AI has been gathering the eye of organizations globally attributable 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 constructing high-cost proprietary tools. But lately, firms are experiencing a major AI price decline.

AI price decline refers to a discount in the price of hardware, software, and services related to AI. The first driver of this decline is a decreasing cost of computational resources. For example, within the Nineteen Fifties, the price of computational power was $200,000/month, which has dropped significantly lately attributable to modern advances like cloud computing.

Hence, business leaders can effectively capitalize on declining AI costs to construct priceless products. Nevertheless, the AI domain presents some major challenges which the business leaders should fastidiously consider before investing in AI. Let’s explore this concept intimately 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 inside their budget. Let’s have a look at them one after the other.

1. Data Quality

AI needs high-quality data. A lot of it. Nevertheless it is just not easy to gather high-value data since greater than 80% of the info in enterprises is unstructured.

The first step within the AI life cycle is to discover and collect raw data sources, transform them into the required high-quality format, execute analytics, and construct robust models.

Hence, for business leaders, it’s crucial to have a comprehensive data strategy that may leverage this data to integrate AI into their business. If relevant data is just not available, then investing in an AI enterprise is just not idea.

2. Computationally Expensive

The computational capability required to execute AI could be an entry barrier for small organizations. AI needs significant computation depending on the complexity of the models which results in high costs. For example, reportedly, it costs about $3 million/month for OpenAI to run ChatGPT.

Hence, to meet the computational needs, specialized and expensive hardware akin to 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 is able to significantly decrease the training time and eventually save computational costs.

Capitalizing on AI Price Decline

Lately, the AI domain has progressed immensely in all dimensions, i.e., software, hardware, research, and investment. In consequence, 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 extend efficiency, improve decision-making, automate repetitive tasks, and enhance customer experience.

For example, generative AI tools like Bard, ChatGPT, or GPT-4 can assist users in generating recent ideas and writing various forms of content, akin to product summaries, marketing copies, blog posts, etc. Over 300 applications are built on top of GPT-3 API.

There are numerous examples in other domains as well. For instance, Transfer Learning techniques are getting used for medical image classification to enhance application accuracy. Salesforce Einstein is a generative AI CRM (Customer Relationship Management) that may 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 example, in 2022, the AI market size was valued at $387.5 billion. It is anticipated to achieve a whopping $1395 billion in 2029, growing at a CAGR of 20.1%.

AI products are getting used to make recent advancements in major industries, like healthcare, education, finance, etc. All the massive 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 Matches In

Before capitalizing on AI price decline, identifying what you are promoting strategy and goals is crucial. Unrealistic expectations are considered 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 the way AI could be integrated into business to boost the general efficiency are essential elements to contemplate before investing in AI.

Construct a High-Quality AI Team & Equip Them With the Right Tools

Before investing in AI, it’s important to discover the required hardware and software resources in your AI team. Equip them with the precise datasets which they’ll leverage to construct higher products. Provide them with crucial training to make sure the success of your AI initiatives. Research suggests that each 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’re unable to deliver the promised end result or returns. In 2012, IBM’s AI software Watson for Oncology received funding value $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. Furthermore, it was costly to establish this software in hospitals. Ultimately, in 2021 IBM abandoned its sales for Watson for Oncology. Hence, it is crucial to judge the price of acquiring or constructing AI technologies before investing in them.

Evaluate AI Regulations

Business leaders must make sure that their AI initiatives comply with relevant regulations. Recently, AI regulations have turn into the main focus of world watchdogs. These AI regulations aim to handle the concerns related to AI data bias, explainability. data privacy and security.

For example, GDPR (General Data Protection Regulation) is one such EU regulation that got here into effect in 2018. It regulates organizational policies on personal data collection, its processing, and usage in AI systems.

Furthermore, in November 2021, all 193 member countries in UNESCO agreed on adopting common values and principles of AI ethics to make sure 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 brilliant future. For example, Microsoft has invested $10 billion in AI while Google has invested $400 million of their AI ventures initially of 2023.

For businesses to remain competitive, it is crucial to capitalize on AI’s declining prices. At the identical time, it is crucial for them to handle and overcome the challenges that AI presents to construct robust systems.

For more interesting AI-related content, visit unite.ai.