Tag: Machine Learning

The advent of AI-powered adaptive commerce is transforming B2B e-commerce for enterprises across various sectors. Beyond merely enhancing customer insights or product recommendations, AI is now driving a new approach that blends physical and digital realms—often referred to as “phygital.” By integrating AI into physical environments, B2B e-commerce firms are crafting seamless and engaging shopping experiences that bridge online and offline spaces. Here’s how AI-driven physical technology is disrupting B2B e-commerce, along with tips based on our experience.

The Rise of Phygital Experiences in B2B E-Commerce

With the rapid rise of digital transactions, B2B companies are now adapting to meet the needs of clients who value both online convenience and tangible engagement. Phygital technology combines digital interfaces and AI-driven data with physical touchpoints, offering an integrated experience. Industries such as manufacturing, retail, and wholesale are especially suited for phygital solutions, as buyers often prefer to inspect products firsthand. For example, AI-AR and AI-VR tools allow B2B firms to offer virtual product interactions, giving clients a hands-on experience even if they’re not physically in a showroom.

How AI Drives Phygital Transformation

AI plays a crucial role in creating phygital experiences, providing insights that seamlessly integrate digital and physical interactions. Here’s how AI contributes to various aspects of phygital solutions:

Predictive Analytics for Personalization: Machine learning processes vast amounts of customer data, enabling B2B firms to deliver relevant recommendations. AI can personalize in-person experiences by presenting tailored information when customers visit a showroom or virtual demo.

Real-Time Data Integration: Phygital experiences require interoperability between digital and physical realms. AI consolidates data across sources to ensure up-to-date information on stock, pricing, and offers is available to both online and in-store customers.

Enhanced Customer Service with Chatbots and Virtual Assistants: Digital assistants and chatbots answer customer questions and guide them in making informed purchases. These can be deployed online or at physical kiosks to provide consistent support.

As AI technology continues to advance, phygital experiences in B2B e-commerce will reach new heights. Future innovations, like generative AI for enhanced virtual assistance and IoT for deeper digital-physical integration, promise even more robust phygital solutions. Companies that embrace these advancements early will likely achieve a competitive edge, offering unprecedented levels of customer satisfaction and convenience. Ultimately, the future of B2B e-commerce lies in balancing the tangible engagement of traditional sales with the efficiency of digital platforms. Phygital technology, supported by AI, will boost client satisfaction, drive sales, and streamline operations—ushering in a new era for B2B e-commerce.

To Know More, Read Full Article @ https://ai-techpark.com/revolutionizing-e-commerce-with-phygital-technology/

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Today, enterprises find themselves on the cusp of a transformative era, poised to unlock unprecedented business value by harnessing the full potential of AI. According to McKinsey, 44% of companies implementing AI report reduced operational costs, with many also experiencing revenue growth. However, successful AI adoption and modernization involve more than just deploying the latest technologies.

To achieve meaningful AI integration, organizations must first deeply understand their existing processes and pain points, establish robust data management practices, and align AI capabilities with broader business objectives. This approach enables improvements in efficiency, accuracy, cost savings, and compliance, empowering companies to excel in competitive markets and maximize AI’s impact and return on investment.

AI’s Role in Driving Business Transformation and Resilience

When implemented thoughtfully, AI can do more than optimize current operations; it can create new pathways for business growth and innovation. By aligning technology investments with strategic objectives, organizations can leverage AI to gain insights, anticipate market trends, enhance customer experiences, and streamline operations.

AI adoption also enhances resilience by supporting proactive risk management and scenario planning. With AI-driven predictive analytics, companies can anticipate challenges, optimize resources, and address potential disruptions. This proactive stance improves efficiency and builds agility, equipping businesses to respond quickly to market changes and competitive pressures. Through strategic AI integration, enterprises establish resilience, enabling them to adapt to uncertainties and sustain high performance.

Beyond technology investments, leadership is essential in this dynamic environment. Cultivating a culture of learning and innovation, supported by AI and modern technology, promotes sustainable growth, nurtures talent, and seizes emerging opportunities to distinguish the organization from its competitors.

AI has already transformed numerous aspects of business, and data-related functions are no exception. The push toward using AI for business value reflects a significant shift toward data-driven innovation, with operational excellence soon becoming an imperative. By strategically leveraging AI’s potential, companies can elevate efficiency, customer experiences, and market leadership. With reliable data as the foundation, businesses are well-prepared to navigate the complexities of an AI-powered world.

To Know More, Read Full Article @ https://ai-techpark.com/ai-data-business-power/

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Throughout 2024, data analytics continued to play a crucial role in driving data-based decision-making for B2B companies, while investment in advanced data technologies surged. These new tools offer deeper insights, predictive capabilities, and more sophisticated analytics to improve governance. The tools listed below are equipped with powerful features across BI, predictive modeling, natural language processing, and governance, reshaping the landscape of B2B data analytics.

Here are our expert reviews on the top five data analytics tools that defined 2024 and how each one can help businesses leverage data more effectively.

Key Trends Shaping Data Analytics in 2024

Before diving into the tools, let’s look at some major trends in data analytics for 2024. AI and machine learning have moved from innovative add-ons to essential components that support real-time insights and forecasting. Data quality and governance have become even more significant, with more companies prioritizing structured data environments for better compliance and integrity.

Natural language processing (NLP) has unlocked new capabilities in handling structured and unstructured data, such as analyzing customer sentiment and predicting market trends. The tools in our list excel in seamless integration, AI capabilities, and enhanced governance, addressing these trends.

Tool #1: Power BI — Leading in Visualization

Overview:

Microsoft’s Power BI retains its top position in data visualization and reporting due to its strong integration and user-friendly interface. It makes complex data accessible, empowering B2B teams to make informed decisions.

Key Features:

Seamless Integration with Microsoft Suite: Easy data extraction, manipulation, and analysis.

AI-Infused Predictive Analytics: Predictive models based on past data trends for future insights.

Enhanced Governance and Compliance: Features such as role-based access and data governance, essential for regulated industries.

Best Use Cases:

Highly effective for sales and operations teams looking for real-time analytics. B2B companies can use Power BI’s KPI and data modeling features to optimize customer service metrics and sales funnels.

The best data analytics tool depends on a company’s specific needs, such as scalability, integration ease, and advanced analytics like NLP or machine learning. Tools like Power BI and Tableau lead in visualization, while Looker Studio offers a seamless cloud-native experience. SAS shines in compliance-focused environments, and ML tools offer robust predictive capabilities.

As we look to 2025, AI-driven analytics, real-time decision-making, and embedded data tools are set to continue their rapid growth. Each of these five tools laid the groundwork in 2024 and will continue shaping the data analytics landscape in the years ahead.

To Know More, Read Full Article @ https://ai-techpark.com/top-5-data-analytics-tools-2024/

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The healthcare sector is undergoing rapid transformation, driven by a deeper understanding of patient needs and the adoption of cutting-edge technologies. At the forefront of this evolution are digital biomarkers and AI/ML, which serve as critical enablers of next-generation healthcare solutions. This paper examines the historical development, impact, and future trajectory of these technologies to explore how they are reshaping the modern healthcare ecosystem.

The Evolution of Biomarkers in Healthcare

Traditionally, biomarkers have played a crucial role in diagnosing diseases, predicting their progression, and evaluating treatment responses. Conventional biomarkers, such as blood tests and imaging, paved the way for the digital evolution of healthcare.

Digital biomarkers utilize technology for real-time data collection and analysis, offering more dynamic insights into health trends. With the widespread adoption of wearable devices and mobile health apps, digital biomarkers are becoming essential tools in patient care, revolutionizing how health professionals monitor and manage individual well-being.

Understanding Digital Biomarkers: A Comprehensive Approach

Definition and Classification

Digital biomarkers refer to objective, measurable physiological or behavioral data captured through digital tools. These markers can range from simple metrics, like daily step counts, to more complex indicators, such as heart rate variability measured through wearables. Unlike traditional biomarkers, which rely on lab tests, digital biomarkers offer real-time insights into a patient’s health status.

Types of Digital Biomarkers

Wearable Device Biomarkers: Smartwatches and fitness trackers measure activity levels, sleep quality, and heart rate.

Electronic Health Records (EHRs): Incorporating digital biomarkers into EHRs enables long-term tracking, improving care coordination and outcomes.

Mobile Health Applications: These apps monitor health metrics and provide personalized recommendations, fostering greater patient engagement.

Integrating Digital and Traditional Biomarkers

The integration of digital biomarkers with traditional ones, such as genetic data, offers a comprehensive view of patient health. For instance, combining genomic insights with wearable data allows healthcare providers to identify risk factors early, enabling timely interventions and personalized care.

To Know More, Read Full Article @ https://ai-techpark.com/digital-biomarkers-ai-ml/

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Artificial intelligence (AI) is a relatively new field that has rapidly evolved into a major influence on the strategic direction of organizations. Its significance extends far beyond automation, enhancing complex decision-making processes. AI is both a risk and a tool for managing risk—a paradox that organizations must confront as they navigate the landscape of 2024 and beyond.

AI as a Catalyst for Transformation

While AI is often associated with task automation, it also plays a critical role in improving decision-making. AI empowers change across various domains, from social to informational, by automating time-consuming processes and driving efficiency. Additionally, AI offers deeper insights to management teams than ever before.

In finance, for example, AI models outperform traditional methods by evaluating a broader set of factors to assess credit risk, predict market trends, detect fraud, and identify optimal investments. Similarly, in healthcare, AI enables early diagnosis and increases diagnostic accuracy, transforming how medical treatments are managed. These examples demonstrate that AI not only mitigates risks but also reshapes operational behavior, opening new avenues for efficiency and effectiveness.

Machine Learning’s Role in Enterprise Risk Management

Machine learning—one of the most crucial AI fields—plays a vital role in Enterprise Risk Management (ERM). By learning from data and detecting patterns beyond human observation, machine learning is particularly useful in industries like cybersecurity, where threats are constantly evolving. AI systems also monitor network activities in real-time, providing alerts for suspicious events to prevent breaches.

According to Gartner’s 2024 report, companies leveraging AI-based risk management tools saw a 30% reduction in data breach incidents. This statistic emphasizes AI’s ability to prevent risk events. Moreover, as data protection laws become stricter, AI helps organizations maintain compliance through precise monitoring and reporting mechanisms.

The dual role of AI as both a risk and a risk management tool defines the modern business landscape. Organizations that recognize AI’s strategic value and incorporate it into their planning will be well-positioned to thrive. The improvements in decision-making, efficiency, and risk forecasting that AI offers are too significant to ignore.

However, these opportunities come with responsibility. Companies must adopt ethical AI practices and ensure robust data protection to avoid negative societal impacts. Failure to address these issues could have serious consequences, not only for businesses but also for society as a whole.

Ultimately, the question is not whether to adopt AI but how to implement it sustainably and responsibly. Leaders with a vision for ethical AI usage will not only mitigate risks but also unlock new opportunities previously beyond reach. As business environments continue to evolve rapidly in 2024 and beyond, organizations that fail to adapt will fall behind. Integrating AI as both a tool and a mandate is essential for any innovative organization looking to succeed.

To Know More, Read Full Article @ https://ai-techpark.com/ai-is-both-a-risk-and-a-tool/

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The rise of artificial intelligence (AI) has transformed many sectors across the business landscape, reshaping how organizations operate. However, the convenience of AI introduces environmental challenges, such as increased energy consumption and hardware waste. These unintended consequences call for thoughtful strategies from chief information officers (CIOs), who must balance technological advancements with sustainability goals.

According to a Gartner survey, environmental issues are now a top priority for tech companies, and CIOs are facing pressure from executives, stakeholders, and regulators to implement sustainability initiatives. The convergence of AI and environmental responsibility requires proactive measures that can drive sustainable transformation.

This article offers a framework for adopting green algorithms—energy-efficient AI solutions—to help CIOs build sustainable IT organizations.

AI Supporting Environmental Sustainability

To integrate sustainability into IT operations, CIOs must establish clear mandates and requirements to track sustainability metrics, such as energy consumption and carbon footprint. The effectiveness of these efforts depends on embedding sustainability KPIs into the organization's digital infrastructure.

A practical example lies in modern data centers. Advanced optical networks, which use fiber cables, are significantly more energy-efficient than copper-based networks. Fiber cables require fewer raw materials and less manufacturing energy, reducing the environmental impact compared to the extraction and refinement processes for copper. In fact, IT companies implementing modern networking technologies have reported up to fourfold reductions in their environmental footprint.

While AI can introduce environmental challenges, it holds tremendous potential to advance sustainability initiatives when used thoughtfully. CIOs and project managers play a pivotal role in designing and implementing AI-driven solutions that align with sustainable development goals.

By focusing AI efforts on the right use cases, businesses can mitigate environmental impacts, enhance operational efficiency, and reduce unnecessary costs. AI has the potential to become a powerful ally, fostering both innovation and environmental responsibility in the IT industry. With proactive strategies, the convergence of AI and sustainability can transform the future of business.

Know More, Read Full Article @ https://ai-techpark.com/the-convergence-of-ai-and-sustainability-in-the-it-industry/

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Yashin, to kick things off, could you share what inspired you to transition from a career in academia and engineering to founding Pvotal Technologies?

Growing up, I thought a lack of proper education was the root of many societal issues and inefficiencies.

Idealistically, I entered academia thinking I could become a professor who would nurture the issues leading to a wavering generation of talent, innovation, and development. Unfortunately, I quickly realized how some processes were limiting, stifling, and stuck in an antiquated age.

I could not build or address problems I saw in my niche field due to software issues, data breaches, the high cost of licensing fees for some critical tools, and the poor integration of tools. These issues led me to lose thousands of hours in frustration fixing technical problems rather than focusing on my growth, thesis, and research. The tools I used became a greater source of frustration than my research, constantly distracting me from my objectives.

My skills and resolve were too limited to reform academia from within, so I decided to focus on the issues within the software industry to limit the problems that more talented academics faced. I co-founded Pvotal with Ashley to build a new generation of solutions that helped customers focus on the value they bring to customers rather than get stuck in an iterative cycle of integrating code and debugging updates.

Pvotal emphasizes creating “Infinite Enterprises.” Could you explain what this concept entails and how it aligns with your overall mission?

While many industries have adopted different interpretations of the ideal Infinite Enterprise, we believe the “infinite enterprise” is any company that has achieved an infinitely scalable, independent, resilient, and secure infrastructure. Once these criteria are met, we observed that it allows businesses to truly innovate, improve, and elevate their value proposition to customers.

The age-old adage of teens or some fresh graduates going into “founder mode” can build the next generation of software in their proverbial garage, shared workspace, or dorm room is simply no longer possible.

The rise of hyperspecialization, wanton integration of third-party code or vendors, and the unmanaged accumulation of technical debt has led most software companies to become antiquated, vulnerable, and overbloated pieces of code that can no longer efficiently protect their customers’ data, provide a competitive edge to their users, and have a reasonable cost/utilization footprint.

Most modern enterprise software has at least 17 paid or free SaaS, PaaS, and third-party code powering its operation or development. With a tough economy, inflation, and squeezed supply chains, these different services are forced to raise prices continually, thus shifting the burden on the end consumer. In addition to the increasing costs, these software are often abandoned or introduce vulnerabilities to the enterprise supply chains, which is why we have experienced a record-breaking number of successful cyberattacks, ransomware, and fraud every year for the past decade.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-yashin-manraj/

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As the world navigates through 2024, cybersecurity gets more unpredictable and dangerous. With increased sophisticated cyberattacks like ransomware, phishing, and APTs, there has never been a higher demand for cybersecurity professionals. But after the rising tide, the industry stands at a significant skills gap, presenting organizations with vulnerabilities to breaches and data theft.

Following a report by ISC², there is a lack of more than 3.4 million cybersecurity workers across the world. The gap becomes an important threat to diverse organizations, majorly finance, healthcare, and technology-related businesses that are more vulnerable to cyber attacks. As we head to the future, B2B businesses need to invest in the next generation of cybersecurity talent and equip them with crucial knowledge and skills to offer sensitive data and systems protection.

This is your roadmap for the business to educate professionals in cybersecurity across all types of skills with education and strategies to create the best plan of workforce development.

The Growing Need for Cybersecurity Talent in 2024 and Beyond

Current Workforce Shortage

Business operations in almost every corner of the globe are being adversely affected by a severe challenge: an acute shortage of cybersecurity professionals. According to Cybersecurity Ventures, by 2025, cybercrime will reach $10.5 trillion in annual damages worldwide, representing an even greater need for experts in that field. There are not enough trained professionals to fill such a high demand. This presents a challenge to businesses, particularly those sectors dealing with sensitive data, such as healthcare, government, and finance.

Evolving Threats

The 2024 threat landscape is not only about malware and phishing schemes. Organizations are becoming increasingly subjected to more advanced attacks, like ransomware-as-a-service (RaaS), attacks by nation-state entities, and supply chain attacks—all of which make much greater technical capability demands than traditional IT knowledge requires. It is thus crucial to train professionals who can predict the magnitude of new threats.

The Role of New Technologies

Emerging technologies such as artificial intelligence (AI), machine learning (ML), and cloud computing are impacting the cybersecurity sector with new vulnerabilities; therefore, professionals have to become adept at securing these systems; thus, the urgency for cybersecurity knowledge coupled with cutting-edge tech becomes highly integral in the future workforce.

To Know More, Read Full Article @ https://ai-techpark.com/preparing-the-next-generation-of-cybersecurity-professionals/

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We live in an age where personalization is key to our experiences, from music and web series to the products and services we use, all tailored to us based on data collected by websites and apps. This capability helps us better understand our needs and improve our overall quality of life.

Similarly, in the healthcare sector, technologies like artificial intelligence (AI), natural language processing (NLP), and machine learning (ML) allow us to monitor our health and receive personalized treatments. Often referred to as AI in healthcare, this technological collaboration is transforming traditional patient care by introducing futuristic clinical and administrative solutions. Doctors, researchers, and healthcare providers are using these advanced tools to enhance healthcare delivery in areas such as preventive care, disease diagnosis and prediction, treatment plans, and administrative tasks.

AI in healthcare is also making strides in recruitment, allowing companies to contribute more effectively to consumer health. The growing use of AI in wearable devices and medical tools is particularly valuable for detecting early-stage heart diseases. These AI-powered sensors and devices enable healthcare professionals to monitor and identify life-threatening conditions at an early stage.

While there are many applications for AI in healthcare, this article will focus on how AI is currently being implemented and what the future holds for healthcare policies in this sector. The concept of patient-centric care is a driving force behind AI-powered prescription medicine, which enhances personal treatment by empowering patients and providing real-time, visual care.

Key Areas of AI in Healthcare

The integration of AI in healthcare is transforming modern healthcare systems, enabling them to diagnose and treat diseases with greater speed and accuracy. These advancements are improving care quality and creating more patient-centered healthcare processes. AI's key focus areas include improving care delivery, strengthening disease surveillance, and accelerating drug discovery.

The future of AI in healthcare holds vast potential to shape public and private health policies. By prioritizing education and training and adopting AI responsibly, leaders in the health tech industry can unlock the full potential of AI, creating innovative, long-lasting solutions to the complex challenges facing healthcare today.To Know More, Read Full Article @ https://ai-techpark.com/ai-in-healthcare/ 

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As cyberattacks advance in their sophistication and frequency, traditional cybersecurity defenders-the firewalls, antivirus software, even intrusion detection systems-are no longer sufficient in protecting companies. Organizations are bound to face advanced persistent threats (APTs), ransomware, as well as insider attacks in 2024 that often go undetected by automated detection tools. This makes proactive cybersecurity a dire necessity.

According to new research findings, the average amount of time taken before it is possible to detect a breach stands at more than 200 days, which is a very long window for cyberthieves to siphon sensitive data and cripple business operations.

This mainly occurs in B2B organizations operating within the finance, healthcare, and technology sectors, as these sectors are mainly characterized by sophisticated attackers seeking high-value data. However, the only solution is in cyber threat hunting-a proactive security approach aimed at detecting threats before they trigger damage.

In the guide here, we will cover the most important steps to implement a robust cyber threat hunting strategy tailored for 2024-overview of all the skills, processes, and technologies that will help in keeping your business safe.

What is Cyber Threat Hunting?

Cyber threat hunting is one of the proactive cyber security practice wherein the trained and well-equipped security analysts proactively search for hidden or undetected threats within an organization’s network.  While the traditional monitoring systems passively wait for alerts, the threat hunters search for malicious activity or a weakness that can be exploited.

Why It Matters in 2024

Today, the threat landscape for cyber defence is no longer passive but active detection. Attackers are continually evolving by attempting to evade detection with tactics like lateral movement, credential dumping, and fileless malware. Threat hunting becomes very critical in this approach since it looks beyond waiting for automated tools to flag an anomaly and instead hunts for and discovers sophisticated attacks made to evade traditional defenses.

Common Cyber Threats in 2024

Some of the prominent threats businesses will face in 2024 include the following:

Advanced Persistent Threats (APTs): Organized cyberattacks that siphon off data for long periods of time without being detected.

Ransomware: A ransomware attack encrypts a victim’s data and demands payment in lieu of providing decryption keys.

Insider Threats: It is an employee or contractor who intends to do evil or shows malacious carelessness in doing his duty that might lead to security breaches.

Zero-Day Exploits: In this case, attacks exploit vulnerabilities that have not been patched yet.

To Know More, Read Full Article @ https://ai-techpark.com/implementing-cybersecurity-threat-hunting/

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