AI is a technology that can be used to support various business processes. Whether you are using it to automate customer support or improve employee productivity, AI can help your company achieve success.
When a company decides to invest in AI, it must consider several factors that influence the overall cost of artificial intelligence. One of the most significant factors is data.
AI software and services are not cheap, especially as data sets grow larger and more complex. This is because they require specialized hardware and computational resources to run, which can quickly add up.
Moreover, AI systems need to be trained on data. This requires labeling, annotating, and organizing the data for use by algorithms.
Furthermore, AI systems need to be maintained and updated, which can also be expensive. This can include repairing and upgrading hardware and purchasing new software licenses.
As an added expense, AI systems also need to be regulated by law and regulatory bodies. This can be time-consuming and costly, particularly if businesses are not familiar with legal requirements for AI use.
Machine learning (ML) models are often built on top of sensitive data that needs to be protected for ethical and legal reasons. This makes preventing attacks and minimizing the risk of security breaches a top priority for CSOs and CIOs.
One of the most common risks is model extraction where an attacker could try to gain access to the AI model and steal its data. Another one is training-data poisoning, where an attacker could alter training data to manipulate the AI model’s predictions.
Cyber criminals have become more sophisticated at devising attack vectors, making it hard for businesses to protect against new threats. They also use techniques such as AI to create advanced attacks and deploy new forms of malware to target both traditional and AI-enhanced systems.
As more businesses turn to AI and machine learning (ML) technologies to streamline operations, it’s important for them to be transparent about how much it will cost. This can help customers make better decisions about their business, and can also reduce their expenses.
However, there is a growing tension between organizations’ desire for transparency and their need to keep their AI tools trade secrets. If an algorithm makes critical decisions for users or the company, transparency can help to prevent malicious actors from gaming it and harming the user or the organization.
Aside from the legal and regulatory requirements, explaining how an AI model works can also increase employee confidence in its decisions. This can improve hiring processes and ultimately lead to higher productivity.
Scalability is a measure of how a system can handle increases in user demand or workloads without suffering a significant drop in performance. This is particularly important in the case of software systems or businesses that are growing rapidly.
As a result, AI solutions need to be designed and engineered in such a way that they can be adapted to meet different demands at scale. This requires a number of measures, such as easily scalable storage and memory, as well as computational resources.
Another aspect of scalability is the ability to provide AI models with access to clean and diverse data at all times. This can be a challenge in the case of healthcare, where privacy laws can prevent data from being moved outside of its source.
To meet this challenge, AI solutions need to be built with a fully standardized and automated workflow that can be replicated for thousands of models. This assembly line enables the fetching of source data, transforming it into a format for machine learning training, developing a model, setting up a validation set and deploying it as a container.