AI Price Decline: How to Capitalize, Challenges & Key Considerations

AI has caught the attention of companies worldwide because of its ability to automate repetitive tasks and improve decision-making. In the past, AI was only available to large companies and universities to conduct academic research or develop expensive proprietary tools. But in recent years, companies have seen a significant drop in the price of AI.

AI price erosion refers to a reduction in the cost of AI-related hardware, software, and services. The main reason for this decline is falling costs for computing resources. For example, in the 1950s the cost of computing power was $200,000 per month, which has fallen significantly in recent years due to modern advances such as cloud computing.

Therefore, business leaders can effectively capitalize on falling AI costs to develop valuable products. However, the AI ​​space presents some major challenges that business leaders should carefully consider before investing in AI. Let’s explore this idea in detail below.

Big challenges when investing in AI

Executives face two major challenges in executing their AI initiatives, namely getting their hands on relevant datasets and keeping the computational costs of AI within their budget. Let’s look at them one by one.

1. Data quality

AI needs high-quality data. Lots of it. But it’s not easy to collect quality data as more than 80% of the data in companies is unstructured.

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The primary step in the AI ​​lifecycle is to identify and collect raw data sources, transform them into the required high-quality format, perform analysis, and build robust models.

Therefore, it is necessary for business leaders to have a comprehensive data strategy that can leverage this data to integrate AI into their business. If no relevant data is available, investing in an AI company is not a good idea.

2. Computationally expensive

The computing power required to run AI can present a barrier to entry for small organizations. Depending on the complexity of the models, AI requires considerable computing effort, which leads to high costs. For example, it reportedly costs OpenAI about $3 million per month to run ChatGPT.

Therefore, to meet the computational demands, specialized and expensive hardware such as graphics processing units (GPUs) and tensor processing units (TPUs) are required to optimize AI operations.

On the software front, researchers are working to reduce the size of the AI ​​model and memory footprint, which will significantly reduce training time and eventually save computational costs.

Capitalize on the AI ​​price drop

In recent years, the AI ​​domain has evolved tremendously in all dimensions, i.e. software, hardware, research and investments. As a result, AI business leaders have overcome and minimized many AI-related challenges.

Accelerated development of AI applications

Today, most AI tools offer free variants. Their paid subscription models are also reasonable. Businesses and individuals use these applications to increase efficiency, improve decision-making, automate repetitive tasks, and enhance the customer experience.

For example, generative AI tools like Bard, ChatGPT or GPT-4 can help users to generate new ideas and write different types of content like GPT-3 API.

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There are various examples in other areas as well. For example, transfer learning techniques are used for medical image classification to improve application accuracy. Salesforce Einstein is a generative AI customer relationship management (CRM) that can analyze data, predict customer behavior, and deliver personalized experiences.

Bigger investments in AI

The drop in AI prices has led to the adoption of mass technologies, making AI a lucrative investment opportunity. For example, the size of the AI ​​market has been estimated at $387.5 billion in 2022. It is expected to reach a whopping $1395 billion in 2029, growing at a CAGR of 20.1%.

AI products are used to create new advances in major industries like healthcare, education, finance, etc. All major tech giants and startups are investing heavily in AI research and development.

Key considerations for executives before capitalizing on the AI ​​price drop

Understand business goals and assess how AI fits in

Before capitalizing on the AI ​​price drop, it is important to identify your business strategy and goals. Unrealistic expectations are one of the main reasons why AI projects fail. The report suggests that 87% of AI initiatives don’t make it to production. Therefore, evaluating your data strategy and integrating AI into the business to increase overall efficiency are important aspects to consider before investing in AI.

Build a quality AI team and equip it with the right tools

Before investing in AI, it’s important to determine the hardware and software resources required for your AI team. Arm them with the right data sets they can use to build better products. Provide them with the training they need to ensure the success of your AI initiatives. Research suggests that both a lack of AI expertise on the part of employees and the unavailability of high-quality data are major reasons for the failure of AI ventures.

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Estimate AI cost and return on investment (ROI)

Many AI projects fail because they are unable to deliver the promised results or returns. In 2012, IBM awarded AI software Watson for Oncology $62 million in funding. It is designed to diagnose and suggest treatments for cancer patients based on the patient’s personal information, medical history and medical literature.

This project was criticized for its accuracy and reliability. In addition, it was expensive to set up this software in hospitals. Ultimately, in 2021, IBM abandoned sales for Watson for Oncology. Therefore, it is important to assess the cost of acquiring or building AI technologies before investing in them.

Assess AI regulations

Leaders need to ensure their AI initiatives are compliant with relevant regulations. Recently, AI regulations have become the focus of global watchdogs. These AI regulations aim to address concerns related to AI data bias and explainability. Privacy and Security.

For example, the GDPR (General Data Protection Regulation) is one such EU regulation that came into force in 2018. It regulates organizational guidelines for the collection of personal data, their processing and use in AI systems.

In addition, in November 2021, all 193 UNESCO member countries agreed to adopt common values ​​and principles of AI ethics to ensure risk-free AI development.

The right time to invest in AI is NOW!

Global tech giants are investing heavily in AI, which tells us that AI has a bright future. For example, Microsoft has invested $10 billion in AI, while Google invested $400 million in its AI projects in early 2023.

In order for companies to remain competitive, it is important to benefit from the falling prices of AI. At the same time, it is important for them to address and master the challenges that AI poses in building robust systems.

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