Classifying the capabilities of advances in artificial intelligence based on their usefulness to solve different business problems is the best way to understand what can and cannot be done for our company.
Although chat to those who are interested in your courses, solve their doubts and close as many sales as possible.
Some do it very well, and others not so well. Creates a data set to train supervised learning algorithms.
Artificial Intelligence: A Modern Approach
Aware that if you went through the transcripts one by one, you would discover that certain groups of words in specific conversations have ended in a sale and others have not, you feed this information into the algorithm so that it begins to identify which sentence patterns and responses have been successful and which not.
Because the direct preparation of a chat board in which to reach potential customers typically work well, but you can not solve the most difficult questions (or questions not previously asked), you opt to create a bot to assist customer service and sales staff, recognize trends and predict possible answers to the various questions.
This way of approaching the labour relationship between people and machines turns out to be very productive, as Sebastian Thrun has seen, applying it to Udacity, his online educational platform, in which its deployment is helping to close the environment to 50% more than sales.
Instead of replacing a person with a robot, a robot used to make the person more productive and learn faster, correcting their mistakes even before they occur.
It is much more practical and profitable for companies to consider the options offered by artificial intelligence from the perspective of business problem-solving capabilities, than from a strictly technological point of view.
Following the classification proposed by Tom Davenport, in general terms, artificial intelligence currently offers three types of solutions: automate business processes (cognitive automation), exponentially increase the capabilities to analyze large volumes of data (cognitive insight), and amplify the possibilities of interaction between people and machines (cognitive engagement).
Artificial Intelligence a Modern Approach Solutions
Let’s See them One by One:
The automation of physical and digital tasks is today the most common category of application of artificial intelligence to the business world.
Its use is more and more frequent in administrative, business support and financial activities, due to its worldly and repetitive nature.
They based on the use of robotic process automation, a more advanced technology than previous process automation tools, in which robots can work with information sources from multiple systems.
Of the three types, they are the least expensive, the easiest to implement and the ones that generate the fastest return on investment.
On the other hand, they are the least intelligent (they are not yet capable of learning “alone”) and although they do not currently pose a significant threat to administrative tasks, as technology improves, jobs will gradually be lost, especially in the field of outsourcing of services.
Among its main applications are tasks such as:
- Transfer of data from emails and customer service centres to registration systems, such as updates and changes.
- Replacement of lost credit/debit cards, accessing multiple systems and managing communications with the client.
- Correction of errors for undue charges through different billing systems.
- Screening of legal and contractual documents to extract provisions through the processing of natural languages.
Should Artificial Intelligence be Capitalized
The increased analytical capacities of large quantities of data are exponential.
The second category is algorithms designed to detect patterns and help interpret information drawn from vast amounts of information from multiple sources and formats.
The patterns obtained through machine learning techniques are far from those that can only be obtained through Big Data.
Systems based on artificial intelligence can absorb much more information and refine it reaching much higher levels of granularity, they can be trained using selected data sets, and their prediction and categorization capacity improves as it receives get more information.
Among its Main applications are Activities Such as:
- Predict the probability that a specific person buys a certain product/service.
- Identify various patterns of fraud in insurance policies, bank accounts, credit/debit cards, and stop your attempt in real-time.
- Real-time analysis of the safety status of products and facilities.
- Automate and personalize the digital customer experience.
- Automate and personalize digital advertising campaigns ( intelligent programmatic advertising ).
- Provide insurance companies with more accurate actuarial models.
- Identify matches between multiple databases in different formats and eliminates redundancies.
- Audit documents and contracts.
- Amplification of the possibilities of interaction between people and machines.
It is the most immature and difficult to manage category, but the most promising for improving brand and employee-customer experience.
It allows you to manage an increasing number of interactions without adding personnel through chatbots that support and optimize the work of the customer service teams, as we saw at the beginning of the article.
Among its main applications we find:
- Smart agents to serve the customer 24 hours a day (still in an embryonic state).
- Intranets to answer questions from employees on standard issues.
- Voice recognition systems (functionality still very limited).
- Personalized recommendation systems for products and services.
- Personalized recommendation systems and real-time monitoring of medical treatments.
As we have just seen, the real capabilities of artificial intelligence are very specific and still far from reaching the levels of general intelligence that characterize people.
Knowing and understanding which technologies do what jobs is the fundamental basic condition before designing any project in which AI will be involved.
The question we must ask ourselves is not how AI will help us achieve our goals, but what goals we have and what type of AI is best suited to help us achieve them.