The answer is yes, absolutely. The only thing that can stop that from happening is the inability to create a vision around it that can feed from you or your company’s creativity. In other words, it is you who can enable the adoption of AI into your job. With this article, you will discover or take inspiration from some examples on how to adopt AI in your professional life and perform more tasks faster and more accurately than before.
We’ll introduce you to one of the most exciting tools to start this journey: our very own, Cogniflow. This is a blog about leveraging the power of AI, especially the no-code AI for many different professions and use cases. How to get more done and with better performance by adopting no code machine learning software. How the future of work and many jobs go hand in hand with the adoption of AI in every profession.
This is a question often asked but, ironically, it is not directed to the right people. Usually, we ask people in our network who, most of the time, are limited to their field of expertise and can't provide insight on what may be possible with Artificial Intelligence. Thus we miss a large part of the opportunities it has to offer us since we fail to see that the approach and adoption of AI might be easier and more practical than we think.
We live in exciting times. You see, the AI behind many IT tools is improving at a fast speed, and with it, the solutions to problems you may have, are increasing exponentially.
You might have heard about AI's potential for HR, marketing, logistics, manufacturing, or many other professional disciplines. You’re intrigued and want to know more about this new digital frontier, but where do you start?
With business leaders, scientists, and technology developers predicting a future of AI-driven innovations in every sector, how can you get ahead of the competition by looking into the capabilities of Artificial Intelligence?
We know the many applications of AI all feed from the same fuel: Data. We also know it comes in different forms, but for now, we’ll keep it simple, and focus on data that originated from text, images, videos, and audio, this also come in many different forms once you go deep inside each of them.
We understand that there are some people who might be hesitant about anything artificial, or the concept of getting replaced in the workplace, but the truth is that the quality of new products, services, and solutions in AI, combined with easy-to-adopt frameworks like No-code are now setting the pace for innovation and the democratization of software development, meaning is not just a developers-only matter, but a paradigm change that empowers any type of professional to is able to focus on gathering data from even the most business process sensitive cases.
Let's start by taking a look at some recent and exciting use cases for different disciplines and industries to get a general idea of its benefits.
The Life Science industry is one area where big data can have a significant impact for the better. The industry includes sectors such as biotechnology and pharmaceuticals, among others. Recent years have seen an explosion of new discoveries and findings in this field thanks to new technologies like artificial intelligence (AI), predictive analytics, and genomics. This means a large quantity of data to track, store, and analyze.
One major example comes in genetic testing which has grown at an incredible rate since its inception in 2003: from testing 834 genes in 2004 to 133,000 genes in 2017, AI has played a major role in providing faster and more reliable identification and classification. AI is also getting smarter by the day and the frequency that is used in different challenges.
Artificial intelligence can be used to make better, faster, or more efficient processes in scientific research such as:
Microscopic image classification
Pathology diagnosis and mapping
Drug manufacturing
Scientific Imaging
X-ray Analysis
Clinical trials
Cancer treatment
Radiological tests
Disease treatment
Another compelling use case can be found in the detection of electroreceptor cells in animals, recently a team of scientists from a reputable research institute in Latam has proven expertise in studying, classifying, and perfecting an AI model capable of doing this without having to write one single line of any programming language.
In finance, AI is being used as an alternative to human financial advisors—helping people manage their money better by analyzing their spending habits and recommending specific actions that will lead to better long-term returns. It can also be used for fraud detection and prevention, which can help banks save money on staffing costs while improving security for customers who use online banking services.
An example of most will know use cases are:
Credit scoring
Financial advisory
Check identification and classification
Document ID, processing, and classification
Risk management
Transaction security
Manual validation processes enhancement
Forecasting
Successful cases are part of our daily interaction with banks, insurance companies, and other players. The fintech industry can also attest to successful implementations of no-code AI such as this one from MiFiananzas, a South American company that managed to do the complex process of visual validation of checks using object detection and computer vision.
The use of AI in manufacturing is one of the most promising applications of this technology. There are many instances where AIs have been used to enhance the efficiency of factories and production lines as well as improve quality control by using robots to perform tasks such as assembly line inspection with high accuracy levels. In fact, it is estimated that by 2025 nearly half of all American jobs will be replaced by automation according to a report from the World Economic Forum which means that there will be an increased demand for automated workers who can do these tasks with less margin of error, continuity or bias which will require an influx of workers with these skill sets over time.
Some popular use cases are:
Machine predictive maintenance
Enhancing safety measures and reduction of human injuries
Personal Protective Equipment detection
Precision in quantity and quality visual control (fluids and solids)
Data anomaly detection
Object counting
Defect detection
AI is being used in all sorts of ways to help marketers, from helping them plan their advertising strategies to analyzing the effectiveness of their campaigns. With it, marketers have access to a new set of tools for collecting and analyzing data about customers' behaviors and preferences. AI can help you identify patterns in your customer base that would have been difficult or impossible to see before.
Another way AI can be used for marketing purposes is through natural language processing (NLP). NLP allows computers to understand human language as it's written or spoken by humans. This means that a computer can read emails or tweets and learn from them—for example: if someone says "I love your product" online, then an NLP algorithm might recognize this statement as positive feedback about the company's products or services; thus, it could use this information as part of its learning process from here on out when responding back to that person's messages online via email or Twitter.
Some other exciting examples of the use of AI in marketing are:
Personalized communication and advertising
Sentiment analysis
Automated decision making
Content generation
Audience targeting and hypersegmentation
Lead generation and nurturing
Customer behavior analysis
Competitor Analysis
AI is changing the way we work. It's a game-changer that will affect every single industry, and it's already made its mark on the HR industry. With AI-based tools, you can automate many of your recruiting processes—no matter what level a person is at in their career.
AI can help you attract and possibly retain talent by using data to identify key trends and patterns in your hiring process and candidates’ information. It can help recruiters and talent acquisition specialists make smarter hiring decisions, screen candidates more effectively, and manage employee performance more consistently.
AI can help with the following use cases in HR and talent attraction:
Identifying candidates who are a good fit for open positions
Identifying best practices for employee retention
Making recommendations about which employees should be promoted
Suggesting ways to improve the employee work-experience
CV/Resumé parsing
Document ID, processing, and classification
Personalized performance management
Talent engagement
Career training & development
According to a recent study by Korn Ferry Institute and IBM Watson, 35% of respondents said they were actively using AI-driven recruiting tools, while 47% said they planned to implement an AI tool in the next two years.
The possibilities are vast, but the purpose of AI in talent attraction is to make it easier for companies to find and hire the best-matching employees for the roles they will be taking, hence improving the chances for people to find jobs in companies that match with their culture, behavior and long term vision.
AI has a lot of potential to streamline logistics and supply chain management. AI can be used to improve the quality and speed of operations, optimize inventory, and reduce costs.
One of the most common uses of AI in logistics and supply chain management is unstructured data collection, recognition, and classification. AI can gather data from a variety of sources, including real-time sensor data from vehicles or equipment, satellite images, weather reports, and more. This data can then be used for predictive modeling, which helps companies better plan their operations by predicting future needs and alerting them if there is an issue that could cause problems down the road.
Other interesting and proven examples of AI adoption in logistics and supply chain management are:
Visual business-rules validation
Document ID, processing, and classification
Resource planning and management
Demand forecasting
Damage detection/Visual Inspection
Safety measures enhancement
Predictive machine/equipment maintenance
Route optimization/Freight management
Customer service or chatbot improvement
Now that we covered some examples in which AI can help some industries, nurture and make better their own operations, we certainly hope you can get an insight or inspiration on how this new paradigm can be useful to your own specific job.
Remember that AI is here to be an advantage for professionals of many different disciplines, not a threat. It's up to you to take the leap of faith into a new framework that's eager to welcome bold and adventurous people that want to be hands-on and not outside of the creativity-made-reality cycle.
AI, AutoML, and no-code tools are three of the hottest trends in the business-tech world right now. No-code development simply means that you don't need to be a developer to build an app or website…or an AI model/experiment. You just need a template that you can customize using pre-made templates, which can then be deployed or integrated on any device or platform.
No-code AI allows you to build apps with little or no coding experience. The difference is that instead of using pre-made templates, your app will use artificial intelligence (AI) so it can learn how to do things on its own, and perform tasks and processes on its own.
We have built an AI-powered builder with pre-trained models and also customized models that allow anyone to build and deploy your ideal AI feature and then integrate it into an app or website in one hour or less. Make sure to give it a try. If you are not certain that your use case or experiment can be done with Cogniflow’s No-code AI you can always reach out for a demo or a schedule a personalized call with one of our team members that will clear your doubts and guide you to the best possible path of adoption.
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