There have been so many articles published about AI and its applications, you can find millions of articles on the internet. You must be tired of hearing quotes like, ‘data is the new oil’ and whatnot. This article is aimed at presenting consolidated information about AI for business in simple language.
AI for Business
The widespread adoption of AI technology is fueled by 3 major challenges that businesses have been facing since the last decade.
Fast shifting trends in consumer behavior
Uncertain economic conditions
Intense competition at every level
And internet penetration is the main reason behind all three, to the point where data analytics is your safety net first, and business driver second.
As a result, finance, logistics, healthcare, entertainment media, casino and eCommerce industries witness the most AI implementation and development. These industries accumulate ridiculous amounts of data on a daily basis. You can also notice real-estate, education and automotive industries trying to leverage data for a better competitive advantage.
Let’s take a look at some facts and figures here-
By 2025, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance. (Source: Gartner Research)
97.2% of organizations that participated in an executive survey back in 2019 claimed they are going to be investing in big data and AI. (Source: TCS)
By 2025, AI will be the top category driving infrastructure decisions, due to the maturation of the AI market, resulting in a tenfold growth in compute requirements. (Source: Gartner Research)
85% of AI (marketing) projects fail due to risk, confusion, and lack of upskilling among marketing teams.(Source: PwC)
AI Adoption and Data Strategy
There are many applications of AI that are helping SMEs and large corporations alike. With respect to the PwC research, we ran a poll asking users what their concerns were regarding the implementation of AI. The top two concerns were-
- Uncertainity about Return on Investment
- Lack of a solid data strategy
First, it is in your best interest to do your own research. Talk to friends and professional consultants. Approach data services companies like ours. How you can make AI work for you is the question you must ask, based on your burning business challenges. You will get enough insights to come to a decision.
In order to adopt AI solutions for your business, the best way forward is to first ensure that you have a strong data strategy in place. Data strategy allows you to build a roadmap to adopt AI. A roadmap that highlights the main challenges you want to solve and the goals you would like to accomplish using data. If you already have a data strategy in place, then it is easier to identify and analyze where AI would be the most useful for your business.Analytics Insight has an informative blog on the wide range of use-cases of AI in prominent industries. Worth a read if you are brainstorming on AI strategy. More use-cases are being tried, tested and built every day, the innovation in this field will not cease for the next few years.
Applications of AI
AI in Marketing
Marketing is known to have the most use-cases when it comes to leveraging AI. AI helps break down consumer data into key insights. The more you know about an individual or a business, the easier targeting and hyper-personalizing becomes.
Marketers also have access to several AI software to save time and optimize their work at every step of the funnel. Content writing, copywriting, video analytics and customer reinvestment, all have AI applications now.
AI applications can also be very niche specific. Gaming companies use AI for segmenting players and predicting churn rates in order to retain them through effective campaigns.
AI in Finance
Not just banking and financial services, but many organizations use big data and AI to forecast revenue, exchange rates, cryptocurrencies and certain macroeconomic variables for hedging purposes and risk management.
High-frequency trading machines or HFTs use AI for making intraday trading simpler. AI is used for investments, automating accounting, fraud detection, claims prediction, credit scoring and risk profiling among others.
AI in Supply chain and Logistics
Tasks that include billing, scheduling, operating machines like forklifts and workforce management can be enabled with an AI-driven warehouse management system, fleet management system or freight management system.
Integrating IoT and route optimization are two other important places that use AI.
AI in Healthcare
The healthcare industry stores ridiculously high amounts of big data- both structured and unstructured for research & development, population health management, technological innovations, patient health history and their medical reports management.
Using AI for simplifying such complex tasks meant saving time and capital while significantly reducing the room for errors.
AI in Ecommerce
Customer satisfaction is the single-most priority that this entire industry is centred around. It can only be achieved through the sophisticated use of AI. AI comes in handy for managing inventory, manufacturing, production and marketing.
Not to be confused with AI-driven platforms. AI-driven platforms are software that provides pre-developed modules for ease of use and fast insights to the business. But AI platforms like TensorFlow, MS Azure and Google AI allow large sets of data to be used for training, testing, developing and deploying AI applications and algorithms.
With the massive influx of big data, several businesses use AI platforms to help save costs in a number of ways including automating certain procedures and speeding up key activities among others.
Enterprise Artificial Intelligence
Enterprise Artificial intelligence (AI) is a common jargon used to refer to how an organization integrates artificial intelligence (AI) into its infrastructure to drive digital transformation.
Artificial Intelligence Analytics
AI can be applied to all 3 major types of analytics:
The entire journey of the descriptive and diagnostic analytics process includes data extraction, data aggregation and data mining; 3 applications where AI is widely used to reduce costs, and eliminate complex actions. A lot of testing AI methods can be utilized for better and more accurate outcomes from mining the data.
Predictive analytics is the most talked about topic of the decade in the field of data science. The aim of predictive analytics is, as the name suggests, to predict and forecast outcomes. For accurate predictions, companies now use various data models, machines and deep learning techniques to continuously improve and refine the quality of the outcome. Predictive analytics, with the help of machine learning, keeps getting more accurate with the continuous inflow of data. Revenue forecasting, exchange rates forecasting, churn prediction, and fraud detection are a few places where predictive analytics comes in very handy.
Prescriptive analytics is the most complex form of analytics. The insights evaluated using AI are directly delivered to the process control systems and their operators, closing the feedback loop in a shorter span of time without compromising on high accuracy.
To make the work of the organizations easier, various IT consulting firms like Oracle and AWS provides a diverse set of prebuilt machine learning models and AI modules that make it easier for developers to apply AI to applications and business operations.
Businesses can directly approach tech consulting companies BizAcuity if they prefer customized AI and ML solutions to enhance their operations and profitability.
As previously mentioned, there are several AI-driven software for almost all kinds of work now that you are only a google search away from finding the right software for your needs. There is AI software for all kinds of purposes from writing, data visualization, feedback analysis and more.
Just another important tech jargon, APIs are short for Application Programming Interface. API is a program that lets you connect systems/software/computers and other applications to build a functional ‘ecosystem’. When you want to add an AI software or function to your business’s big data ecosystem, the API is often referred to as an AI API.
That’s all you need to know to get started. Hope the article helped. Do let us know by commenting below how we can improve the blog and what we can add to it. Thank you!