Artificial Intelligence and Cognitive Systems

Artificial Intelligence and Cognitive Systems
The previous study established that Artificial Intelligence (AI) is intelligence showcased by machines and is not intuitive, as shown by people and animals. Artificial Intelligence qualifies as an emerging technology as it is still developing; it is yet to be entirely accepted and appreciated and rising to prominence slowly but surely (Surblyte, 2016). Although people may have ideas about AI technology, they do not fully understand how technology can improve business operations and processes while reducing costs. The typical example is the 2017 Darrell M. West and John R. Allen’s research. It established that out of 1500, senior business leaders only a handful, less than 15% had an idea about AI usage in business processes (Miller, 2019).
Nonetheless, AI has become one of the hottest topics in the globe under discussion. Researchers are spending sleepless nights to ensure that new advancements are made in technology. Consequently, there is a need to study markets and derive the requirements which AI  can meet.
This paper examines the assessment approach to be used for future markets, develops attribute matrix for the products, identity market niches for the AI technology, and analyzes how AI technology can be used to create competitive advantage in organizations. The paper also reviews the external environment, which shapes the organization in terms of using AI products and services.

Assessment Approaches for Future Markets
Exploration and Learning
            Artificial Intelligence is a type of technology that is ever advancing as new concepts and ideas are incorporated to bring out the best cognitive system. Every day, Artificial Intelligence is making differences in lives, bringing a real change in the futuristic expectations of the society which already embraces AI products and services.  According to Khan (2018), “Artificial Intelligence has now changed and has been changing there forth, the scenario of the technology and related services, and being applied and successfully being tested in other fields.” Further insights and studies indicate the transformation of AI in the working systems, and up to the condition, the setting algorithm will be proven vital in the upcoming time.
As a result, exploration and learning market assessment approach is appropriate for this product. The method is based on active learning, which helps the researcher explore the market while identifying the needs of artificial intelligence. Every day, people come across the knowledge of Artificial Intelligence as the essence of the human brain, robotic society, and inventing systems. The experience of AI increases when we explore the way to meet current needs.
How are people getting used to AI technology? After the offset of the Corona Virus in the world, research shows that the use of virtual assistants has increased by over 65% in business operations and processes. Besides, developers of video games have advanced, and they, too, incorporate AI technology to ensure great qualities of video games are manufactured. All these are examples of the explore-and-learn approach. As they explore the needs of the market, the developers of AI products learn how to increase their market share.
Attribute Matrix for Artificial Intelligence
Artificial Intelligence is an abstract form of machine intelligence that resembles human intelligence. Some of the critical attributes of reliable Artificial intelligence include the ability to reason, solve puzzles, make judgments, plan, learn, and communicate. Besides, researchers note that sturdy AI should have consciousness, dispassionate thoughts, self-awareness, sentience, and sapience.
When AI is fully developed and advanced, it should go beyond the normal imitated human cognition to comprise problem-solving, learning, and development attributes. One key attribute for strong AI is the human resemblance. According to researchers, a machine with robust AI should go through the same developmental processes as humans, starting with a naive mind and developing a mature mind through learning. Furthermore, the machine should interact with the environment and learn from it while acquiring its ordinary sense, behavior, and language.
The graph below indicates the two attributes selected for analysis.
From the above graph, problem-solving ability and human resemblance are directly related. Implicitly, the more social a machine with AI technology looks, the better it is in solving puzzles and problems. One driving force for machine learning is the need to address complex or repetitive issues in society. As a result, AI is developed to execute such tasks as humans would do.
Technology Barriers
One of the critical barriers to the development and adoption of AI Technology into the market is human resistance. Human beings are creatures of habit.  Once we find a method of carrying out a task that seems to get the job done effectively and efficiently, we like to stick with it. It takes some prevailing before we leave the approach to a new.  Similarly, as people are used to the current technology and working strategies, acceptance of AI is limited. This is also promoted by the lack of knowledge of the advantages of AI in business processes.
Another significant barrier is the fear of the unknown.  HP Lovecraft comments that fear is the “oldest and strongest emotion of humanity. Similarly, there is a vast amount that is still indefinite when it comes to the part AI will play in our future. The growth and development of AI at workplaces leads to a fear that people are losing control and may no longer be considered as the “experts” in their field of work.
Artificial Intelligence Market Niches
One key market segment that would require machine intelligence is the healthcare sector. According to            Adam (2019), Americans spent more than $ 3trillion in healthcare annually. As the Baby Boomers generation knocks retirement and millennials enter childhood, the cost will even double. As a result, the sector may need machines adapted to work as human beings to save the cost of operations and the industry’s complexity. That way, healthcare may feel the impact of AI in a more significant way than any other area.
Another critical niche is the education sector, which requires problem-solving and to facilitate individualized learning for growth and development. One of the problem-solving Artificial Intelligence is Synap. This platform uses prognostic algorithms to assist students share questions and develops neuroscience-backed plans for each student’s needs. As it learns the preferences of each student, Synap makes available the right content to maximize information retention.
AI Drives Competitive Advantage
The AI technology influences value activities, thus allowing organizations in the healthcare and education sector to gain competitive advantage. The use of machine intelligence in the organization reduces the cost of operation, enhancing the company’s cost leadership. Besides, AI gives organizations a competitive advantage in marketing. Notably, Ai offers insights into marketing approaches, when to market, and who to market to, based on is the ability to learn and train based on the available consumer preferences.  In other words, AI can know the customer on a personal level that humans may not achieve due to different barriers. Most organizations in the educations and health sector are considering incorporating AI or instead machine intelligence services, regardless of the technology constraints that affect the adoption of machine intelligence.
Forces in External Environment
One of the critical factors that influence AI adoption is competition among firms.  The study conducted by the McKinsey Global Institute established that rivalry among organizations is one of the significant influencers of AI adoption in the market. This is consistent with game theory in which the marginal propensity to adopt AI depends on the proportion of rivals that have already decided to take. If a new technology is diffused and adopted in the market, early adopters may enjoy misappropriating benefits. However, as more organizations adopt the technology, the marginal incentive to take diminishes since technology becomes less competitively advantaged.
Another force is the regulatory authority between countries and states.  For instance, when comparing rates for AI adoption between countries, it’s plausible that the more stringent data protection regulations in Europe could delay AI adoption in European firms compared to US firms. Every country’s economy has different rates for diffusion and adoption of AI and separate costs of exploring markets. Ultimately, regulatory authorities of that economy will influence the price of adopting the new technology.

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