We recently helped a Real Estate Investment Trust (REIT) use machine intelligence to inform the design of an entirely new system. During our analysis, we used the data to understand if there were predictive qualities in the numbers.
The Emergence of Design Thinking
Design thinking has emerged in the last few years as a reputable model for solving complex business problems and innovating new products and services.
With publications like Forbes and Harvard Business Review publishing articles on the topic, it's no surprise that enterprise organizations around the world are bringing design thinking best practices into their cultures.
Simultaneously, it’s hard to go a day without hearing about the importance of big data and analytics.
Learn more about digital design:
Though organizations understand the opportunity that big data presents, many struggles to find a way to unlock its value and use it in tandem with design thinking - making “big data a colossal waste of time & money.”
How Architech Has Personally Used Artificial Intelligence (AI)
We recently helped a Real Estate Investment Trust (REIT) use machine learning to inform the design of an entirely new system.
During our analysis, we used the machine learning and deep learning data to understand if there were predictive qualities in the numbers.
By doing so we were able to uncover a number of important patterns.
Our data based approach gave us the perfect stepping stone to create an innovative solution to address the company’s ask.
Our Human-Centered Design team stepped in and began to uncover the underlying causes behind the patterns we found, all with the help of machine learning and deep learning.
Insights From Machine Learning / Deep Learning
For example, the machine learning and deep learning data showed us that leasehold improvement expenses (investments made by the landlord) had limited correlation to the length of tenancy.
This led us to question why a landlord would make such investments and how they factored into the tenant’s decision to stay or move.
Employing a variety of design thinking methods, such as end-user interviews and scenario modelling, we began to uncover the unmet needs of tenants and came up with a value hypothesis for the developer.
Learn even more about digital design:
- Supporting Collaboration Between Data Science & Design
- 6 Ways Designers Need to Adapt in the Age of AI
Only by combining quantitative insights gathered using machine learning, deep learning, and qualitative research through direct user interaction were we able to paint a complete picture of the problem at hand and help drive towards a solution that would create value for all stakeholders.
On May 5, 2016, Ramy Nassar & Tal Bevan presented at the AI Summit in London, UK on how AI Fuels Design Thinking by exploring how AI’s data-driven and iterative approach can be combined with Design Thinking to create the ideal solution to address end-user needs. Ramy will also be presenting at the AI Summit in New York City on December 1, 2016.
Interested in transforming your dated systems with insights powered by your data and predictive AI?
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