Media Article Archives - Movate

Quantum computing technology pushes for IT advantage

Movate Admin — Mon, 02 Dec 2024 07:30:49 +0000

Media: TechTarget
Spokesperson: Gourishanker Jha

Quantum computing, overshadowed in recent years by the stunning rise of generative AI, is making quiet — and, in some pockets, accelerated — progress toward realizing its full potential.

This still-emerging technology aims to dramatically speed up the processing of complex mathematical and computational problems, employing the principles of quantum mechanics. Quantum computing, and the related fields of quantum sensing and quantum communication, are poised to affect a variety of industries, from life sciences to financial services and others, as well as the government and defense sectors. Enterprise IT leaders can already explore early manifestations of quantum technology offered by an array of quantum hardware vendors and hyperscalers for access in the cloud — and they might discern some familiar patterns as they do so.

Indeed, the quantum sector has begun to show signs of the evolution that signaled the emergence of conventional computing decades ago. Those trends include a shift toward software development versus a hardware-centric view of computing, previously seen in the unbundling of mainframe software in the 1960s. In addition, the U.S. military’s sizeable commitment to quantum technology, reflected in recent multimillion-dollar contracts, mirrors the Pentagon’s early use of computers such as ENIAC in the 1940s.

Technical challenges, however, block the path of mainstream quantum adoption. Error handling is a particularly difficult challenge, as researchers work to create methods for reducing the large number of errors that commonly crop up in quantum computations. Other issues include the need for more capable algorithms and software that can take advantage of quantum hardware.

Surmounting such hurdles will help propel quantum technology from its current stage, referred to by researchers as noisy intermediate-scale quantum (NISQ), to the envisioned next phase, which the industry has dubbed quantum advantage. That transition will mark the point at which a quantum machine can outperform a classical computer in certain use cases, such as simulation, optimization and cryptography. When it will happen is an open question. Estimates generally range from five to 10 years. But recent advances in software and hardware — and the potential for unexpected breakthroughs — could speed up the arrival of quantum advantage.

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Reinventing CX Business Models with Immersive Tech

Movate Admin — Thu, 07 Nov 2024 09:12:35 +0000

Immersive technology is poised to become a transformative force in our everyday lives, much like the personal computer did in the 1980s or the smartphone did in the early 2000s. This evolution presents businesses with an unparalleled opportunity to rethink their strategies and innovate new models.

Imagine a busy IT manager at a growing company. Traditionally, troubleshooting a complex server issue could involve endless phone calls, back-and-forth emails, and potentially days of downtime. Now, picture that same manager donning a VR headset to be virtually transported into the server room, resolving the issue swiftly and effectively. This isn’t science fiction; it’s the future of customer experience (CX) powered by immersive technologies.

The CX landscape, once dominated by standardized service contracts and impersonal interactions, is undergoing a radical transformation. The global customer experience management market is expected to reach $20.4 billion by 2028, growing at a compound annual growth rate (CAGR) of 12.2 percent.< Today’s customers no longer settle for generic solutions or reactive support. They expect proactive, tailored experiences that address their unique needs and challenges. This is where artificial intelligence (AI), immersive technologies such as augmented reality (AR) and virtual reality (VR), and advanced analytics converge to revolutionize CX.

Personalization is just the beginning of this new frontier. AR and VR unlock entirely new dimensions of engagement. Imagine a field technician facing a complex hardware issue on site. With AR, he can overlay critical data directly onto the physical equipment, diagnosing the problem and guiding the customer through troubleshooting steps in real time. Or consider a software developer using VR to immerse himself in the codebase of a new application, visualizing data flow and pinpointing potential bugs before they impact functionality. These immersive experiences enhance service delivery, improve problem-solving efficiency, and deepen the understanding of customer needs, leading to faster resolutions.

Integrating immersive technologies into CX strategies is critical for organizations aiming to offer more than just transactional interactions. In fact, Gartner found that 60 percent of consumers report becoming repeat buyers after a personalized purchasing experience. By offering interactive, engaging digital experiences, companies can capture customer attention and foster brand loyalty through memorable, impactful interactions.

Innovation thrives in environments of agility. Traditional, siloed approaches to customer interactions no longer suffice in today’s dynamic landscape. Organizations are now embracing omnichannel strategies, ensuring seamless transitions across multiple touchpoints. Whether it’s shifting from a mobile app to an in-store visit or following up a VR product demo with a personalized email, each interaction contributes to a cohesive customer journey that builds trust and loyalty.

NVIDIA’s collaboration with Apple Vision Pro, for example, demonstrates how hybrid rendering and real-time cloud computing enhance spatial computing across industries. Disney’s investment in Epic Games further highlights how immersive technologies can redefine customer engagement. By leveraging AI-driven applications, companies can gamify experiences and deliver real-time personalization. These examples illustrate how agility is now the cornerstone of modern business strategy. Companies that adopt agile methodologies and iterative development processes can swiftly respond to shifting market demands and evolving consumer expectations, continuously enhancing their digital platforms with user feedback and emerging technologies.

The future of CX goes beyond personalization and omnichannel experiences. It lies in co-creation, where customers are active participants in shaping the platforms with which they interact. Imagine a scenario where users co-design product features, offer feedback during development, and influence the design of digital experiences. This collaborative approach strengthens brand loyalty, fosters a sense of ownership, and ensures that products and services are continuously refined to meet evolving customer needs.

PwC data shows that 86 percent of buyers are willing to pay more for a great customer experience, with a premium of 13 percent to 18 percent for luxury or indulgence services. By involving customers in the development process, companies build not only trust but also a future where CX becomes a fundamental pillar of their business models. Immersive tech is not merely a novelty; it’s a tool for forging deeper connections, fostering trust through interactive experiences, and creating future-proof strategies that transcend current limitations.

As AI and immersive technologies evolve, so will the way businesses create meaningful customer interactions. The convergence of AI and immersive tech represents a paradigm shift in customer engagement. By adopting these technologies, companies can unlock new growth opportunities and differentiate themselves in an increasingly competitive marketplace.

Prioritizing personalized experiences, agility in response to market changes, and co-creation with customers will allow businesses to transform every customer interaction into something truly transformative.

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AI vs AI: Generative AI’s Impact on Digital Defense and Cyber Attacks

Movate Admin — Mon, 21 Oct 2024 07:29:41 +0000

Cybersecurity is now a battleground not just for humans, but for AI systems, with Generative AI (GenAI) playing a dual role in both strengthening defenses and enabling new types of cyberattacks. On one hand, GenAI is revolutionizing cybersecurity by improving threat detection, automating responses, and bolstering defensive mechanisms.

On the other hand, hackers are leveraging the same technology to develop more sophisticated, evasive techniques, penetrating even the most secure systems. This AI vs AI scenario poses significant challenges for organizations to safeguard their digital assets.

The stakes have never been higher. With the average data breach cost reaching $4.45 million in 2023, GenAI’s role in enabling and preventing these breaches is more critical than ever. Furthermore, Gartner forecasts that enterprise spending on combating misinformation will exceed $500 billion by 2024, consuming a substantial portion of cybersecurity and marketing budgets. As AI-driven threats escalate, businesses must adapt quickly to stay ahead of malicious actors.

The Rise of AI Threats

Cybersecurity experts predict a dramatic increase in AI-driven threats. Cybercriminals are increasingly using GenAI to create sophisticated malware, craft highly convincing phishing attacks, and deploy deepfake technologies to impersonate individuals or manipulate information. GenAI enables attackers to generate adaptive cyberattacks that evolve in real-time, making them harder to detect and defend against. AI-powered disinformation campaigns and the weaponization of AI for automated exploits have further heightened the stakes. With 53% of organizations recognizing cybersecurity as a GenAI-related risk, the dual nature of this technology places businesses in a race to innovate faster than cybercriminals can adapt.

Cyberattacks are becoming more potent. 98% of cyberattacks rely on social engineering, with an average business facing over 700 attacks annually. The rise in GenAI-powered phishing attacks has made them even more difficult to thwart, with 83% of U.S. businesses falling victim to phishing and 95% of successful network intrusions relying on spear phishing. These tactics, combined with AI-augmented botnets, have led to more devastating attacks, including automated ransomware and deepfake exploits.

The Role of GenAI in Strengthening Cybersecurity

While GenAI poses significant risks, it also brings revolutionary benefits for defenders. GenAI algorithms can analyze massive datasets to detect suspicious activity, reducing breach detection time and improving real-time response capabilities. By 2027, GenAI is expected to contribute to a 30% reduction in false positive rates for application security testing and threat detection, refining results from traditional techniques and enhancing defenses.

Organizations can leverage GenAI to predict and block potential attack vectors by analyzing anomalies and behavior-based patterns. AI-driven systems can also automate defensive responses, enabling real-time threat adaptation and minimizing human intervention. GenAI’s ability to predict potential vulnerabilities before they are exploited is driving companies to integrate it deeper into their cybersecurity architectures, elevating defenses and enabling more proactive countermeasures.

Best Practices for Safe Utilization of AI Technology

Despite its promise, GenAI also presents significant risks when not handled with care. Cybercriminals exploit the technology to find loopholes in enterprise defenses, launch sophisticated attacks, and craft malware that bypass conventional security measures. To harness GenAI’s benefits and mitigate its risks, organizations should:

* Adopt AI-driven threat intelligence tools: These tools can detect and mitigate AI-generated threats, especially as cybercriminals develop more advanced techniques.
* Establish regular AI audits: Conduct periodic audits of AI systems to ensure they remain secure, compliant, and ethical, preventing external manipulation or exploitation.
* Train employees on AI-driven phishing threats: With social engineering tactics evolving, equipping employees to recognize AI-generated scams is critical to reducing vulnerabilities.

By integrating these practices into a comprehensive cybersecurity framework, businesses can safeguard their operations while benefiting from GenAI’s potential.

Conclusion

The ongoing AI arms race compels businesses to stay vigilant, constantly innovating to outpace cybercriminals. By leveraging GenAI to reinforce digital defenses, while embedding ethical and safe AI practices, organizations can overcome the emerging challenges of AI-driven cyberattacks. With AI-driven threats rising, businesses must invest in cutting-edge cybersecurity technologies and ethical practices to secure their future.

The post AI vs AI: Generative AI’s Impact on Digital Defense and Cyber Attacks appeared first on Movate.

Optimizing generative AI through effective prompt engineering: A key to unleashing cost-efficiency and high-performance

Movate Admin — Thu, 03 Oct 2024 12:30:22 +0000

Media: nasscom
Spokesperson: Pallab Chatterjee

Generative AI (GenAI), particularly models powered by Large Language Models (LLMs), offers transformative potential across industries. However, without proper planning, the operational costs associated with deploying these models can escalate quickly. This article demonstrates how prompt engineering drives significant cost savings while delivering highly customized outputs for various use cases. This article will also provide a comprehensive analysis of LLM models, their pricing structure, and the operational cost implications. Additionally, it outlined strategies for optimizing expenses, fine-tuning model attributes, and implementing guardrails for efficient operations. This article also addresses the security implications of prompt engineering and introduce specific techniques that enable businesses scale GenAI in a financially sustainable way.

Introduction

GenAI, powered by LLMs such as OpenAI’s GPT series, Google’s PaLM, Anthropic’s Claude, and Meta’s Llama has revolutionized industries by enabling machines to generate text, code, and even creative outputs. These models are potent but resource-intensive, and their improper use can lead to skyrocketing operational costs.

Prompt engineering has emerged as a vital discipline that can help businesses leverage LLMs effectively and efficiently, balancing both performance and cost. This paper will explore how careful manipulation of prompts can reduce costs while maintaining high-quality outputs.

A Quick Overview of LLM Pricing Model

LLMs are neural networks trained on extensive datasets. These models operate by predicting the next token in a sequence based on the input text or “prompt”. Model performance scales with factors such as the number of model parameters, the length of the input context, and the volume of training data. The cost of utilizing LLMs is typically influenced by several factors:

Number of tokens processed (both in input and output)
Model size (larger models cost more to use)
API call frequency

LLM pricing structures differ across service providers and are primarily driven by usage metrics. Below is an analysis of current pricing models and a comparison of associated charges as of the time of writing. It is crucial to acknowledge that these costs are subject to fluctuation, and future readers—especially those reviewing this article six months or more after publication, may observe difference in unit pricing.

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The strategic role of prompt engineering in AI evolution

Movate Admin — Wed, 25 Sep 2024 05:16:59 +0000

Media: nasscom
Spokesperson: Saraswati Kadel

As generative AI (GenAI) models advance at an unprecedented pace, the art of crafting intuitive and contextually relevant prompts has emerged as a critical capability. Prompt engineering has become a key factor in harnessing the full potential of AI, particularly for enterprises aiming to enhance customer engagement and streamline complex interactions. The global prompt engineering market size is projected to grow at a compound annual growth rate (CAGR) of 32.8% from 2024 to 2030.

What is Prompt Engineering?

Prompt engineering, often referred to as “in-context learning,” involves the deliberate design of inputs—whether text, images, or commands—to steer generative AI systems toward producing precise and meaningful outputs. Much like selecting the finest ingredients for a gourmet dish, the quality of these prompts directly influences the effectiveness of AI responses. In essence, prompt engineering shapes the behavior and performance of AI models, particularly those grounded in natural language processing (NLP).

Why Prompt Engineering Matters?

Mastering prompt engineering is a key competency for anyone working with Large Language Models (LLMs). This discipline provides AI models with a well-defined framework, ensuring that responses are not only accurate but also aligned with specific business objectives. By optimizing prompts to align with organizational strategies, organizations can leverage AI to enhance decision-making, improve operational efficiency, and drive competitive advantage. The efficacy of AI in tasks such as natural language processing, content generation, and complex problem-solving is intrinsically linked to the sophistication of the prompts employed.

Tailoring AI to Industry-Specific Needs
One of the primary advantages of prompt engineering lies in its ability to customize AI models for industry-specific applications. By aligning prompts with the unique demands of a particular sector, organizations can significantly enhance the relevance and accuracy of AI-generated outputs. By 2027, more than 50% of the GenAI models that enterprises use will be specific to either an industry or business. For example, in the healthcare industry, prompt engineering can be leveraged to craft prompts that address medical inquiries with precision, ensuring the delivery of trustworthy information.
Enhancing User Experience (UX)
Prompt engineering plays a pivotal role in refining user experiences by optimizing the way AI systems interact with users. By crafting clear, concise, and contextually aware prompts, businesses can ensure that AI-generated responses are not only correct but also resonate with the user’s intent. This is particularly crucial in applications where seamless communication is paramount, such as customer service chatbots, virtual assistants, and automated support systems.
Upholding Ethical Standards
In an era where ethical AI practices are under increasing scrutiny, prompt engineering helps in mitigating biases and ensuring fairness in AI outputs. By carefully designing prompts and conducting regular audits of AI-generated content, organizations can proactively address potential biases and uphold the ethical integrity of their AI systems. This is not just a technical requirement but a strategic imperative, as biased AI responses can have far-reaching consequences for brand reputation and regulatory compliance.
Driving Continuous Improvement in AI Models
By 2025, 39% of worldwide organizations will be at the experimentation stage and 14% will be at the expansion stage. Prompt engineering also facilitates the continuous refinement of AI models. By iterating on prompt design, evaluating outcomes, and adjusting inputs, organizations can ensure that their AI systems evolve in line with shifting market demands and consumer expectations. This iterative approach to prompt optimization is crucial for maintaining the relevance and effectiveness of AI-driven solutions.
Cross-Domain Versatility
Another significant aspect of prompt engineering is its cross-domain applicability. Engineers can design prompts that enable AI models to integrate knowledge from multiple disciplines, thereby enhancing their versatility and utility. This is especially valuable in scenarios where users require insights that span diverse fields or intersect various business verticals.
Crafting Precision Instructions
Precision in prompt design is paramount for achieving desired outcomes in AI applications. By eliminating ambiguity and clearly defining the tasks or intents that AI models should focus on, organizations can drive more accurate and relevant AI-generated responses, thereby maximizing the strategic value of their AI investments.

Core Techniques of Prompt Engineering

Understanding the Problem and Defining Objectives
Clearly define what you want to achieve with the AI interaction. What specific task or question do you need the AI to address?
Iterative Prompting and Testing

Don’t expect perfect results on the first try. Refine your prompts based on the AI’s initial responses to get closer to desired outcome.

Transfer Learning and Fine-Tuning

Leverage pre-trained AI models as a foundation, then tailor them with targeted prompts for specific task. This approach can save time and effort compared to developing an AI model from the ground up.

The Future of Prompt Engineering in Learning & Development:

AI-powered Coaching and Mentorship:

Imagine virtual coaches using AI to provide personalized guidance and feedback based on learning progress and performance data.

Immersive Learning Experiences:

AI-powered hyper-realistic simulations can immerse learners in real-world business scenarios for practicing decision-making and problem-solving skills.

Adaptive Learning Systems:

Training programs can dynamically adjust based on learner performance, offering personalized learning paths and targeted resources.

Ethical Considerations of AI

While AI offers immense potential, responsible development and use are crucial. We must consider factors like:

Transparency and Explainability:

AI decision-making processes should be transparent and explainable to ensure fairness and trust.

Data Privacy and Security:

Data used to train AI models needs robust safeguards to protect privacy and prevent misuse.

Algorithmic Bias:

Mitigating bias in AI algorithms requires diverse datasets, continuous monitoring, and human oversight to address potential biases.

The Strategic Importance of Prompt Engineering

Prompt engineering is no longer just a technical task; it is a strategic function that shapes the trajectory of AI-driven innovation. Its influence extends from optimizing user experiences to ensuring ethical AI practices and tailoring AI models to meet industry-specific challenges. As AI technologies continue to evolve, the importance of this function will only increase, positioning it as a crucial lever for organizations seeking to gain a competitive edge. For business and technology leaders, prioritizing prompt engineering is essential for staying ahead in a rapidly transforming market.

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The strategic role of data in unleashing GenAI’s potential

Movate Admin — Thu, 18 Jul 2024 08:31:42 +0000

Media: Express Computer
Spokesperson: Krishnan Gopalrao

Generative AI needs no introduction as it continues to make waves everywhere. Boasting human-like conversational ability, remarkable precision, and unwavering consistency, GenAI has emerged as a transformative force, revolutionising various industries. Its impact is particularly pronounced in customer service and support, where applications range from fundamental chatbots to cutting-edge technologies. It also holds promise in creating hyper-realistic videos and sparking creativity in product design, signaling a future of boundless possibilities. The journey of GenAI’s evolution has indeed been a fascinating one! Amidst the enthusiasm for technological breakthroughs, the key to success lies in an often-underestimated asset: data.

A Harvard Business Review study reveals that over 80% of AI projects fail due to poor data quality. Without a well-crafted data strategy and efficient management, an organization’s GenAI aspirations might remain unfulfilled. Data is the foundation of today’s technological landscape – it can propel organizations to new heights or leave them behind. A robust data foundation is crucial to unlock GenAI’s true potential and navigate this dynamic environment. The percentage of companies that have created a data-driven organization doubled from 24% in 2023 to 48% in 2024, indicating a significant shift towards data-oriented cultures.

Fueling Innovation with Robust Data Management – GenAI’s Lifeline

A robust data strategy isn’t just an option for GenAI; it’s its lifeblood. Like an engine that thrives on quality fuel, GenAI relies on meticulously curated and diverse data to learn and innovate. Without it, GenAI outputs can become inaccurate and potentially harmful. Think of data as the fuel powering the GenAI engine. To ensure a robust strategy, organizations need consistent data inspection to eliminate biases and inaccuracies, providing a rich and varied dataset. By harnessing clean data, organisations pave the way for a future where AI serves humanity responsibly and inclusively.

Integrating advanced AI solutions, such as natural language processing models and predictive analytics tools, can significantly boost productivity and introduce a personalised approach to business operations. From enabling GenAI to providing reliable technical support and maintenance, a comprehensive strategy ensures that investments in technology yield effective, ethically aligned models.

Here are some critical elements to consider for a successful data management journey:

Data Strategy and Data Readiness	Develops robust data strategies aligning goals with initiatives, ensuring readiness for AI.
Data Cleansing	Identifies/rectifies errors, ensuring clean and accurate data for enhanced reliability of AI models.
Content Curation	Uses advanced techniques for effective content curation, facilitating better decision-making and insights generation.
Structured & Unstructured Data Management	Provides comprehensive solutions for organizing, storing, and extracting value from diverse data types for AI applications.
Data Integrity Management	Ensures non-negotiable data integrity through robust management, maintaining accuracy and consistency throughout the lifecycle.
Data Privacy and Security Management	Implements stringent measures to protect sensitive information, comply with regulations, and secure the AI operational environment.

Unlock Enterprise Business Transformation

There is no doubt that the technology is catapulting mass AI adoption in ways unseen hitherto. As service providers evolve with GenAI’s rapid surge to support enterprises, the emphasis is on responsible and inclusive development, data integrity, and privacy measures for trustworthy AI operations. With data as the lifeline, establishing best practices in data strategy and management is fundamental to the success of an enterprise-wide Gen AI adoption.

In conclusion, robust data management emerges as the linchpin in the journey of Generative AI, defining the path towards transformative innovation and responsible AI adoption. As organisations prioritise data integrity, ethical considerations, and responsible AI development, they not only unlock the full potential of GenAI but also pave the way for a future where AI serves humanity responsibly and inclusively. By anchoring our strategies in data governance frameworks and ethical principles, we harness GenAI’s power as a transformative force, driving innovation, efficiency, and ethical business practices across enterprises, while safeguarding data integrity, privacy, and trust.

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Boosting sales through personalization: Impact of GenAI-driven CRM

Movate Admin — Mon, 24 Jun 2024 12:33:07 +0000

Media: nasscom
Spokesperson: Prabhat Mehrotra

As traditional, uniform sales strategies fade, the push for personalized customer engagement intensifies. Moreover, with an increasing number of customers expecting personalized experiences from businesses that match their buying preferences, relying solely on manual methods for tailoring each interaction can present challenges. Embracing GenAI technology within CRM systems is becoming essential for businesses aiming for excellence in this digital era.

With GenAI, CRM platforms are undergoing a transformation that is enhancing productivity and delivering unprecedented efficiency in customer management. And we have real examples: last year, Microsoft launched its Viva Sales, now part of Microsoft Sales Copilot, which integrates with Microsoft 365 apps and CRM systems to enhance productivity and streamline the entire sales journey. Similarly, Salesforce introduced Einstein for sales which includes Copilot and other Gen AI features, that boosts sales and customer engagement by providing insights, automating tasks, and improving decision-making for sales professionals. As traditional, uniform sales strategies become obsolete, the focus on personalized customer engagement is growing more intense

GenAI -powered CRM systems transforming organizations into a Sales Powerhouse

With GenAI-powered CRM systems, businesses can reimagine their sales funnels. How does it operate? Imagine a system that prioritizes high-value leads, offers tailored product or solution recommendations based on customer behaviors, and drafts targeted communication—all automated. Sounds incredible, isn’t it? By offering personalized touchpoints throughout the customer journey, these systems enhance engagement and boost conversion rates. When customers receive relevant recommendations and messages tailored to their preferences, their likelihood of purchasing increases. This personalization not only improves satisfaction but also fosters loyalty and advocacy, driving repeat business and attracting new customers through word-of-mouth recommendations.

To understand what goes in the background, here are a few factors that help boost sales:

Smart Lead Scoring

GenAI evaluates vast data arrays to pinpoint high-potential prospects, enabling sales teams to concentrate on the most promising leads. Unlike traditional static scoring methods, GenAI applies dynamic criteria based on real-time data, such as engagement levels, past interactions, and predictive insights. This approach helps optimize resource allocation and increase deal closures. According to industry reports, 66% of sales representatives find AI tools crucial for understanding customer preferences even before the initial contact.

Targeted Product/Solution Recommendations:

Wouldn’t it be ideal if your CRM could suggest the perfect product for each customer?

GenAI-powered CRM systems use customer data to customize product recommendations, matching each prospect’s needs. By analyzing browsing habits and past purchases, GenAI suggests products likely to resonate with each customer, mimicking the personalized service of in-store experts but on a broader scale. This approach boosts higher engagement and encourages repeat purchases, creating a loyal customer base, where customers feel understood and valued. Moreover, the timely presentation of relevant products can greatly enhance cross-selling and upselling opportunities.

Personalized Communication

Every interaction, from emails to sales calls, is an opportunity for connection, personalized by GenAI. This technology crafts messages that resonate personally with each customer by analyzing data points like past interactions and behavioral patterns. Such personalized engagement captures attention and strengthens emotional connections, thereby increasing customer satisfaction and loyalty, which are key drivers of conversion rates. Business leaders report a 67% increase in sales due to personalized communication via tools like chatbots.

The Future of Sales is Personal!

In conclusion, integrating GenAI into CRM systems represents a significant shift in customer engagement and overall sales strategy. By leveraging intelligent automation and tailored experiences, GenAI-enhanced CRMs not only improve conversion rates but also build enduring customer loyalty and advocacy. As organizations adopt this innovative approach, they position themselves at the forefront of market competition, offering exceptional experiences that distinguish them in today’s dynamic business environment. The future of sales is indeed personal, and GenAI is paving the way toward this new era of customer-centric strategies.

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Harnessing the Potential of Digital Twins

Movate Admin — Wed, 22 May 2024 05:00:49 +0000

Media: nasscom
Spokesperson: Amardeep Juneja

Welcome to the fascinating domain of digital twins, where the line between the physical and the virtual blurs, opening doors to unprecedented possibilities.

Digital twins are transforming industries with innovation and insight. In the fast-evolving landscape of technology, digital twins have emerged as transformative agents across diverse industries. Their growing significance lies in their ability to revolutionize communication, predictive analysis, and collaboration and even transcend the barriers of time and space. As we delve into the concept of digital twins, we’ll unravel the threads that weave this digital tapestry and explore its profound impact on our world.

Reflections from the background

Delving into the concept of digital twins, we uncover their vast potential and address the challenges of their implementation. From data security concerns to ensuring seamless integration with existing systems, navigating the landscape of digital twins requires a strategic approach. Despite these challenges, the immense business value they bring is unmistakable, making them indispensable in our tech-driven era.

Digital twins are digital replicas of physical objects that use real-time data to imitate real-world behaviour.

This capability is a game-changer for the manufacturing, energy, healthcare, and transportation industries. Digital twins enable continuous performance monitoring, prompt issue identification, and streamlined process optimisation by providing companies with a dynamic toolset.

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The new age of recruiting: Technology and human interaction at the forefront

Movate Admin — Wed, 15 May 2024 12:12:39 +0000

Media: People Matters
Spokesperson: Asha Malini

A balanced approach in talent acquisition, combining advanced technology with human-centric strategies, is crucial for effectively securing and engaging top talent.

Securing top talent in today’s job market requires more than just competitive pay; candidates these days seek meaningful connections with organisations whose values align with their own. For organisations to acquire the right skilled talent and thrive in this competitive landscape, a blend of cutting-edge technology and a personalised, human-centric approach plays a vital role.

Technology: A dynamic catalyst for transformative talent acquisition

The influence of technology on TA is undeniable. Automation, AI-powered insights, and Generative AI are some of the innovations that have unlocked new potential in modern recruitment.

The recruitment process has undergone a digital transformation through automation and data-driven insights, with tools like Applicant Tracking Systems (ATS) streamlining the process. These tools automate time-consuming tasks like screening resumes and scheduling interviews, freeing up valuable time for recruiters to build meaningful connections with potential hires. Predictive analytics, used by over half of organisations, elevate recruitment to a strategic level, allowing companies to identify talent aligned with future goals. Gartner states that by 2025, 60% of global midmarket and large enterprises will have deployed recruitment process automation., indicating a shift towards a more strategic and data-driven approach to recruitment and retaining the talent they need to thrive.

Building upon these automated solutions, Gen AI is taking talent acquisition to a new level. The ascent of Gen AI in recruitment marks a pivotal moment in the evolution of talent acquisition strategies. It goes beyond automation, providing deeper insights into candidate compatibility with company culture, potential career paths, and adaptability, heralding a shift towards proactive recruitment practices that enable businesses to stay agile amidst market fluctuations. 76% of HR leaders believe that if their organisation does not adopt and implement AI solutions, such as generative AI, in the next 12 to 24 months, they will lag in organisational success compared to those that do. The ability of Gen AI to enhance recruitment by creating personalised content and improving candidate interactions through custom job descriptions and chatbot assistance thereby enriches the overall recruitment experience while setting a new standard for talent acquisition excellence.

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How a container-first cloud modernization approach benefits organisations?

Movate Admin — Tue, 14 May 2024 07:26:55 +0000

Media: Express Computer
Spokesperson: Pallab Chatterjee

Introduction
In the race to stay ahead in today’s tech-driven world, organisations are constantly under pressure to revamp their IT systems while cutting operational costs. One pivotal challenge they encounter is the limitations posed by traditional virtual machine-based architectures, particularly in the context of microservices. While virtual machines have served as a cornerstone of IT infrastructure for years, their inherent overhead and lack of agility hinder the seamless deployment and scaling of microservices-based applications. Even in today’s world, where 92% of global organisations are running at least one production workload with a cloud Hyperscaler, VM-based infrastructures still dominate.

That’s where container-first cloud modernization steps in. It’s a game-changer, using containerisation and cloud-native tools to shake things up. In this article, we explore the transformative benefits of Container-First Cloud Modernization and its profound impact on organisations seeking to achieve operational excellence and optimise operational costs.

What is a container-first approach?
A container-first approach involves developing or refactoring applications to run in Docker containers. These containers are lightweight yet contain everything necessary for an application to run, source code, runtime, system tools, dependencies, external libraries, and configurations. Once packaged as a container, applications can run universally, regardless of computer architecture or operating systems. Often coupled with cloud-native services, this approach amplifies its advantages.

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