The adoption of artificial intelligence (AI) and its impact on businesses is at a turning point. The global adoption of AI continues to increase each year as organizations see its tangible benefits, says Rajan Nagina, AI practice lead at Newgen Software.
According to a PWC report, the potential contribution of AI by 2030 to the global economy will be around $15.7 billion! A recent IBM survey identified key drivers driving AI adoption, including the need to reduce costs and automate key processes, increasing competitive pressure, and changing 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, business leaders need to understand the trends and direction in which the AI space is moving.
AI predictions for 2023
As global investments in AI continue to increase, let’s take a look at the next AI trends expected in 2023 and their potential impact on businesses:
- Low Code AI is making strides in the industry: The process of developing AI models 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-and-drop interface provided by low-code data science platforms helps build experiments faster. Intuitive GUI, visual repeatability and collaboration are the main strengths of a low-code platform. This allows the entire data science team to quickly run many experiments. Low-code AI platforms are also ideal for developing data engineers and business analysts into citizen data scientists and reducing reliance on expert data scientists that are scarce in the industry.
- Distributed model training is at the heart of AI modeling: Data science teams have to experiment with thousands of models. And AI models can get quite complex, with millions of parameters. With little barcode, the ability to work on multiple experiments simultaneously increases many times over. But to perform those thousands of experiments, data science teams need a cost-effective computing system that scales with requirements. Training these complex and memory-intensive experiments using conventional methods is a great challenge. Training in models based on distributed computing can help solve this challenge and is central to a scalable implementation of enterprise AI.
- MLOps is on the rise: McKinsey, in its 2021 report, revealed the use of MLOps as a decisive factor behind successful AI returns for a business. MLOps is gaining popularity among AI leaders and data scientists as it brings machine learning from the experimentation phase into a production environment and covers an important part of the data science process for enterprises. This ensures better governance when data science managers need to manage and prune hundreds of models in the production environment with capabilities like version control, rapid scaling, etc.
- Confidence and explainability in AI: AI is no longer seen as a black box. More and more people are investing in AI to make strategic decisions. Therefore, it becomes essential to overcome the challenges of trusting AI to automate sensitive processes. This whole scenario has led to the emergence of explainable AI that helps understand the factors that contributed to decision-making. Explainable AI transparency is key to building trust in AI and increasing its adoption.
- AI in cybersecurity: As the complexity of cyber threats increases, companies are integrating AI into their security solutions. Artificial intelligence now handles the routine storage and securing of sensitive data as the next step in automating cyber threat prevention and protection. It is leveraged to enhance intelligence in analysis to detect potential threats or patterns to identify potential attacker intentions.
The winning formula
Follow trends and strategically evolve AI
According to a Accenture Studycompanies that strategically scaled AI experienced a two-fold increase in success rate and three-fold increase in return compared to companies that pursued a proof-of-concept in silos.
Organizations that are in the early stages of AI adoption are likely to see a steady return on investment. AI needs to be extended across the organization to ensure the technology can make a meaningful contribution to the business. Organizations can optimize their day-to-day operations and decision-making tasks by integrating AI into core business processes, workflows, and customer journeys. Research by McKinsey predicts that organizations that take this approach are highly likely to realize value and scale, with some even adding around 20% of revenue and tapping between $9 trillion and $15 trillion in potential economic value offered by the AI.
Scaling for success
The key to successfully scaling AI depends on specific factors, such as people, AI software, and IT infrastructure. For companies to move up the AI maturity ladder, they need to understand the ins and outs of data insights and integrate it into their business processes.
An important requirement is a system that can efficiently and effectively support day-to-day business engagements, such as payments, transaction volumes, sales, or even quarterly reporting. With AI, people from different departments can easily access data insights without facing any interdepartmental interference. And as the organization grows, AI can help it explore new territory for its current offerings or develop new products.
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 take advantage of new opportunities and increase their influence with their employees, customers and stakeholders.
How do you think organizations can benefit from the growing adoption of AI? Let us know on Facebook, Twitterand LinkedIn.
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