Artificial Intelligence Development Forecast in 2023 How can companies successfully adopt AI?

The adoption of artificial intelligence (AI) and its impact on businesses is now at an important inflection point. AI adoption is growing every year as businesses witness the tangible benefits AI brings.

According to a survey report released by PricewaterhouseCoopers, AI’s potential contribution to the global economy will reach US$15.7 trillion by 2030. A recent IBM survey identified key factors driving AI adoption, including the need to reduce costs and automate key processes, rising competitive pressure and changing customer expectations.

In order to successfully benefit from AI investments, business managers need to understand the development trends and directions in the AI ​​field.

2023 AI Development Trend Forecast

As global AI investment continues to increase, people need to understand the future of AI in 2023 Development trends and their potential impact on enterprises:

1. Low-code AI has made great progress in industry applications

The development process of AI models is complex, laborious and Iteratively, building a good set of models takes days and thousands of experiments. Low-code AI/data science platforms change all that, providing drag-and-drop interfaces that help create experiments faster. Intuitive graphical user interfaces (GUIs), visual reproducibility, and collaboration are the biggest advantages of low-code platforms, which enable data science teams to quickly perform large numbers of experiments. Low-code AI platforms are also ideal for promoting data engineers and business analysts into citizen data scientists, reducing reliance on expert data scientists that are scarce in various industry sectors.

2. Distributed model training is the core of AI modeling

The data science team needs to conduct experiments on thousands of models. AI models have become quite complex these days, with millions of parameters. And under the control of low-code, the ability to conduct multiple experiments simultaneously increases many times. But to implement these thousands of experiments, data science teams need a cost-effective computing system that scales with demand. Training these complex, memory-intensive experiments using traditional methods is a huge challenge. Distributed computing-led model training can help solve this challenge and is core to enabling scalable enterprise AI.

3. The application of machine learning operations (MLOps) is growing rapidly

McKinsey pointed out in its survey report released in 2021 that the use of MLOps is the decisive factor for enterprises to obtain successful returns from AI. MLOps is growing in popularity among AI leaders and data scientists because it takes machine learning from the experimental phase into production and covers a major part of the enterprise data science process. This ensures better governance when data science leads have to manage and prune hundreds of models in production using features like version control, rapid scaling, and more.

4. Trust and explainability of AI

AI is no longer viewed as a black box. More and more people are investing in AI to make critical business decisions. Therefore, overcoming the challenge of trusting AI to automate sensitive processes becomes critical. This entire scenario has led to the emergence of explainable AI, which helps in understanding the factors that go into making decisions. Transparency in explainable AI is key to building trust in AI and increasing its adoption.

5. Application of AI in cybersecurity

As the complexity of cyber threats increases, enterprises are integrating AI into their security solutions. AI is now handling day-to-day storage and protection of sensitive data as the next step in automating cyber threat prevention and protection. It is being used to further enhance intelligence analysis capabilities to detect potential threats or patterns and identify the potential intentions of cyber attackers.

Secrets to Successful AI Adoption

An Accenture study shows that businesses that strategically scale AI are more successful than those that pursue a single proof of concept The rate and return are twice and three times that of the former respectively.

It turns out that the return on investment for companies in the early stages of AI adoption may not be high. AI must be scaled throughout the organization to ensure the technology can make a significant contribution to the business.

By integrating AI into core business processes, workflows and customer journeys, their daily operations and decision-making tasks can be optimized. McKinsey predicts in a research report that companies that adopt this approach are likely to achieve growth in value and scale, with some even increasing revenue by about 20%.

Successful scaling of AI

The key drivers of successful scaling of AI depend on specific factors such as people, AI software and computing infrastructure. To increase AI maturity, companies need to understand the ins and outs of data insights and incorporate them into business processes.

One of the important needs is to adopt AI systems that can effectively and efficiently support daily business, such as payments, transaction volume, sales, and even generate quarterly reports. People in all departments of the enterprise can use AI to easily access data insights without being restricted by any department. As a company expands, AI can help it explore new areas or develop new products for existing products.

Conclusion

Businesses need to explore AI benefits and possibilities and take a strategic approach to their AI investments. With AI, companies can do more than just accelerate or automate existing processes. They can also take advantage of new opportunities and increase AI's influence among employees, customers and stakeholders.

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