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AI, Featured, Technology

Human Centric AI Paradigm: How Does It Get Better?

The implementation of AI and Machine Learning in different sectors is increasing with each passing day. Deemed by industry experts as the most advanced specimen of human technological progress, it is still a developing market. Theoretically conceived during the 1950s, the AI industry is still developing and may take another couple of years before encapsulating almost all aspects of human daily life. This article will shed light on the special case of human centric AI, along with its implications and possible future.

Superhuman Intelligence: The Basics of AI

In simplest terms, AI refers to the intelligence displayed by machines. From the first computers, humans have technologically advanced at an exponential rate. Giant leaps have been made in AI developments with the help of programming, ML, Deep Learning, Data Analysis. The machines developed today are capable of “learning”- acquiring insight from routine observation and using them to improve performance and capabilities. 

Artificial Intelligence and the Future of Humans

Human-Centric AI learns from inputs made by humans and cooperations. The AI model concentrates on algorithms present in a bigger environment centered around humans. These models display consistent improvement from observing human input. They improve the interaction between humans and machines.

The Human Centric AI Concept

The primary focus and implementation of AI technology revolve around the augmentation of human workers in different designations and areas of automation. Experts foresee the trend to be soon transforming markets. Human effort will be delegated only to areas that actually require human intervention. 

Viewing AI as a performance-augmentation tool makes more sense than assuming it to be a superpower of some sort. In its bare fundamentals, AI strives to add the element of “science” in work, as humans supplement with the “art”. The symbiotic combination will drive work in the future. Human centric-AI acts as an interface between these two. The results are achieved by innovating machine learning with the aim of comprehending human patterns- behavior, emotion, and language. 

Impact of Human Centric AI on Business

From the business perspective, human centric AI solutions apply aspects of human science and combine them with high-quality data to recognize and apprehend the customer mindset. This is used to decode consumer patterns across different markets. The technology successfully integrates knowledge of human behavioral patterns with data science. The derived context analytics can help an enterprise to make a major positive change in the customer service experience. Implemented strategies have a much larger chance of becoming successful, if the decision is made after knowing what consumers like, dislike and expect.

To sum it up, a human centric AI improves the overall business through:

  • Information-backed strategizing.
  • Trustworthiness and Scalability.
  • Comprehensive software and product development.

Human Centric AI Implementation

  • Finance Sales

Awareness and a global shift towards digital marketing can negatively impact the efficiency of a financial institution looking forward to market credit cards. This is more imminent when the target demographic comprises small and medium-scale businesses. Market research fundamentals always prescribe observing the customer and learning as much as possible. 

Small enterprises like to manage the business accounts and finance options by themselves. What may surprise tech-savvy marketers is the fact that a significant percentage of them prefer to call local banks for lines of credit- over the Telephone. 

Improvements in existing AI-based finance institution models with similar functions are not impossible. The model can include a telephone preference. As explained above, most small business owners manage finance by themselves. This information is invaluable and forms the basis of creating hypotheses, data proxies and implementing them in the existing AI model. Other factors can also be fed into the algorithm- card offer timings suggested corporate cards and more.  The improved model is much more likely to provide better insight- including points of interaction with the highest probability of success. 

Also Read: Role of AI and ML in Customer Acquisition and Customer Retention in Banking

  • Power and Utility Bill Payment

Organizations providing utility bill payment services can implement human-centric AI to improve customer service relations. The model finds its uses all over Europe. A before-and-after frame comparison is usually the best for analyzing the impact of activities based on AI-derived insights. Global enterprises like HashCash Consultants are building AI-blockchain prototypes for electricity distribution and sales applications.

Most utility bill payment companies cease offering bonus features to their consumers after a period of time. The service is the only thing that attaches the customers and companies- upgrades and loyalty programs disappear over time. With emerging options in the energy sector, companies can seek out a fresh path to engage customers and grow.

Business organizations are using AI insights to rise above the slump. Data sets comprising parameters like energy consumption, consumer profiles, and contact logs integrate into existing AI models. The updated algorithms indexes consumer based on usage patterns. The implementations are yielding results- changing engagement topics around carbon footprints and energy cost reduction instead of conventional bill queries.

Also Read: AI-Blockchain Prototype for Energy Consumption and Distribution Patterns

AI for Humanity: The Need for a Balance

Gathering big and thick data are resource-intensive. Setting things right require walking through the fine line in between. Yes, an optimum balance between big data and thick data is instrumental to the success of any business’s AI initiatives. 

The following priorities can help in figuring out the next right step.

  • Observe upcoming business challenges with a human centric AI approach.
  • Finalize whether additional metrics will boost ROI in big data. 
  • Take time in solving issues ahead and revisit if necessary.
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