The use of artificial intelligence in business is gaining popularity. According to SAS, 80% of companies from different industries expect that AI will significantly affect their activities over the next five to 10 years. Fiery enthusiasm can slightly cool when there is a lack of organizational readiness. Some businesses have had their doubts about adapting too early. A common reason for this is the conventional wisdom that artificial intelligence can only be played by big players. But is this true? Here’s how to tame artificial intelligence with a brief guide for business.
A couple of examples of how to tame artificial intelligence
A small accounting office is making great efforts to make bookkeeping quick and easy. It launches a series of research and develops cloud software using AI, to automate bookkeeping for its clients. In 2017, it was awarded the title of Practice Excellence Pioneer. This is the most prestigious award in accounting. In the same year, its income exceeds $1 million. Pretty good result for a company with just 30 employees.
The Big Apple Diamond Jewelry Store fights in the toughest competition. AI became a salvation for the virtual seller, which uses it to analyze the global diamond market. In a few seconds, it processes a million pieces of information to find the perfect wedding ring option for the client. Buyers are delighted: the end of an exhausting search!
“Okay,” you say. “They were convinced. But where to start?” Because everyone is talking about AI today, but explanations of how to approach it are less obvious. Well, it makes sense to start with definitions.
What is artificial intelligence explained in a narrow sense and in a broader sense?
The term “artificial intelligence” today is used in a narrow and broad sense. In the narrow sense of AI — this software that simulates the work of the human brain. In a broader sense, “artificial intelligence” is a generic term used to denote a range of technologies:
1. Machine learning
Machine learning is based on the use of statistical tools. With it, computer systems learn to use a variety of data to improve their own performance. This is done with minimal human intervention, and with none at all. An example is a face-recognition system on Facebook.
2. Smart robotics
Today’s smart machines are systems based on artificial intelligence, learning by analyzing information from the outside world. They are already used in a number of industries and perform a wide variety of tasks, starting with creating dental implants and ending with cooking pizza.
3. Virtual assistants
Virtual Assistant is a software product that provides customers with round-the-clock assistance in using websites or finding the right information. Perhaps you already had the chance to meet Amy from X.ai or Watson from IBM — that’s them.
4. Automated solution management
The operation of such services is based on the ability of regulated systems to make decisions regarding recurring issues without human intervention. AI-based solution management systems are already used in logistics and human resource management.
5. Data processing using natural language
This technology is aimed at data processing and their transformation into a text that is understandable to humans. Forbes uses it to generate income reports, and the Canadian Ministry of the Environment uses it to generate weather forecasts.
Introduction of artificial intelligence in business
It is impossible to introduce artificial intelligence in the work of the company in a couple of days. Preliminary audit and optimization of existing processes is a long process. If you do not want to be overboard in 5-7 years, it’s time to start.
Explore artificial-intelligence technologies
Find out what is meant by each of them and how each can be applied. There are many educational materials on Udemy, Coursera, and Udacity. NVIDIA has developed a detailed list of AI courses for various levels, from beginners to advanced. Use the products with artificial intelligence yourself to understand what you like about them and what doesn’t. Think how your customers can use something like this when dealing with your products.
Review your business
Ask yourself where in your industry you can use advantages from artificial intelligence. Or, from the other side, what is slowing down your company compared with competitors, and how would AI help you gain momentum?
For example, if you have a problem with leads, consider the possibility of using machine learning for lead generation. If with attracting customers through the site — maybe you need a chatbot. Consider how you can improve existing services with it, such as options for improving the website, optimizing marketing campaigns, improving customer service, and building a social strategy. Identify measurable goals and contact with a technical consultant for help.
Make a preliminary assessment and love the data
Estimate the cost of implementation. AI differs from conventional software in that it’s not possible to install and forget. The machine-learning process needs to be monitored. To learn, AI needs data — a lot of data collected in one place. So collect it. Include everything: your CRM data, advertising campaigns, traffic analysis, customer behavior on social networks, public competitor data, etc.
Check whether your IT department needs to be rebuilt to meet the requirements of implementing AI-based solutions.
Just as the internet has completely changed our lives over the past 20 years, so artificial intelligence will soon become the most powerful engine of transformation in the near future. And the sooner you begin to analyze how to use it to promote your business, the better you will be prepared for new market challenges.
The success of the introduction of artificial intelligence depends largely on practical readiness. The latter implies the availability of the necessary tools and qualified personnel. The initial steps for rooting AI into your company’s business processes include:
- Acquaintance with artificial intelligence technologies.
- Thorough business analysis for the introduction of new technical solutions.
- Preliminary assessment of the cost and quality of labor.
- Data consolidation.
- IT service adaptation to new working conditions.
- Identification of an experimental segment for a trial launch.
All this may seem too complicated at first glance. But do not panic. In fact, there are many more chances to fail when running traditional software than when it comes to AI.