Analyzing the Disruption Potential of AI: Exploring Human Relevance

The industries such as manufacturing, logistics, customer service, and data analysis have deep-rooted and complex processes.

Defining each function to a bot and making them work on it will take much work for humans. People say it can lead to job displacement and require workers to acquire new skills.

AI has always been termed as a disruptive force in different industries and sectors. But if we analyze it deeply, we can see that this AI is not disruptive for us. There are many fields today where AI is used widely. Some are automation, decision-making, personalization, financial services, health care, transportation, and creativity. Let us analyze these few sectors and understand how a human brain or human thinking can be replaced with the artificial thinking of a bot. Let us check and see how it can behave in a certain manner to help humans solve many issues and get to that level of solving the problems where it can be a threat to humans and their capability.  
Automation is where we create bots that will replace humans in doing the tasks and processes humans usually perform. I am into the Automation process in my company, and for the last 5 years, I have seen how difficult it is to create a bot that will perform different and complex tasks, which has always failed. The only task it has accomplished is repetitive and very simple. That also requires human intervention when it fails. It still needs to be fully automated but only semi-automated, even after using all the tools of AI and automation. Why is this happening? This is happening because the AI bot needs 100% perfect things in place, and only they can perform at their best. Even when one of the things is not in place, it fails, and immediately we need a human to check and put everything in place. Thus, automation of processes and tasks take much work to achieve, and it will require a person to always keep an eye on it. The more AI and bots we use and add to the system will make the system more complex to manage; it will require people with new skills; instead, there will be no job displacement, but jobs will increase if we increase the bots and cobots in our system. 
Decision Making: Decision-making for humans also is a very tough task. It requires all the details and the SWOT analysis to be done for any sector, say finance, healthcare, marketing, and transportation; it requires not just one person but the entire hierarchy of the organization to make some big decisions, and if we leave this to the AI and bots to decide it will be a very disruptive thing, since with a single wrong decision many things get affected. It can turn things upside down in an organization. Can you rely on a machine and AI capability to make such decisions without enough knowledge acquired by the AI system? For example, in the IT industry, decision-making plays a crucial role in determining organizations' direction, strategies, and allocation of resources. The technology adoption needs evaluation and selection of the appropriate technologies and tools for their organizations. This involves assessing the needs of the business, considering available options, and making informed decisions about hardware, software, networking infrastructure, and cloud services. Factors such as scalability, security, compatibility, and cost-effectiveness are considered during this process. 
The very skilled resources in IT also sometimes cannot judge correctly that such and such tools and technologies be selected for the project and many experiments are happening yet above that, yet every day, new technologies keep adding up in the stack, making it more difficult to select. Then comes the resources; if we choose a particular technology that is very new, will we get those resources who will know those technologies, and will they be able to start work from day one? Then comes project planning, and the decisions regarding project management methodologies, team composition, and procurement of necessary equipment and software tools are also made during this phase. Then comes IT Infrastructure Management, which is related to hardware, networking, storage, data centers, server architecture, virtualization technologies, network design, data backup and recovery solutions, and security measures. IT decision-makers must consider scalability, performance, reliability, and cost optimization factors while making infrastructure-related decisions. 
Security and Risk Management involves evaluating and implementing security measures to protect the organization's data, networks, and systems and deciding about implementing firewalls, intrusion detection systems, encryption, access controls, security protocols, compliance requirements, industry standards, and emerging threats about risk mitigation strategies. Software Development includes decisions related to programming languages, frameworks, development methodologies, and software development life cycle (SDLC) models. Factors such as project requirements, scalability, maintainability, and time-to-market, select the most suitable approaches and technologies for software development projects. 
Vendor Selection and Outsourcing often need to decide whether to build or buy specific solutions and services. Evaluating vendors and service providers, considering reputation, quality, pricing, support, and long-term partnerships. Outsourcing decisions are also made based on the organization's needs, cost analysis, and the availability of specialized skills. Then comes Data Management, which determines data storage, integration, and analytics strategies—data security, accessibility, scalability, and compliance with data privacy regulations. 
Decisions may include selecting database management systems, data warehouses, business intelligence tools, and data governance practices. Effective decision-making in the IT industry requires a combination of technical knowledge, understanding of business objectives, consideration of risks and opportunities, and awareness of industry trends. It involves evaluating alternatives, assessing potential impacts, and making choices that align with the organization's goals and objectives. Can all this be evaluated and done by the AI bots or the machines to decide and then decide on its alternatives if it fails? Will it be a very difficult task? This is only the IT industry that I have taken into consideration. Going into other very old sectors will require more skilled people to make decisions.
 Personalization: AI allows for personalized experiences and recommendations tailored to individual users. This has disrupted how businesses interact with customers in e-commerce, entertainment, and advertising. By leveraging user data, AI algorithms can provide personalized product recommendations, content suggestions, and targeted advertisements. The best experiences are when we go to some place and experience on our own, and the best offers we get from our friends. Have you ever gone to any shopping website and often found some weird recommendations by the AI and wondered why these recommendations are given to us? In the entertainment industry, content recommendations, customized playlists, targeted advertising, and interactive experiences have been created for people to take advantage of it. Still, given this, people need help with huge and unnecessary advertisements and useless product recommendations. 
Personalization in e-commerce contains customized offers and discounts, personalized email marketing, dynamic pricing, user-focused search results, and customized user interfaces. Personalization in e-commerce aims to create a more engaging and tailored user shopping experience. By leveraging user data and personalization techniques, e-commerce businesses can increase customer satisfaction, drive conversions, and foster long-term customer loyalty. Personalization in advertising as tailoring advertisements and marketing messages to individuals based on their preferences and behaviors to deliver targeted and customized ads to increase relevance, engagement, and conversion rates. 
Targeted Advertising is to target specific demographics, interests, and behaviors. Here AI can play a good role by analyzing user data such as browsing history, search queries, social media activity, and demographics; advertisers can deliver ads that are more likely to resonate with individual users. This increases the chances of capturing their attention, generating interest, and driving conversions. Retargeting and Remarketing, Personalized Ad Content, Contextual Advertising, Location-Based Advertising, and Personalized Ad Experiences are some places where AI can deliver optimum results. 
Personalization in advertising helps advertisers deliver more relevant and targeted messages to consumers. By leveraging user data and employing personalization techniques, advertisers can improve ad effectiveness, increase engagement, and drive better returns on investment. However, it's important to ensure that personalization is conducted ethically and transparently, respecting users' privacy and adhering to applicable data protection regulations. 
Healthcare with AI
AI is shown to transform healthcare through various applications, including disease diagnosis, drug discovery, and patient care. Machine learning algorithms can analyze medical data, identify patterns, and provide more accurate diagnoses. AI-powered robots and virtual assistants can enhance patient care by giving reminders, monitoring vital signs, and assisting with basic medical procedures. Healthcare refers to the system, services, and practices involved in maintaining and improving the health of individuals and communities. It encompasses various activities, from diagnosing and treating diseases and injuries to preventive care, health promotion, and support for overall well-being. Healthcare involves a multidisciplinary approach that includes medical professionals, healthcare facilities, pharmaceutical companies, insurers, policymakers, and other stakeholders. Here are some key aspects of healthcare: Medical Services, Preventive care, Pharmaceutical Services, Allied Health Services, Health Systems and Infrastructure, Health Information Systems, Public Health Initiatives. 
The healthcare sector is vast and constantly evolving as new medical discoveries, technologies, and approaches emerge. It plays a crucial role in addressing health challenges, promoting well-being, and providing individuals with the necessary support and resources to maintain and improve their health. In the healthcare sector, the HIS (Health Information System) is where AI can be introduced and used to support the records of the patients, which can be easily done and managed by AI tools. But other things like doing the checkup and then providing the prescription may fail as that requires expert doctors to decide and to prescribe. AI has a very limited role in this field since it involves the lives of the people, and no one can play with the lives of people being the fundamental right of people to live a healthy and nice life.  
Financial services with AI
Financial services refer to various services provided by financial institutions and other organizations that manage money, assets, and financial transactions. These services help individuals, businesses, and governments manage financial resources, achieve financial goals, and mitigate risks. Let's see where AI and bots can help and how much they can help us in financial services:
Banking Services: Banks are at the core of financial services offering a wide range of services, including savings accounts, checking accounts, loans, mortgages, credit cards, and other financial products. They provide essential functions such as deposit-taking, lending, and payment services to individuals and businesses. The AI can do only the repetitive tasks in this, but a human must check if these are correctly done.
Investment Services: Financial institutions, such as investment banks, brokerage firms, and asset management companies, offer investment services. These services include providing investment advice, executing trades in stocks, bonds, and other securities, managing investment portfolios, and offering investment products like mutual funds and exchange-traded funds (ETFs). AI can provide some suggestions but only possible to do some of the processes here.
Insurance Services:  Reinsurance Services, Financial Planning and Advisory Services, Payment and Transaction Services, and Wealth Management are where AI can manage and keep track of it. Financial services are crucial in supporting economic growth, facilitating commerce, and providing individuals and businesses with the necessary tools to manage their finances effectively. These services are regulated by financial regulatory bodies to ensure transparency, stability, and consumer protection in the financial industry. But AI has very little to contribute here as these are money matters, and money does matter a lot; hence, we must be extra careful in these transactions. 
Transportation with AI 
Transportation refers to moving people, goods, and services from one location to another. AI has a good role to play in this. Transportation systems involve a wide range of infrastructure, vehicles, and services designed to ensure the efficient and safe movement of goods and individuals. The AI can monitor the Signals; the cameras can monitor the movement of the vehicles. Here are some key aspects of transportation: Modes of Transportation include Road Transportation, Rail Transportation, Air Transportation, Water Transportation, and Pipeline Transportation for oils, gas, and other commodities. Infrastructure, Public Transportation, Freight and Logistics, Personal Transportation, Safety and Regulations, and Sustainable Transportation. Artificial Intelligence can, through its humongous amount of data, regulate the flow of transportation, including Road, Rail, Air, and Water. The signals can be automated and monitored with AI but with human intervention. Transportation is a fundamental component of modern society, enabling economic activities, social interactions, and the exchange of goods and services. Efficient and well-developed transportation systems are essential for economic growth, regional connectivity, and overall quality of life. 
Creativity is one thing where humans generate new and original ideas, concepts, solutions, or expressions. It involves thinking much thinking and approaching things in unconventional ways, breaking free from established patterns, and producing innovative and unique outcomes. Creativity is not limited to artistic or aesthetic domains but is applicable across various fields, including science, technology, business, and everyday problem-solving. Some key aspects of creativity are Novelty and Originality, Thinking Outside the Box, Flexibility and Openness, Problem Solving, Innovation, Imagination, Expression, Risk-Taking and Resilience, Context and Relevance. All this is not possible for the machine that is learning from the content that is already existing, and it has no brains to think out of the box since it has a pattered thinking process. 
If we also get into the deep learning process of a Machine, it is based on the different layers of filtering the data, which again relies on the already existing data. New ideas can only be done when people have seen all the alternatives and want to develop some new innovative idea. For example, we have heard that while inventing the electric bulb, Thomas Edison had 1000 failed experiments, and then 1001 were successful. All these innovations and thinking are not possible for an AI. Also, if we got into the music field or writing songs and lyrics are innovative forms of creativity that require a different level of thinking.
It's important to note that while AI cannot be that disruptive and as we have seen above in many cases at a very subtle level and in some cases a bit deeper but still not that disruptive, it can bring some benefits and opportunities with it, through which the humans can only prosper. However, its widespread adoption, which is unnecessarily overrated, requires careful consideration of ethical, privacy, and security implications since there are no job losses. Still, the more the AI gets deeper, the more humans will be required to manage the machines. Now it may seem that one device can replace two resources, but in the future, it may happen that to operate one machine, we will require two resources., If there are unproductively add-ons of the machines and AI and bots where it's optional. The best way to tackle these AI machines and bots is, to begin with very simple and repetitive experiments that will take much time to resolve and then get into deeper and more unique cases of manual intervention. Also, the last should be health care and finance domains since these should operate in highly efficient and effective environments.
The author is Manager - IT at Atos India
Image Source:   Freepik  

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