AI has been gathering the attention of organizations globally due to its ability to automate repetitive tasks and enhance decision-making capabilities. Earlier, AI was only available to big corporations and universities for conducting academic research or building high-cost proprietary tools. But in recent years, companies are experiencing a significant AI price decline.
AI price decline refers to a reduction in the cost of hardware, software, and services related to AI. The primary driver of this decline is a decreasing cost of computational resources. For instance, in the 1950s, the cost of computational power was $200,000/month, which has dropped significantly in recent years due to modern advances like cloud computing.
Hence, business leaders can effectively capitalize on declining AI costs to build valuable products. However, the AI domain presents some major challenges which the business leaders should carefully consider before investing in AI. Let’s explore this idea in detail below.
Major Challenges Faced While Investing In AI
Business leaders mainly face two major challenges while executing their AI initiatives, i.e., getting their hands on relevant datasets and keeping AI’s computational expenses within their budget. Let’s look at them one by one.
1. Data Quality
AI needs high-quality data. Lots of it. But it is not easy to collect high-value data since more than 80% of the data in enterprises is unstructured.
The primary step in the AI life cycle is to identify and collect raw data sources, transform them into the required high-quality format, execute analytics, and build robust models.
Hence, for business leaders, it is necessary to have a comprehensive data strategy that can leverage this data to integrate AI into their business. If relevant data is not available, then investing in an AI venture is not a good idea.
2. Computationally Expensive
The computational capacity required to execute AI can be an entry barrier for small organizations. AI needs significant computation depending on the complexity of the models which leads to high costs. For instance, reportedly, it costs about $3 million/month for OpenAI to run ChatGPT.
Hence, to fulfill the computational needs, specialized and expensive hardware such as Graphic Processing Units (GPUs) and Tensor Processing Units (TPUs) are required to optimize AI operations.
On the software front, researchers are working on reducing the AI model size and memory footprint, which will significantly decrease the training time and eventually save computational costs.
Capitalizing on AI Price Decline
In recent years, the AI domain has progressed immensely in all dimensions, i.e., software, hardware, research, and investment. 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 are using these applications to increase efficiency, improve decision-making, automate repetitive tasks, and enhance customer experience.
For instance, generative AI tools like Bard, ChatGPT, or GPT-4 can assist users in generating new ideas and writing various types of content, such as product summaries, marketing copies, blog posts, etc. Over 300 applications are built on top of GPT-3 API.
There are various examples in other domains as well. For example, Transfer Learning techniques are being used for medical image classification to improve application accuracy. Salesforce Einstein is a generative AI CRM (Customer Relationship Management) that can analyze data, predict customer behavior, and deliver personalized experiences.
Greater Investment in AI
The decline in AI prices has led to mass technology adoption, making AI a lucrative investment opportunity. For instance, in 2022, the AI market size was valued at $387.5 billion. It is expected to reach a whopping $1395 billion in 2029, growing at a CAGR of 20.1%.
AI products are being used to make new advancements in major industries, like healthcare, education, finance, etc. All the big tech giants and startups are investing heavily in AI research and development.
Key Considerations For Business Leaders Before Capitalizing on AI Price Decline
Understand Business Goals and Evaluate How AI Fits In
Before capitalizing on AI price decline, identifying your business strategy and goals is essential. Unrealistic expectations are one of the leading causes of AI project failure. Report suggests that 87% of AI initiatives don’t make it to production. Hence, assessing your data strategy and how AI can be integrated into business to enhance the overall efficiency are important aspects to consider before investing in AI.
Build a High-Quality AI Team & Equip Them With the Right Tools
Before investing in AI, it is vital to identify the required hardware and software resources for your AI team. Equip them with the right datasets which they can leverage to build better products. Provide them with necessary training to ensure the success of your AI initiatives. Research suggests that both lack of AI expertise in employees and non-availability of high-quality data are major reasons for the failure of AI ventures.
Estimate AI Cost & Return On Investment (ROI)
Many AI projects fail because they are unable to deliver the promised outcome or returns. In 2012, IBM’s AI software Watson for Oncology received funding worth $62 million. It was designed to diagnose and suggest treatments for cancer patients based on the patient’s personal data, medical history, and medical literature.
This project was criticized for its accuracy and reliability. Moreover, it was costly to set up this software in hospitals. Ultimately, in 2021 IBM abandoned its sales for Watson for Oncology. Hence, it is essential to evaluate the cost of acquiring or building AI technologies before investing in them.
Evaluate AI Regulations
Business leaders must ensure that their AI initiatives comply with relevant regulations. Recently, AI regulations have become the focus of global watchdogs. These AI regulations aim to address the concerns related to AI data bias, explainability. data privacy and security.
For instance, GDPR (General Data Protection Regulation) is one such EU regulation that came into effect in 2018. It regulates organizational policies on personal data collection, its processing, and usage in AI systems.
Moreover, in November 2021, all 193 member countries in UNESCO agreed on adopting 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 instance, Microsoft has invested $10 billion in AI while Google has invested $400 million in their AI ventures at the start of 2023.
For businesses to stay competitive, it is important to capitalize on AI’s declining prices. At the same time, it is important for them to address and overcome the challenges that AI presents to build robust systems.
For more interesting AI-related content, visit unite.ai.
Credit: Source link
