hats that correspond to the different ways of thinking. The end result is – hopefully – a well-rounded conversation that takes many factors into account.

The colors, or ways of thinking, are as follows:

Blue Managing the thinking process – what’s the end goal? How are we going to get there? Look at the big picture!

White Information-centric – What’s available? What’s needed to make a decision or move ahead?

Red Emotional – gut reaction, hunch-driven, emotional

Black Judgement-oriented – practical, realistic, often plays devil’s advocate

Yellow Optimistic – seeking value, benefits, looking for ways to ‘make it work’

Green Creative – looking for new possibilities and opportunities; exploratory.

Take, for example, one of next week’s topics: Business Management. One of the questions posed is “how can we make use of data while maintaining client trust?”

The green hat (or “Green”) might start the conversation with a provocative statement, perhaps throwing out the beginning of an idea to develop. Red might have heard of a similar suggestion, but is concerned of its applicability in AEC. White might start searching for statistics that support (or refute) Green’s initial statement. Black will be scouring her brain for the exception that might invalidate the idea. And so on.

As you prepare for DTS, keep in mind these colors and the perspectives they represent, and be prepared to share your thoughts. We’re looking forward to a day filled with your thoughts and ideas!

Setting the Stage

DTS is almost here – in just one week we’ll be getting together – minus the armor, swords, and medieval lighting – and tackling what Artificial Intelligence (AI) and Machine Learning (ML) means to the AEC industry, and how we can best position our firms and ourselves in this evolving landscape.

 

According to Gartner’s 2017 Emerging Technologies Hype Cycle report, ML is just about ready to take a roller coaster-like dive into the ‘trough of disillusionment’. After lots of talk and inflated expectations, there will be a letdown as the realities of early implementation come to light, and the hype gets put in check. So how can we even out this trough in our firms? What can we propose that demonstrates the positive aspects of AI, while keeping the disillusionment at bay?

 

One idea we’ve discussed in the DTS Committee is to focus our conversations on the next several years, while keeping the longer-term view in the background. Using this framework to think about ‘driverless car’ technology, we know it isn’t mature enough yet to eliminate the need to drive, but today it is useful in providing useful feedback and assistance to drivers.

 

In the AEC space, two things that come to my mind are Proving Ground’s LunchBox ML plugin, and WeWork’s desk layout automation research.  The former is a tool that can be adopted now, and the latter describes a proof-of-concept that could be expanded to address more complex design scenarios.

 

So be prepared for an intense, fun, and hopefully satisfying day and a half of conversations. As you’re preparing for next week, keep the following in mind:

 

* What improvements can we begin over the next twelve months

* How will they benefit our practices, our work, and our lives, and

* How can they inform and accommodate the next wave of technological advancement?

 

Image credit: https://www.pinterest.com/pin/499758889889807127/

Setting the Stage

Setting the Stage 

credit:  https://www.pinterest.com/pin/499758889889807127/

 DTS is almost here – in just one week we’ll be getting together – minus the armor, swords, and medieval lighting – and tackling what Artificial Intelligence (AI) and Machine Learning (ML) means to the AEC industry, and how we can best position our firms and our selves in this evolving landscape. 

 

According to Gartner’s 2017 Emerging Technologies Hype Cycle report, ML is just about ready to take a roller coaster-like dive into the ‘trough of disillusionment’.  After lots of talk and inflated expectations, there will be a letdown as the realities of early implementation come to light, and the hype gets put in check.  So how can we even out this trough in our firms?  What can we propose that demonstrates the positive aspects of AI, while keeping the disillusionment at bay?  

 

One idea we’ve discussed in the DTS Committee is to focus our conversations on the next several years, while keeping the longer-term view in the background.  Using this framework to think about ‘driverless car’ technology, we know it isn’t mature enough yet to eliminate the need to drive, but today it is useful in providing useful feedback and assistance to drivers.  

 

In the AEC space, two things that come to my mind are Proving Ground’s LunchBox ML plugin, and WeWork’s desk layout automation research.  The former is a tool that can be adopted now, and the latter describes a proof-of-concept that could be expanded to address more complex design scenarios. 

 

So be prepared for an intense, fun, and hopefully satisfying day and a half of conversations.  As you’re preparing for next week, keep the following in mind:   

 

  • What improvements can we begin over the next twelve months 
  • How will they benefit our practices, our work, and our lives, and  
  • How can they inform and accommodate the next wave of technological advancement? 

Knowledge Management, AI, and Mentorship

Are you as crazy about Google Maps as I am?  I really love being able to check it before leaving on a drive – if there’s traffic on my way, it will tell me.  If I need to reroute, I can avoid complications before I run in to them.  I can evaluate the best route, be on my way, and not worry.  Google will even correct mid-trip if it sees congestion and finds a better route.

 

In spite of my enthusiasm for Maps, I’m a little disappointed when I use it.  I find that I don’t recall the directions as well as I do when I have to figure out the route on my own.  I focus on obeying Google’s discrete, turn-by-turn directions at the expense of understanding the overall route.  In some ways I feel less prepared to make that trip again should my phone be unavailable, because I rely more on Google, and less on my brain.

 

Bringing this back to the topics at hand, I’m curious to see if there are parallels between my use of Maps and a person’s use of AI-assisted Knowledge Management.  While I’m always in favor of retaining knowledge, I hope we can find a way to do so that augments rather than compromises the learning experience.  How can we make information available in a way that stimulates learning, and reinforces the mentorship model?

 

As one of our topics for this year’s DTS, I hope you’ll bring some thoughts on this topic to DTS this July.  Let’s discuss!

 

 

Translating Technology

In doing research for his recent book, Jay Conger identified one particular set of talents that resonated with me, that of “technology translation”.  It’s the ability to absorb large amounts of data, discern its essence, understand different perspectives and stakeholders, and come up with a simple, strong message that’s tailored properly to the intended audience.

 

Jay’s example centered around software companies and their customers – namely, that there might be some feature changes that would impact enterprise customers.  A technology translator would bee able to understand those changes, and find the most salient points and build explanations for different audiences:  the software company CEO, the board, the marketing heads, and so on.

 

I’ve found these skills especially useful in our line of work too.  Technology can mean many things to many people, and how I’d explain something to a firm’s partner could be pretty different than how I’d explain it to a first-year graduate.  Design-minded folks will respond differently than technically-minded ones, Construction Admin experts will have different needs than interior designers and urban planners.  Engineers will find yet a different set of priorities to focus on.  You get the picture.

 

How have you been able to use these techniques to your advantage?  Have there been times when you’ve found the messages more convergent?  And some that are less convergent?  Have you found some principles to be ‘universal’, or do they always depend on the audience? I’d love to hear your thoughts, hopefully at DTS this summer.  Please apply to join the conversation!

 

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