Tag: Generative Ai

As we approach 2025, the technological landscape continues to evolve at an unprecedented pace. The rapid development of emerging technologies is poised to revolutionize industries ranging from transportation to healthcare over the next decade. Innovations like causal AI and next-generation large language models (LLMs) are set to transform traditional methods, enabling businesses across sectors to make accurate, data-driven decisions derived from experimentation and insights.

In this exclusive AITech Park article, we explore the perspective of Mridula Rahmsdorf, CRO at IKASI, on how the coming years hold immense promise for groundbreaking advancements that will redefine the way we work and interact.

Key Insights:

Integration of Causal AI in Decision-Making

The year 2025 and beyond will witness significant technological advancements as businesses incorporate causal AI alongside generative AI and LLMs. While current machine learning (ML) models remain invaluable, they are expected to undergo upgrades in the near future. Although causal AI has yet to enter the mainstream, experts predict it will enhance decision-making by improving accuracy, especially in scenarios involving complex, conflicting indicators. By understanding cause-and-effect relationships rather than mere correlations, organizations can leverage causal AI to bolster the reliability of generative AI, producing more coherent and relevant outcomes.

Expanding Critical Use Cases Across Industries

As confidence in causal inference grows, its integration with other AI technologies will unlock impactful use cases across various sectors. For example, in healthcare, causal AI can analyze patient history and lifestyle data to predict disease onset, enabling personalized treatment plans and interventions. Financial institutions can use it to develop sophisticated trading algorithms that adapt to market shifts, reducing risks and maximizing returns. Similarly, retailers can optimize pricing, loyalty programs, and promotions with unparalleled precision.

Growth in Community and Open-Source Development

Tech giants like Google, AWS, Uber, Netflix, and IBM are heavily investing in causal AI research, aiming to transition from correlative models to solutions that enable reasoning and real-time cause-and-effect analysis. Mridula highlights the role of open-source initiatives in democratizing access to advanced causal AI frameworks for startups, researchers, and public organizations with limited resources. However, open-source development faces challenges such as scalability, quality control, ethical considerations, and compliance, which require experienced teams and proven technologies for successful implementation.

To Know More, Read Full Article @ https://ai-techpark.com/technological-predictions-causal-ai/

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Generative AI (GenAI) has the potential to transform enterprise operations by driving automation, boosting efficiency, and fostering innovation. However, its implementation is not without challenges, particularly around data privacy and security. According to Gartner's Generative AI 2024 Planning Survey, 39% of data and analytics leaders identify data protection and privacy as major concerns. What fuels these challenges? Traditional data management practices, characterized by fragmented data sources and siloed governance protocols, are proving inadequate in the era of Large Language Models (LLMs). This inefficiency has prompted organizations to explore modern solutions, like the data fabric, to address security and governance hurdles more effectively.

Historically, enterprises have managed data across multiple sources and storage systems, each with its own security protocols and policies. While this approach was sufficient in simpler environments, it becomes problematic with LLMs, which require extensive, diverse datasets for optimal performance. Siloed systems complicate seamless data integration, creating inefficiencies and exposing security gaps. This complexity makes training and fine-tuning LLMs more challenging, as point solutions often lack the comprehensive data context that LLMs need.

Traditional approaches frequently demand either consolidating all data into a single warehouse—a costly and inefficient process—or sending data to public LLMs, risking exposure of sensitive information and potential security breaches. To fully capitalize on GenAI’s potential while maintaining robust security and governance, enterprises must adopt a more cohesive data management strategy.

Data Fabric and Active Metadata: Enhancing Security and Governance

A data fabric provides a unified and intelligent framework to overcome the security and governance challenges associated with integrating GenAI into enterprise environments. By acting as an abstraction layer between data and LLMs, leveraging active metadata for secure interactions, and offering centralized API access, it effectively addresses these concerns.

Protecting Sensitive Data

One critical risk when deploying LLMs is exposing sensitive data to public systems. A data fabric mitigates this by acting as an intermediary, ensuring sensitive data is never directly accessed by LLMs. Instead, it manages secure data access and retrieval, enabling the LLM to interact only with the necessary data in a controlled environment. This approach prevents unauthorized access, reduces the risk of breaches, and ensures that LLMs process information securely without directly handling raw data.

As enterprises increasingly adopt GenAI, robust data security and governance are paramount. Traditional, fragmented data management structures are insufficient for effectively and securely integrating LLMs. By adopting a data fabric, organizations gain a scalable framework that ensures sensitive data is never directly sent to LLMs, leverages active metadata for secure prompt engineering, and streamlines governance through a single API—all without exposing underlying data sources. This modern approach enables enterprises to harness the full potential of GenAI while maintaining rigorous security and compliance standards.

To Know More, Read Full Article @ https://ai-techpark.com/navigating-security-and-governance/

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The holiday season brings a significant surge in demand for customer service, driven by the rise of online shopping. Businesses increasingly turn to artificial intelligence (AI) and automation to bridge the gap in support during these high-pressure periods. With AI-powered tools like chatbots and automated systems, companies can handle demand spikes effectively without compromising performance. Let’s explore key strategies for scaling AI in customer service to ensure smooth operations during the holiday rush.

Why AI Customer Service Is Essential During the Holidays

The holiday shopping season, particularly Cyber Week, generates massive web traffic. Salesforce projects global sales to reach $311 billion this year, with AI influencing 19% of those transactions. Retailers leveraging AI-powered agents have reported improved conversion rates, enhanced customer engagement, and a 7% increase in average order value, showcasing AI’s ability to boost revenue and customer satisfaction during high-demand periods.

Scaling AI for customer service is more than deploying chatbots; it’s about real-time adaptability to meet customers' needs for fast, personalized assistance.

Optimize AI Chatbots for Holiday Traffic

AI chatbots are crucial for handling the increased volume of customer inquiries during the holidays. They manage everything from order tracking to product recommendations, alleviating pressure on human agents.

Practical Steps:

Integrate Across Platforms: Ensure chatbots are available on websites, social media, and mobile apps for a seamless customer experience.

Personalize Recommendations: Use AI to provide tailored product suggestions based on customer preferences, increasing satisfaction and conversion rates.

Automate Email Responses: Send personalized emails for order confirmations, shipping updates, and holiday offers to keep customers informed.

With finely tuned chatbots, businesses can deliver rapid, relevant responses, reducing wait times and enriching the shopping experience.

Manage Demand with Real-Time Forecasting and Scaling

AI-driven demand management tools help predict and prepare for traffic spikes, ensuring teams are equipped to handle surges effectively.

Practical Steps:

Leverage Demand Forecasting: Use AI analytics to predict traffic peaks and allocate resources accordingly.

Monitor Real-Time Behavior: Adjust chatbot scripts or human responses based on real-time customer queries.

Scale Backend Support: Utilize AI for inventory and shipping planning to avoid bottlenecks and ensure timely responses.

By smoothing operations and reducing delays, AI ensures a seamless experience for customers.

To Know More, Read Full Article @ https://ai-techpark.com/ai-customer-service-holiday-traffic/

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Future of QA Engineering

Generative AI – a technology wonder of modern times – has revolutionized our ability to create and innovate. It also promises to have a profound impact on every facet of our lives. Beyond the seemingly magical powers of ChatGPT, Bard, MidJourney, and others, the emergence of what’s known as RAG (Retrieval Augmented Generation) has opened the possibility of augmenting Large Language Models (LLMs) with domain-specific enterprise data and knowledge.

RAG and its many variants have emerged as a pivotal technique in the realm of applied generative AI, improving LLM reliability and trustworthiness. Most recently, a technique known as Graph RAG has been getting a lot of attention, as it allows generative AI models to be combined with knowledge graphs to provide context for more accurate outputs. But what are its components and can it live up to the hype?

What is Graph RAG and What’s All the Fuss About?

According to Gartner, Graph RAG is a technique to improve the accuracy, reliability and explainability of retrieval-augmented generation (RAG) systems. The approach uses knowledge graphs (KGs) to improve the recall and precision of retrieval, either directly by pulling facts from a KG or indirectly by optimizing other retrieval methods. The added context refines the search space of results, eliminating irrelevant information.

Graph RAG enhances traditional RAG by integrating KGs to retrieve information and, using ontologies and taxonomies, builds context around entities involved in the user query. This approach leverages the structured nature of graphs to organize data as nodes and relationships, enabling efficient and accurate retrieval of relevant information to LLMs for generating responses.

KGs, which are a collection of interlinked descriptions of concepts, entities, relationships, and events, put data in context via linking and semantic metadata and provide a framework for data integration, unification, analytics and sharing. Here, they act as the source of structured, domain-specific context and information, enabling a nuanced understanding and retrieval of interconnected, heterogeneous information. This enhances the context and depth of the retrieved information, which results in accurate and relevant responses to user queries. This is especially true for complex domain-specific topics that require a deeper, holistic understanding of summarized semantic concepts over large data collections.

To Know More, Read Full Article @ https://ai-techpark.com/graph-rags-precision-advantage/

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AI-Powered Wearables in Healthcare sector

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Have you encountered a bad situation that was made worse by something that is meant to help? Here’s a recent example of mine – I had to take my son to an emergency room while vacationing in Asia but the most frustrating part was dealing with insurance when we got home. The agent who initially processed my claim put me (and my money) in limbo – no external or internal follow-up communication, inaccessible and invisible in the client portal – because they didn’t follow the process for handling non-English documents. This poor customer service was entirely preventable and, though I’m not an insurance industry expert, I’m going to tell you how.

I started this article with my personal experience because all service providers need to consider customer impact when designing their AI adoption. Unfortunately for me, health insurance is a relatively inelastic service. The insurance company – let’s start to see ourselves in their position now – has many customers locked in for the year irrespective of individual satisfaction. It also means that customer acquisition is relatively fixed. Insurance companies are not alone in having profit margins that are won and lost in processes. They’re also not alone in having a customer base that includes stubborn engineers who will spend above-average time investigating problems to discover a root cause (hi, that’s me). Even though I can’t switch medical insurance, the original agent’s mistakes followed by my persistence led to an undesirably high touch time for the insurance company (getting personal again, I digress…)

Whether your organization manages insurance claims, manufactures automotive components, or facilitates the food and beverage supply chain, profitability is influenced by how well your people, processes and systems are harmonized. Fortunately, some of the up-and-coming solutions embedded with AI have started to measurably improve the balance with people, processes and, ultimately, profit. One of the solutions with a high yield potential from relatively low effort is called Process Mining. Gartner defines it as “a technique designed to discover, monitor and improve real processes (i.e., not assumed processes) by extracting readily available knowledge from the event logs of information systems”. What gives process mining the potential for high yield with low effort is that it leverages information that your business processes already generate but traditionally ignore outside of IT troubleshooting. Process mining users are provided with unprecedented visibility of process flows and deviations. Analysis of those deviations turns into data-driven continuous improvement with the possibility of incorporating process improvements that were already proven through execution even though they weren’t pre-planned.

To Know More, Read Full Article @ https://ai-techpark.com/ais-role-in-process-mining/

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Can you tell us about your journey as CEO and co-founder of Corti?

Corti was founded in 2016 in Copenhagen by Lars Maaløe and me. Lars, with his PhD in machine learning, and my experience as a multi-entrepreneur in AI from Scandinavia to Silicon Valley, has made for a solid partnership. We’ve always been driven by a belief in AI’s transformative potential for healthcare. We started Corti as a research company, with a bold thesis that Generative AI would become an integral part of every patient interaction in real time. At the time, this was a radical idea, but it has since proven to be viable. Initially, we focused on emergency medicine, assisting in detecting cardiac arrests and managing COVID-19 calls. Today, we are building the most reliable and effective Generative AI platform tailored to healthcare’s unique needs, scaling globally to enhance real-time consultations across healthcare and reducing the margin for error by up to 40%.

What inspired you to focus on healthcare technology, and how does your personal connection influence Corti’s mission and innovation?

From day one, we’ve been driven by the desire to reduce disparities in healthcare. Our vision is that everyone, everywhere, should have access to medical expertise. The uncomfortable truth is that millions of healthcare professionals are missing, and this gap is widening. From a young age, I saw firsthand the impact of overburdened healthcare systems on patients, which inspired me to seek a solution. That’s what drives me every day – to think that, thanks to Corti, a patient somewhere might get more time with their doctor, or avoid a misdiagnosis that could have been life-altering.

Could you provide an overview of Corti’s solutions?

Corti offers an AI-powered platform that enhances decision-making across the entire patient journey. Our AI provides a “second opinion” and integrates seamlessly into nearly any system, enabling healthcare professionals to quality assure, journal, code, nudge, prompt, and document every patient interaction. This drastically improves care, documentation, and revenue through expert guidance and support. We know there is no one-size-fits-all in healthcare, so Corti is fully customizable. It fits effortlessly into any workflow, effectively becoming the easiest employee you’ve never hired – no downtime, no breaks. With Corti, healthcare professionals save an average of two hours of documentation time per day and up to 80% on administrative tasks, freeing them to focus more on what truly matters: patient care.

What are the core values that Corti is built upon, and how do they guide the company’s objectives and mission in revolutionizing healthcare?

At Corti, we believe that everyone is a patient at some point, and we strive to support those who care for us when we need it most. Working at Corti means being committed to making the healthcare system better for all. Our team is driven by this belief – that we can and must do more to support caregivers. This mindset fuels us to go above and beyond, embracing challenges, staying curious, learning from failure, and waking up every day ready to try again.

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

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Human Potential with Augmented Intelligence

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Sebastian, can you start by sharing your background and what led you to your current role as VP of Engineering at Storyblok?

My journey in the tech industry began with a deep interest in software development and a passion for creating innovative solutions. Over the years, I have held various roles in engineering and management, which have provided me with a broad perspective on technology and its applications.

Before joining Storyblok, I worked with several startups and established companies, focusing on building scalable and secure software solutions. My experience in these diverse environments has been instrumental in shaping my approach to engineering and leadership. With Storyblok, I was drawn to the company’s vision of transforming content management and the opportunity to lead a talented team in driving this innovation forward.

In what ways can generative AI be utilized to create malicious content such as phishing emails and social engineering attacks?

Generative AI can produce highly realistic and personalized phishing emails by analyzing vast amounts of publicly available data about potential targets. This allows attackers to craft messages that are more likely to deceive recipients into divulging sensitive information. Similarly, AI can generate fake social media profiles or impersonate trusted contacts, enhancing the effectiveness of social engineering attacks. The ability to produce high-quality, contextually relevant content at scale means that these AI-generated threats can bypass many traditional security filters designed to catch generic phishing attempts.

The current cybersecurity measures seem adequate. What specific measures do you believe are most effective against AI-driven attacks?

While current cybersecurity measures provide a foundation, they need to be enhanced to effectively counter AI-driven attacks. Key measures include advanced threat detection where AI and machine learning are used to detect and respond to threats in real-time, behavioral analytics, which is the monitoring of user behavior to identify deviations that may indicate compromised accounts. Zero Trust Architecture is also important which involves implementing a model where verification is required for every access request, regardless of its origin.

Keeping staff informed about the latest threats and best practices to mitigate human error are also key measures in reducing the threat of AI-driven cyber attacks as is Multi-Factor Authentication (MFA) where an extra layer of security is added to verify user identities.

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

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Paige, kindly let our readers know how Seismic perceives the role of AI in evolving customer experiences and go-to-market (GTM) processes based on recent data.

At Seismic, we believe enablement is a mission-critical function that turns strategy into reality, and generative AI is creating an industry-defining moment for GTM and enablement teams. It is changing everything about the sales process, from prospecting to meeting preparation, content and presentation development, follow-up, training and performance tracking.

In fact, an overwhelming majority (93%) of enablement tech users acknowledge AI as the driving force behind their future investments. Based on this data, it’s evident that organizations neglecting to integrate AI into their GTM processes risk lagging behind and losing competitiveness in today’s industry, a sentiment echoed by 73% of respondents in our research.

What’s more: our customers know this to be true. In a Seismic customer survey, 65% of respondents cited AI as a primary reason for increased enablement investment. Specifically, they view Sales Content Generation & Optimization as the most valuable use cases to explore and implement for their teams. Over half (52%) are currently using or evaluating AI-powered tools within sales enablement processes, with 61% sharing that they are familiar with these tools to varying degrees.

Your report suggests a significant impact on both internal processes and customer experiences for businesses leveraging AI. How does Seismic observe this impact in terms of tangible ROI and enhanced customer engagement?

In addition to the clear revenue growth teams have witnessed, GTM leaders predict an average of 23% of that growth will be directly attributed to AI utilization over the next five years. In fact, 63% believe that AI is the primary force behind evolving customer experiences today. AI solutions are poised to touch nearly every corner of customer engagement.

For context, GTM leaders in the United States are leveraging AI-powered enablement tools for three primary functions: 53% utilize them for content analytics, 50% for content distribution, and 48% for learning and coaching. Businesses are experiencing significant benefits, with 91% of those who have implemented AI tools reporting an increase in customer satisfaction since integrating AI into their enablement processes.

How is Seismic addressing the gap in understanding how AI is used in GTM processes, and what educational initiatives or tools are being introduced without delving into specific numbers?

At Seismic, we work to consistently update our AI product offerings and tools to ensure they are meeting the needs of our customers and empowering them to build more strategic relationships, effectively engage with buyers, and speed up the entire buying process.

In fact, just last year we introduced an AI customer community, which is dedicated to sharing best practices, knowledge and strategies to seamlessly integrate AI into revenue teams’ operations. Additionally, we offer both the Seismic Advocacy Program and Seismic Community – two spaces for our customers to hear more directly from Seismic leadership about our products, as well as share with each other what they’re learning and doing with AI-powered enablement at their company.

To Know More, Read Full Interview @ https://ai-techpark.com/ai-transforming-gtm-processes/

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Generative AI has swiftly become popular among marketers and has the potential to grow to a $1.3 trillion industry in the next 10 years. OpenAI’s ChatGPT is just one growth example—rocketing to over 100 million users in just two months of its release.

Many have hailed generative AI as a process-changing tool that can quickly produce swaths of content with minimal human intervention, drastically scaling content production. That’s the claim anyway. But as AI becomes more prevalent, its use in content production opens several questions — does generative AI actually produce quality content? Can it match what human marketers can produce?

With the digital landscape already saturated with content, marketers in the AI era need to fully understand the strengths and weaknesses of current generative tools so they can build (and protect) high-quality connections with their audiences.

Human-generated content beat out AI-generated content in every category.

Though the AI tools had strengths in some areas, no one tool mastered multiple criteria across our tests. When it comes to accuracy, readability, and brand style and tone, the AI tools could not reach the level of quality that professional content writers provided. It also lacked the authenticity of human-written content.

The lesson: Brands and marketers must keep humans at the center of content creation.

Unsurprisingly, AI is not the end-all-be-all solution for creating content that truly connects with human audiences.  

Yes, AI is an efficient and capable tool that marketers can leverage to supercharge specific content tasks. Using AI for tasks such as research, keyword analysis, brainstorming, and headline generation may save content creators money, time, and effort.

Even so, marketers should prioritize humanity in their writing. AI can only give us an aggregate of the staid writing available across the internet. But highly skilled human writers are masters of contextualization, tapping into the subtleties of word choice and tone to customize writing to specific audiences.

As some have pointed out, quantity can never win out over quality.

In the race to adopt AI tools, we must remember what makes content valuable and why it connects with human audiences. The online marketing landscape is becoming increasingly competitive, and brands can’t risk the ability to build trusting connections with consumers in their rush to streamline workflows. Ultimately, humans must remain the central focus as brands invest in unique and authentic content that connects.

To Know More, Read Full Article @ https://ai-techpark.com/ai-vs-human-content-quality/

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The rapid advancement of AI has revolutionized industries worldwide, transforming the way businesses operate. While some organizations are still catching up, AI is undeniably a game-changer, reshaping industries and redefining enterprise operations.

Estimates from Goldman Sachs suggest that AI has the potential to increase global GDP by approximately 7% (almost $7 trillion) over the next decade by enhancing labor productivity. Even with conservative predictions, AI is poised to drive significant progress in the global economy.

The Importance of Training and Development

Training and development also play a critical role in this AI-driven evolution. Recent data showed that 66% of American IT professionals agreed it’s harder for them to take days off than their colleagues who are not in the IT department, which has serious implications for burnout, employee retention, and overall satisfaction. This makes AI integration more important than ever before. But first, proper training is essential.

As IT professionals are beginning to leverage AI’s power, emphasis must be placed on cultivating skills in data analysis, algorithm development, and system optimization. Especially as organizations embrace closed-loop AI systems, considerations around data security, ethics, and workforce upskilling become imperative.

AI companions are becoming increasingly essential to ensure efficient IT operations. Luckily, innovative solutions are emerging with capabilities like ticket summaries, response generation, and even AI solutions based on device diagnostics and ticket history to help streamline daily tasks and empower IT professionals to focus on higher-value issues.

Integrating Closed-Loop Systems to Supercharge Your AI Integration

The evolution of AI technology and closed-loop systems is set to revolutionize enterprise operations. As businesses navigate this future, embracing these advancements responsibly will be crucial for staying competitive and efficient. AI’s ability to enhance decision-making, streamline processes, and drive innovation opens new avenues for growth and success.

By integrating closed-loop systems and prioritizing responsible AI, enterprises can create more responsive and adaptive environments, ensuring continuous improvement and agility. The future of enterprise technology is here, and those who adapt and leverage these powerful tools responsibly will undoubtedly lead the way in their industries.

To Know More, Read Full Article @ https://ai-techpark.com/ai-evolution-enterprise-future/

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