Artificial Intelligence in Business – What is the Future in 2022?

Artifical intelligence in business

Artificial intelligence has made its way into our daily lives, allowing organizations in a variety of industries to use it to improve marketing prospects and streamline operations. Every year, new advancements in artificial intelligence are produced that organizations may leverage in a variety of inventive ways.

The year 2022 is expected to follow in the footsteps of its predecessors and leaders in a new era of artificial intelligence.

AI in Business – Present

Augment CX (Customer Experience)

The use of AI technology is being driven by a dynamic and competitive corporate climate, as well as customer interaction, to provide personalised services in real-time. Businesses in industries such as e-commerce, financial services, healthcare, and others are turning to artificial intelligence (AI) to improve consumer experience.

Users’ online experiences are streamlined, and AI assists consumers in discovering suitable products. By providing a tailored search, this technology improves the customer experience. Netflix, for example, creates a unique home page for each user based on their viewing habits, preferences, and search history.

AI-powered search saves consumers time by eliminating the need to scroll through numerous pages and avoids the aggravation that comes with impersonal searches. Furthermore, AI-assisted customer service (through chats, emails, texting, and voice) is available 24 hours a day, 7 days a week, to help businesses grow.

Because AI can evaluate large amounts of data in a short amount of time, it employs predictive analytics to generate insights that forecast brand-customer interactions. This predictive engagement provides information on how to contact with customers and when to do so.

Predictive Analytics

AI prediction tools help doctors be more efficient in their workflows, treatment plans, and medical choices. Artificial intelligence technology analyses excess data from EHR/EMR or telemedicine platforms to forecast diseases, diagnoses, patient requirements, and prescriptions.

Predictive analytics is critical for ICU patients because their lives are dependent on timely interventions. Patients’ vitals are continuously monitored, and an AI algorithm forecasts the highest likelihood of healthcare intervention based on the data. Healthcare providers take preventive measures before a patient’s health deteriorates.

Furthermore, by assisting in diagnosis procedures, predictive analytics relieves pressure on healthcare professionals. For example, Michigan State University is developing an AI software that scans language and speech patterns to diagnose Alzheimer’s signs using predictive analytics.

Researchers employed predictive analytics and machine learning to highlight risk factors for the Covid-19 disease in a study. This system can detect dangers because the severity of the Covid-19 differs from person to person.

Customer Relationship Management (CRM)

Only 12% of CRM users use an AI tool and only 11% think that AI-driven CRM helps to focus on high-value customers. However, 75% of CRM users want to switch to a new CRM platform to take advantage of AI’s potential.

Meetings, calendars, phone conversations, follow-ups, and notes are all managed by AI solutions. Customers are segmented using algorithms based on their purchase habits, demographics, dislikes, and likes. This enables companies to forecast consumer behaviour, allowing them to better estimate target audiences and outreach operations.

AI in Business – Future

AIoT – Artificial Intelligence of Things

The number of connected devices is growing, and businesses are collecting more data. Artificial intelligence (AI) integrated with the Internet of Things (IoT) gives organisations a leg up by resolving their data issues. By providing data value solutions, AIoT helps organisations become more competitive, nimble, and productive. AIoT is now being used by enterprises to improve their operations.

By breaking away from traditional cloud computing, AIoT offers AI in a unique way. To begin with, AIoT devices do not share data, ensuring privacy. Second, AIoT can make quick judgments since it doesn’t have to deal with the latency issues that come with cloud data transmission. AIoT smart devices can be found almost anywhere and are unaffected by network faults or the lack thereof. For example, due to connectivity, bandwidth, and latency difficulties, a traditional AI-powered cloud model cannot support autonomous autos. However, AIoT overcomes this problem, as well as a slew of others, across a wide range of electronic devices.

AIoT is implemented in low-cost CPUs that provide good performance over a long length of time. This guarantees its efficacy in industries like manufacturing, electronics, transportation, and healthcare.

AI Chips

AI chips are the latest business trend since they are inexpensive, tiny, produce little heat, and use very little power, making them suitable for usage in smartphones and robots.

Enabling AI computations on the chips eliminates the need to transport large amounts of data to remote places, resulting in benefits such as speed, usability, privacy, and data security. These processors are designed to handle machine learning workloads using programming frameworks like Facebook’s PyTorch and Google’s TensorFlow.

AIOps Technology

Massive amounts of data are generated by systems and applications, and this data continues to rise. Traditional IT models are incapable of intelligent event segmentation, predictive analysis, or real-time insights. As a result, AIOps is employed to handle these challenges. This technology analyses data, extracts events, and notifies IT workers of problems as well as solutions.

AIOps enhances IT functions with personal, proactive, and dynamic insights using machine learning, big data, and analytics technology. Data collecting strategies, the usage of numerous data resources, analytical, and presentation technologies are all possible with AIOps platforms.

This technology is meant to take in data from a variety of sources in IT, including apps, networks, cloud, infrastructure, storage, and more. AIOps corrects flaws and selects an automated answer to provide an accurate solution to the problem.

AIOps pinpoints fundamental problems and provides precise solutions in real time. This enables companies to set goals and achieve them based on data. AIOps teams also receive notifications when certain criteria or thresholds are fulfilled. The warnings give the most accurate diagnostic necessary to take the appropriate action. This enables professionals to make a significant and strategic contribution to enterprises.

Cloud adoption is a gradual process that ends in a hybrid multi-cloud setup with constantly changing inter-dependencies. AIOps provides a clear picture of inter-dependencies, lowering the operational risks of hybrid cloud and cloud migration strategies.


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