If your business operates online, then the question is no longer whether to adopt predictive AI, but how to implement models that are right for you. To build or buy is always a question surrounding the emergence of any new technology, and predictive AI is no exception. But given the huge investments of time and money that go into AI development, the survival of your business might hinge on how you answer.
Let’s take a look at the pros and cons of building and buying predictive AI solutions, so you can determine which option suits your business best. We’ll focus on cases of using AI for digital-native purposes such as app optimization.
Predictive AI models are taking over the business world due to their ability to excel at a huge range of innovative labor and revenue-saving functions, such as customer service, fraud prevention, product recommendation, content creation, accounting, inventory forecasting, digital assistance, and recruitment (and countless others).
Awhile one recent study shows that 77% of companies are now using or exploring the use of AI, and research by PwC predicts that global GDP will be 14% higher in 2030 because of AI, which is forecast to contribute $15tn to the global economy.
However, while companies are near-unanimous in their willingness to adopt predictive AI, they remain divided on how exactly to bring it into being. Some companies dedicate in-house software developers to the task of building AI models specifically for the goals they most need help with. Others buy AI tools from companies that specialize in their research and design.
In a nutshell, build-it schemes are not for the faint of heart. Rather, they are for businesses that intend to revolve around a central, highly specified AI algorithm, such as Uber or Spotify – and have a lot of time and money to do it. This approach has huge risks, but as these examples demonstrate, there are huge rewards also.
Buying a predictive AI model is a lot more reliable and efficient for the vast majority of functions most businesses wish to run. Teams of dedicated experts are constantly developing the capabilities of these tried and tested models further. For instance, in a study of 152 AI projects by various companies, leading AI researcher Thomas Davenport found that those who opted to build ‘ambitious moon shot’ projects were far less likely to succeed than companies that used established forms of AI, or ‘low hanging fruit’. However, there are a range of nuances that are important to understand about each approach. Let’s take a closer look at their benefits and drawbacks.
As stated above, certain circumstances favor building predictive AI models. If you have a dedicated, salaried team of expert software designers with extensive experience in building AI models, reams of data already collected, can afford to wait several months, and have a very specialized goal for the AI, then build it and they will come.
The main advantage of building your own predictive AI models is that you can theoretically tailor it endlessly to unique physical and digital infrastructures, and purposes that aren’t well catered for by commercial solutions. In a best-case scenario, you could then find yourself with a unique and powerful solution that forms the axis around which your business spins.
However, caution is essential when following this approach as you might find yourself pouring endless time and resources into a solution, only to run into inherent limitations that prevent you from reaching a successful deployment.
A situation that occasionally arises when companies opt to build AI models is that they spend vast sums designing and building an advanced model, only to discover that it can’t perform its function as there is no data on which to train it. The data collection process takes a long time and is plagued by ethical and legal obstacles, such as GDPR, CCPA, and the decline of third-party cookies. This can result in years of delay to the deployment of an AI model.
Even after data collection, there is the problem of training – another time-intensive process where the data must be fed into the AI model. The model is only as good as its training, which means that skimping on this stage is guaranteed to produce a lackluster program. You also need the data to be structured in a certain way, for example, according to common attributes and the type of device in which it will be employed.
All of the above is assuming the model is fit for purpose. However, skilled data scientists are notoriously hard to find; many companies tend the maturity of their in-house AI capabilities. You need to consider whether your existing engineering teams can handle the challenges of designing and deploying a model that runs in real-time, especially in cases where they have additional mission-critical responsibilities.
Although buying a predictive AI model reduces some of the capacity for specialization, the wide and growing range of solutions available means that almost any relatively specific needs can be met with prebuilt solutions – and, to quote Thomas Davenport, “It’s a cheaper solution than spending millions of dollars hiring data scientists”.
However, the main advantage of going with a prebuilt solution is that you can already leverage existing data and models that have been optimized by specialists over several years to best deal with common predictive AI functions like personalization, forecasting, and fraud detection. The best solutions come with data, taking in billions of non-PII data points from customers of other businesses, meaning that these solutions get more accurate the longer the provider is in operation. This is particularly useful for the vantage point of a small company, as you will have far less data than your larger competitors: prebuilt models even the playing field.
Simply put, data is what drives AI and this is also what separates prebuilt and DIY solutions. The former comes data-equipped and the latter often fails because it lacks access to adequate data reserves, or doesn’t account for the work that collecting, storing, and formatting this data requires.
At the end of the day, prebuilt predictive AI solutions are going to suit far more businesses than building models in-house or contracting developers to do so. This is partly because of money: even using open source tools, designing a predictive AI model capable of real-time deployment can cost millions of dollars.
Building a model also takes huge amounts of time developing software, collecting and formatting data, and training the AI model. Prebuilt solutions come with non-PII data from all of the businesses the providers serve, allowing for a far more accurate, cheaper, and time-efficient means of achieving more predictive AI tasks. The bottom line: unless you’re specifically targeting unique functions that aren’t covered by expert solutions providers, buy every time.
