Artificial Intelligence (AI) is a technological trend that has been much talked about in recent months, not only because it is a very promising market, but also because it opens up interesting prospects and new opportunities for many companies.
US analysts estimate a global business that, in the next 10 years, will continue to grow to over $60 billion and according to Gartner Inc. it will have already reached $18.3 billion by the end of 2017.
The automation technologies described in the Forrester report impose new business challenges to be faced through the implementation of the Digital Transformation process so as not to remain out of the game.
In order to understand the most interesting opportunities that may result from technological innovation linked to AI solutions, the recent “TechRadar report on Artificial Intelligence” by Forrester shows the top ten technologies to keep an eye on:
1 Voice Recognition: a technology that allows voice and speech recognition by people and transcribes it in useful code for software applications.
It is used in particular for interactive voice response systems and mobile apps: Nuance Communications, OpenText, Verint Systems, NICE are some of the vendor names analysed by Forrester;
2Natural Language Generation (NLG): in this case, the solutions will produce text in a common language from computer data. For example, they are useful systems for speeding up Business Intelligence reporting or for customer service analysis;
3 Text Analytics and Natural Language Processing (NLP): systems that use natural language processing to perform text and document analysis.
In order to obtain an effective understanding of the content both from the point of view of syntax and semantics, these systems succeed in using sophisticated technologies to understand the tone and mood with which a sentence is expressed or written.
These are solutions that are already being used to prevent fraud and are also favoured by many companies for analysing unstructured data, such as web conversations and social networks, or to improve the effectiveness of automated and virtual assistants;
4 Virtual Assistants: although there is an ever-increasing number of chatbots, employed in customer care services and corporate help desks, technological evolution is increasingly geared towards virtual assistants, which will reach maturity over the next 3–5 years.
The latter are able to interact with humans and to carry out specific actions, especially if employed in IoT projects such as in the case of Smart homes;
5 Robotic Process Automation: one of the most mature technological trends in AI because the use of many industry-specific solutions, such as Manufacturing, has enabled the automation of many human activities and the optimizing of successful business processes;
6 Machine Learning Platform: these are pre-configured technological platforms that exploit the AI that integrates within them various functional artificial intelligence capabilities already developed and are “ready to use”, in particular with machine learning algorithms, in a simple and immediate way.
They are usually cloud solutions that incorporate mathematic algorithms, development and training kits, computing power, and infrastructures needed to work out mathematic models and use them to develop applications. Currently, they are mainly used for the implementation of predictive analysis solutions;
7 Deep Learning Platforms: platforms where technologies employ motors and automatic learning algorithms based on artificial neural networks.
These are solutions that will be used in pattern recognition and grading, complex problem solving, or Big Data Analytics projects;
8 Biometric Recognition: these are very promising solutions that will improve man-machine interaction by recognizing not only natural language, but also the face, tone of voice, body movements and, in general, the non-verbal language of humans.
At this time, it is one of the areas in which scientific and technological research is focused worldwide;
9 Decision Making & Management: these are systems and solutions that integrate logical and policy rules within AI applications to accelerate activities such as configurations, algorithm and maintenance training, with the aim of providing effective tools and data for decision-making management.
These are systems aimed at not only making better decisions but also at making the decision-making process more efficient and effective;
10 Hardware Optimization: this is one of the least developed technology areas because a company is more likely to buy and use an AI software solution for a particular project, such as chatbots and facial recognition.
However, from the point of view of current research and trends, hardware technologies designed and optimized to carry out actions and support AI applications are extremely powerful because they are systems designed with the precise aim of providing the right computing, network, or memory needed to run software and artificial intelligence algorithms.
Technological fields of greater impact on businesses
Starting from this top ten, it is important to understand what technologies might have the greatest impact within companies, through what application solutions and what type of adoption could we witness over the next 5–10 years.
The four main application areas to be monitored are:
1 Self-service customer solutions: in Forrester’s TechRadar, those with the highest adoption rate and the prospect of continued growth over the next 3–5 years.
These are physical applications that integrate AI systems to better serve and support users: for example, through an interactive kiosk or a smart touchscreen with which the user can interact using natural language.
These are solutions that may have a very large market, both as digital services for citizens and tourists, but also as innovative solutions in the retail sector (as a touch-point in omni-channel strategies where they are allied to AI-based software, virtual assistants or marketing automation solutions);
2 Robots and sales assistants: retail is one of the areas where AI could show the greatest potential, as in the case of robots used in both sales and warehouse management.
Robots with a motor-like ability similar to that of man are still under research and experimentation. Although the estimate for technological growth is expected to mature in the next 1–3 years, many companies have already started concrete projects: also with the use of robots like Pepper for welcoming and sales assistance in retail;
3 Industrial robots: robotics for the automation of production processes is one of the technological fields where AI has long been used.
With IoT, the most sophisticated data analysis and predictive analytics can record a new wave of adoption over the space of 2–3 years, especially robots intended for collaboration with humans (cobots) for better security of the workers themselves;
4 Sensory intelligence: these are areas where biometric technologies will have wider applications in order to improve the ability of software to understand (and even express) human emotions and feelings by recognizing images, sounds, body language, or facial expressions.
Sensory intelligence is applicable to many activities, including robotics and customer care and help desk solutions, and even medical care.
As mentioned, it is one of the areas where technology research efforts are combined with deep learning and artificial neural networks, but according to analysts such as Forrester, it will still be 5–10 years before we see mature and applicable business solutions with optimum results.
Where and how artificial intelligence is being exploited
Digital Over the Tops like Facebook, Google, Amazon, Apple and Microsoft, also known as the “Big Five”, have been investing in start-ups and technical skills using artificial intelligence, and just by looking at these giants, we get a glimpse of the areas where AI is already employed today and is showing solid growth potential.
The four areas where artificial intelligence is already applied
1Marketing: customer relationship management and their loyalty have always been the foremost challenges for those involved in marketing, company sales, and those who work in B2B.
Today, AI marketing-specific solutions are based on:
- voice recognition solutions used in applications such as voice servers, from Apple’s Siri to Microsoft’s Cortana;
- for the analysis of natural language (in this case for the analysis of unstructured data from which to derive habits and consumer “sentiment”);
- for the intelligent management of marketing and communication campaigns, especially in support of contextual marketing projects through which to reach their targets with focused offers, promotions and messages.
Chatbots, voice assistants and even the most sophisticated virtual assistants have also been very successful in after-sales services such as help desks, customer support and customer service;
2 Supply Chain: the optimization of the chain that goes from product or service distribution is another of the areas where AI is having particular success because it is able to connect and monitor the whole process through various analytical systems.
Some examples of usage may include:
- order management, through real-time analysis of consumption or commodity markets;
- preventive distribution management by analysing correlative data that may also relate to weather forecasts or geolocation for the analysis of people’s movement flows like on special occasions or during events, demonstrations or concerts.
Artificial intelligence systems for supply chains are also often integrated into other solutions such as marketing. For example, these systems serve to intelligently manage distribution based on certain campaigns or promotional offers, therefore predicting potential demand and consumer buying habits;
3 HealthCare: voice assistants in support of sick or disabled people are now quite commonly used and their effectiveness will mature even more when these systems start using biometric sensors and the analysis of unstructured data.
Artificial intelligence is also already in use in operating rooms with systems that perform as real assistants to surgeons and doctors.
More revealing is the use of machine and deep learning systems for the prevention of rare diseases and cancers since AI solutions can analyse data, even unstructured, such as documents, texts, and Scientific publications, with a greater speed not even comparable to that of man;
4 Risk management: as mentioned, the prevention of fraud is one of those areas where artificial intelligence has managed to best develop by becoming the technological engine of a number of solutions.
Some of these solutions can predict, and therefore reduce, the risk of fraud or loss of data or money by making real-time correlations of large amounts of data, including those related to consumer behaviour.
Today, most business projects focus on these four areas where Marketing and Healthcare dominate.
As for the latter, Deepmind Health, purchased in 2014 by Google, is one of the companies launching new initiatives that combine healthcare and AI and has launched a project that can deliver better services in very little time, extrapolating an extraordinary amount of medical information in minutes.
An example showing AI applied to Marketing is Netflix, who have made the most of this technology: currently 75% of movies and TV shows viewed by members are suggested by the recommendation system.
However, over the next 5 to 10 years, technological development will sprout many other application fields such as tourism, food & beverage or real estate, thanks to continuous improvement of AI solutions and more widespread confidence by companies that will start to evaluate, experiment and adopt different applications in a variety of sectors.
