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Understanding the 4 Types of Artificial Intelligence: Gartner Report 2018

Artificial Intelligence, with a vast ocean of various evolutionary innovations, has marked a new epoch in the enterprise industry worldwide. There is seldom any aspect of life that has not interspersed with the tech-revolutions brought by AI. Moving away from human lives in general, Artificial Intelligence is taking the world of business beyond boundaries, providing it with far more depth and reach.

Different AI technologies are giving ways to tech strategic planners for redrawing long-term and short-term product strategies. AI is anticipated to be more pervasive in the forthcoming years. In the future, enterprises are expected to leverage AI for multiple goals spanning different industries, domains, and technologies.

Recently, Gartner’s forecast of AI enterprise levels has broken down Artificial Intelligence into four major project types including, decision support/augmentation, agents, decision automation, and smart products.  In the following content, let’s put a spotlight on these four project types that will have AI functioning as the fulcrum. Take a look:

Decision Support Augmentation

Breaking out the international enterprise value derived from Artificial Intelligence type, decision support/augmentation is expected to represent almost 36% of the worldwide AI-derived enterprise values in the year 2017. By the year 2030, decision support/ augmentation is expected to surpass every other type of AI initiative in order to account for around 44% of the international AI-derived enterprise values.

The usage of DNNs in decision support/augmentation improves AI systems that are developed on conventional statistical and analytics techniques. These are capable enough to enhance the quality of different business decisions. DNNs would let organizations perform pattern recognition and data mining across various datasets. These tools can also classify complicated inputs which feed conventional programming systems.

These programming systems can offer insights, predict events, provide personalization, and make probabilistic recommendations at a more spectacular scale than any other conventional technologies. These systems have an influence on the capacity of different companies for automating decisions and interacting processes. This new kind of automation can minimize risks and expenses in leaps and bounds. These can also enable increased revenues via an upgraded segmentation, micro-targeting, marketing, and selling.


Agents generally, count on voices or texts to interact with users in a natural language.  Agents have mainly gained their pride of place by Microsoft’s Cortana and Amazon’s Alexa. These are apparently ubiquitous in text messaging applications. They can transform the spoken words into messages, capturing several other attributes from the speech of the users. Capturing words or even intents are just a part of the issue.

These days, consumer service apps still need manual coding for dealing with a better extraction of intent, which is possible with the previous systems. Upon being implemented properly, the automated systems can handle different steps of a consumer interaction impeccably. These systems can capture the nature of an issue and identify different information. It owns the capacity of examining probable resolutions sans engaging a human assistant.

An agent can connect to a human assistant to address complicated issues in one step. These systems can drive consistent results in some domains. This apart, it can deal with different languages as well. Different cultures, as well as languages, might materially impact a decision tree though.

The virtual agents can allow different corporate hubs to minimize labor expenses as they take over various simple requests. The virtual employee assistants or virtual agents can aid in scheduling, calendaring and several other administrative tasks like feeding up the workers’ time for a higher value-added work or minimizing the requirement of human assistance.

The virtual agents are capable of accounting for almost forty percent of the international AI-oriented business values in the year 2017. It’s a matter of anticipation that by the year 2030 numerous other types of AI will grow and add to business values.

Decision Automation

Decision automation system relies on Artificial Intelligence to optimize business procedures or automate tasks.  They are effective in accomplishing different tasks such as translating voice to messages and vice versa, classifying different data contents, and processing hand-written images or forms.

The statistical process is capable of automating routing or recommending next steps dependent on heuristics, which enhance over time with experience. The importance of decision automation is paramount, especially when there is enough ambiguity and non-quantitative objects are engaged.

The rule-based repetitive procedures with a structured data could be addressed by a lot more simple RPA or Robotic Process Automation. As ambiguity and unstructured data are the staples of today’s corporate industry or decision automation. As it grows, it ensures to bring tremendous enterprise values to different companies.

At present, decision automation can account for only two percent of the international AI-oriented enterprise values in the year 2017. But, this ratio is expected to increase to some extent by the year 2030.

Smart Products

Smart products can account for almost eighteen percent of the international AI-oriented enterprise values in the year 2017. However, it would shrink to almost thirteen percent by the year 2030 because several other DNN-driven system types grow smart products in their contribution to enterprise values.

Smart products own AI-embedded in them, generally in the shape of cloud systems, which can implement data about the users’ overall performance from different interactions and systems. They can learn about their users and their overall performances to hyper-personalize their experience and drive more engagements.

A smart products’ subset is embedded with AI, which uses a sophisticated model for moving in and interacting with a robot’s environ. Some examples include factory robots and autonomous vehicles that can eliminate the necessities of putting human beings at risk in a difficult environment. A few humanoid robots have recently come into being that can interact via emotion, body language or speech and classify different tones of voice.

Final Words

Almost every discovery of Artificial Intelligence comes with its own unique trademark. Each of them emphasizes accomplishing that ultimate goal – to transform human lives. The influences of AI have percolated down to the world of enterprise worldwide. Looking at the monumental proliferation of AI, we can state that the day is not far when Artificial Intelligence would emerge as a springboard for upward enterprise mobility.

Aarsh, Co- Founder & COO, Gravitas AI



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