SK Telecom Releases A Korean Sovereign LLM Built From Scratch

Over 80% of Nubank’s customers handle their needs through these digital self-service features without the need for additional support. A Cornerstone Advisors study showed digital banking users reached 77% of checking account customers in 2024, reflecting that digital adoption is at near-saturation among many demographics. By 2029, 80% of common customer services issues will be resolved autonomously, without human intervention. The firm also predicts that 33% of enterprise software applications will include agentic AI by 2028, and 15% of all day-to-day work decisions will be made autonomously. Generative AI and machine learning are revolutionising the digital transformation landscape – especially for banks and telcos, where customer demands, regulatory complexity and competition are constantly evolving.

• According to a McKinsey study, 71% of consumers now expect personalized experiences from their banks, and 76% feel upset when personalization is lacking.
• A contract is in the works, and SKT, alongside rivals Naver and Kakao, is expected to compete for it.
• OpenAI is expected to release its own agentic software engineer platform soon, A-SWE, which stands for agentic software engineer.
• At cybersecurity firm Abnormal AI, between half and three-quarters of the company’s 350 engineers are currently using these tools, says Dan Shiebler, the company’s head of machine learning.

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If you’re unsure of how to implement AI in your retail business, here are seven ways retail leaders are using AI today, based on our company’s recent report, The State of AI in Retail. An often underappreciated aspect of AI in banking is how it can improve accessibility and inclusivity of digital services. In 2024, banks began applying AI to ensure that digital banking works better for all customers, including those with disabilities or special needs. AI also enabled conversational engagement through chat and messaging interfaces. In 2024, many banks rolled out or upgraded chat services (e.g., in-app chat, WhatsApp banking, etc.) in which AI would be the first to respond.

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The A.X 3 Series, like 3.1 Lite, is built entirely from scratch, focused on sovereignty, compactness, and speed. The 4 Series, by contrast, is much larger and optimized for performance through continued pretraining. Beyond simply improving existing processes, AI is unlocking entirely new workloads and insights from unstructured documents that older technologies could not achieve.

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As real-world use cases show, the value lies in both improved efficiency and elevated customer experiences. A Forrester study on CX drivers noted that feeling “valued” is a key loyalty driver, and providing accessible, inclusive digital services is a concrete way to show every customer they are valued. In the UK, banks collaborated with fintechs specializing in inclusive design—for instance, leveraging AI fintech solutions that help dyslexic users by changing fonts or reading out text. Another development was AI-driven image recognition for bill payment or check depositing, simplifying processes for those who struggle with manual input, just snapping a photo and letting AI do the work. These kinds of features improved the overall usability of digital banking for a wider audience. For instance, AI can detect when a customer is having difficulty navigating an app (through behavior patterns) and proactively offer simplified explanations or switch to a voice-assisted mode.

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The J.D. Power 2024 banking satisfaction study found that while overall satisfaction with big banks rose, the one area that saw a dip was when customers tried to contact the bank through self-service digital channels for help. This suggests that some banks’ self-service tools in 2024 still left a gap when customers had more detailed questions or problems. Banks are learning from this, enhancing their knowledge bases and making AI bots more context-aware so they can handle nuanced inquiries. Especially for tech-savvy segments (millennials and Gen Z), digital banking is the default – 60% of millennials primarily use mobile banking apps as their main way to bank. In 2024, banks responded to this demand by expanding what customers can do without human intervention. AI turns one-size-fits-all into one-size-fits-one, driving double-digit revenue and CSAT jumps.

This is another example of using AI to analyze customer behaviors and detect patterns—this time, looking for patterns that don’t align with expected behavior. By analyzing customer behavior and trends, AI can detect and stop unauthorized purchases or chargebacks made with stolen cards. It can also detect if customer accounts were compromised, putting customer data at risk. Be sure to continuously update your AI systems with both new customer data and broader financial alerts on fraud trends to improve your detection accuracy. AI excels at analyzing and extracting insights from data, which can be put to good use in customer behavior analytics. According to a recent HubSpot survey, 62% of customer service specialists say that AI helps them understand their customers better.

• According to Gartner, AI agents enable developers to fully automate and offload more tasks, transforming how software development is done — a change that will force 80% of the engineering workforce to upskill by 2027.
• Generative AI is not just a technological advancement – it’s a strategic enabler for innovation, agility and competitive differentiation in banking and telecommunications.
• This evolution will improve the speed, availability and consistency of customer service, thereby lifting overall customer satisfaction despite initial skepticism.
• As AI model context windows get larger, these tools can look through more and more code at once to identify problems or suggest fixes.

Personalization emerged as a top priority for banks’ AI initiatives in 2024, driven by customer expectations for services tailored to their needs. According to a McKinsey study, 71% of consumers now expect personalized experiences from their banks, and 76% feel upset when personalization is lacking. Creating more personalized customer experiences is an opportunity for financial institutions, and most want to move quickly. In summary, 2024’s AI push in banking was not only about efficiency and personalization, but also about humanizing digital experiences and leaving no customer behind. From adjusting to a user’s pace (as simple as noticing if someone is scrolling slowly and might need extra help) to providing emotionally intelligent responses via chat, AI made digital banking more accommodating. As AI model context windows get larger, these tools can look through more and more code at once to identify problems or suggest fixes.

Generative AI is Revolutionizing How Banks Approach Customer Experience

According to McKinsey, consumer packaged goods companies were able to reduce their inventory by up to 20% by using AI, which can help optimize costs while providing a better experience for customers. By using AI for predictive analysis, you can tailor messaging and product recommendations to your customers and be at the top of your mind—like reminding them to reorder when they’re most likely to buy. Dynamically explore and compare data on law firms, companies, individual lawyers, and industry trends. As of this writing, there are 138 agents offered on the platform, from companies like Deloitte, VMware, Amdocs, Palo Alto, Wipro, and Dun & Bradstreet. Generative AI is good at, say, summarizing, or pulling out specific information.