AI Adoption Strategy: An enterpreneur's Perspective

The real estate and AEC (architecture, engineering and construction) industries are complex; each project is different, and the data and knowledge processing is so fragmented. The majority of AI attempts I have seen in these industries so far have been too narrow or not sophisticated enough to create transformative value. Most AI point solutions rarely go beyond proof-of-concept (PoC) and pilot projects.

One cannot disagree with the position that the real estate industry’s approach of relying heavily on external technology developed by third parties (usually supported by VC funding), has created several challenges.

As a venture-funded start-up founder and CEO, I see two main challenges among my entrepreneurial colleagues:

1. Many tech start-ups, at times, get too passionate about solving “complex technology problems”, and instead of solving the real-world problems their customers face—the infamous tech-before solution dilemma.
2. Tech start-ups, especially those that have raised(or intend to raise) external capital, are tempted to roll out half-baked technologies under pressure to show commercial traction. False promises lead to disappointments and undermine customer confidence in novel technologies.

Therefore, a firm heavily or solely relying on external technology developed by third parties is not a good approach. However, at the same time, relying on in house capabilities alone would have its own set of challenges. In particular:

1. An AI team is not just few machine learning engineers, but a full-stack team of ML engineers, data annotators, data engineers, cloud engineers, MLOps, data scientists and product designers. Putting such a group in one place and retaining them as a team is a challenge even for technology companies and well-funded start-ups. Real estate corporates are not seen as attractive enough for leading AI talent, and many won’t match the compensation benchmarks of the tech industry.
2. The pace with which AI technology is evolving, its practically impossible to stay abreast with in house capabilities alone. In-house R&D initiatives are always at risk of external disruptions.
3. Introducing an R&D mindset to the real estate industry would warrant a major cultural shift, as well as a structural change in their financial statements and value chain economics. In addition, the pace of change is starkly different between the real estate and technology sectors.
4. Companies may have the knowledge and skills in house to solve their problems, they often do not have the time and tools to do that.

We should not see “external” and “internal” as a dichotomy, but rather view it complementarily. As an analogy, I am sure that all sizeable real estate firms have great in-house lawyers, but do they not work also with external law firms? I bet they do, and I bet having good lawyers in-house makes it a lot easier to work with external lawyers.

At CONXAI, we believe that AI start-ups like us won’t go anywhere without our customers’ in-house innovation and data analytics teams. And without external AI technologies or start-ups like us, the in-house innovation, AI and data analytics teams of our customer would only go so far and no further. Working closely with our customer teams provide us with an(external) governance and solution orientation. This allows us to incorporate their industry knowledge and wisdom, and right usability concepts into our solutions early on, as well as refine our operating model.

It reminds me of an author who wrote “we don’t know who discovered water, but we are certain that it wasn’t a fish”. Sometimes we get so immersed in our environment that we fail to discover the realms of our own reality or notice the changes around us and overlook our “blind spots”. From time to time, we do need a different “outside-in” worldview to better perceive and understand our own environment. That’s when innovation and transformation happen.

We strongly believe that the real estate industry must invest in building in-house data and analytics capabilities. And at the same time, they must be poised to leverage the AI technologies developed by third parties to accelerate the implementation and lower the technology risks associated with in-house developments. Their goal should be to maximize their operational leverage by becoming more data driven.Analytics and AI are means to achieve that.

The real estate industry should invest in building(what I would like to call) the “data circuitry” within their organizations, to which they can plug in all sorts of in-house and externally developed technology products to illuminate their organizational blind spots.