3 Key Factors Making AI Adoption Hard For Startups
Technology has evolved over the last decade. We have witnessed a significant maturity of the IOS, the rise of cloud computing, the scattering of mobile apps development, and new tech giants born out of a more competitive and more accessible tech market.
The growth of Artificial Intelligence (AI) is like the icing on the cake. Everyone is excited by AI, and most technology discussions revolve around AI and its possible integration with human activities. Companies now find ways to invest in AI
Currently, AI is being used in two ways. The vertical use and the horizontal use. In summary, the vertical use encompasses what major people want in using AI-the coexisting of AI and human beings. It involves significant human participation and serves as a support for human beings.
The vertical users of AI tend to disrupt the traditional manner of doing things in a specific industry while still making sure there is a significant amount of human participation. For instance, vertical AI in law involves using AI to make research easier for paralegals and lawyers. In medicine, algorithms can help doctors with diagnosis.
In horizontal use of AI, the focus is on developing and improving AI by building AI focus software used by different businesses. So, there is always a collaboration between the vertical use of AI and the horizontal use. Horizontal builds the AI software, and Vertical users integrate it into their workflow.
With the obvious benefits of AI and the current global value of AI at $327.5 billion, more companies are embracing AI. The bigger companies find this easier to do as they have the resources to commit to integrating AI into their businesses.
However, start-ups are not finding it easy to integrate AI into their businesses as they have fewer resources, among others. This post will explore 3 reasons for this as provided by experts at a writing service.
Big Data Challenges
To run an efficient AI software, you need data, lots of data. The more data you have, the more efficiency you will achieve. Currently, gathering large sets of data is easier these days due to technology and the maturity of the internet. However, start-ups can’t compete with big companies that have more resources to gather more data. Thus, big companies have more efficiency in their use of AI than start-ups. Only big companies have the resources the required type of data analytics to gather data easier and gather crucial data.
For instance, in computer vision, large amounts of image data are needed for efficiency, which gives big companies like Google and Facebook an advantage. Facebook built a computer vision model that runs on a billion images from Instagram within the limits that data protection laws would allow.
Resources like this cannot be replicated by start-ups who rely on public image banks such as ImageNet for data. ImageNet itself has just 14 million images even after the significant involvement of data collection experts. So, there is no way start-ups can provide adequate data for their AI with their limited resources. Start-ups also lack their unique computer vision model that will work on a personalized basis as Facebook does.
Experts at dissertation writing submit that another big data challenge apart from gathering data is gathering quality data. AI needs both important and quality data to function optimally. After gathering the essential data, it has to be appropriately categorized for it to be used. Small businesses usually end up being lost in the quantity of data they have gathered that they don’t correctly categorize these data. The lack of proper categorization makes their data subpar. Subpar data means inadequate AI functionality. Larger companies have the resources to gather these data and categorize them to provide efficient results.
Lack Of AI Talent
Experts widely discuss AI. It is still growing, hasn’t matured, and nowhere near maturity. There are few experts in the field, owing to the technical knowledge required for an AI expert. To have expertise in AI, you must know statistics and understand linear algebra. In addition to that, you must develop your AI models. Its framework, design, and capacity to understand and solve a problem. There is so much to know, and there are few people who know. Also, you would need data analysts and a project manager. Needless to say, the infrastructure for the AI model is expensive.
Few experts available in the AI field have caused a scarcity of talented people with the necessary ability. Scarcity causes a high demand and makes acquiring the service of an expert costly. As a start-up, there is no way for you to finance an expert with their salary demands.
Many companies have revealed their inability to hire these experts. They have lamented the challenges they have faced in hiring these experts when they do find them. Without these experts, nothing can be done. This presents an unfortunate paradox where AI, a disruptive tech meant to give small businesses a chance to compete, is taking away their ability to compete.
The Problem of Costs
AI will make running a business cheaper in the long run. However, it is challenging for small businesses to have an accurate prediction of the profits to be made from investing in AI. This flows in part from the fact that they don’t have enough resources to maintain an efficient AI in the first place.
The other part is, the financial benefits of AI might not be visible in the short run. It takes time and more resources. For instance, a computer vision model can help you provide image search products to potential customers. It is going to take time for you to know if customers are satisfied with what they see or not.
Another aspect of expenses is the resources spent on training your AI model to achieve its results. You have to expend resources to optimize your AI, improve its computational capabilities, etc. Also, you have to retrain your AI model to keep it sharp and efficient. Apart from monetary expenses, this training takes time. So, you are investing both time and money.
As a start-up, apart from investing in the machine itself, you also have to invest in the human beings that would run it. Okay, not investment per se but rather paying them for their services, and it doesn’t come cheap. The United States of America labor statistics peg the average salary for a data scientist at $100,560 without bonuses and other benefits. With the bonuses and additional benefits, the figure significantly rises to about $144,000.
More prominent corporations can afford the time and expenses to make an AI investment, but start-ups don’t have these resources. So, we ask, has AI come to kill off start-ups? The answer to this lies in the future AI trends.
3 Key Factors Making AI Adoption Hard For Startups Written by
Charlie Svensson is an interactive freelance writer with expertise in content writing and blogging. He writes mainly on education, social media, marketing, SEO, motivation blogging, and self-growth. Charlie has also done some pro bono work for a custom essay writing service and dissertation writing services
