What’s Next in AI: 2023 Predictions and Scaling AI to Win
Take a look at upcoming AI trends expected in 2023.
The adoption of artificial intelligence (AI) and its impact on businesses stand at a turning point. The global adoption of AI continues to increase yearly as organizations witness its tangible benefits, says Rajan Nagina, Head of AI Practice at Newgen Software.
According to a PWC report, the potential contribution that AI will have by 2030 to the global economy will be to the tune of $15.7tr! A recent IBM survey identified top factors driving AI adoption, including the need to reduce costs and automate key processes, rising competitive pressure, and evolving customer expectations. Undoubtedly, AI will radically shape the dynamics of many industries. And that’s why many digital leaders are rushing into AI investments. To successfully reap the benefits of AI investments, organizational leaders need to understand the trends and the direction in which the AI space is evolving.
AI Predictions for 2023
As global AI investments continue to increase, let’s have a look at the upcoming AI trends expected in 2023 and their potential impact on businesses:
- Low Code AI is making strides in the industry: The AI model development process is complex, laborious, and iterative. And building a good set of models requires days and thousands of experiments. Low code AI/data science platforms have changed all that. The drag ‘n’ drop interface provided by low code data science platforms helps create experiments faster. Intuitive GUI, visual repeatability, and collaboration are the biggest strengths of a low code platform. This enables the entire data science team to execute numerous experiments quickly. Low code AI platforms are also ideal for upskilling data engineers and business analysts into citizen data scientists and reducing dependency on expert data scientists that are scarce in the industry.
- Distributed Model Training is at the Core of AI modeling: Data science teams need to experiment with thousands of models. And AI models can get pretty complex, with millions of parameters. With low code at the helm, the ability to work on multiple experiments simultaneously increases multifold. But to realize those thousands of experiments, data science teams need a cost-effective computing system that scales up per the requirements. Training these complex, memory-intensive experiments using conventional methods is a big challenge. Distributed computing-led model training can help solve this challenge and lies at the core of a scalable enterprise AI implementation.
- MLOps is on the Rise: McKinsey, in their 2021 report, has revealed the use of MLOps as a deciding factor behind the successful returns from AI for an enterprise. MLOps is gaining popularity amongst AI leaders and data scientists as it brings machine learning from the experimentation stage into a production-grade environment and covers a major part of the data science process for enterprises. This ensures better governance when data science leads have to manage and prune hundreds of models in the production environment with abilities like version control, quick scale-up, and more.
- Trust and Explainability in AI: AI is no more seen as a black box. An increasing number of people are investing in AI to make business-critical decisions. Hence, it is becoming critical to overcoming the challenges of placing their trust in AI to automate sensitive processes. This whole scenario has led to the emergence of explainable AI that helps understand the factors that went into making the decision. Transparency from explainable AI is the key to establishing trust in AI and increasing its adoption.
- AI in Cybersecurity: As the complexities of cyber threats grow, organizations are weaving AI into their security solutions. AI is now handling routine storage and securing sensitive data as the next step to automating cyber threat prevention and protection. It is being leveraged to further power intelligence into analytics to detect potential threats or patterns to identify the potential intentions of attackers.
The Winning Formula
Keeping up with the Trends and Strategically Scaling AI
According to an Accenture study, companies that have strategically scaled AI have experienced a 2x success rate and 3x the return compared to companies that pursued siloed proof of concepts.
Organizations in the initial stages of AI adoption are likely to see flat ROI. AI has to be scaled across the organization to ensure that the technology can make a sizable contribution to the company. Organizations can optimize their daily operations and decision-making tasks by integrating AI into the core business processes, workflows, and customers’ journeys. Research by McKinsey predicts that organizations that adopt this approach are very likely to realize value and scale, with some even adding around 20% of earnings and tapping between $9-$15 trillion in economic value potential that AI offers.
Scaling for Success
The key driver to successfully scale AI depends on specific factors, such as the people, the AI software, and the computing infrastructure. For companies to move up the AI maturity ladder, they need to understand the ins and outs of data insights and incorporate them within their business processes.
An important requirement is a system that can effectively and efficiently support everyday business engagements, such as payments, transaction volumes, sales, or even generating quarterly reports. With AI, people from various departments can easily access data insights without facing any inter-department interference. And as the organization expands, AI can support it in exploring new territories for its current offerings or developing new products.
In Conclusion
Organizations need to explore the possibilities of AI and take a strategic approach to their AI investments. With AI, organizations can do more than accelerate or automate existing processes. AI can enable organizations to capitalize on new opportunities and strengthen their influence among their employees, customers, and stakeholders.
How do you think organizations can benefit from the increasing adoption of AI? Let us know on Facebook, Twitter, and LinkedIn.
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