Research in the corporate and business world has shown that enterprise has gotten into a new era, with the data application taking a center stage. Sometimes back, artificial intelligence (AI) used to be a realm of science fiction, only to emerge as part of the daily business and operations. In the current century, organizations are better placed when it comes to adopting machine algorithms in the identification of trends together with insights into vast data. Basically, the approach enhances the decision-making process, with less time directed towards the same, and potentially positioning companies to be highly competitive in real-time. On that note, this paper seeks to provide a detailed analysis of the different AI techniques applied in the corporate world, as well as their integration in small business.
Challenges facing Artificial Intelligence Adoption
Studies by Hashiguchi (2017) have confirmed the presence of a few technologies that players in the corporate world are quite excited about as compared to AI. Basically, AI has the capability of reshaping the operations of companies across different functions such as marketing, customer service, and finance. However, the emergence of different technologies, quite a number of challenges has come into play, with the AI presenting no shortage of the same. Below are some of the obstacles in the realization of AI potential: Data AccessJust as Kowert (2017) puts it, the lifeblood or rather engine of the digital world as well as for organization seeking to embrace AI is data.However, access to data has emerged as a major challenge, inhibiting the adoption and success of AI application in the corporate world. It is for this reason that quite a number of organizations have embarked on investing heavily in infrastructural development with regard to data collection and storage. The heavy investment has further been directed towards recruiting talent that can successfully make use of the availed data.
The absence of Emotional Intelligence
Players in the corporate world have been increasingly looking to embrace AI technology as a way of enhancing their customer service efforts. For instance, companies have been coming up with AI-powered chatbots, to enhance client interaction through such platforms like Facebook Messenger. Despite all this, the AI applications have been lacking emotional intelligence as a relevant factor in ensuring its success. The system has failed when it comes to demonstrating empathy, which in turn has inhibited realization of AI objectives through such applications like chatbots. Inability to Collaborate Studies by Vejendla and Enke (2013) have put it clear that success in marketing campaigns calls for coordination of different specialized tasks. This implies that organizations should embrace the corporation of various AIs, as a way of ensuring that the approach takes over the entire product awareness campaign. However, coming up with this kind of collaboration and self-driving marketing campaigns is quite complex and costly, thus a challenge.
Artificial Intelligence Techniques Case-Based Reasoning
Case-based reasoning, abbreviated as CBR, is an AI technique entailing the process of coming up with solutions to unsolved problems, with the approached following the principles defined by the pre-existing solutions of same nature. Shokouhi, Skalle and Aamodt (2014) argue that the CBR technique is quite analogous, where the individual is expected to come up with a solution for the problem presented to him/her. While undertaking a review on the past problems and the solutions associated with the same, the act is referred to as CBR. However, they also come with some pros and cons over the human experts.
Some of the advantages include:
The major benefit that comes with the adoption of CBR is consistency. Considering the fact that ES is computer-based, logics are programmed or rather integrated into the same. Once the programming is in line with the intended solution, they provided the same decision whenever they are expected to. This is based on the fact that the decision-making process will be defined by the same principles and logic.
Memory is another advantage of the CBR. The system comes with huge memory for storage of vast knowledge, which is easily accessible as well. Unlike other techniques, the ES gives no room for memory loss. Despite the above advantages, the CBR technique comes with disadvantages, among them including:
The system requires a regular update, which in this case has to be done manually. This implies that the CBR does not learn, hence making data entry as a major disadvantage. Considering the dynamic world, the CBR has to be updated manually, where experts in that domain are called upon to step in.
Time and Cost
The initial set up of the CBR is quite resourceful, with respect to time and cost. Gaining the knowledge for developing and updating the CBR requires a lot of time. It is more unfortunate that there are a few knowledge engineers who can do the same, and the available ones are quite expensive.
According to Vejendla and Enke (2013), neural networks (NN) refer to a paradigm of information processing integrating the principle of information process by biological nervous systems like the human brain. The fundamental element of the NN paradigm is based on the novel structure that comes with the system of information processing. Basically, the system comprises of numerous interconnected processing elements, referred to as neurons, perfuming their functions in unison in the process of solving a given problem. The adoption of NN comes with various advantages, among them including:
Unlike other systems, the NN has the capability of learning organically. Basically, this implies that the system comes with networks whose outputs are not limited by data, which in this case is the input. Similarly, they are neither limited by the outcomes supplied by the expert system during the initial phase. They independently generalize their outputs; a key ability for robotics recognition systems.
Nonlinear Data Processing
With nonlinear data processing as a key feature, the NN systems can come up with a shortcut which facilitates the process of generating solutions that are computationally expensive. Furthermore, the systems are better placed to infer connections between the relevant data points, instead of relying on data source for explicit linkage.
One key advantage of NN is the potentiality towards high levels of artificial tolerance. Whenever they are scaled across a number of machines and servers, they are designed in such a way that they can successfully route around nodes that have communication problems. Other than the above identified advantages, the NN further comes with a number of disadvantages, among them including:
The NN can only work when installed with processors that have parallel processing abilities. Basing on the above assertion, the achievement of system’s objectives become independent.
This is the major problem that comes with the adoption of the ANN approach. When the system develops a probing solution, no clue is offered to support the reason and process.
According to Koch and Wäscher (2016), genetic algorithm (GA) is an approach used in seeking solutions for optimization functions and based on the idea of natural selection. Basically, the prove entails repeated modification of a population made up of individual solutions in the problem of seeking a solution for different optimization problems that have not been defined for standard optimization algorithms. Just like other techniques, the approach comes with its own advantages such as:
Non-dependence on Error Surface
Execution of the genetic algorithm does not rely on the error surface. The non-dependency paves way for solving multi-dimensional problems. The solution extends to problems that are not continuous as well as those that are non-differential in nature.
Solution Solving Process
Through the structural genetic algorithm, the GA technique is better placed to simultaneous come up with solutions for structure and parametric problems. Other than the advantages, the approach comes with various disadvantages, among them including:
Unlike other approaches, the GA fails to scale with complexity. In other words, whenever there is an exponential increase in the population of elements exposed to mutation, a rise in search spaces accompanies the same. As a result, it becomes quite challenging to adopt techniques in designing different structures. TechnicalityThe only way through which the problems can be made tractable with respect to evolutionary search is by breaking them down in the simplest forms to facilitate their representation.
Rule-based systems (RBS) are considered as number in the simplicity index, for the various forms of AI. The technique embraces the principles of information presentation and coding, in the respective systems. It is this set of rules that makes decision on the way forward and elements for inclusion in various situations. As the simplest techniques of AI, its adoption comes with advantages such as:
The approach captures emergent phenomena. In this case, they offer a natural environment for analyzing a number of systems.
The model is flexible, and most specifically when the work entails coming up with the geospatial model. The techniques come with some advantages like: Only useful for the main purpose for which it was defined for. It has to be constructed at the exact level of description for each and every phenomenon.
Solving a Problem Using Case-based Reasoning
Problem Statement: The chief executive officer (CEO) at Dominion Firm, who doubles as the production officer, wants to get away with the production of rice and cotton for a while. He has managed to save up to $800, which he plans to use on the highly anticipated vacation. He has an off that goes up to five days from work.
When seeking a vacation, the CEO is expected to identify the areas interest in the region he is to visit, and come up with a plan to realize the same. The plan calls for complete knowledge of the region, when the tourist sites are open to the public and the cost attracted by the same, among others. All this information is modeled by use of CBR, as shown the in following screen-shots: nmWith the CBR technique, the above information is presented using CLIPS. This will basically entail coming up with templates through utilization of the CLIPS deftemplate command, followed by data storage.
The next step will be pattern matching, where decisions are made on how to develop partial matches by use of the availed rules. The coding will be done on regular basis, considering the fact that the respective partial matches are addresses each problem. Further AnalysisIn a given work entailing the sale of specialty teas, the approach should go for the same type of AI systems, as for the online and storefront used in the same. This is largely based on the fact that the enterprise will be selling the same items. In the case of a small business, the organization tends to have fewer resources. This will inhibit the organization from going for decision support and AI. Basically, decision support systems are adopted for the purposes of gathering data, subjecting them to analysis as well as shaping them. Through this approach, organizations can come up with sound decisions as well as construct strategies. In the case of a smaller business, they use AI tend to seek less information as compared to larger enterprises.
Conclusion and Recommendations
From the above discussion, it is clear that the adoption of machines algorithms in the identification of trends together with insights into vast data have improved efficiency and the general corporate decision making. Organizations embracing the AI approaches have reported cases of enhanced decision-making process, with less time directed towards the same, and potentially positioning companies to be highly competitive in real-time. Despite all this, it is clear that different techniques of AI come with their advantages and challenges as well. As a way responding to the above assertion, organizations should go for AI techniques that are quite appropriate with respect to the type of data available; the resources directed towards the same and defined outcomes or expected objectives. With respect to the general challenges coming with the adoption of AI techniques, companies should consider undertaking the following measures: Embark on investing heavily in infrastructural development with regard to data collection and storage. The heavy investment should be directed towards recruiting talent that can successfully make use of the availed data.