For the last decade, artificial intelligence has transformed the way companies summarize information, design technologies, and serve their customers. The technology is so powerful that in one form or another, the 50 biggest Fortune 500 companies have already put AI to work.

In the real estate market, use cases for artificial intelligence are flourishing. Big data analytics are driving all aspects of the real estate sales process from identifying highly-qualified buyers to accurately predicting a property’s sale price. Innovative real estate companies like Redfin and Rex are using AI to streamline their operations and drive down the costs of commissions paid to agents.

Companies in the property space that are looking to finally tap into the power of AI have a decision to make: do they staff up a machine learning dev team in-house or should they engage an outside firm?

On the one hand, the benefits of hiring an in-house team may seem obvious. AI is an expansive technology with many, many use cases. Having a dedicated team means that you can take advantage of all AI has to offer by comprehensively deploying models across every aspect of your business.

On the other hand, engaging an outside team can save costs and streamline the complex process of data ingestion, transformation, training, and validation.

When it comes to getting in the machine learning game, speed is critical. Unlike other business assets, AI models actually appreciate in value over time. Continuous training leads to an increase in model accuracy over time. Simply put, systems that are in place longer and earlier perform better. 

Companies, therefore, hugely benefit from being first movers. This advantage alone is enough to forego all of the delays associated with hiring and training an AI team in-house. Getting approval and budget for a new AI team can take months. Staffing and training that team can take even longer. This can put companies at a significant disadvantage if their competitors spend those same precious months building and deploying AI solutions via an external organization.

In addition to the obvious advantages of being first to AI, engaging an outside team also provides a level of ease that can make even the most uptight CIO sleep easier. In a world where unprecedented seems to be the norm, the best companies are the ones that can adapt and pivot in an instant.

Here in the northwest, we have two property companies that have approached their AI needs differently. For NDA reasons, we’ve withheld the names of these companies; however, we felt their differences are particularly illustrative.

The first company, a real estate brokerage, engaged AI experts for their machine learning needs. By contrast, the second company, a vacation rental site, raised nearly $400m and used it to employ a large, high-end data science team to build out their AI-powered vacation rental capabilities.

Now that we are in the throes of a global pandemic, the impact of each of their approaches has become increasingly clear.

While most companies have suffered under the strain of the global contraction, the vacation rental company — not to mention the entire travel industry — has been especially slammed. In March, it announced large-scale layoffs, including the entire AI team.

To a lesser extent, the real estate firm has also suffered. At the end of March, the company’s stock hit a record low. A few weeks later, they, too, announced layoffs and other cuts. Despite the setback, the company is feeling optimistic about its position. In a recent earnings call, they reported with optimism that they’re adapting rapidly to a world defined by social distancing.

Now, obviously, the greatest indicator of whether these two companies would come to be brutalized by Covid has nothing to do with whether or not their AI team was in-house. Even if its AI resources came from an outside source, the vacation rental company would still have suffered the greatest.

Nonetheless, these two stories illustrate parallel universes. The real estate company’s decision to fulfill its AI needs with outside experts means that it had much more flexibility to adapt to the evolving global landscape. By contrast, the vacation rental company’s option was to bring dozens of loyal employees through a cutthroat round of layoffs. 

Furthermore, once the pandemic finally recedes, starting up the ML engine may not be so simple for the rental company. By then, many of their laid-off experts will, in all likelihood, have transitioned to new opportunities. The loss of IP, tribal understanding, and institutional knowledge could be significant in that case — perhaps even unrecoverable.

At the beginning of June, the vacation rental company announced that it had raised over $100m in a Series D round. Many industry experts concluded that the terms of the deal could have been far favorable for the company had the pandemic not put them in a tough spot.

By contrast, companies like the real estate company that engage outside vendors tend to amass far more detailed documentation about the systems and architectures these firms have produced.

It’s not enough to put an AI model into production, the institutional knowledge and intellectual property must also be handed-off to the client business. This documentation empowers organizations to stop and start their ML initiatives as their needs change.

Undoubtedly, the much better model is the one many small and medium-sized businesses use for legal advice. Although there can be great value in bringing your legal team in-house, the pay-as-you-go model has worked well for most organization’s legal needs. We see this model repeated across many business units such as accounting, cloud computing, public relations, and SaaS.

In a similar way, CIOs should consider engaging outside teams for their machine learning needs. As the world becomes increasingly unpredictable, we think it’s best to err on decisions that enable flexibility.