According to McKinsey’s State of AI Global Survey, artificial intelligence (AI) adoption in at least one business process increased by 56% vs 50% from 2021. Consulting giant Accenture argues that AI can boost rates of profitability by an average of 38% and could drive an economic boost of a whopping $14 trillion in additional gross value added (GVA) by 2035. Businesses have reported a positive impact on their bottom line citing at least 5% growth attributed to AI.
In the height of the COVID-19 pandemic, where most industries slashed their total expenses to stay afloat, many companies invested in AI in 2020 notably in the service operations, product and service development, and marketing and sales. They have harnessed machine-learning operations (MLOps) and took advantage of cloud technologies. Experts say this is just the beginning of what AI can do and can be harnessed to create new products and services.
Artificial intelligence is a broad term that refers to any type of computer software that engages in learning, planning and problem-solving. Common AI applications used in business are machine learning and deep learning.
Machine learning is primarily used by businesses to process large amounts of data quickly and have algorithms that “learn” to see patterns over time. It is useful for digesting large gathered data into comprehensible bits. Deep learning is an even more specific version of machine learning that relies on neural networks to perform advanced functions like fraud detection.
The findings of McKinsey’s research barely scratches the surface of the positive benefits of AI for all kinds of businesses.
Initially, some employees thoughtthat AI will take away their jobs from them. However, acceptance became positive since they have seen it working. By employing AI to handle repeatableand mundane tasks, it gives the employee more time to focus on higher-value tasks that will benefit the company and department better. Apart from that, AIcan also help in reading data better, transferring data smoothly and revealing trends.
With ML algorithms, data accumulated can assist marketing and business development to optimize products for customers. The emergence of digital media has brought on an influx of bigdata that marketers which can assist them to assess their performance and decide on their next steps. This can lead to a personalized marketing approach and enhanced measurement.
In this same process, new products can be created based on the gathered data. Since we don’t have a crystal ball to see the future, this can lead to a more scientific approach in the development of new product.
With machine learning, organizations can implement real-time monitoring capabilities that enable them to monitor issues, alert for irregularities, recommend action and can also automate a response.
Customer service was once a feature but is now considered a key element in a company’s culture. With contact center automation, it aims to successfully handle some of those interactions through AI intelligent algorithms with reduced communication with a human agent. When you fuse AI to your CRM system it can be a self-updating and auto-correcting system that your customers can relate to. Of course, human interaction will still be needed as problems get complicated but the simpler ones would have been resolved faster.
There has been an ongoing skill shortage for developers for the past years. Companies who might want to use AI might find themselves the lack of talent or budget to implement it. This is the challenge that no-cod ecan help you overcome. Back then, you will need a team of seasoned engineers to build a piece of software but with drag and drop, citizen development is on the rise. We discussed how no-code is driving AI to mainstream usage HERE.
According to Fabio Moioli in a Forbes’ article on AI-driven organizations, a no-code/low-code strategy is becoming of utmost importance in sustaining an AI-drive organization. No-code/low-code make the creation of new applications feasible for anyone who have problems to solve or an idea that can improve functions.
No-code AI democratizes the use of AI and ML so different departments can manage data and analysis that came from other AI processes within the business. Right now, it is not yet highly customizable but the functions can depend on the platform. Advances in A.I. itself are making no-code platforms perform better. Right now, you can find platforms that allow anyone to create software that can do predictive analytics, risk management and create accurate binary yes-no predictions.
No-code AI is at the forefront with its flexibility for large and small businesses. For now, most no-code-A.I. users want to streamline the way things are done without having to involve a programmer. Eventually, it will be more than just a cost-cutting solution but rather a library of possibilities to improve different aspects of the business. No-code AI is never about the replacement of the human brain but maximizing the potential of it. It lets you think at a higher level on the results presented by innovation and data science.
If you want to start applying no-code in your organization but feel overwhelmed, we are the team for you! You can book a schedule HERE and let a team of experts like Estel guide you.
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