<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:wfw="http://wellformedweb.org/CommentAPI/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:sy="http://purl.org/rss/1.0/modules/syndication/" xmlns:slash="http://purl.org/rss/1.0/modules/slash/" xmlns:georss="http://www.georss.org/georss" xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#" > <channel> <title>GenAI Archives - CDInsights</title> <atom:link href="https://www.clouddatainsights.com/tag/genai/feed/" rel="self" type="application/rss+xml" /> <link>https://www.clouddatainsights.com/tag/genai/</link> <description>Trsanform Your Business in a Cloud Data World</description> <lastBuildDate>Sat, 24 Feb 2024 18:23:53 +0000</lastBuildDate> <language>en-US</language> <sy:updatePeriod> hourly </sy:updatePeriod> <sy:updateFrequency> 1 </sy:updateFrequency> <generator>https://wordpress.org/?v=6.6.1</generator> <image> <url>https://www.clouddatainsights.com/wp-content/uploads/2022/05/CDI-Favicon-2-45x45.jpg</url> <title>GenAI Archives - CDInsights</title> <link>https://www.clouddatainsights.com/tag/genai/</link> <width>32</width> <height>32</height> </image> <site xmlns="com-wordpress:feed-additions:1">207802051</site> <item> <title>GenAI, AIOps, Hybrid Cloud and Beyond: 4 Cloud Computing Trends to Watch This Year</title> <link>https://www.clouddatainsights.com/genai-aiops-hybrid-cloud-and-beyond-4-cloud-computing-trends-to-watch-this-year/</link> <comments>https://www.clouddatainsights.com/genai-aiops-hybrid-cloud-and-beyond-4-cloud-computing-trends-to-watch-this-year/#respond</comments> <dc:creator><![CDATA[Kausik Chaudhuri]]></dc:creator> <pubDate>Sat, 24 Feb 2024 18:23:47 +0000</pubDate> <category><![CDATA[Cloud Data Platforms]]></category> <category><![CDATA[GenAI]]></category> <category><![CDATA[multi-cloud]]></category> <guid isPermaLink="false">https://www.clouddatainsights.com/?p=5038</guid> <description><![CDATA[Besides GenAI and AIOps, the growing adoption of hybrid and multi-cloud architectures and agendas such as cost-savings will top the list of major shifts within the cloud realm this year.]]></description> <content:encoded><![CDATA[<div class="wp-block-image"> <figure class="aligncenter size-full"><img fetchpriority="high" decoding="async" width="1000" height="552" src="https://www.clouddatainsights.com/wp-content/uploads/2024/02/cloud-Depositphotos_44884349_S.jpg" alt="" class="wp-image-5041" srcset="https://www.clouddatainsights.com/wp-content/uploads/2024/02/cloud-Depositphotos_44884349_S.jpg 1000w, https://www.clouddatainsights.com/wp-content/uploads/2024/02/cloud-Depositphotos_44884349_S-300x166.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2024/02/cloud-Depositphotos_44884349_S-768x424.jpg 768w" sizes="(max-width: 1000px) 100vw, 1000px" /><figcaption class="wp-element-caption"><em>Besides GenAI and AIOps, the growing adoption of hybrid and multi-cloud architectures and agendas such as cost-savings will top the list of major shifts within the cloud realm.</em></figcaption></figure></div> <p>It is that time of year when everyone has opinions about what’s next in the realm of tech. We do like to believe that our deep immersion in the technology industry – especially domains like cloud computing and automation – qualify us to offer insightful predictions about major technology trends that are likely to play out this year.</p> <p>To that end, keep reading for a look at our key predictions in cloud computing for the coming year.</p> <h3 class="wp-block-heading"><a></a>1) Cost-savings and security drive cloud migration</h3> <p>There are many potential reasons for migrating to the cloud, such as increasing infrastructure scalability, reducing the burden of infrastructure management, and speeding up time-to-value – all of which have historically driven businesses to move workloads into the cloud.</p> <p>In 2024, however, we believe two other motives – saving money and improving cybersecurity – will become key considerations for a majority of cloud migrations. Businesses remain keen to trim budgets in a time of ongoing economic turbulence, and moving to cloud environments that offer pay-as-you-go pricing is one way to help do that.</p> <p>At the same time, growing concern with data security and compliance, especially in sectors like healthcare, makes the cloud attractive to organizations seeking to take advantage of the consistent security controls and airtight physical security protections that cloud environments offer.</p> <p><strong>See also: </strong><a href="https://www.clouddatainsights.com/the-impact-of-generative-ai-on-cloud-storage/" target="_blank" rel="noreferrer noopener">The Impact of Generative AI on Cloud Storage</a></p> <h3 class="wp-block-heading"><a></a>2) Even more adoption of hybrid and multi-cloud architectures</h3> <p>Hybrid clouds, which integrate on-prem infrastructure with public cloud resources or services, have existed for years. Multi-cloud architectures, which involve using more than one cloud platform simultaneously, also exist.</p> <p>But in 2024, we expect organizations to double down on hybrid and multi-cloud strategies. The reasons reflect the same considerations we described above: Saving money and improving security outcomes. Hybrid and <a href="https://www.clouddatainsights.com/why-people-choose-public-private-hybrid-or-multi-cloud-solutions/" target="_blank" rel="noreferrer noopener">multi-cloud architectures</a> offer more flexibility, which can translate to cost savings. They also offer greater control over where and how data is stored, helping organizations to protect sensitive information more effectively.</p> <p>The caveat is that hybrid and multi-cloud strategies are also more complex. To unlock <a href="https://www.clouddatainsights.com/why-people-choose-public-private-hybrid-or-multi-cloud-solutions/" target="_blank" rel="noreferrer noopener">cost-savings</a> and security benefits, it’s critical to have tools and processes in place for taming that complexity by, for example, being able to monitor and secure data that is spread across multiple cloud environments.</p> <h3 class="wp-block-heading"><a></a>3) GenAI boosts cloud service delivery</h3> <p>The cloud industry has spent the past year oohing and ahhing at the potential capabilities that generative AI unlocks for cloud operations. 2024, however, is poised to be the year when businesses actually begin taking advantage of GenAI-based features in the cloud.</p> <p>We expect organizations to focus, in particular, on using GenAI to enhance cloud service delivery. With the help of GenAI, cloud systems will offer enhanced resource management, ensuring that computational and storage resources are allocated and scaled in the most effective manner. This intelligent allocation not only optimizes the workload but also significantly cuts down on unnecessary expenditure and risk, making cloud services safer and more cost-effective.</p> <p>At the same time, GenAI will allow for the personalization of Cloud experiences, tailoring services to the unique requirements and preferences of each user. The integration of genAI into cloud services thus represents a leap towards a more responsive, user-centric cloud environment, where services are crafted to fit the diverse needs of users.</p> <h3 class="wp-block-heading"><a></a>4) AIOps becomes a cornerstone of cloud operations</h3> <p>Complementing the adoption of GenAI to assist cloud service delivery, more businesses are poised to embrace AIOps fully in 2024.</p> <p>AIOps, which refers to the use of AI/ML to automate IT operations processes, has been a buzzword in the tech industry for years. But to date, real-world implementation of AIOps-based tools has been limited.</p> <p>That is likely to change in the near future, however, due to the growing adoption of hybrid and multi-cloud architectures. As cloud strategies grow more complex, IT teams will need more efficient and automated means of managing their clouds – which is exactly what AIOps helps them do. At the same time, the growing maturity of AI-based tools, including but not limited to those within the generative AI category, means that AIOps solutions are becoming more capable than ever.</p> <p>More cloud complexity combined with better AIOps tools makes now the right time for more organizations to take full advantage of AIOps as a pillar of cloud operations.</p> <h3 class="wp-block-heading"><a></a>Conclusion</h3> <p>Again, we can’t predict the future with greater accuracy than anyone else. But when it comes time about a year from now to look back on which key trends reshaped the cloud computing space in 2024, we expect that technologies like GenAI and <a href="https://www.gartner.com/en/information-technology/glossary/aiops-artificial-intelligence-operations" target="_blank" rel="noreferrer noopener">AIOps</a>, as well as practices like growing adoption of hybrid and multi-cloud architectures and agendas such as cost-savings, will top the list of major shifts within the cloud realm.</p> <div class="saboxplugin-wrap" itemtype="http://schema.org/Person" itemscope itemprop="author"><div class="saboxplugin-tab"><div class="saboxplugin-gravatar"><img decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2024/02/Kausik-Chaudhuri.jpg" width="100" height="100" alt="" itemprop="image"></div><div class="saboxplugin-authorname"><a href="https://www.clouddatainsights.com/author/kausik-chaudhuri/" class="vcard author" rel="author"><span class="fn">Kausik Chaudhuri</span></a></div><div class="saboxplugin-desc"><div itemprop="description"><p>Kausik Chaudhuri is the Chief Innovation Officer at <strong><a href="http://www.lemongrasscloud.com/">Lemongrass</a></strong>. Kausik is a thought leader known for designing, deploying, migrating, and running complex technical solutions for mission-critical enterprise applications, including SAP. At Lemongrass, he is responsible for Platform and Enterprise Architecture, Product Management Capability in alignment with Sales and Product teams, and platform enablement of the Delivery Service Team.</p> </div></div><div class="clearfix"></div></div></div>]]></content:encoded> <wfw:commentRss>https://www.clouddatainsights.com/genai-aiops-hybrid-cloud-and-beyond-4-cloud-computing-trends-to-watch-this-year/feed/</wfw:commentRss> <slash:comments>0</slash:comments> <post-id xmlns="com-wordpress:feed-additions:1">5038</post-id> </item> <item> <title>The Impact of Generative AI on Cloud Storage</title> <link>https://www.clouddatainsights.com/the-impact-of-generative-ai-on-cloud-storage/</link> <comments>https://www.clouddatainsights.com/the-impact-of-generative-ai-on-cloud-storage/#respond</comments> <dc:creator><![CDATA[Elizabeth Wallace]]></dc:creator> <pubDate>Thu, 25 Jan 2024 13:55:12 +0000</pubDate> <category><![CDATA[AI/ML]]></category> <category><![CDATA[GenAI]]></category> <category><![CDATA[hybrid cloud]]></category> <guid isPermaLink="false">https://www.clouddatainsights.com/?p=4910</guid> <description><![CDATA[Generative AI is changing how companies are thinking about cloud storage. ]]></description> <content:encoded><![CDATA[ <figure class="wp-block-image size-full"><img decoding="async" width="1000" height="667" src="https://www.clouddatainsights.com/wp-content/uploads/2024/01/Depositphotos_287898424_S.jpg" alt="generative AI and cloud storage" class="wp-image-4911" srcset="https://www.clouddatainsights.com/wp-content/uploads/2024/01/Depositphotos_287898424_S.jpg 1000w, https://www.clouddatainsights.com/wp-content/uploads/2024/01/Depositphotos_287898424_S-300x200.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2024/01/Depositphotos_287898424_S-768x512.jpg 768w, https://www.clouddatainsights.com/wp-content/uploads/2024/01/Depositphotos_287898424_S-930x620.jpg 930w" sizes="(max-width: 1000px) 100vw, 1000px" /></figure> <p>Generative AI (GenAI) is a rapidly evolving and increasingly influential force. This domain, encompassing sophisticated machine learning models that generate text, images, and other complex outputs, is reshaping just about everything, technology-focused or otherwise. As GenAI systems grow in complexity and capability, so too do their demands for vast, diverse, and ever-expanding datasets. These escalating data requirements are not just a technical challenge; they represent a pivotal shift in how enterprises approach data and cloud storage and management.</p> <p>In response to this shift, a significant transition is occurring within the storage strategies of organizations. Enterprises, recognizing the limitations of traditional storage paradigms, are increasingly turning towards hybrid cloud storage solutions. This move is not merely a trend but a strategic adaptation to the unique demands posed by GenAI. Hybrid cloud storage, with its blend of on-premises and cloud-based resources, offers a flexible and scalable approach to data management. It provides the agility to handle large volumes of data generated by GenAI applications while also addressing security, compliance, and cost-efficiency concerns.</p> <p>See also: Cloud Control: <a href="https://www.clouddatainsights.com/cloud-control-effective-strategies-for-navigating-the-multi-cloud-era/">Effective Strategies for Navigating the Multi-Cloud Era</a></p> <p>As GenAI continues to advance and integrate deeper into various sectors – from healthcare and finance to entertainment and academia – the role of hybrid cloud storage becomes ever more critical. This integration is not a simple plug-and-play solution; it requires a nuanced understanding of both the capabilities of GenAI and the intricacies of cloud and on-premises storage systems. The evolution of storage strategies in response to GenAI is a testament to the dynamic nature of technological progress and the continuous adaptation enterprises require to stay at the forefront of innovation.</p> <h3 class="wp-block-heading">Drivers for the Shift to Hybrid Cloud Storage</h3> <p>The migration towards hybrid cloud storage in the realm of Generative AI (GenAI) is driven by a confluence of factors, chief among them being the burgeoning data volumes and the intricate nature of GenAI applications.</p> <h4 class="wp-block-heading">Escalating Data Volumes and Complexity in GenAI</h4> <p>GenAI applications are renowned for their voracious appetite for data. As these models delve into more complex tasks, such as generating high-fidelity images or understanding nuanced human languages, the quantum of data required scales exponentially. This data isn’t just vast in volume; it’s varied in type and complex in structure. Handling such multifaceted data necessitates a storage solution that’s expansive and adept at managing the complexities of data types and formats.</p> <p>Traditional on-premises storage systems, while robust and secure, often lack the scalability and flexibility required for such dynamic data handling. Conversely, cloud storage offers the necessary scalability but can sometimes fall behind in areas of data sovereignty and latency-sensitive operations. This is where the hybrid cloud model comes into play, presenting a balanced solution that harnesses the best of both worlds.</p> <p>See also: <a href="https://www.clouddatainsights.com/a-roadmap-to-boost-data-team-productivity-in-the-era-of-generative-ai/">A Roadmap to Boost Data Team Productivity in the Era of Generative AI</a></p> <h4 class="wp-block-heading">Balancing Flexibility and Control</h4> <p>In the hybrid cloud storage model, the scalability and flexibility of the cloud are leveraged to handle large-scale data processing and storage needs. This is particularly beneficial for GenAI applications that continuously evolve and require swift scaling of resources. The cloud component allows enterprises to adapt to these changing needs without the capital expense and physical constraints of expanding on-premises infrastructure.</p> <p>Simultaneously, the hybrid model retains critical aspects of on-premises storage, notably control and security. Data security and compliance are non-negotiable for organizations, especially those in regulated industries like healthcare and finance. The on-premises element of hybrid cloud storage provides the control needed to manage sensitive data, meet regulatory requirements, and ensure that critical operations are not entirely dependent on external cloud environments.</p> <p>This balance is not just a matter of convenience; it’s a strategic imperative. The flexibility of the cloud enables enterprises to experiment with and deploy GenAI applications rapidly. At the same time, controlling on-premises storage ensures that they can manage and protect their core assets effectively. In essence, hybrid cloud storage is emerging as a foundational component in the GenAI era, facilitating innovation while safeguarding the integrity and security of enterprise data.</p> <h3 class="wp-block-heading">Challenges in Adoption</h3> <p>Adopting hybrid cloud storage in response to the demands of Generative AI (GenAI) presents several significant challenges, encompassing technical, logistical, security, and compliance aspects.</p> <h4 class="wp-block-heading">Technical and Logistical Integration Challenges</h4> <p>Integrating cloud storage with existing on-premises infrastructure is a complex endeavor. It involves ensuring compatibility between different systems and technologies and managing data transfer and accessibility across diverse platforms. Additionally, streamlining workflows to optimize data management across hybrid environments is essential but can be intricate. Challenges also arise in scaling the infrastructure to meet evolving data needs without disrupting existing operations.</p> <h4 class="wp-block-heading">Security and Compliance Concerns</h4> <p>Maintaining robust security as data traverses between cloud and on-premises systems is a major challenge. This is compounded by the need to adhere to industry-specific regulations and data residency requirements. Ensuring regulatory compliance and data sovereignty across both environments is crucial but often challenging. Proactive risk management and continuous monitoring are imperative to address potential threats and vulnerabilities in a dynamic hybrid environment.</p> <h4 class="wp-block-heading">Pathways to Overcome Adoption Challenges</h4> <p>Successfully navigating the complex landscape of hybrid cloud storage requires strategic and thoughtful approaches. Organizations must focus on several key areas to overcome the technical, logistical, security, and compliance challenges associated with integrating cloud and on-premises solutions.</p> <ul class="wp-block-list"> <li>Skilled Personnel and Training: Invest in training for existing IT staff. Hire specialists in hybrid cloud solutions.</li> <li>Partnering with Experienced Vendors: Collaborate with vendors with expertise in hybrid cloud integrations. Utilize their specialized tools and support.</li> <li>Iterative Implementation and Testing: Adopt a phased approach to integration. Conduct thorough testing at each stage.</li> <li>Advanced Security Measures: Implement end-to-end encryption and robust security solutions. Use data governance tools for policy enforcement.</li> <li>Compliance and Data Governance: Develop comprehensive strategies for regulatory compliance. Employ tools to manage data sovereignty issues.</li> <li>Scalability and Infrastructure Flexibility: Use modular infrastructure designs for scalability. Implement flexible resource allocation strategies.</li> <li>Effective Data Management: Develop efficient data migration strategies. Implement unified data management tools.</li> </ul> <p>By focusing on these areas, <a href="https://www.rtinsights.com/the-definitive-guide-to-generative-ai-for-industry/">organizations can effectively manage</a> the complexities of hybrid cloud storage, ensuring a secure, compliant, and scalable infrastructure that fully supports the demands of GenAI applications.</p> <h3 class="wp-block-heading">Industry Adoption and Examples</h3> <p>The adoption of hybrid cloud storage <a href="https://www.clouddatainsights.com/unlocking-business-success-in-2023-and-beyond-the-power-of-cloud-and-generative-ai/">by enterprises</a> is being shaped not only by its potential benefits but also by the challenges it presents. These challenges influence how and why industries implement hybrid cloud solutions.</p> <p>Adoption Trends in Various Industries</p> <ul class="wp-block-list"> <li><strong>Healthcare</strong>: In healthcare, secure and compliant data management is paramount. Hybrid cloud storage allows sensitive patient data to be kept on-premises while leveraging cloud capabilities for large-scale data analysis, such as in genomic research or patient data analytics. The balancing act between security and scalability is a key driver for hybrid adoption in this sector.</li> <li><strong>Financial Services</strong>: Financial institutions are adopting hybrid cloud storage to manage the vast amounts of data required for real-time processing and analysis, such as for fraud detection and risk assessment. The need for stringent data security and regulatory compliance, while also requiring scalable computing resources, makes the hybrid model suitable.</li> <li><strong>Retail and E-commerce</strong>: These sectors utilize hybrid cloud storage for handling customer data and analytics while maintaining compliance with consumer data protection laws. The flexibility to scale during peak shopping periods while maintaining control over sensitive customer information is a critical consideration.</li> </ul> <h4 class="wp-block-heading">Influence of Challenges on Adoption</h4> <p>The challenges associated with hybrid cloud storage, such as technical integration complexities and security concerns, are influencing the pace and manner of its adoption across industries. Sectors with high regulatory compliance needs, like healthcare and finance, are particularly cautious, ensuring that security and compliance are not compromised while pursuing scalability and flexibility. The technical challenge of integrating disparate systems is leading some enterprises to seek partnerships with experienced vendors who can provide expertise and tailored solutions.</p> <p>Overall, the industry adoption of hybrid cloud storage is marked by a careful balancing act, where the benefits of scalability and flexibility are weighed against the need for security, compliance, and seamless integration. This cautious yet strategic approach is guiding the transformation of enterprise data management in the era of GenAI.</p> <p>So what does that look like in the real world? Here are some hypothetical examples: </p> <ul class="nv-cv-m wp-block-list"> <li><strong>A Global Bank’s Hybrid Strategy</strong>: One prominent global bank implemented a hybrid cloud solution to manage its financial data. The bank stores sensitive customer information on-premises to comply with data residency regulations while employing cloud-based analytics to gain insights into customer behavior and market trends.</li> <li><strong>Healthcare Research Organization</strong>: A leading healthcare research organization uses hybrid cloud storage to manage its vast repositories of medical data. Patient records are stored in secure on-premises servers, while cloud computing resources are used for computational-intensive tasks like DNA sequencing and drug discovery research.</li> <li><strong>Retail Giant’s Scalable Solution</strong>: A major retail corporation adopted a hybrid cloud storage approach to manage customer data and online transaction processing. During high-demand periods like Black Friday or Cyber Monday, they leverage the cloud’s scalability to handle the surge in online shopping traffic and data processing while keeping sensitive data under their control.</li> </ul> <p>See also: <a href="https://www.clouddatainsights.com/explore-the-mutual-advantages-of-generative-ai-and-the-cloud/">Explore the Mutual Advantages of Generative AI and the Cloud</a></p> <h3 class="wp-block-heading">Future Prospects</h3> <p>As Generative AI (GenAI) continues to grow and evolve, the landscape of hybrid cloud storage is expected to undergo significant transformations. These changes will be a result of both the escalating demands of GenAI applications and the continuous advancements in storage technology.</p> <h4 class="wp-block-heading">Evolution of Hybrid Cloud Storage with GenAI</h4> <ul class="nv-cv-m wp-block-list"> <li><strong>Increased Automation and AI Integration</strong>: Future hybrid cloud storage solutions will likely incorporate more advanced AI and machine learning algorithms to automate data management tasks. This includes predictive analytics for capacity planning, automated data tiering, and intelligent data caching, which can significantly improve efficiency and reduce operational costs.</li> <li><strong>Enhanced Security and Compliance Tools</strong>: As GenAI applications delve into more sensitive areas, the need for robust security and compliance measures in hybrid cloud storage will become more acute. Expect advancements in encryption technologies, AI-driven security monitoring, and more sophisticated compliance management tools to ensure data protection and regulatory adherence.</li> <li><strong>Greater Scalability and Flexibility</strong>: The future of hybrid cloud storage will emphasize even greater scalability and flexibility. Technologies like containerization and microservices architectures will play a crucial role, allowing businesses to dynamically allocate resources across cloud and on-premises environments depending on their changing needs.</li> </ul> <p>See also: <a href="https://www.clouddatainsights.com/navigating-the-cloud-in-2024-ai-disruptions-emerging-players-and-standardization-challenges/">Navigating the Cloud in 2024: AI Disruptions, Emerging Players, and Standardization Challenges</a></p> <h4 class="wp-block-heading">Technological Advancements to Watch Out For:</h4> <ul class="nv-cv-d nv-cv-m wp-block-list"> <li><strong>Edge Computing Integration</strong>: The integration of edge computing with hybrid cloud storage is anticipated to be a significant trend. This will facilitate faster data processing and decision-making at the edge, which is particularly beneficial for real-time GenAI applications like autonomous vehicles and IoT devices.</li> <li><strong>Quantum Computing’s Impact</strong>: As quantum computing matures, its potential impact on hybrid cloud storage and GenAI is immense. Quantum computing could increase processing capabilities exponentially, enabling more complex AI models and potentially revolutionizing data encryption and security.</li> <li><strong>Sustainable and Energy-Efficient Storage Solutions</strong>: Sustainability will become a more pressing concern. We can expect innovations in energy-efficient storage technologies and strategies to reduce the carbon footprint of large data centers.</li> <li><strong>Advanced Data Fabric and Interoperability Solutions</strong>: It is likely that sophisticated methodologies like data fabrics will evolve to enable seamless data movement and access across hybrid environments. This will enhance interoperability between different cloud services and on-premises systems, making data integration and management more efficient.</li> </ul> <p>The future of hybrid cloud storage in the context of GenAI should see dynamic growth and innovation. With an emphasis on automation, security, scalability, and sustainability, these advancements will not only cater to the increasing demands of GenAI but also pave the way for new capabilities and applications in various sectors. As technology evolves, so too will the strategies and solutions for managing the ever-growing data needs of GenAI, offering exciting prospects for enterprises and technology providers alike.</p> <div class="saboxplugin-wrap" itemtype="http://schema.org/Person" itemscope itemprop="author"><div class="saboxplugin-tab"><div class="saboxplugin-gravatar"><img loading="lazy" decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2022/05/Elizabeth-Wallace-RTInsights-141x150-1.jpg" width="100" height="100" alt="" itemprop="image"></div><div class="saboxplugin-authorname"><a href="https://www.clouddatainsights.com/author/elizabeth-wallace/" class="vcard author" rel="author"><span class="fn">Elizabeth Wallace</span></a></div><div class="saboxplugin-desc"><div itemprop="description"><p>Elizabeth Wallace is a Nashville-based freelance writer with a soft spot for data science and AI and a background in linguistics. She spent 13 years teaching language in higher ed and now helps startups and other organizations explain – clearly – what it is they do.</p> </div></div><div class="clearfix"></div></div></div>]]></content:encoded> <wfw:commentRss>https://www.clouddatainsights.com/the-impact-of-generative-ai-on-cloud-storage/feed/</wfw:commentRss> <slash:comments>0</slash:comments> <post-id xmlns="com-wordpress:feed-additions:1">4910</post-id> </item> <item> <title>What’s the future of GenAI? Checking in with Industry Leaders</title> <link>https://www.clouddatainsights.com/whats-the-future-of-genai-checking-in-with-industry-leaders/</link> <comments>https://www.clouddatainsights.com/whats-the-future-of-genai-checking-in-with-industry-leaders/#respond</comments> <dc:creator><![CDATA[Elizabeth Wallace]]></dc:creator> <pubDate>Mon, 04 Dec 2023 01:00:41 +0000</pubDate> <category><![CDATA[AI/ML]]></category> <category><![CDATA[GenAI]]></category> <category><![CDATA[generative AI]]></category> <guid isPermaLink="false">https://www.clouddatainsights.com/?p=4680</guid> <description><![CDATA[Find out where GenAI is headed in the future from some of the industry's best and brightest in this roundup.]]></description> <content:encoded><![CDATA[<div class="wp-block-image"> <figure class="aligncenter size-full"><img loading="lazy" decoding="async" width="1000" height="563" src="https://www.clouddatainsights.com/wp-content/uploads/2023/11/Depositphotos_608774152_S.jpg" alt="Generative AI" class="wp-image-4681" srcset="https://www.clouddatainsights.com/wp-content/uploads/2023/11/Depositphotos_608774152_S.jpg 1000w, https://www.clouddatainsights.com/wp-content/uploads/2023/11/Depositphotos_608774152_S-300x169.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2023/11/Depositphotos_608774152_S-768x432.jpg 768w" sizes="(max-width: 1000px) 100vw, 1000px" /><figcaption class="wp-element-caption"><em>Find out where GenAI is headed in the future from some of the industry’s best and brightest in this roundup.</em></figcaption></figure></div> <p>Generative AI is rapidly reshaping the landscape of industries and businesses, opening up new horizons while raising questions about its potential impact. What’s ahead for a technology that has changed everything? Let’s explore what experts in the field predict.</p> <h3 class="wp-block-heading">OpenAI and Box: Balancing disruption and promise</h3> <p>In <a href="https://www.techtarget.com/searchcontentmanagement/news/366555335/Box-and-OpenAI-CEOs-discuss-the-future-of-enterprise-AI?utm_campaign=20231020_Box+and+OpenAI+CEOs+discuss+the+future+of+enterprise+AI&utm_medium=email&utm_source=MDN&asrc=EM_MDN_279924379&bt_ee=T7RZydu/etzLzpZPWRgPs+X9aerru2mW5gffFsZJRThrpp7uVdVXb29B4TbduEuF&bt_ts=1698069771511" target="_blank" rel="noreferrer noopener">a recent conversation</a> between OpenAI founder and CEO Sam Altman and Box CEO and founder Aaron Levie at the BoxWorks user conference, the disruptive power of generative AI took center stage. The discussion revolved around the impact of AI, particularly<a href="https://www.clouddatainsights.com/whats-so-amazing-about-chatgpt-a-quick-recap-of-large-language-models/"> ChatGPT</a>, on various industries, the fears and hopes of CIOs, and the potential future of enterprise AI.</p> <p>One of the key takeaways from the conversation is the acknowledgment of GenAI’s immediate impact on job markets. GenAI has already begun reshaping industries, resulting in the displacement of jobs in certain sectors. However, experts, including Sam Altman, believe that GenAI’s true potential extends beyond its initial disruptions. While jobs have been lost to AI-driven efficiency gains, there is a growing consensus that GenAI will ultimately alleviate workers from mundane tasks, providing them the time and freedom to focus on more creative and innovative aspects of their roles. This transformation is anticipated to lead to the creation of more high-quality jobs, an improved quality of life, and increased wealth, although in a significantly altered job landscape.</p> <p>Sam Altman emphasizes that the future of GenAI will bring about profound changes. While recognizing that this transformation may not offer immediate consolation to those affected by job displacement, Altman envisions a future where the integration of GenAI into enterprise IT will bring about “more jobs, better jobs,” leading to a higher quality of life and greater wealth. This optimism is tempered, however, by the recognition that this future will be markedly different from today’s job market, requiring adjustments at various levels, including government regulation and cost efficiency in AI.</p> <p>As GenAI continues to evolve, experts anticipate the reduction of AI costs over time, although this will be accompanied by the demand for increased processing power to accommodate added functionality. While AI developers work to make AI more efficient and cost-effective, the focus remains on keeping power consumption in check. This balance between innovation and energy efficiency is crucial to ensuring that GenAI remains accessible and economically viable for businesses.</p> <h3 class="wp-block-heading">Forrester’s “Magic and Mayhem”: Impacting Jobs and Industry</h3> <p>A recent <a target="_blank" href="https://www.forrester.com/report/forresters-2023-generative-ai-jobs-impact-forecast-us/RES179790" rel="noreferrer noopener">Forrester report</a> paints a complex picture of GenAI’s future, highlighting both the enchantment and the challenges that GenAI presents. Experts here anticipate the technology’s journey will be marked by significant job <a target="_blank" href="https://medium.com/@alltechmagazine/generative-ai-will-replace-2-4-million-jobs-in-the-us-by-2030-says-forrester-be082b4664a9" rel="noreferrer noopener">disruptions and reconfigurations</a>.</p> <p>One of the central findings of the report is the notion of “hyperadoption,” where GenAI, driven by innovations like ChatGPT, is embraced at an unprecedented rate. This rapid adoption is fueled by the remarkable results GenAI can achieve, often described as magical. However, this magic comes with a flip side: mayhem in the job market. Forrester predicts that GenAI will eliminate approximately 2.4 million jobs by the decade’s end while fundamentally reshaping more than 11 million others.</p> <p>The report also underscores the profound impact of GenAI on various job categories. GenAI is expected to impact 4.5 times as many jobs through transformation than it will directly replace through automation. Jobs that are easier to automate and have a high GenAI influence, such as technical writers and proofreaders, are more likely to be lost. In contrast, roles that are harder to automate but still influenced by GenAI, such as editors and creative writers, are anticipated to evolve through augmentation rather than complete replacement.</p> <p>Office and administrative jobs are expected to bear the brunt of GenAI’s impact, particularly mid-level positions with mid-level wages. Higher-level positions with better compensation are predicted to be more resistant to GenAI, primarily because they rely on skills less susceptible to automation, such as human judgment and leadership.</p> <p>To prepare for the evolving landscape of GenAI, experts recommend several strategies. These include investing in the “robotics quotient” (RQ) to assess individuals’ adaptability to AI and automation, making augmentation a central component of business strategies, and conducting proactive analyses to identify which job roles will benefit most from GenAI. Equipping employees with the necessary tools and fostering GenAI development skills within organizations are also crucial steps in navigating this transformative journey.</p> <h3 class="wp-block-heading">McKinsey: Thrilling, yet cautious, optimism</h3> <p>Since the debut of ChatGPT in November 2022, Generative AI (GenAI) has rapidly become a focal point in technology discussions, with businesses vying to harness its potential. Early indications suggest that GenAI could be a game-changer, with <a target="_blank" href="https://www.mckinsey.com/featured-insights/mckinsey-explainers/whats-the-future-of-generative-ai-an-early-view-in-15-charts" rel="noreferrer noopener">McKinsey research</a> estimating that GenAI features could contribute up to $4.4 trillion to the global economy annually within its initial months of existence.</p> <p>A swift pace of development has marked the evolution of GenAI. Notably, since ChatGPT’s launch, several iterations of GenAI technology have been released at an impressive rate. In March 2023 alone, there were six significant advancements, including solutions for customer relationship management and financial services support. These developments have accelerated the journey toward human-level performance in various technical capabilities, with some areas achieving this milestone decades ahead of prior estimations.</p> <p>GenAI is poised to reshape various industries, particularly in knowledge work, where it is expected to have the most substantial impact. Fields such as education, law, technology, and the arts may see portions of their roles automated earlier than anticipated due to GenAI’s ability to predict natural language patterns dynamically.</p> <p>Industries are already exploring GenAI’s potential, focusing on specific use cases. Applications tailored to particular industries and functions are expected to provide more significant value than generic ones, further fueling GenAI’s adoption.</p> <p>While GenAI’s potential is thrilling, organizations must exercise caution. The technology presents risks, such as biased or factually incorrect content generation and potential legal issues related to copyright infringement. Integrating a “human in the loop” approach, where human oversight is applied to GenAI outputs, is recommended to mitigate these risks.</p> <p>Despite the promise, the adoption of GenAI within organizations remains relatively limited, and there is a growing demand for GenAI-literate employees. Organizations must prioritize talent management and offer a rewarding working environment to retain skilled GenAI workers.</p> <p>See also: <a href="https://www.clouddatainsights.com/is-tech-changing-the-world-or-is-the-world-changing-tech/">Is Tech Changing the World or is the World Changing Tech?</a></p> <h3 class="wp-block-heading">Amy Webb of Future Today: The uncertain future of GenAI</h3> <p>Amy Webb, CEO of Future Today Institute and NYU professor, wrote in the <a target="_blank" href="https://hbr.org/2023/08/how-to-prepare-for-a-genai-future-you-cant-predict" rel="noreferrer noopener">Harvard Business Review</a> about the future of Generative AI (GenAI). She mentions that various industries, including banking, face a workforce problem, with a gap between the demand for skilled personnel and the willingness of workers to return to traditional office settings. Many executives are considering GenAI as a solution to create cost savings and efficiencies through automation while potentially addressing talent shortages.</p> <p>However, she argues that it’s too early to predict the exact future of AI, as it is just one area of a complex field with many interdependencies. The precise jobs AI will replace–and when it will happen– are uncertain. The output must be trustworthy, integrated into existing workflows, and managed for compliance and regulatory issues.</p> <p>Moreover, Webb emphasizes that leaders should not focus solely on immediate gains but should consider how AI will transform their entire value network in the future. She draws a parallel with the early days of the internet, where the transformative impact was not initially foreseen.</p> <p>Webb provides several steps for leaders to prepare for an uncertain future where GenAI and human workforces coexist and evolve. These steps include:</p> <ul class="nv-cv-d nv-cv-m wp-block-list"> <li>Tempering expectations about what GenAI can achieve and developing a realistic strategy.</li> <li>Evaluating the data generated by the company and how GenAI can use it.</li> <li>Shifting the focus from cost reduction to revenue generation by understanding how to delegate tasks effectively between humans and AI.</li> <li>Adopting the IDEA framework (Identify, Determine, Extrapolate, Anticipate) to predict workforce dynamics and plan for the future.</li> </ul> <p>Webb urges leaders to methodically plan for what comes next, understand GenAI’s limitations and strengths, and create a future where AI is leveraged by a highly skilled workforce, fostering productive and efficient collaboration between humans and AI.</p> <h3 class="wp-block-heading">Gartner: On the hype cycle</h3> <p>According to <a target="_blank" href="https://www.gartner.com/en/newsroom/press-releases/2023-10-11-gartner-says-more-than-80-percent-of-enterprises-will-have-used-generative-ai-apis-or-deployed-generative-ai-enabled-applications-by-2026" rel="noreferrer noopener">Gartner</a>, the future of generative artificial intelligence (GenAI) is poised for significant growth and adoption across enterprises. By 2026, it is expected that over 80% of enterprises will have utilized GenAI application programming interfaces (APIs), models, or deployed GenAI-enabled applications in production environments, a substantial increase from the less than 5% reported in 2023. GenAI has become a top priority for C-suite executives, driving innovation in various industries, including healthcare, life sciences, legal, financial services, and the public sector.</p> <p>Three key innovations highlighted in the 2023 Gartner Hype Cycle for Generative AI are expected to shape the future of GenAI. First, GenAI-enabled applications are set to transform user experiences and augment tasks, impacting a broad range of skill sets within the workforce. Although they offer democratized access to specialized tasks through natural language-based prompt engineering, they face challenges like hallucinations and inaccuracies.</p> <p>Second, foundation models are becoming increasingly crucial for AI, with their extensive pretraining and broad applicability across use cases. They are predicted to drive digital transformation in enterprises, enhancing productivity, customer experiences, and the cost-effective development of new products and services. Gartner anticipates that by 2027, foundation models will underpin 60% of natural language processing (NLP) use cases.</p> <p>Finally, AI Trust, Risk, and Security Management (AI TRiSM) is emerging as a crucial framework for ensuring AI model governance, trustworthiness, fairness, and security. Organizations that effectively manage AI risks are expected to experience improved adoption, business outcomes, and user acceptance, with AI TRiSM becoming mainstream within two to five years. In summary, experts from Gartner see a future where GenAI adoption will continue to grow, enabling significant advancements in various industries and requiring robust frameworks for governance and trust.</p> <p>See also: <a href="https://www.clouddatainsights.com/the-3-steps-to-make-responsible-ai-a-reality/">Three Steps to Make Responsible AI a Reality</a></p> <h3 class="wp-block-heading">Ken Mugrage of Thoughtworks: Niche Specialization and Real-World Integration”</h3> <p>Amidst the recent hype surrounding generative AI, experts like Ken Mugrage, Principle Technologist, Office of the CTO at Thoughtworks, are cautioning against overlooking more immediate concerns such as sustainability and bias while also recognizing the genuine value of these systems. Rather than viewing generative AI as all-encompassing chatbots, experts envision it as a class of tools designed for specific niches, offering innovative ways to navigate specialized information domains. This perspective—outlined in Mugrage’s recent piece published for <a target="_blank" href="https://www.technologyreview.com/2023/04/27/1072102/the-future-of-generative-ai-is-niche-not-generalized/" rel="noreferrer noopener">MIT Technology Review</a>— acknowledges that generative AI’s true significance lies in its capacity to interact with vast and complex datasets.</p> <p>The emergence of ChatGPT plugins developed by various companies exemplifies this trajectory, where generalized chatbots won’t handle every task but can provide valuable solutions within niche contexts, as seen with Expedia simplifying travel planning. While the question remains whether this will lead to an “iPhone moment” or challenge Google search, the initial shift will likely involve organizations integrating large language models (LLMs) to learn from their own data and services.</p> <p>Recognizing generative AI’s future is not a matter of widespread societal transformation but rather of unlocking new ways to engage with extensive data and information, OpenAI and other entities are actively exploring commercial opportunities. The emergence of self-hosted LLMs and domain-specific language models underscores the trend toward specialization. These developments, while still nascent, promise benefits like enhanced privacy and tailored information retrieval tools, applicable to areas such as customer support and content creation. </p> <p>This evolving landscape suggests that the future of AI lies in seamlessly integrating generative AI into specific contexts, transforming it from an all-knowing entity into a pragmatic and embedded tool. Technologies like GitHub Copilot already exemplify this trend, offering context-sensitive support to users and signaling that generative AI’s success may manifest as a discreet and indispensable tool in our daily workflows.</p> <h3 class="wp-block-heading">The future of AI is…complicated</h3> <p>As we look ahead, the success of Generative AI will be measured not by its ability to grab headlines or fuel grandiose visions but by its capacity to quietly enhance our work, assist our endeavors, and provide pragmatic solutions within specific domains. When Generative AI becomes so seamlessly integrated into our workflows that we hardly notice its presence, that could mark the true milestone of its success. And even as more big industry names express reservations about the impact of AI, stopping its progress doesn’t seem to be on the radar. Ultimately, its future is complicated.</p> <div class="saboxplugin-wrap" itemtype="http://schema.org/Person" itemscope itemprop="author"><div class="saboxplugin-tab"><div class="saboxplugin-gravatar"><img loading="lazy" decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2022/05/Elizabeth-Wallace-RTInsights-141x150-1.jpg" width="100" height="100" alt="" itemprop="image"></div><div class="saboxplugin-authorname"><a href="https://www.clouddatainsights.com/author/elizabeth-wallace/" class="vcard author" rel="author"><span class="fn">Elizabeth Wallace</span></a></div><div class="saboxplugin-desc"><div itemprop="description"><p>Elizabeth Wallace is a Nashville-based freelance writer with a soft spot for data science and AI and a background in linguistics. She spent 13 years teaching language in higher ed and now helps startups and other organizations explain – clearly – what it is they do.</p> </div></div><div class="clearfix"></div></div></div>]]></content:encoded> <wfw:commentRss>https://www.clouddatainsights.com/whats-the-future-of-genai-checking-in-with-industry-leaders/feed/</wfw:commentRss> <slash:comments>0</slash:comments> <post-id xmlns="com-wordpress:feed-additions:1">4680</post-id> </item> </channel> </rss>