Hey Alexa, Are you more intelligent than humans?
Alexa will entertain you with some uncanny answers.
From the customised advertisements on websites, chat-bots, captchas, or search engines like Google and Siri, you are deliberately and consciously made to interact with the Artificial Intelligence (AI) of machines every waking moment of your life.
Therefore, this blog will uncover the technologies behind Alexa: ML/AI in Business, their Benefits, and a few Tips.
Metaphorically, non-AI/ML machines are like grapes, while AI/ ML machines are like a bottle of wine that keeps tasting better with the passage of time.
In simple terms, AI is the capability of machines to imitate and showcase human-like intelligence.
And ML is a technique within AI, to assess the incoming data, learn from and mimic human-like cognitive functions, give an appropriate result, and reprogram themselves by learning from its solutions.
This is done using techniques like Deep Learning, Neural Networks, Predictive Analytics, and many more.
The grandness of these disruptive technologies can be estimated by corporate giants investing about $50 billion by 2022 in AI systems, which will reach $110 billion by 2024.
In 2022, approximately 92% of companies investing in these technologies have already started to gain returns on their investments.
So, having noted the importance of the topic, let us dig a bit deeper and learn more about AI and ML for businesses in this blog.
- Learn about Artificial Intelligence (AI) in Business
- Also, Learn about Machine Learning (ML) in Business
- Read the AI/ML Business Benefits
- Know the Tips for applying AI/ML in Business
Artificial Intelligence in Business
The buzzing high-tech AI techniques of Machine Learning, Deep Learning, NLP (Natural Language Processing), Robotics, Image Recognition, and so on, are like a metauniverse against Earth’s rudimentary mechanical tools, like hoardings and mechanical vehicles.
The increase in efficiency, human-like task performance, logical reasoning, cognitive learning, the capability to self-learn implicitly, and dynamicity in customer-centric products and services make them a revolutionary technology of the 21st century.
Here, the target is to make data-driven decisions against repetitive, high-volume, and rule-based problems unique to the business that usually lead to human-related errors.
So, let us see the seven best use cases of AI in businesses that can help them out in addressing their queries:
1. Market Research and Sales
AI sorts out customer preferences, offers personalised messages to the target audiences, and predicts customer needs and choices to streamline the product and market it to consumers.
For example, IBM’s Watson Studio leads the market in deep and thorough market research software.
2. Digital and Email Marketing
The AI machines assess and analyse the most influential content, posts’ timing, customer behaviour, keywords to use, and many more. It can create a large amount of marketable and trending content.
3. AI Robots for Finance
AI-based robots hone the skills of rule-based advisors to create financial portfolios and help make data-backed decisions, supporting the economic backbone of the company and its assets.
4. AI in Decision Making
These machines facilitate businesses’ engagement with the public, understand their satisfaction better, and use factual information to give rules-based solutions while strengthening the company’s finances, which are usually complex to manage.
5. AI in Medical
AI-Robots offer seamless services as virtual nurses assisting in surgery, as virtual-advisor to patients, or as pharmacists.
6. AI and Education
It can customise the courses for students and assist teachers in clarifying which courses, subjects or topics need more attention according to students’ learning capabilities and histories.
7. AI and Agriculture
Using AI, farmers can dynamically test the soil health or learn about pests’ attacks per the geo-climatic conditions, assist in vertical agriculture and smart food agriculture, develop new agri-based food products, and support the sustainability of natural resources.
Machine Learning in Business
We interact with technological processes of AI like ML and Deep Learning (DL) day in and day out.
You search for a product on an e-commerce website, and later you see advertisements for the same product appearing everywhere on web pages and social media posts. Quite spooky, right?
This is because the raw material for ML is the big data from consumers’ credentials, search history, bank transactions, pictures, location history, consumer purchasing habits and preferences, browsing patterns, market trends, the popularity of products and services, and so on.
The relearning and cognitive improvement methods make computers behave like humans and perform tasks like thinking, reading, writing, learning, problem-solving, decision-making, and interacting humanely.
Some examples of machine learning are Google Maps location tracking, Fitbit tracking your health data, booking cabs in real-time and dynamic food-delivery services.
We will read some of the significant use cases of ML in businesses here:
Companies use ML in predictive maintenance for equipment failures, determining faulty products, and automating the inspection and supervision processes.
The machine will gain inputs on demand, supply, industry-related conditions, cost-cutting, customer-related conditions, etc. and help maximise revenue by deciding the prices.
Automated Chatbots and Voice Assistants
The voice assistants of today, like Alexa, Cortana, and Siri, take charge of redundant or repetitive tasks for humans, streamlining their preferences and enhancing customer satisfaction.
Stock Market Trading
Sentiment analysis by assembling data from various digital platforms, news, surveys, etc., of people and businesses, predicting the potential risks and stock prices, credit scoring and underwriting, and so on.
Detect phishing attacks, malware attacks, hacking and information breaches; strengthen cybersecurity, conduct fraud detection; use NLP to analyse emails, identify good or bad bots; detect loopholes in the cybersecurity of browsers; and so on.
Customer experience optimisation for purchase patterns, pricing, competitive analysis, recommending curated products and services to customers, and online search history pattern recognition.
The new-age cars from the Bond movies have become a reality in the shape of automated, self-driving cars.
In Space and Non-space Scientific Applications
ML can recognise unexpected patterns and distinguish different types of galaxies before we even know their existence. ML can also be used in wildfire and flood detection.
It is observed that AI helps improve collaboration within teams and enhances teamwork in almost 78% of those groups.
Benefits of ML/AI in Business
The benefits that AI and ML offer to businesses and people are as follows:
1. Innovation, efficiency and high economic value
Through deep business insights and fact-driven solutions, leading in the competition-driven world and driving business growth.
2. Improve operational processes
Automating the processes, recognising patterns, making predictions, and improvising the product.
3. Robustness and Algorithmic interpretability
The system’s robustness, privacy and security, fairness of the outcome, interoperability, user-friendliness, and minor human-machine interaction are some of the features of AI.
4. Remove monotonicity of workforce
Drastically reduce mundane tasks for a large industry workforce and secure better socio-economic opportunities for people.
5. Remove life-threatening risks
In industries with risky tasks, companies can replace humans with AI-embedded intelligent robots, for example, in mining and resource exploration missions.
6. Reducing human-related errors
It will increase task accuracy.
7. Cyberstructure and financial security
Integrating AI/ML machines and cyber-infra with buzzing blockchain technology.
8. Revolutionise Metaverse and Art world
The GPUs (Graphic Processing Units), blockchain technology, NFTs (Non-Fungible Tokens), and fast processors have revolutionised the automotive, AR, VR, and Metaverse industries.
These techniques hold the potential to alter how you perceive the world, for example, wallpapers, machines, art, aesthetics, commercial advertisements, and many more.
Tips for ML/AI in Business
Here are the best six tips for you to use AI/ML in business as follows:
- It is essential to understand AI/ML’s fundamental principles, potential benefits, limitations, implications, etc., to not get wrecked by these disruptive technologies.
- While still being a niche area, these unconventional technologies raise emotional and ethical issues., Detecting the truth or lying, facial recognition inclusion and exclusion errors, and moralistic errors are the grey areas that demand more clarity, research, and countermeasures.
- The query should address either one of the situations of repetitive, high-volume, or rule-based problems that are unique to the business. This novel technique should be used for significant problems, not trivial ones.
- The prerequisites to work miracles with AI/ML are unlimited virtual data, affordable data storage options, and less costly and fast computing systems.
- Keep tweaking the input data and search queries per the business’s requirements for suitable solutions.
- Data should be fed with appropriate context to find relatable correlations.
AI and ML, the technologies of the golden age of the 21st century, are leading us to the cusp of the 4th Industrial Revolution, demanding us to benefit from them for the betterment of humanity.
AI has the potential to handle a huge palette of tasks exceeding the limits of human capacity or where there is scope for human-related errors. It showcases human-level psyches and offers an endless sea of possibilities.
The world’s leaders and business houses, along with all the stakeholders, need to conduct appropriate studies to churn out the best products and services out of this technology.
Also Read : Future of Technology in Business
Frequently Asked Questions (FAQs)
1. What are the benefits of machine learning in business?
Machine learning in businesses is used for cognitively strategising, preparing efficient business plans, and framing actionable consumer service plans. It ensures cybersecurity, innovation, efficiency, and high economic value in a competition-driven world. ML has automated and improved operational processes, recognising patterns, making predictions, and improvising the product.
2. What companies use machine learning?
Some examples of machine learning around us are Google Maps location tracking, Fitbit tracking your health data, Ola or Uber booking cabs in real-time, Zomato or Swiggy dynamic food-delivery services, and customer-centric product recommendations on Amazon or Nykaa or Instagram, etc.
3. How can artificial intelligence be used in business?
AI is the capability of machines programmed to assess the incoming data, learn from them and mimic human-like cognitive functions, give an appropriate result, and reprogram themselves by learning from the feedback datasets of the process, using techniques like Deep Learning, Deep Learning, Robotics, Facial Recognition, Natural Language Processing (NLP), and so on.
4. How is machine learning used in business?
Companies embed machine learning techniques to extract meaningful data from crude datasets like personal credentials, and search histories, recognise patterns using deep learning and neural networks, make appropriate predictions, and reincorporate the analysed information into the feedback loop to get a suitable solution to the particular problem.