Implementing Artificial Intelligence in Your Enterprise

The term “artificial intelligence” was coined in 1956 by an American computer scientist John McCarthy, the founder of the discipline. Until recently, it had only been the subject of science fiction movies and people’s imagination. Today, artificial intelligence is a hot topic, as it has become a part of our daily lives. It is not only about creating robots that imitate human behavior (as most people used to think). AI comprises a set of smart technologies that are able to learn, analyze, conclude, solve problems and make decisions, thus outperforming people in various tasks.

Digital transformation, Big Data and the Internet of Things create a perfect environment for AI expansion. By implementing AI solutions, numerous industries innovate their processes and the way they communicate with their customers. “Intelligent” applications are already being used in healthcare, transportation, public security, education and entertainment, and are going to penetrate many other areas in the next few years.

Artificial Intelligence for Enterprise

Consumers are becoming more demanding in the modern digital environment and expect more than ever from their providers: always-on access to data, full engagement with the service, immediate and reliable notifications through the chosen platform, etc. AI software can’t solve all problems, but it can make interaction with clients more convenient, improve the efficiency of companies and drive production through more accurate data analysis and automated processes. AI can also affect optimization and network management.

Business leaders around the world realize that artificial intelligence is likely to be a new engine for economic growth, and they can greatly benefit from adopting AI. Practical use cases emerge regularly and many enterprises are undergoing the transformation.

At the moment, AI technology has the greatest impact on telecommunication. Machine learning tools are applied to Big Data, as the human is not able to process the amount of information in play today — from millions connected devices, M2M connections, embedded sensors and other sources. AI systems can provide sound management and analysis of this data. It can also connect different sectors of infrastructure and allow them to share information and best practices.

Machine learning applications, for instance, predict customers’ wishes and needs by analyzing their previous inquiries and purchases. On the basis of the obtained knowledge, such “smart” systems can offer relevant and attractive goods or services that can definitely enhance clients’ engagement and drive sales.

Chatbots have revolutionized working processes in call centers. A chatbot is an irreplaceable tool for any organization with millions of users. It eliminates queues to connect to an operator, processes a great number of requests without delays and allows to reduce the number of operators.  

Various industries rapidly increase investment in AI and use technologies for their own specific purposes.

Current Use Cases of AI:

  • Self-driving cars
  • Virtual reality assistance in medicine
  • Personal financial management and fraud detection in banking
  • Customer-centric search in eCommerce
  • Defense against cyber-attacks
  • Training simulators in aviation, military and other fields
  • Games

Many AI applications have been so deeply embedded in the infrastructure of some industries that they are not called AI anymore but perceived a matter-of-course.

      Related Posts:

  1. Five Myths About Artificial Intelligence (AI) You Must Know
  2. How to Create an App with Artificial Intelligence
  3. How Will Artificial Intelligence Innovations Affect Your Healthcare Needs?
  4. How to Create an Artificial Intelligence Software?

Artificial Intelligence Technologies

AI includes a set of technologies. Some of them are already mature and widely used, others are at their inception.

Artificial Intelligence Technologies

Source: Forrester

  1. Natural Language Generation is the conversion of the computer data into the real narration. It is used for automatic creation of written analytic reports, blog posts or product descriptions.
  2. Speech Recognition technology perceives oral speech and transforms it into a digital text format that is clear for computer applications. A speech recognition API is used in voice response systems and mobile apps.
  3. Virtual Agents are artificial computer characters that can communicate with users, answer their questions and perform the non-verbal behavior. They are typically used in chatbot industry and serve as customer service representatives.
  4. Machine Learning Platforms provide algorithms, APIs, development and training tools to create learning models and deploy them into machines and applications. They usually deal with classifications and predictions. Google’s cloud machine learning engine is an example.
  5. AI-Optimized Hardware includes processing units and appliances that are specially designed for AI-oriented tasks.
  6. Decision Management Systems set up rules and logic for AI solutions, maintain and tune them. They are used in various decision-making apps. At present, it’s the most mature technology.
  7. Deep Learning Platforms build many layers of artificial neural networks that should sort and process information the way human brains do.
  8. Semantic Technology is a blending of several AI concepts. Its aim is to encode meaning into content and enable computers to think like people do, understand different requests based on their meaning, respond to them and draw conclusions. This technology can be applied in machine learning systems and cognitive computing.
  9. Biometrics are sensors that capture and analyze human body characteristics (fingerprints, facial features, speech, body language) and use them for various purposes — security control, authentication, detection.
  10. Image and Video Analysis is widely used in real-time city monitoring through surveillance cameras. Its aim is to detect objects in order to protect the population from crime and accidents, inform about traffic and perform other socially important duties.
  11. Robotic Process Automation replaces human workers in hard and time-consuming processes to make these processes cheaper and more efficient. It’s currently used in manufacturing and other industries.
  12. Text Analytics and Natural Language Processing (NLP) make sense of massive text data by analyzing and structuring it. Various automated assistants and applications use these technologies to extract significant information from great data volume.
  13. Swarm Intelligence Technology is an emerging field inspired by the social behavior of insects and other creatures. It refers to a group of numerous autonomous robots or devices that are governed by simple rules. In cooperation, they produce intelligent behavior in order to reach a common goal.

How to Implement AI in Your Company

Many companies have a desire to use the power of AI in their daily processes. They need help understanding where to begin and how to make practical steps for artificial intelligence implementation in the business. Here are five tips to get you started.

1. Find out the potential benefits for your company

Any changes and implementations in a company should be economically sound. If you are going to use AI technologies in your working processes, first of all, you should find out what advantages it can deliver and whether it will boost production. You should also consider the price of this innovation, including technical features and training courses for the staff. Education and knowledge are critical in this case, as engineers must have a clear understanding of the technology and its potential impact on services, products and customers.

2. Investigate best practices of your competitors who are already using AI

Artificial intelligence is still a work in progress, so it’s not so easy to implement AI technologies from scratch. That’s why the accumulated experience of your competitors is a priceless and extremely helpful tool that can help avoid many mistakes.

3. Choose a platform

Amazon, Google, Microsoft, IBM and other vendors offer AI platforms for the enterprise. It’s hard to say which one is better because all of them are quite similar in price and functionality. You have to decide which platform is more suitable for your business depending on your personal requirements and specific features.

4. Develop a strategy

In your strategy, you should identify problems you want to solve and figure out points you would like to predict using the intelligent technologies; gather necessary data and decide on the outcomes you strive to achieve; prepare the right tools and ensure that the employees are skilled enough to support implementation; identify possible risks and consider constant infrastructure improvement in order to keep it up to date.

5. Make a deployment plan

To deploy a product, you need to set up an application, a database and the related infrastructure. Then you need to test the app and refine the code. So a plan should include a detailed description of all the settings.

Potential AI Opportunities and Challenges

Any technological or industrial revolution brings together incredible opportunities and daunting challenges. They should be considered beforehand as a full AI implementation in the enterprise touches upon every aspect of the business and may fundamentally change it.

Opportunities

  • Higher profit margins are expected due to optimized production and maintenance as compared to the companies that don’t adopt AI. The best results can be reached in the healthcare, financial services and retail industries.
  • The predictive capabilities of technology make it possible to anticipate customer needs and preferences. This leads to their increased engagement and satisfaction. Moreover, providers can run more effective sales and marketing campaigns.
  • Network management greatly benefits from AI, as it can manage traffic routing and capacity more efficiently, quickly identify faults and correct them. Networks can be automatically configured according to data analysis.
  • AI is expected to have a great socio-economic impact in all industries. New technologies are going to outperform humans in many tasks, provide more effective customer care and services, and boost productivity. Society will experience cultural changes as well, because the mindset of employees and business leaders will be significantly transformed.

Challenges

As artificial intelligence is gaining traction in more and more segments, some serious issues emerge with it. These issues concern people.

  • Impact on the job market is the main nightmare for many employees. Really, even now robots can already replace humans in various job positions. Many people are afraid to lose their workplaces, as producers can reduce the workforce to save money on salaries. Nevertheless, some researchers are sure that robotics adoption may create millions of workplaces, as technologies need to be developed and maintained. Thus, employees will have to obtain new knowledge, skills and training to adapt to the changing reality.
  • Regulation and control is another big question. AI programs can sometimes act in an unpredictable way, deviating from training and creating their own algorithms that can’t be interpreted by operators. One vivid example is when a program at Facebook created its own languages, though it was trained to use English. It can’t be affirmed that such deviations are dangerous, but the fact that people are losing control over machines is frightening.
  • Machines are taught to learn, act and make decisions on their own. With minimal or zero participation of programmers, it’s hard to put the responsibility on somebody if faulty results occur. Machines can’t explain why they take this or that action that leads to malfunctioning, so they can’t be blamed. In most companies, programmers are not accountable for such flaws as well; otherwise, they would be unwilling to innovate anything. But the main problem is that users may greatly suffer in such situations, and nobody can be legally punished. That’s why this question needs careful regulations to clarify liability issues for vendors and engineers.

Forecast and Statistics

Artificial intelligence is the subject of focus for many researchers, as it is flourishing in the market and investment in enterprise AI implementation is increasing as well.

  • IDC predicted the growth of worldwide AI revenues from nearly $8.0 billion in 2016 to more than $47 billion in 2020.
  • The number of enterprises using cognitive computing and intelligent technologies is forecasted to increase to 62% by 2018.
  • Gartner says that by 2020, 85% of all customer interactions will be handled through chatbots without a human agent.
  • Employees are not so optimistic about automation at work as business leaders.

Forecast and Statistics

Source: Statista

Summary

AI adoption can improve operational efficiencies and create positive relationships between vendors and customers. Many providers today are ready for a complete transformation, as they understand that these innovations are the driving force for the future. However, some companies are still cautious about it. Moving to AI is risky and complicated, so it shouldn’t be a rushed process. To maximize opportunities and reduce risks, all cons and pros should be evaluated, and a sound strategy should be developed. In this case, the delivered benefits will be great.

SaM Solutions has experience in creating cognitive computing solutions and software with AI elements. Our team participated in the development of the following services on the basis of Microsoft Azure:

  • Bing Speech API: speech recognition in real-time mode; speech into text translation; supports some languages.
  • Language Understanding Intelligent Service (LUIS): commands recognition; the program recognizes speech, selects typical phrases, for example, “turn off the light,” and performs the necessary action; can be applied in smart homes.
  • Face API: facial recognition according to the created database; on the basis of this service, the project for partially sighted users was developed.
  • Computer vision API: allows cataloging images on the basis of tags.
  • Video indexer: allows searching images/events in a video on the basis of tags; for example, select goal shots in a football match video.

For detailed information, contact our specialists.

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About the author

Natallia Sakovich
Natallia Sakovich

A copywriter at SaM Solutions, Natallia is devoted to her motto — to write simply and clearly about complicated things. Backed up with a 5-year experience in copywriting, she creates informative but exciting articles on high technologies.