AI can be considered the light bulb of the 21st century, soon it will be taken for granted. Most likely it will find its way into every activity sector since it is already used by solopreneurs start-ups and by giants like Google. There is no shortage of ideas for applications, which range from fraud prevention to cancer detection and from customizing the client’s experience to growing crops. Even companies that don’t have the upper hand in technology want to be part of the hype and seize some of the opportunities. Here are four ways of joining the revolution, from enhancing your business with data science to making AI your primary activity.
Enhance your current business
It is not mandatory to create a new business around AI, but you can boost sales numbers or optimize processes by incorporating the insights provided by Big Data. Most companies already collect numerous streams of information to use in their BI systems. Not all qualify as Big Data, but some of it can be combined with already existing data to get a new perspective on what clients, want, need or prefer, as experts from the data science consulting firm InData Labs explain.
1 Use the data you already have
Some companies have precious data just lying around, stored due to compliance requirements or to be there, just in case. A simple example is the CCTV footage in retail stores or the recording of prescriptions in pharmacies. Usually, such data is recorded for security reasons or for payment purposes, but it could prove way more valuable.
In the security camera example, properly tagged and categorized data could make a difference in mapping the movements of clients and creating an interest heat map. This could show the store management team where to place items or what areas are prone to shoplifting. Prescriptions can be used to create databases with interactions and the most effective drugs for each disease. It could also be used to avoid cross-effects and distribute health budgets better.
To make money out of data, first, list all the data sources you have. Be open-minded and consider both data in tables as well as free-form data like customer reviews, call recordings, and footage. Think about ways of using these yourself like these companies did when they started analyzing their client representative calls to gain knowledge about what sells.
2 Transform business models
Creating new business models or changing old ones are some of the most interesting applications of AI and a good enough reason to adopt it. Of course, automatization and new insights can improve a company’s bottom line dramatically by saving time and money, but CEOs should look beyond that. It should be more about a new way of doing old tasks more intelligently, driven by data. The best examples are related to finance, transportation and agriculture.
For example, AI can completely change the processes related to financial auditing and fraud detection. A company providing these services can replace the work of auditors while also reducing the time and the type of work dramatically.
Similarly, for agriculture, data-driven models and predictive analytics can dictate the types of crops to be grown, or help with insurance claims once a meteorological hazard has occurred. Logistics can go a long way from pen and paper planning and benefit from dynamic route planning, and dynamic pricing based on demand, much like Uber is doing for passenger transportation.
3 Build new AI tools
Of course, the best way of building a company around AI is to offer tools and services in this activity sector for others. The market for businesses that generally have pay per use services is growing almost exponentially and is expected to reach $47 billion by 2020, according to the IDC new Spending Guide. The general trend is that once a startup has defined its core product and is off the ground to become attractive and get acquired by one of the giants like IBM, Google, and others. Each of them is trying to become an AI power center, and this is a win-win-win (startup-large company-user) situation.
The real money will belong to those companies who will be able to make the quality leap towards autonomous intelligence, where there is no more need for human intervention, supervision or calibration.
4 Data is the new world currency
Since data is the raw material on which AI algorithms feed, it will soon be more valuable than traditional currencies. Information can be the cornerstone of new businesses or help extend existing ones, it will become a new production factor or a new type of capital. We can’t expect to have data listed on the stock exchange market like any other commodity, but the companies investing in this resource will certainly have a competitive advantage.
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