If previous technological progress affected the only production, today it encapsulates the entire chain: consumers, manufacturers, suppliers, and marketers.
ML, AI, and API technologies drive real digital transformation, which also profoundly changes operational business models and approaches. As we’ve seen with Uber, Amazon, Booking, and Expedia, etc., many success stories stemmed from the application of an innovative business model, rather than unique products.
So, what exactly should company executives do to accurately assess the impact of these technologies on their business model and strategy? For starters, stop treating these as a checklist that needs to be handled by IT department guys; instead, try to look at them from a different angle to find in the new opportunities.
API: capitalizing on features
To put it, API is a standard way of communication between one computer program and another for the joint execution of a common task. Let’s get back to Uber: if API of Google Maps weren’t integrated into Uber’s application, you would not be able to order a car or Uber would have to develop its map instead. Another example: when you search for hotel rooms on Expedia and Booking.com, they brush up across various websites and extract suitable results via API
Today, it’s much easier to apply some finished feature with API to build your technology or app. There are more than 50,000 public APIs, and tech giants like Google or Facebook share them with those developers who want to integrate their functionalities.
Let’s take another example with Spotify: At first, they opened their API so that app developers could create third-party widgets right inside the Spotify desktop app. Later on, they restarted their API so that developers could create separate applications based on it.
If your digital business is competitive, the best way you can earn an income is to keep your API free of charge for a specified period. This can be a trial version for potential buyers that helps to realize the true potential of your API before paying commission. In case you only plan to develop an app, it’s also a win-win solution: ‘borrowing’ a feature from a renown API provider you access proves quality for a low price and without having to build the feature from scratch.
AI: SaaS and PaaS
Software as a service and platform as service models are changing the way companies capitalize on innovative AI-driven technologies that otherwise may appear incomprehensible.
Now it is possible to rent particular resources of the company or use the whole technological platform (PaaS) in exchange for a fee.
In a SaaS model, a company gets a fully deployed and supported solution. PaaS offers a ready-made supported platform that features a much higher level of customization. Developers and individual entrepreneurs, as a rule, want to be able to manage the platform settings but do not want to waste time on administering the infrastructure. If you want to control the administration and make individual adjustments, you may want to consider PaaS. SaaS will better suit entrepreneurs who don’t have experience in administration and only want to receive a ready-for-use product.
Both services are widely applied by big companies, medium companies and startups. They make things faster for those who can’t afford to keep an extensive pool of IT professionals on staff but who still need to install, configure and maintain complex AI tech systems. The development of these models gave rise to the White Label concept based on PaaS.
Here is a good example. In the ad tech industry, where the problems of hidden margins, commissions, and ad fraud are especially acute, companies are shifting to independent, in-house media purchasing. In these circumstances, White Label becomes a tool that enables businesses to Build an ad Network on their own. They acquire the platform from the White Label service provider, connect a media partner of their choice and start to capitalize it under their brand name. That’s how it all works.
This way, they invite-only exclusive partners inside their network. When each partner inside the chain is known, there’s less room for manipulations to occur. In the long run, this model is a win-win. While big companies obtain more money for selling additional value (service), small ones don’t have to invest tremendous resources in developing their solutions, testing it, licensing and hiring tech professionals.
ML: additional business value
It looks like in the next couple of years, technologies, like AI and ML, will be able to automate 90% of the manual workflow of every business. In such circumstances, the attention of entrepreneurs should be shifted towards the additional value that their products can bring to customers, especially in the b2b segment. Customers will be interested not only in the physical features of the product but also in the opportunities and values that these products can provide.
Let’s take an example with drones. These days they are smart and can visualize the environment themselves with Simultaneous Localization and Mapping based on Machine Learning. It helps them to evaluate the distance to the destination point automatically. At the same time, the pure selling of the drones can’t be considered a unique or competitive business strategy anymore.
The same technology can be applied differently; for example, Sharper Shape uses drones as a service that helps to inspect utility networks. This way, drones have become a precious service since they eliminate danger associated with manual human inspection and significantly speed up the entire process.
To add additional value to the primary product, you can equip it with software individually wired to execute a target action for your customers that hail from different industries. Of in-built, analytical software into such systems will also help customers to analyze collected data and drive actionable insights automatically.
To wrap it up
Improvements in business performance don’t necessarily start with revolutionary changes or entire industry transformations. The experience of businesses operating in IT, finance, ad tech, and online retail industries proves that you don’t always have to step out of the traditional framework of work. Instead, try to underscore the existing strengths of your products or services. Then, find out how you can enhance them with AI, ML, API and White Label technologies that can serve as a value-added layer on which new business models can be built.
Irina Kovalenko
Irina Kovalenko holds the position of CMO at SmartyAds programmatic advertising company. The company develops best in class and superior programmatic media buying and selling software. Irina has a passion for all digital marketing fields and has a strong experience in programmatic advertising.