Who’s Betting, Where, and Why in AI – SwissCognitive AI Investment Radar

As global AI funding levels remain elevated, this week’s investment activity reveals a tightening pattern: fewer hubs, bigger bets, and sharper focus. Silicon Valley, Beijing, and Paris now account for 80% of global AI funding, while other regions navigate capital scarcity and look for niche leverage. Meanwhile, Amazon’s CEO used his annual letter to justify billions already spent, calling AI investments a necessity for long-term competitiveness.

In San Francisco, startup Virtue AI secured $30 million to tackle deployment risk, a concern that’s becoming more pronounced as adoption scales. UK-based Synthesia reported $100 million in revenue and welcomed Adobe Ventures as a new backer, underscoring the value of enterprise AI tools that are already delivering results. And in China, a newly launched $8 billion AI fund backed by government and finance ministries will channel early-stage investments into foundational research and startup formation.

CEE continues to gain investor attention as a cost-efficient and increasingly capable AI development region, while Korea saw a domestic political pledge of $70 billion toward AI initiatives. On the infrastructure front, Nvidia’s $500 billion long-term strategy—including chips and supercomputing partnerships—continues to drive share price gains, while nEye Systems closed a $58 million round to push optical chip development further into the AI stack.

Big tech players aren’t staying out of the startup scene either. Alphabet and Nvidia reportedly invested in SSI, the new venture by OpenAI co-founder Ilya Sutskever, and ex-OpenAI CTO Mira Murati’s startup is reportedly eyeing a massive $2 billion seed round. CMA CGM’s €100 million partnership with Mistral AI brings logistics into the funding spotlight, and the trend toward agentic AI for financial research continues to spread across fintech.

Previous SwissCognitive AI Radar: AI Funding Highlights.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

Der Beitrag Who’s Betting, Where, and Why in AI – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Major AI Funding Shifts – SwissCognitive AI Investment Radar

The AI investment landscape continues to evolve, with new funding rounds and strategic commitments driving the industry forward. This week, key players across finance, technology, and infrastructure have made major moves to expand AI capabilities, focusing on both software and cloud expansion. Salesforce pledged $1 billion toward AI development in Singapore, while Honor committed $10 billion to integrating AI across its product line.

Investment priorities are shifting from AI chips to software, with analysts predicting that software firms will capture more value in the coming years. Microsoft is expanding its cloud and AI infrastructure in South Africa with a $298 million investment, reflecting the rising demand for AI-driven services. Meanwhile, Barclays analysts note that AI models are evolving from training-based systems to more advanced reasoning engines, signaling a new phase in AI capabilities.

DeepSeek’s breakthrough continues to drive activity in China’s venture capital sector, attracting fresh funding after years of stagnation. Elsewhere, private equity firms are adjusting their investment strategies to keep pace with AI-driven business transformations.

With AI playing a bigger role in stock markets, investor sentiment is shifting as automation takes on a larger role in financial decision-making. The rise of AI-powered fintech solutions, such as Finnomena’s partnership with Google Cloud, further highlights the increasing role of AI in investment strategies.

Stay tuned as we track these developments and more, bringing you the latest insights from the growing AI investment world.

Previous SwissCognitive AI Radar: $100B for AI Chips, $40B for AI Bets.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

Der Beitrag Major AI Funding Shifts – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Samir Anil Jumade – “The Relentless Tide of Technological Disruption: Are You Ready?”

The world is evolving at an unprecedented pace, driven by rapid technological advancements. Many industries that once seemed invincible have either vanished or are on the verge of collapse due to their failure to adapt. The rise of artificial intelligence (AI), automation, blockchain, and digital platforms is fundamentally reshaping how businesses operate.

In this article, we explore how past giants like Kodak and Nokia disappeared, how today’s industries are facing a similar existential crisis, and how individuals and businesses must prepare for this inevitable transformation.

The Rise and Fall of Industry Giants

Remember Kodak? In 1997, they employed 160,000 people and dominated the photography market, with their cameras capturing 85% of the world’s images. Fast forward a few years, and the rise of mobile phone cameras decimated Kodak, leading to bankruptcy and the loss of all those jobs. Kodak’s story isn’t unique. A host of once-dominant companies, like HMT, Bajaj, Dyanora, Murphy, Nokia, Rajdoot, and Ambassador, failed to adapt and were swept aside by the relentless tide of technological change. These weren’t inferior products; they simply couldn’t evolve with the times.

This isn’t just a nostalgic look back. It’s a stark warning. The world is changing faster than ever, and we’re on the cusp of another massive transformation – the Fourth Industrial Revolution. Think about how much has changed in the last decade. Now imagine the next ten years. Experts predict that 70-90% of today’s jobs will be obsolete within that time frame. Are we prepared?

Look at some of today’s giants. Uber, the world’s largest taxi company, owns no cars. Airbnb, the biggest hotel chain, owns no hotels. These companies, built on software and connectivity, are disrupting traditional industries and redefining how we live and work. This disruption is happening across all sectors.

Consider the legal profession. AI-powered legal software like IBM Watson can analyze cases and provide advice far more efficiently than human lawyers. Similarly, in healthcare, diagnostic tools can detect diseases like cancer with greater accuracy than human doctors. These advancements, while offering immense potential benefits, also threaten to displace a significant portion of the workforce.

The automotive industry is another prime example. Self-driving cars are no longer science fiction; they’re a rapidly approaching reality. Imagine a world where 90% of today’s cars are gone, replaced by autonomous electric or hybrid vehicles. Roads would be less congested, accidents drastically reduced, and the need for parking and traffic enforcement would dwindle. But what happens to the millions of people whose livelihoods depend on driving, car insurance, or related industries?

Even the way we handle money is transforming. Cash is becoming a relic of the past, replaced by “plastic money” and, increasingly, mobile wallets like Paytm. This shift towards digital transactions offers convenience and efficiency, but also raises questions about security, privacy, and the future of traditional banking.

From STD Booths to Smartphones: A Revolution in Communication

Think back to the time when STD booths lined our streets. These public call offices were once essential for long-distance communication. But the advent of mobile phones sparked a revolution that swept STD booths into obsolescence. Those who adapted transformed into mobile recharge shops, only to be disrupted again by the rise of online mobile recharging. Today, mobile phone sales are increasingly happening directly through e-commerce platforms like Amazon and Flipkart, further highlighting the rapid pace of change.

The Evolving Definition of Money

The concept of money itself is undergoing a radical transformation. We’ve moved from cash to credit cards, and now mobile wallets are gaining traction. This shift offers convenience and efficiency, but it also has broader implications. As we move towards a cashless society, we need to consider the potential impact on financial inclusion, security, and privacy.

The Message is Clear: Adapt or Be Left Behind

The message is clear: adaptation is no longer a choice; it’s a necessity. We must embrace lifelong learning and upskilling to navigate this rapidly changing landscape. We need to foster creativity, critical thinking, and problem-solving skills – qualities that are difficult for machines to replicate. The future belongs to those who can innovate, adapt, and thrive in a world increasingly shaped by technology. The question is: will you be ready?

Additional Points to Consider:

· The environmental impact of technological advancements, both positive and negative.

· The ethical considerations surrounding AI and automation.

· The role of government and education in preparing the workforce for the future.

· The potential for new industries and job roles to emerge. By staying informed and proactive, we can harness the power of technology to create a better future for all.

References:

1. D. Deming, P. Ong, and L. H. Summers, “Technological Disruption in the Labor Market,” National Bureau of Economic Research, Working Paper No. 33323, Jan. 2025.
2. K. Hötte, M. Somers, and A. Theodorakopoulos, “Technology and Jobs: A Systematic Literature Review,” arXiv preprint arXiv:2204.01296, Apr. 2022.
3. D. Acemoglu and P. Restrepo, “Assessing the Impact of Technological Change on Similar Occupations,” Proceedings of the National Academy of Sciences, vol. 119, no. 40, e2200539119, Oct. 2022.
4. D. Acemoglu and P. Restrepo, “Occupational Choice in the Face of Technological Disruption,” National Bureau of Economic Research, Working Paper No. 29407, Oct. 2021. 5.S. Y. Lu and R. Zhao, “Artificial Intelligence for Data Classification and Protection in Cross-Border Transfers,” IEEE Transactions on Big Data, vol. 7, no. 3, pp. 536-545, 2021.

About the Author:

Samir Jumade is a passionate and experienced Blockchain Engineer with over three years of expertise in Ethereum and Bitcoin ecosystems. As a Senior Blockchain Engineer at Woxsen University, he has led innovative projects, including the Woxsen Stock Exchange and Chain Reviews, leveraging smart contracts, full nodes, and decentralized applications. With a strong background in Solidity, Web3.js, and backend technologies, Samir specializes in optimizing transaction processing, multisig wallets, and blockchain architecture.

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AI Market Adjustments and Billion-Dollar Bets – SwissCognitive AI Investment Radar

The AI investment landscape remains as active as ever, with Alphabet announcing a staggering $75 billion commitment to AI infrastructure. This substantial bet highlights Big Tech’s continued push into AI dominance, following Meta’s $240 billion market value surge as investors back its open-source AI approach. Meanwhile, SoftBank is doubling down on AI, unveiling a joint venture with OpenAI in Japan and backing a $230 million investment into Indian AI startup Krutrim as the country accelerates its AI ambitions.

While these headline moves dominate discussions, AI hype versus reality is becoming a growing concern for investors. Early Nvidia investor Jonathan Cohen warns about “AI washing,” with companies inflating their AI capabilities to attract capital. This skepticism extends to financial institutions, where banks are ramping up AI investments, yet most are focused on incremental gains rather than disruptive overhauls.

Across the Atlantic, European AI startups raised $8 billion in 2024, setting the stage for the upcoming Artificial Intelligence Action Summit in Paris, where global leaders will debate AI’s role in economic growth. In fintech, AI-powered tools are attracting fresh funding, with Jump securing $20 million to develop AI-driven financial advisory solutions, while Marlin Equity Partners takes a majority stake in Napier AI, reinforcing AI’s role in financial crime prevention.

For investors looking at the financial impact of AI, new data suggests that mid-sized businesses can break even on AI investments within 9.5 months, achieving a 281% ROI in just three years. Yet, with Alphabet set to report earnings soon, investors are keen to scrutinize its AI-related capital expenditures, questioning whether such massive spending will translate into real returns.

Finally, China-based DeepSeek remains a wildcard in the AI trade, following its disruptive AI model that rattled the market last week. Whether its breakthrough is a game-changer or an overhyped anomaly remains a key debate among industry watchers.

As AI investments continue to shape global industries, we’ll be tracking the key shifts, opportunities, and market reactions in next week’s AI Investment Radar.

Previous SwissCognitive AI Radar: The AI Market Shake-Up: Where the Investments Are Headed.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

Der Beitrag AI Market Adjustments and Billion-Dollar Bets – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: cio.com – “12 AI predictions for 2025”

This year we’ve seen AI move from pilots into production use cases. In 2025, they’ll expand into fully-scaled, enterprise-wide deployments.

Generative AI has seen faster and more widespread adoption than any other technology today, with many companies already seeing ROI and scaling up use cases into wide adoption.

Vendors are adding gen AI across the board to enterprise software products, and AI developers haven’t been idle this year either. We’ve also seen the emergence of agentic AI, multi-modal AI, reasoning AI, and open-source AI projects that rival those of the biggest commercial vendors.

According to a Bank of America survey of global research analysts and strategists released in September, 2024 was the year of ROI determination, and 2025 will be the year of enterprise AI adoption.

“Over the next five to 10 years, BofA Global Research expects gen AI to catalyze an evolution in corporate efficiency and productivity that may transform the global economy, as well as our lives,” says Vanessa Cook, content strategist for Bank of America Institute.

Small language models and edge computing

Most of the attention this year and last has been on the big language models —  specifically on ChatGPT in its various permutations, as well as competitors like Anthropic’s Claude and Meta’s Llama models. But for many business use cases, LLMs are overkill and are too expensive, and too slow, for practical use.

“Looking ahead to 2025, I expect small language models, specifically custom models, to become a more common solution for many businesses,” says Andrew Rabinovich, head of AI and ML at Upwork. LLMs aren’t just expensive, they’re also very broad, and not always relevant to specific industries, he says.

“Smaller models, on the other hand, are more tailored, allowing businesses to create AI systems that are precise, efficient, robust, and built around their unique needs,” he adds.[…]

Read more: www.cio.com

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Copyright: theconversation.com – “Do People Trust AI on Financial Decisions? It Depends on Who They Are”

When it comes to investing and planning your financial future, are you more willing to trust a person or a computer?

This isn’t a hypothetical question any more.

Big banks and investment firms are using artificial intelligence (AI) to help make financial predictions and give advice to clients.

Morgan Stanley uses AI to mitigate the potential biases of its financial analysts when it comes to stock market predictions. And one of the world’s biggest investment banks, Goldman Sachs, recently announced it was trialling the use of AI to help write computer code, though the bank declined to say which division it was being used in. Other companies are using AI to predict which stocks might go up or down.

But do people actually trust these AI advisers with their money?

Our new research examines this question. We found it really depends on who you are and your prior knowledge of AI and how it works.

Trust differences

To examine the question of trust when it comes to using AI for investment, we asked 3,600 people in the United States to imagine they were getting advice about the stock market.

In these imagined scenarios, some people got advice from human experts. Others got advice from AI. And some got advice from humans working together with AI.

In general, people were less likely to follow advice if they knew AI was involved in making it. They seemed to trust the human experts more.

But the distrust of AI wasn’t universal. Some groups of people were more open to AI advice than others.

For example, women were more likely to trust AI advice than men (by 7.5%). People who knew more about AI were more willing to listen to the advice it provided (by 10.1%). And politics mattered – people who supported the Democratic Party were more open to AI advice than others (by 7.3%).[…]

Read more: www.theconversation.com

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SwissCognitive Guest Blogger: Muhammad Irfan – “AI in Investment: How AI is Transforming the World of Financial Portfolios”

Key Takaways:

– Analyze past and current data to give a clearer picture of portfolio risks for better management.
– Integrating AI provides insights that help investment managers make data-driven decisions and maximize returns.
– Helps in portfolio management by automatically deciding how to distribute assets, rebalancing, and managing risks.
– Personalized investment strategies are just a click away with AI.
– Offer insights into market behavior to capitalize on investment opportunities.

Artificial intelligence (AI) has been making tides in every sector—be it finance or healthcare. The use of AI in finance has caught the eye of all stakeholders. According to Statista, the market size of AI in fintech is $44.08 billion in 2024, which is expected to exceed $50 billion in 2029.

At this point, you might be asking yourself what significant changes AI brings to finance. The short answer is that it improves efficiency, supports informed investment decisions, maximizes returns, and more. The long answer? Well, for that, you have to stick around till the end.

AI has completely changed the way investment professionals operate. It automates various administrative tasks and helps to make well-informed investment decisions by analyzing vast amounts of data. In today’s article, we will be understanding the role of AI in investment, financial portfolio management, and making smart decisions.

The Impact of AI on Finance: AI in Investments

AI isn’t just a catchy term. It offers numerous benefits to the finance industry, especially in smart investment, risk assessment, and portfolio management. It aids investment professionals in decision-making by analyzing huge amounts of data.

We all know that it’s highly possible for humans to overlook investment opportunities, but AI systems? Not a chance. Moreover, AI helps in portfolio management by optimizing asset allocations based on market conditions and investor preferences. Enough of the basics; let’s explore the benefits of AI for smart investments in-depth without wasting more time:

Understand Marketing Trends

Will you invest without having any knowledge about the market? Will you be relying on your gut feeling? Surely not. AI plays a pivotal role here and provides investors with insights by analyzing huge amounts of market data.

Utilizing these insights, investors can identify risks and opportunities for investment. It helps professionals make proactive decisions and maximize returns. So when you have a technology like AI, why make guesses? Investors should rely on AI for profitable investment strategies as it is the way forward.

Supports Risk Management

Talking about AI’s role in investment management and not mentioning risk assessment and management is nothing less than a crime. Investment isn’t kid’s play; even the most experienced among us face risks. Manual risk assessment and management is a tedious and time-consuming process that is prone to errors. However, that’s not the case when AI is involved.

AI technology helps to identify potential risks in financial portfolios and provides invaluable recommendations to mitigate them. Curious to know how things work?

AI analyzes large amounts of market data and takes multiple scenarios into consideration to provide an accurate assessment of risks like fraud or market decline and their impact on investment portfolios. This way, investment managers can optimize portfolio performance and achieve their investment goals.

Provide Personalized Investment Advice

Imagine having a companion who knows your risk tolerance and goals and provides personalized advice that leads towards your set goals. Isn’t it great? What if I say you have such a companion but aren’t aware of his capabilities? Yes, you heard it right.

Any guesses as to who it could be? Have you guessed AI? Spot on! AI capabilities extend to wealth management, tailoring investment plans to individual preferences and financial goals. AI helps manage wealth by customizing investment plans to fit personal preferences and financial goals.

Improve Portfolio Management

Another benefit of AI in investment is that it helps with portfolio management. It enables investors to easily automate and improve how they allocate and adjust their assets with the changing market. Name a thing that AI can’t do. Bet you won’t find any!

These modern AI systems evaluate market trends, investor profiles, and economic signs to optimize portfolios so they match individual goals and risk levels. What’s more, investors can predict asset performance using AI, which helps in proactive adjustments.

Factors to Weigh When Using AI in Investment Decision-Making

Bias & Fairness

We all know that AI systems are trained on data. It means any biases in data can lead to discrimination or undesired outcomes. To avoid such challenges, cleaning your data and regularly checking your AI systems for fair results is crucial. It helps organizations prevent legal issues and protects their reputation and trust.

Transparency & Explainability

Do you know that most AI systems are black boxes? Didn’t know what a “black box” is? It is a scenario where a system takes a decision without answering the question “how.” The need of the hour is to overcome this challenge by providing explanations on how an AI system draws conclusions.

Data Privacy & Security

Securing data is another crucial factor to consider when using AI. Organizations can overcome privacy and security challenges by complying with international regulations like HIPAA in the US and GDPR in the EU for data protection.

Conclusion

The recent adoption of modern technologies like artificial intelligence in finance has reshaped the industry completely. It not only helps to improve operational efficiency and save time but also aids in decision-making and financial portfolio management. We can confidently say that utilizing AI isn’t just a basic need; it has become imperative for investment professionals in order to achieve greater returns.

About the Author:

Irfan Malik is pursuing a PhD in artificial intelligence and is the founder and CEO of Xeven Solutions. He’s tech-savvy and always stays one step ahead when embracing new technologies. He is passionate about solving real-world problems and is dedicated to making AI technology accessible and beneficial for all.

Der Beitrag AI in Investment: How AI is Transforming the World of Financial Portfolios erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

See what AI has achieved around the globe this week:

Social services enhance healthcare and banking support using AI
Dalith Steiger examines how AI is reshaping roles within the banking sector
Strategies for successful AI leadership in Andreas Welsch’s new handbook.
AI to diagnose autoimmune diseases in women
AI chatbot reduces conspiracy theories by engaging and persuading users
AI in infrastructure management, demanding significant investment for full potential realisation
…and more!

Stay tuned as we decode more AI innovations together!

Kind regards,

The Team of SwissCognitive

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Credit: This article with Dalith Steiger-Gablinger has been published in German as “ZHAW-Veranstaltung: Wie KI die Rolle der Bank-Mitarbeitenden verändert” – “How AI Is Changing The Role Of Bank Employees – ZHAW”

The next Finance Circle will take place on 16 September 2024 under the title “Banking Skills in the Age of AI”; organized by the Zurich University of Applied Sciences (ZHAW) and in collaboration with the Zurich Bankers Association (ZBV). finews.ch is a media partner of the ZBV.

Beforehand, artificial intelligence (AI) expert Dalith Steiger-Gablinger addresses the topic in a guest article and talks about the potential changes in banking and what skills bank employees will need in the future to remain successful.

AI takes over data-intensive tasks – but not everything

Everything that is connected to data processing and preparation will be taken over by AI in the near future. AI can provide enormous support, especially in the area of portfolio management and customer advice.

The role of emotional intelligence

Artificial intelligence gives us more time to invest in interpersonal relationships, both with clients and within teams. In a world where technology is becoming increasingly dominant, skills such as empathy and emotional intelligence are more in demand than ever. Accordingly, socially critical and philosophical questions are becoming increasingly central.

Collaboration between humans and machines can only be successful if humans build the emotional bridge between the data analysis provided by AI and the needs of the customer. It’s not just about the data provided by AI, but also about how we can interpret this information in human terms and communicate it to customers.

Key skills in dealing with AI

It is a misconception that AI makes us think less. On the contrary: when dealing with AI, you have to think carefully about the goal you are pursuing and ask the AI the right questions. The result depends heavily on how precisely we formulate the task.

Dealing with ChatGPT is comparable to communication between a boss and a secretary: In the past, bosses had to communicate very clearly what they wanted to say in a letter. If the instructions were unclear, the letter was not what they had in mind. The situation is similar with ChatGPT: the more precise and well thought-out the input, the better the result.

Technological understanding required

Although technical knowledge is not the main focus when dealing with AI, it is still important that bank employees understand the “power of the technology”. It’s similar to a smartphone. You don’t need to know how it works on the inside, but you should understand the possibilities it offers.

Employees don’t need to know the technical details of an AI application, but rather recognize its potential and be able to correctly assess when and how they can use it.

Further training and gut feeling as decisive factors

In the past, stenography and typewriting skills were basic requirements. Today and in the future, it will be essential to master the use of AI applications. Bank employees who find it difficult to use these technologies will find it harder to hold their own in the industry in the future.

Another key point is gut feeling. Even if AI delivers a result that seems logical, we still have to trust our gut feeling. If we sense that an AI result doesn’t suit the customer, even though the numbers are right, we need to listen to that intuition. Humans have the unique ability to evaluate situations in context and this ability remains essential.

Ultimately, it is not about using technology at all costs, but about where it supports us in a meaningful way and where it does not. Just because something is technically possible does not mean that we should do it. Humans must always remain in control and define the framework conditions for how AI can be used in different areas – from medicine to banking.

Conclusion: Humans remain crucial

The development of AI is progressing relentlessly, but humans remain indispensable in many areas. Emotional intelligence, critical thinking and the correct assessment of technologies are examples of the crucial skills needed to survive in the job market of the future.

Register for ZHAW-s free event today and meet Dalith Steiger-Gablinger, and the fellow esteemed participants:

– Dr. Michel Neuhaus, Head AI & Analytics, UBS Switzerland
– Dr. David Schlumpf, Head Learning & Leadership Development, JB Academy, Julius Bär
– Matthias Läubli, Vorsitzender der Bankleitung Raiffeisenbank Zürich
– Mark Dittli, Geschäftsführer und Redaktor, The Market

The event will be conducted in German.

Original article in german.

Der Beitrag How AI Is Changing The Role Of Bank Employees – ZHAW erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: technologyreview.com – “Readying Business For The Age Of AI”

Rapid advancements in AI technology offer unprecedented opportunities to enhance business operations, customer and employee engagement, and decision-making. Executives are eager to see the potential of AI realized. Among 100 c-suite respondents polled in WNS Analytics’ “The Future of Enterprise Data & AI” report, 76% say they are already implementing or planning to implement generative AI solutions. Among those same leaders, however, 67% report struggling with data migration, and others cite grappling with data quality, talent shortages, and data democratization issues.

MIT Technology Review Insights recently had a conversation with Alex Sidgreaves, chief data officer at Zurich Insurance; Bogdan Szostek, chief data officer at Animal Friends Insurance; Shan Lodh, director of data platforms at Shawbrook Bank; and Gautam Singh, head of data, analytics, and AI at WNS Analytics, to discuss how enterprises can navigate the burgeoning era of AI.

AI across industries

There is no shortage of AI use cases across sectors. Retailers are tailoring shopping experiences to individual preferences by leveraging customer behavior data and advanced machine learning models. Traditional AI models can deliver personalized offerings. However, with generative AI, these personalized offerings are elevated by incorporating tailored communication that considers the customer’s persona, behavior, and past interactions. In insurance, by leveraging generative AI, companies can identify subrogation recovery opportunities that a manual handler might overlook, enhancing efficiency and maximizing recovery potential. Banking and financial services institutions are leveraging AI to bolster customer due diligence and enhance anti-money laundering efforts by leveraging AI-driven credit risk management practices. AI technologies are enhancing diagnostic accuracy through sophisticated image recognition in radiology, allowing for earlier and more precise detection of diseases while predictive analytics enable personalized treatment plans.[…]

Read more: www.technologyreview.com

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