Latest news with #predictiveanalytics
Forbes
a day ago
- Business
- Forbes
2 Innovations For Financial Markets
1 2 5 10 100 Different dollar bills in a pile as background. Finance concept It's easy to understand intuitively that financial markets are predictive to some extent. In other words, there are things we can learn through data, and other things that are fairly opaque and mysterious, at least until you apply some pretty advanced analysis. But I think that a lot of people would associate finance, in general, with learning. People want to understand methodologies, and how finance works – they want to be able to make better decisions by being more informed, not just about what markets are doing or how to budget money, but how to forecast and make predictions and plan for the future, which is kind of what budgeting is when you think about it. In fact, investing is the same, in a way. Obviously, it works better if you have a long-term plan. Using AI for Finance So given all that, it seems like finance is a perfect application for the products of large language models, where these analytical machines are so highly cognitive that they can make predictions and teach us more about how to handle money. I recently saw a range of presentations by some great young career professionals who are developing real-world use cases for artificial intelligence. Two of them had to do with finance, but they had very different angles applied. Event Prediction The first one was a predictive engine from Aroosh Krishna that analyzed the Kalshi exchange – although it has an Indian-sounding name, the exchange was created in the U.S., fairly recently. I wasn't familiar with Kalshi, but when I read up on it, the platform gets at least 1 million users routinely, and you can bet on everything from natural disasters and elections to things like what Trump is going to say in a certain time frame. They call it event-based investment. In any case, a project called AnalyseKalshi uses sentiment analysis to help predict outcomes. There are two APIs involved, according to a flow chart showing how this engine works. Chart Krishna notes that part of the goal is to level the playing field between hedge funds and what he calls 'casual investors.' I found this to be an interesting use of AI, although applied to a market that seems to lack a certain seriousness. In other words, our tools can make highly predictive recommendations about certain kinds of outcomes based on the capricious emotions of, in some cases, rather unstable people, or more serious and substantial predictions … driven by highly technical pattern observations that LLMs can help us with. Teen Budgeting The second presentation had to do with creating personal finance tools for teenagers or young adults. Presenter Viren Kedia noted that the app is meant to help teens to be able to do things like invest, and budget, and understand taxes. Notes on program research on the presentation banner show that in two days, teens got a 30% increase in literacy quiz scores. Chart That's important because the quiz scores show you, as a user, what you're learning, and help with the benchmarking that the app brings to the table. Users, Kedia adds, like three major things about the application: free version, a good interactive chatbot, and video functionality. Teens learn the difference between a budget and financial plan, and setting financial goals. It's all part of educating this segment of the user base in better money management, and yes, in a way, predictive capabilities. Can these tools achieve what we need them to? The plans look actionable, and there's a clear value proposition to both of them. I'll continue to bring some of the most interesting and compelling projects to the blog, so stay tuned.
Finextra
a day ago
- Business
- Finextra
The End of Average: AI Is Rewriting the Rules of Digital Banking CX: By Alex Kreger
In 2024, banking crossed a critical threshold. No longer satisfied with being simply digital and inspired by interactions with Chat GPT, customers began demanding something deeper—experiences that understand them. Banks turned to a powerful ally: artificial intelligence. The result? A seismic shift from transactional to transformational. AI didn't just automate services—it began shaping intimate, context-aware journeys that feel less like banking and more like a personal concierge for your financial life. From predictive insights to adaptive interfaces, the age of 'one-size-fits-one' in banking CX has arrived—and it's only getting started. Banking, Tailored: Personalized Banking Experiences AI turns one-size-fits-all into one-size-fits-one, driving double-digit revenue and CSAT jumps. 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. Banks responded by leveraging AI (including machine learning and predictive analytics) to analyze vast troves of customer data—from transaction histories to digital behavior—and deliver more individualized experiences. In fact, a global marketing survey found personalization is the predominant use case for AI in finance, with 44% of organizations scaling AI to tailor customer experiences, anticipate needs and boost loyalty. This shift toward hyper-personalization is yielding tangible results: one analysis reported that using AI-driven insights for customer experience led to 'double-digit boosts in revenue, customer satisfaction and campaign conversions' for financial institutions. In other words, AI isn't just a gimmick; it's translating into happier customers and improved business outcomes. Banks around the world rolled out AI-powered personalization features in 2024. Many mobile banking apps started offering AI-generated insights and advice customized to each user's financial situation. For example, some banks use AI to automatically notify customers of unusual spending, predict upcoming bills or suggest budget adjustments—all tailored to the individual. In the US, Wells Fargo began leveraging predictive analytics and generative AI for dynamic content creation, aiming to deliver 'hyper-personalized interactions that drive engagement and revenue.' Regional banks also saw success: First Horizon Bank's marketing team integrated AI to predict client needs and personalize messages, reporting 'promising results' in deepening customer relationships. Meanwhile, in the UK, both incumbents and digital challengers invested in personalization. Digital-only banks like Starling Bank and Revolut (which reached 52.5 million customers in 2024) built their growth in part on personalized financial tools, prompting incumbents to follow suit. The demand is high: a large majority of banking customers (in all age groups) want more personalized experiences, and almost half are willing to let banks use their data for this purpose. This pushed banks in 2024 to use AI not only for targeted product offers, but also to tailor content, notifications and even the user interface to each customer's profile. Beyond marketing, AI-driven personalization extended to financial advice and planning. Some banks piloted virtual financial advisors that analyze a customer's goals and spending patterns to offer bespoke advice—for example, suggesting how to save for a house or which debt to pay down first. While these AI advisors gained some traction, trust remained a hurdle for sensitive advice: a survey found only 27% of consumers fully trust AI for financial guidance. To address this, banks paired AI insights with human validation or made AI recommendations fully transparent. The overall trajectory is clear: personalization became a must-have aspect of digital banking, and AI provided the engine to deliver it at scale. Banks that executed well saw improvements in customer loyalty, whereas those with generic one-size-fits-all digital experiences risked falling behind. As Capgemini's global head of data for financial services noted, 'AI and generative AI are rapidly transforming how we view personalized banking experiences… enabling analysis of vast data to generate tailored content, recommendations and interactions,' which is proving 'transformational' for customer engagement. DIY Banking Takes Over: Rise of Self-Service AI adoption in 2024 also turbocharged self-service capabilities in banking. Customers increasingly prefer to handle their banking needs digitally, without having to visit a branch or call a helpline for routine tasks. Banks used AI to make these self-service options smarter, more intuitive and more comprehensive than ever. The result was a continued climb in digital banking usage across markets. 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. Even in markets with traditionally high branch usage, the trend is similar. In the UK, for instance, 87% of adults use online banking, and 60% use mobile banking as of 2024, according to UK Finance, and 40% of Brits in 2025 have an account with a digital-only bank—a number that has risen sharply in recent years. This mass migration to digital channels has been facilitated and accelerated by AI-driven enhancements to self-service. One of the clearest impacts of AI is the proliferation of virtual assistants and chat interfaces that empower customers to get things done on their own. From simple tasks, like checking a balance or paying a bill, to more complex actions like applying for a loan, AI has made self-service more convenient. A survey by Zendesk noted that self-service adoption in financial services has grown 5.4× in recent times, as banks provide more useful tools like searchable knowledge bases and intelligent chatbots. Customers appreciate these improvements: about 75% say self-service is a convenient way to address issues, and 67% actually prefer self-service over speaking to a human rep. 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-powered chatbots and voice assistants played a dual role in boosting self-service. First, they act as 24/7 guides: an AI bot can understand a customer's query in natural language and either provide the answer or walk the user through the steps to complete a task. Second, these bots leverage AI to handle multi-step requests that previously might have required an employee. For example, Brazil's Nubank (one of the largest digital banks) offers a self-service model in which customers can do 'everything from paying a bill or raising their credit limit to setting travel notices' via the app. Over 80% of Nubank's customers handle their needs through these digital self-service features without the need for additional support. This kind of comprehensive self-service became a benchmark in 2024. In the US, Bank of America (BofA) expanded the capabilities of its virtual assistant, 'Erica,' to allow in-app money transfers and more complex queries, further reducing the need to visit a branch or ATM. And at Wells Fargo, the new AI assistant, 'Fargo,' was introduced to simplify everyday banking tasks in the mobile app, contributing to a staggering 245.4 million customer interactions via the assistant in its first year. The convenience of AI-driven self-service translated into higher digital engagement and cost savings. Automated self-service options deflect routine inquiries that would otherwise flood call centers, freeing human staff to focus on high-value or complex cases. Industry analysis estimated that bank chatbots will save around $7.3 billion globally in customer service costs, roughly $0.70 per interaction handled by AI instead of a human. Moreover, customers who get quick, effective answers on their own tend to be more satisfied. A survey by Zendesk in 2024 found 77% of consumers say AI is helpful for simple issues, indicating that when AI works, it meets customer expectations. However, it's worth noting that AI self-service is not yet perfect. 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. In summary, 2024 cemented self-service as an essential component of banking CX—largely enabled by AI—with customers embracing the flexibility to 'do it yourself' and banks enjoying higher digital adoption rates as a result. Next-Gen CX in Banking: Proactive Customer Engagement Beyond enabling transactions, AI has transformed how banks engage and build relationships with customers through digital channels. In 2024, banks used AI to become more proactive, conversational and timely in customer interactions, rather than just reactive. This led to higher customer engagement, measured by increased logins, more frequent interactions and deeper usage of digital services. A striking example comes from Bank of America, which reported that its clients logged into digital banking 14.3 billion times in 2024, and nearly 38 million customers subscribed to proactive digital alerts (up 7% from the prior year). Those alerts, powered by AI analysis of account activity, delivered nearly 12 billion personalized notifications in 2024, giving customers real-time insights into things like low balances or unusual charges. This kind of AI-driven engagement keeps customers continuously connected with their finances and with the bank's app. As a result, BofA saw users logging in on average more than once per day, and digital interactions overall jumped by double digits year-over-year. A Forrester analyst noted that BofA's mobile app 'at least meets and often exceeds customers' expectations' in most categories, thanks in part to useful features like these AI-powered alerts. The lesson echoed globally: when digital banking is helpful and personalized, customers engage with it frequently, creating a virtuous cycle of value for both the user and the bank. 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. This effectively turned customer service into a continuous, two-way dialogue. For instance, NatWest (UK) saw its chatbot, Cora, manage millions of conversations that not only answered questions but also guided customers to relevant services. With the launch of Cora+ (a generative AI-enhanced version), NatWest's assistant became even more engaging—able to converse more naturally about products and financial guidance, almost like a human agent. Early pilots suggest that these AI chats can handle complex queries with a more conversational tone, making customers feel heard and supported. Similarly, banks in the US like Capital One expanded their AI chat capabilities. For example, Capital One's Eno can proactively warn customers about suspected fraud or duplicate charges, engaging them in real-time via text message. By analyzing customer data and context, AI allows banks to initiate contact at the right moment: sending a helpful tip, a security alert or a personalized offer exactly when it's relevant. 70% of consumers value a consistent experience across channels, and AI helps achieve this by maintaining context across mobile apps, web and chat. A customer might start a task in a chatbot and finish in the app, or vice versa, with AI ensuring a seamless handoff. This omnichannel fluidity was a key engagement theme in 2024. Major markets demonstrated the impact of AI on engagement through concrete metrics. In the U.S., banks that invested heavily in AI-driven CX saw higher Net Promoter Scores (NPS) and loyalty drivers. While overall NPS can depend on many factors, leading banks like Chase and Capital One (both noted for their advanced digital experiences) continued to rank at the top of customer satisfaction surveys. In the U.K., digital challengers dominated customer advocacy: Forrester's 2024 survey of European banks found that app-centric banks, such as Starling Bank, earned the highest NPS in the UK market (beating even other fintechs by a few points). From a qualitative standpoint, AI-powered engagement in 2024 meant banks could anticipate customer needs instead of waiting for customers to reach out. Predictive models identified which customers might be shopping for a home, need a savings boost or be at risk of overdraft—and then proactively offered assistance or deals. This not only drives sales; it also improves the customer's financial health, thus building goodwill. One survey noted that 62% of consumers would immediately try AI-driven personalized alerts to help avoid fees, showing that engagement efforts that clearly benefit the customer are warmly welcomed. In summary, AI made digital banking more engaging in 2024 by turning it into an ongoing conversation—through alerts, chats and personalized content—rather than a static utility. Banks in the U.S. and UK that mastered this saw stronger customer loyalty and higher usage of their digital platforms, while globally the norm shifted toward AI-enabled 'smart' engagement as a key to CX success. AI Takes Care: Support at the Speed of Thought Perhaps the most visible impact of AI in banking has been in customer support automation. In 2024, nearly every major bank either launched or upgraded an AI-driven virtual assistant to handle customer inquiries, marking a significant shift in how service is delivered. These AI assistants (e.g., text-based chatbots and increasingly voice bots) became front-line support, capable of resolving many issues that used to require a phone call or branch visit. The volume of inquiries handled by AI is staggering: by late 2024, it's estimated that more than 987 million people worldwide use AI chatbots for various services, and banking is a leading sector in this trend. Banks like Bank of America reported that since launching the 'Erica' chatbot a few years ago, it had facilitated over 2.5 billion interactions in total, with hundreds of millions in 2024 alone. In the UK, NatWest's Cora not only answered routine questions but also helped perform transactions, effectively handling workload equivalent to thousands of support agents. Cora handled 11.2 million retail banking conversations in 2024, matching the scale of their human-assisted contacts. These examples underscore how AI automation scaled customer support to new levels, improving response times and availability. Customers can get help 24/7, in seconds, which is reflected in high satisfaction with these tools: a Capgemini study found 89% of customers were satisfied with generative AI virtual assistants, and 73% trust content written by AI, indicating growing confidence in automated support. NatWest's Cora+ was trained with safeguards (in partnership with IBM) to avoid the pitfalls of open AI models (like inaccurate 'hallucinations' or bias), emphasizing trust and reliability in its answers. In the U.S., banks were more cautious but still active in exploring generative AI for support. JPMorgan Chase, for example, invested in developing AI models (sometimes dubbed 'IndexGPT') for both customer service and investment advice, while Wells Fargo's Fargo assistant used Google's Dialogflow AI for robust understanding of customer requests. Even regional banks and credit unions began deploying AI chatbots through cloud providers or fintech partners, recognizing that automated support is becoming an expected part of digital banking. The quantitative benefits of support automation are significant. AI assistants resolve a large portion of common inquiries—balance requests, card activation, password resets, loan rate questions, etc.—in a self-service manner. This has led to shorter wait times and faster issue resolution for customers. For example, one report highlighted that customer service teams using AI can scale productivity without adding headcount, deflecting repetitive inquiries and freeing human agents for more complex issues. This improved first-contact resolution rates and reduced call center volumes. A Gartner study noted that 61% of banking executives planned to increase AI investments specifically to enhance areas like customer service in the coming year. In 2024's AI strategy, leading banks integrated a seamless escalation to human agents when needed, often with AI assisting in the background by summarizing the issue for the agent. This human-in-the-loop approach ensured that while AI handles the heavy load, the human touch remains essential for sensitive matters. Both the U.S. and UK saw case studies of improved support due to AI. BofA's Erica not only answered questions but could perform actions (like bill pay or Zelle transfers via voice commands), and the bank attributed part of its high digital engagement to Erica's success. 20 million BofA clients used Erica in 2024, and feedback has been positive enough that Erica's capabilities keep expanding. In the UK, Lloyds Bank and HSBC also enhanced their AI chat assistants in 2024, with HSBC launching an AI-powered customer support chat on its mobile app to complement phone service (especially valuable as branch networks contract). As a broader trend, a PYMNTS report noted only 12% of banks provided AI-powered customer service as of early 2024, but about half plan to implement it in the next months—signaling that support automation was a major focus industry-wide. Consumers increasingly embraced these AI helpers when well-implemented: one survey found 72% of retail banking customers actually prefer an 'intelligent virtual assistant' over a standard chatbot, likely because the former provides a more accurate and conversational experience. Further AI-driven support in banking will evolve from simple chatbots to more advanced virtual agents. This evolution will improve the speed, availability and consistency of customer service, thereby lifting overall customer satisfaction despite initial skepticism. Inclusive by Design: Enhancing Accessibility 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. Traditionally, accessibility in finance focused on accommodating visual or hearing impairments (e.g., screen reader compatibility or text-to-speech for phone banking). But 2024 brought a broader vision: banks started to tailor digital experiences for people with cognitive differences, mental health challenges, language barriers and more—areas in which AI's adaptability is key. Accessibility and inclusivity are finally receiving greater attention across the banking sector, with advances in AI helping to drive this progress. 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. AI-driven personalization also means interfaces can adjust to a user's needs in real time. Financial products should be designed for users with anxiety, depression or other mental health conditions in mind, not just physical disabilities, and AI can stitch together behavioral data across touchpoints to help banks understand and support each customer holistically. This represents a more compassionate use of AI: using data to identify when a customer might be financially stressed or confused and responding with empathy through the digital channel. In practical terms, 2024 saw banks introduce features like AI-powered voice assistants for those who prefer speaking to navigating a screen. These go beyond basic IVR systems; they use natural language processing to carry out banking tasks via voice (useful for visually impaired customers or the elderly who find apps challenging). Some banking apps added real-time transcription and translation services (AI translating speech or text on the fly), making services accessible to non-native speakers or customers with hearing impairments. For example, a customer could speak in one language and have the chatbot respond in another, bridging communication gaps. 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. In the U.S., where regulatory focus on accessibility is growing, large banks worked to ensure their websites and apps meet ADA standards and WCAG guidelines, often using AI tools to test and fix accessibility issues (like adjusting color contrast or adding AI-generated descriptions for images). 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. The benefits of AI-driven accessibility enhancements are both social and commercial. They empower more customers to confidently use digital banking, which in turn increases digital adoption among groups that might have been left behind. A more accessible app can turn reluctant users into active digital customers, raising adoption rates. It also strengthens trust and loyalty: customers feel the bank cares about their individual needs. While quantitative metrics here are harder to measure, one can look at engagement from traditionally underserved segments. Banks reported, for example, increased usage of mobile apps among senior customers after adding voice-command features and larger, adaptive text, indicating that AI features can bring in demographics that previously stuck to branches. Moreover, designing for extreme use cases often improves the experience for everyone (the curb-cut effect). 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 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. Both the U.S. and UK saw forward movement: U.S. banks like Citi invested in AI-driven document readers for visually impaired users, while UK banks such as Lloyds emphasized mental health–friendly banking apps with calming designs guided by AI feedback. These efforts, while still emerging, set the stage for a more inclusive digital banking landscape. Conclusion: As AI continues to infuse every corner of banking, the ultimate question is no longer whether machines can think, but whether banks can feel. Can they recognize struggle? Anticipate need? Offer not just efficiency, but empathy? The winners of the next era in banking won't be the ones with the most features—but the ones that can form the strongest emotional connection through digital channels. CX isn't just a battleground for loyalty; it's where trust is earned or lost in milliseconds. In the race toward hyper-personalization, AI is the engine—but humanity is the destination.
Yahoo
5 days ago
- Business
- Yahoo
Cognitive Supply Chain Market to Hit USD 32.58 Billion by 2032, Fueled by Rising Demand for Predictive Analytics in Global Logistics
Rising AI and real-time data use in supply chains drives efficiency, agility, and predictive decision-making, fueling market growth. Austin, July 10, 2025 (GLOBE NEWSWIRE) -- Cognitive Supply Chain Market Size & Growth Analysis: The SNS Insider report indicates that the Cognitive Supply Chain Market size was valued at USD 8.14 billion in 2023 and is projected to reach USD 32.58 billion by 2032, growing at a CAGR of 16.70% from 2024 to 2032. In the U.S., the Cognitive Supply Chain Market was valued at USD 2.01 billion in 2023 and is projected to reach USD 7.13 billion by 2032, growing at a CAGR of 15.11% during 2024–2032. Expansion is propelled by early adoption of AI, strong digital infrastructure, and an increased need for supply chain transparency. As enterprises around the country look to automate, create more resilient, and implement predictive analytics into their logistics operations, the future looks a Sample Report of Cognitive Supply Chain Market@ Major Players Analysis Listed in this Report are: IBM Corporation (IBM Sterling Supply Chain Suite, IBM Watson Supply Chain Insights) Oracle (Oracle Fusion Cloud Supply Chain Management, Oracle Supply Chain Planning Cloud) Amazon Web Services (AWS) (AWS Supply Chain, Amazon Forecast) Accenture plc (Accenture Intelligent Supply Chain Platform, myConcerto Supply Chain Suite) Intel Corporation (Intel Supply Chain Optimization Tools, Intel AI for Supply Chain Analytics) NVIDIA Corporation (NVIDIA AI Enterprise, NVIDIA Omniverse for Logistics) Honeywell International Inc. (Honeywell Forge Supply Chain Suite, Honeywell Connected Logistics) C.H. Robinson Worldwide, Inc. (Navisphere Vision, Navisphere Optimizer) Panasonic (Panasonic Supply Chain Solutions, Panasonic Logiscend System) SAP SE (SAP Integrated Business Planning, SAP Digital Supply Chain) Microsoft (Dynamics 365 Supply Chain Management, Azure AI for Supply Chain) Kinaxis (Kinaxis RapidResponse, Kinaxis Maestro) Anaplan (Anaplan Supply Chain Planning, Anaplan Demand Planning) Infor (Infor Supply Chain Planning, Infor Nexus) Manhattan Associates (Manhattan Active Supply Chain, Manhattan Demand Forecasting) Cognitive Supply Chain Market Report Scope Report Attributes Details Market Size in 2023 US$ 8.14 billion Market Size by 2032 US$ 32.58 billion CAGR CAGR of 16.7% From 2024 to 2032 Base Year 2023 Forecast Period 2024-2032 Historical Data 2020-2022 Regional Analysis North America (US, Canada, Mexico), Europe (Germany, France, UK, Italy, Spain, Poland, Turkey, Rest of Europe), Asia Pacific (China, India, Japan, South Korea, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (UAE, Saudi Arabia, Qatar, South Africa, Rest of Middle East & Africa), Latin America (Brazil, Argentina, Rest of Latin America) Segment Analysis By Deployment: On-Premise Dominates While Cloud Leads in Growth The On-Premise segment dominated the market in 2023. It accounted for 66% of revenue share, due to higher security, data control, and compliance requirements among the large enterprises, particularly in manufacturing and BFSI verticals. Historically, these industries have operated in silos of in-house infrastructure to manage private supply chain data. The Cloud deployment segment is expected to register the fastest CAGR during 2024–2032. Due to their inherent scalability, low cost of ownership, and ubiquitous accessibility capabilities, Cloud computing technology is ideally suited for SMEs and businesses operating in multiple geographies. Along with this structural trend towards remote work and the need for real-time collaboration tools, cloud adoption is being boosted. By Enterprise Size: Large Enterprises Dominate, SMEs See Rapid Growth The Large Enterprises segment dominated the market in 2023 and accounted for 68% of revenue share, as these organizations invested significantly in cognitive technologies at an early stage and often require complex and multidimensional supply chain orchestration activities. Such organizations are using AI to lower logistics costs, improve forecasting accuracy, and improve supplier networks. SMEs are projected to grow at the fastest CAGR from 2024 to 2032 during the forecast period, attributed to the growing availability of low-cost SaaS-based cognitive platforms. That gives smaller companies access to sophisticated analytics and automation without needing to invest heavily in infrastructure, letting them better compete in global supply chains. By Automation Used: IoT Dominates, ML Grows Fastest The Internet of Things (IoT) dominated the automation segment in 2023 and accounted for 45% of revenue share, owing to its significant role in providing immediate insight into the location of assets, inventory levels, or simply the health of machines to identify when predictive maintenance occurs. Here, IoT devices are generating the foundational data that all AI algorithms build off of across the supply chain. The Machine Learning (ML) segment is set to grow at the highest CAGR through 2032, owing to greater implementation of predictive analytics, risk detection, and adaptive learning over ML models by organizations. ML also enables businesses to continuously improve their capabilities to fine-tune forecasts and speed up responsiveness across operations. By Industry Verticals: Manufacturing Leads, Logistics Grows Fastest The Manufacturing sector accounted for the largest market share of more than 30% in 2023, as it was the first one to implement AI and cognitive automation in production and supply. Cognitive tools for inventory optimization, demand forecasting, and supplier risk management help manufacturers. The Logistics and Transportation segment is forecasted to grow at the fastest pace during the forecast period due to increasing e-commerce demand and last-mile delivery optimization and fleet intelligence solutions. Players of this segment are targeting investments in AI for optimizing route planning, route visibility for shipments and reduction in fuel cost. For A Detailed Briefing Session with Our Team of Analysts, Connect with Us Now@ Cognitive Supply Chain Market Segmentation By Deployment Cloud On-Premise By Enterprise Size SMEs Large Enterprises By Automation Used Internet of Things (IoT) Machine Learning (ML) Others By Industry Verticals Manufacturing Retail & E-commerce Logistics and Transportation Healthcare Food and Beverage Others By Region: North America Dominates, Asia-Pacific Emerges as Fastest Growing North America held the largest market share of more than 35% of revenue in 2023, due to the presence of robust technological infrastructure, rapid adoption of AI across major industries, and some of the leading market vendors, including IBM, Microsoft, alongside Oracle. Further, the focus of this region on supply chain resilience and regulatory compliance is also supporting the market growth. Asia-Pacific is poised to register the fastest CAGR through 2032. Countries like China, India, and Japan are witnessing a surge in demand due to rising industrialization, growing retail and manufacturing sectors, and increasing government initiatives to embrace AI in supply chains. Recent Developments – 2024 May 2024: IBM launched an AI-enhanced cognitive supply chain suite aimed at boosting real-time logistics analytics. March 2024: Oracle introduced an updated version of its SCM Cloud integrating generative AI features. February 2024: SAP partnered with NVIDIA to integrate cognitive AI models into its supply chain systems. Buy a Single-User PDF of Cognitive Supply Chain Market Analysis & Outlook Report 2024-2032@ Table of Contents – Major Key Points 1. Introduction 2. Executive Summary 3. Research Methodology 4. Market Dynamics Impact Analysis 5. Statistical Insights and Trends Reporting 6. Competitive Landscape 7. Cognitive Supply Chain Market by Deployment 8. Cognitive Supply Chain Market by Enterprise Size 9. Cognitive Supply Chain Market by Automation Used 10. Cognitive Supply Chain Market by Industry Verticals 11. Regional Analysis 12. Company Profiles 13. Use Cases and Best Practices 14. Conclusion About Us: SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company's aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the world. CONTACT: Contact Us: Jagney Dave - Vice President of Client Engagement Phone: +1-315 636 4242 (US) | +44- 20 3290 5010 (UK) Email: info@ in to access your portfolio
Yahoo
03-07-2025
- Business
- Yahoo
JMP Maintains Price Target and Market Outperform Rating on Evolent Health (EVH)
Evolent Health, Inc. (NYSE:EVH) is one of the top 10 healthcare AI stocks to buy according to hedge funds. JMP Securities reaffirmed its Market Outperform rating and $13.00 price target on Evolent Health, Inc. (NYSE:EVH), highlighting a more stable and promising outlook for the company. The firm described Evolent's current risk/reward profile as 'favorably skewed,' citing a series of operational improvements and strategic shifts that have put the company on firmer footing than in recent years. A doctor looking at their computer, discussing their patient's care options with a group of experts. Evolent, which provides value-based care solutions to payers and providers, has made notable progress in restructuring its risk-based arrangements and enhancing contracting mechanisms. These efforts have contributed to a healthier balance of performance obligations and upside potential. JMP noted that a strong pipeline of new business and improved visibility into existing partnerships support the firm's bullish view. While 2025 is expected to be a trough earnings year, JMP expressed increased confidence in Evolent's profit trajectory going forward. A key factor is the anticipated moderation in oncology-related cost trends, which have historically posed volatility in the company's earnings profile. Evolent Health, Inc. (NYSE:EVH) operates as a healthcare AI company, using predictive analytics and machine learning to manage complex populations, optimize clinical interventions, and reduce unnecessary costs. Its platform supports providers in making data-informed decisions, particularly in specialties like oncology and cardiology. With improved structural alignment and growing technological leverage, Evolent is well-positioned to capture long-term value in the transition to outcome-driven healthcare. While we acknowledge the potential of EVH to grow, our conviction lies in the belief that some AI stocks hold greater promise for delivering higher returns and have limited downside risk. If you are looking for an AI stock that is more promising than EVH and that has 100x upside potential, check out our report about this cheapest AI stock. READ NEXT: 13 Best Biotech Stocks To Invest In Now and 12 Best Healthcare Stocks to Buy Now. Disclosure: None.
Entrepreneur
17-06-2025
- Business
- Entrepreneur
AI's Impact on Business Operations in Europe: Startup Strategies to Stay Ahead of the Curve
Opinions expressed by Entrepreneur contributors are their own. You're reading Entrepreneur Europe, an international franchise of Entrepreneur Media. Artificial intelligence (AI) has transitioned from a speculative frontier to a core driver of business innovation and transformation. Whether enhancing decision-making through predictive analytics, delivering personalized customer experiences or shaping organizational strategy, AI is becoming indispensable across industries. What felt like cautious experimentation between 2023 and 2024 will evolve in 2025 into AI becoming deeply embedded in the strategic fabric of businesses, particularly for startups and their enterprise scaling needs. The shift is underpinned by significant investment and market momentum. According to a report by the European Parliament, Euro AI companies attracted €32.5 billion in investments by the third quarter of 2023. Moreover, AI spending in Europe could reach approximately $144 billion by 2028, as per a recent research by IDC. In 2025, AI's impact will extend beyond technological adoption—it will drive profound cultural and strategic realignments within organizations. While innovation typically drives transformative change, AI has progressed more rapidly than many other technologies due to the significant benefits it offers. Over the past few years, organizations have recognized the tangible advantages of AI, prompting many to move beyond pilot projects. Both startups and emerging firms will be required to leverage AI to redefine efficiency, scalability and innovation, making it a cornerstone of modern operations. PwC's 2025 AI Business Predictions Survey found that nearly half (49%) of technology leaders reported AI as already "fully integrated" into their core business strategies, with a third indicating its deep integration into products and services. In a blog post, PwC emphasized that integrating AI into an organization isn't just about achieving breakthrough innovations. "Big leaps, like new business models, are one source of game-changing AI value," they wrote. "But another, equally important, is the cumulative impact of incremental value at scale—20% to 30% gains in productivity, speed to market, and revenue—spreading across the organization until it's transformed." However, for startups and emerging businesses, thriving in this AI-driven landscape demands more than just adopting new technologies. "When AI is seen as a force for process or cost optimization, it can lead to automating tasks that were previously impossible to automate," JD Raimondi, Head of Data Science at Making Sense, told me. "While it may take time for all companies to join the AI movement, those that demonstrate clear benefits early will likely gain a competitive edge in both innovation and market share." Essential strategies for engineering scalable AI solutions Experts suggest that developing scalable AI systems should be a key focus for 2025. Investing in modular architectures that evolve alongside dynamic operational demands will be critical to maintaining adaptability and effectiveness. However, as with any technology, starting with a solution and searching for a problem rarely leads to meaningful outcomes. "A more practical and scalable strategy is to begin by identifying unsolved problems or challenges where current solutions are overly complex, time-consuming, expensive, or difficult to scale," Ruban Phukan, former Data Scientist at Yahoo and CEO of GoodGist, told me. "Once these problem areas are clearly defined, businesses can assess whether AI offers an efficient, innovative solution." Phukan emphasized that a problem-first approach streamlines AI development and makes it easier to demonstrate proof of value early in the process. "By targeting a specific solution to a well-defined problem space, startups can more effectively build scalable business models and position themselves for growth. Starting with a clear problem-to-solution alignment ensures that AI is not just a technological tool but a value-driven enabler of impactful business outcomes," he added. This approach can empower lean teams to achieve disproportionate impact by optimizing resource allocation and operational efficiency. For startups and scaling businesses, the decision to prioritize AI should be based on its potential to solve well-defined problems or unlock growth opportunities—rather than succumbing to hype or pressure to "keep up." By adopting this pragmatic, goal-oriented strategy, companies can incorporate AI as a driver of efficiency and growth without compromising other critical areas of their business. AI is becoming a cornerstone of Industry 5.0—a human-centric approach to technology development. AI systems can provide workers with real-time insights into quality errors and dynamically update instructions based on their inputs to resolve issues efficiently. "Large language models, in particular, can be integrated with software systems across industries to parse past records and serve as a 'knowledge base' for how employees have addressed various circumstances in day-to-day work," explained Arjun Chandar, Founder, Chairman & CEO of IndustrialML. Even startups that aren't explicitly AI-focused can leverage AI to enhance operations. "Using LLMs to help build detailed procedures as practices are established can align new hires with founders' knowledge much more quickly, streamlining onboarding and operational consistency," Chandar noted. How to break through organizational resistance and drive change The operationalization of AI comes with its share of challenges. Organizations must address internal resistance, establish robust training programs, and ensure AI initiatives align with broader strategic goals. "The AI system is going to compete with someone we don't want to compete against or displace jobs," cautioned JD Raimondi. Proper preparation, training, and reskilling of workers are crucial to creating a win-win scenario. "If that's not possible, the scope of AI implementation must be strategically planned to allow for a gradual transition, minimizing disruptions," Raimondi advised. In scenarios where AI's outputs could negatively impact individuals or groups, the role of human oversight becomes critical. "Human supervisors and ethical boards can help address concerns, quantifying and mitigating risks in a way that balances the benefits," Raimondi explained. Supervisors can step in to provide explanations or make exceptions (overrides) for AI decisions when necessary. Meanwhile, ethical boards can analyze the broader impact of AI-driven decisions, shaping company policies and setting limitations to ensure responsible use. "Most organizations, especially mid-market ones, will require humans to oversee, enhance, and complement AI systems," Raimondi added. Building trust and confidence within teams starts with involving employees early in the process. Engaging them in shaping how AI tools will integrate into workflows fosters a sense of ownership and reduces resistance. A practical approach is to begin with small-scale implementations, such as pilot programs. By inviting feedback and iterating on the system, organizations can fine-tune their strategies while demonstrating the tangible benefits of AI to their workforce. Unlocking customer loyalty with trust-driven, transparent AI solutions As customer-facing AI applications continue to proliferate, emerging businesses should place greater emphasis on simplifying these technologies and aligning them with user expectations. From AI-driven chat interfaces to predictive analytics, these tools are being leveraged to personalize user experiences and enhance customer satisfaction. Phukan stresses that AI should only be implemented if it provides a faster, more cost-effective, and scalable solution compared to existing alternatives. "By adopting this approach, businesses can align AI initiatives with measurable outcomes, making it easier to justify the return on investment," he explained. This strategic prioritization ensures that AI becomes a core driver of operational efficiency and revenue growth, rather than a discretionary expense. Phukan suggests that "Instead of relying on generic messaging, businesses can use AI to dynamically adapt communications to resonate with individual customers, demonstrating an understanding of their needs and a commitment to solving their specific challenges." This level of personalization, he noted, should span the entire customer journey—from interactions (both human and automated) and service delivery to feedback collection, problem resolution, and every touchpoint in between, with minimal friction. Agentic AI, according to Phukan, will be pivotal in achieving this depth of personalization. By conducting in-depth customer research and analyzing massive amounts of granular data—from server logs to communication records and notes—agentic AI can generate tailored communications and actions in real-time at scale. "These capabilities, which would otherwise be impractical to achieve manually, empower businesses to offer meaningful, responsive, and streamlined customer interactions that drive loyalty and satisfaction," he added. The specific points at which human involvement is required will vary across businesses and workflows. However, integrating human-in-the-loop (HITL) automation strategically can ensure that AI enhances efficiency while preserving the authenticity and personal connection that customers value. Opportunities and challenges for startups in 2025 The trajectory of AI in 2025 and beyond will be defined by its integration into business processes and its ability to deliver measurable value. Organizations that embrace AI's transformative potential will secure lasting competitive advantages, while those that hesitate risk falling behind. "So many innovative ideas have not yet been set in motion, and the constantly shifting market creates new opportunities," said JD Raimondi. "The landscape is still evolving, making it an ideal time for fresh ideas to emerge. That said, while many startups will rise with remarkable concepts, adoption is challenging and requires careful planning." For startups, the message is clear: AI has moved beyond speculative innovation to become a critical force shaping both current and future operational landscapes. "The opportunity to address long-standing challenges across industries, which have historically constrained growth, is here," explained Ruban Phukan. "AI is eliminating these barriers and delivering real, tangible value to enterprises—what was once a distant aspiration is now a reality." However, Phukan also stressed the responsibility that comes with AI's transformative power. "It's crucial for businesses to implement proper guardrails, robust security and privacy controls, and strong checks and balances to prevent biases in AI learning," he emphasized.
