Tag: Data and Artificial Intelligence

The Impact of GenAI on Customer Service in Retail

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Key Takeaways:

  • Generative AI is becoming a retail essential
  • It’s transforming customer loyalty, marketing efficiency, speed of support and more
  • Detailed insights are enabling personalized, automated service
  • Multi-modal and agentic AI will bring further scope for transformation

The Impact of GenAI on Customer Service in Retail

As with many other industries, the capabilities of Generative AI are making a real impact in the retail industry, especially in the quality and scale of the customer service that retailers can deliver. Research has found that the majority of retailers are already using GenAI to boost their customer service capabilities, and this is helping them meet the ever-increasing expectations of consumers.

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Data as Currency: The Emerging Economy of Digital Value

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Today’s hyper-connected digital economy thrives on innovation. From barterable items to coins, notes, and even crypto, the world has undergone a steady transition from one form of currency onto the next. And while all of these have made a significant impact on global trade – the real power now, and still, lies in data. Over the years, data has become a dynamic force in the global digital economy, leading to smarter decisions, more personalized experiences, and smarter AI. According to IDC’ Data Age 2025 journal, the global datasphere is expected to grow to over 175 zettabytes by 2025, reflecting just how central data has become a core component in modern business operations. To put 175 zettabytes into perspective, assuming a standard HD movie is about 5 GB, and you watch one movie per day, it would take you ~95 billion years to watch them all.

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Building Trustworthy and Scalable AI Models for US FinTech Compliance

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Key Takeaways:

  1. Establish clear AI governance frameworks and quality standards
  2. Standardized processes support the transition from POCs to production
  3. Implementing MLOps early streamlines regulatory compliance audits
  4. Integrated teams enable clear roles and shared accountability for AI outcomes

If you’re a FinTech organization operating in the United States, then you’re facing a constantly evolving maze of US fintech regulations and compliance mandates regarding artificial intelligence. The failure to comply can lead to serious legal, financial and reputational consequences, including regulatory fines and legal penalties, reputational damage, operational disruptions, lawsuits and liability issues, and loss of market access.

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Modern Data Technology: Building an AI-Ready Data Infrastructure for Enhanced BFSI Analytics

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Key Takeaways:

  1. AI is enabling BFSI operations across personalization, fraud and risk management, and more
  2. A strong infrastructure and architecture is vital to maximize AI’s potential
  3. Compliance and human skills development should be major areas of focus
  4. Working with external expertise can add clarity to the modernization journey

The potential of artificial intelligence for banking, financial services and insurance (BFSI) is increasing all the time. The global market value for AI in finance is set to reach $374 billion by 2029, fueled by the ability to increase customer personalization, combat fraud and better manage risk.

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From Diagnosis to Claims: Where AI Actually Works in Healthcare

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Key Takeaways:

  1. AI is critical to address growing global care demands and increasing skills shortages
  2. Transformation possible in diagnosis, proactive care delivery, and efficient administration
  3. Ethical, responsible AI is key to counter workforce resistance and wariness
  4. Small-scale, high value use cases are an excellent place to start

The pressures on healthcare are considerable and global. Healthcare systems all over the world are struggling with rising populations, budget and resource constraints, increasing care demands, and data complexity. 

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Easing the Impact of Tariffs with AI and Automation

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Key Takeaways:

  1. 96% of senior leaders are concerned about the impact of trade policy.
  2. Tariff volatility is disrupting how global businesses plan, price, and source.
  3. AI-powered tools give companies an edge in unstable trade environments.
  4. Predictive AI is helping businesses anticipate setbacks in time.

Tariffs Are Changing the Rules of Global Trade

Trump-era tariffs have triggered a wave of uncertainty, with knock-on effects reaching far beyond rising costs of goods. For companies dependent on overseas suppliers, sudden policy shifts strain budgets, add new layers of paperwork, delay timelines, and force difficult choices across the product lifecycle.

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Agentic AI and the Future of Personalized Healthcare

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Key Takeaways:

  1. Autonomous AI enhances care quality and patient outcomes.
  2. Personalized healthcare improves survival rates in cancer patients.
  3. Predictive analytics spots outbreaks of diseases early.
  4. Advancements in robotic surgery and human-AI collaboration are imminent.

Agentic AI: A Turning Point for Healthcare

AI is already part of our everyday lives, changing how we perform simple tasks and go about our work. But when it comes to healthcare and medicine, it’s a different ball game. The healthcare industry is complex, costly, and full of moving parts. For a long time, we didn’t have technology powerful enough to ease that burden. Until now.

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Things to Consider When Hiring a Generative AI Development Partner

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Key Takeaways:

  1. Working with an external AI development outsourcing partner cuts costs and eases access to expertise
  2. Choosing the right partner depends on your organization’s existing skill sets and business objectives
  3. Industry experience and proven track record are critical differentiators
  4. The best partners will provide support through deployment and beyond with a clear GenAI implementation plan

Did you know, as many as 85% of generative AI deployments fail? Businesses all over the world recognize the importance of this transformative technology, and yet so many are struggling to make it work for them and justify their investment. The reasons are many, with unclear scope, poor data infrastructure and generic AI models all too commonplace.

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The Ethics of Trend Prediction: Balancing AI Insights with Consumer Privacy

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Key Takeaways:

  1. Consumers are more aware of retailers potentially misusing their data
  2. Ethical use of AI tools and data is now essential to avoid brand damage
  3. Baking in transparency and compliance can act as a differentiator
  4. Strong ethical frameworks and flexible solutions are key to evolve in the future

Retailers all over the world are using data and AI in order to maximize sales and meet consumer experience expectations. The likes of predictive analytics, personalized shopping and omnichannel integration are allowing retailers to build more individualized and effective buying journeys.

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Agentic RAG in Banking: 5 AI Use Cases for Fraud Detection and Regulatory Compliance

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Key Takeaways:

  1. Fraud is on the rise, and customers expect banks and finance firms to respond
  2. Agentic RAG expands on the possibilities of AI in fraud detection and compliance
  3. Retrieving more diverse data and generating insights enables smarter decisions
  4. A phased, expert-led approach can maximize the value of an agentic RAG deployment

Artificial intelligence has transformed the world of banking and finance, in terms of customer experience and personalization of products. But it’s proving just as instrumental behind the scenes in improving fraud detection and regulatory compliance for finance firms big and small.

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