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STACK’s proven design supports the next generation of data centers and their artificial intelligence workload requirements

STACK Infrastructure (“STACK”), the digital infrastructure partner to the world’s most innovative companies and a leading global developer and operator of data centers, positions itself as the premier developer for the future era of data centers, leveraging its established AI-Ready capabilities built upon STACK’s proven, customizable design. With a history of supporting high-density workloads, STACK reinforces its position as a pioneering partner to the world’s foremost technology providers.

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In the rapidly evolving landscape of technology, characterized by generational advancements in AI, the demand for data center capacity capable of accommodating high-density workloads is reaching unprecedented levels. A prime example is OpenAI’s ChatGPT, the generative AI chatbot launched just over a year ago and, within two months, became the fastest-growing consumer application in history. With such remarkable growth of AI and other machine learning applications, STACK’s cutting-edge data center solutions and its extensive portfolio of powered land holdings have become even more crucial to today’s technology companies. Purposefully crafted to meet the escalating high-density requirements of clients, STACK’s resources are positioned at the forefront of innovation.

“STACK was built for the world’s largest innovators, and from the beginning, we have prioritized the long-term scalability of our clients with a flexible data center design,” said Matt VanderZanden, Chief Operating Officer of STACK Americas. “As an AI-Ready digital infrastructure company with a proven track record of high-density deployments, STACK is building upon this foundation to continually address evolving client needs.”

STACK achieves optimal cooling for high-density AI workloads through its closed-loop water cooling systems, offering flexibility to meet diverse cooling requirements including customizable solutions that can support up to 30kW per rack with traditional air cooling; up to 50kW per rack with rear door heat exchangers; up to 100kW per rack with direct-to-chip liquid cooling; and up to and exceeding 300+kW per rack with immersion cooling in the future. STACK’s extensive portfolio of operating data centers, all designed based on cost-efficient and proven models, exemplifies its ability to swiftly deliver gigawatts of scale, coupled with expertise in cooling systems enabling greater and greater deployment densities, and affirms STACK as the ideal data center developer for sustained growth in the age of AI.

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STACK’s global footprint of campuses spans major data center markets across the Americas, EMEA, and APAC, offering a vast powered land portfolio, sustainable building solutions, and high-density cooling optionality for the next generation of AI-Ready data centers. With 2.5+GW built or under development and an additional 4.0+GW of planned and potential development, STACK strategically supports the hyperscale requirements and groundbreaking initiatives of leading global technology firms through the development of AI-Ready campus developments including:

• A planned five-building data center campus offering 250MW of scale in Central Phoenix with a dedicated on-site substation.
• A 48MW Santa Clara data center, featuring immediately available shell space powered by an onsite substation with rare contracted capacity.
• A 56MW Toronto campus, spanning 19 acres, includes an existing 8MW data center and 48MW expansion capacity, all supported by committed power.
• A 48MW build-to-suit opportunity in the Dallas/Fort-Worth area, boasting abundant power and connectivity options.
• A 200MW campus in Portland spanning 55 acres with 96MW currently available for leasing.
• A New Albany, Ohio 58MW data center campus with immediately available capacity and build-to-suit expansion opportunities.
• A strategically located data center campus in Osaka, Japan with 72MW of capacity across three planned buildings.
• A 36MW facility delivered in Australia, launching a 72MW campus in Melbourne, one of the fastest growing markets in Asia Pacific.
• A 30MW data center campus in Stockholm with 18MW under development.

AiThority Interview Insights : AiThority Interview with Itamar Kandel, Chief Executive Officer at Vista.ai

 [To share your insights with us, please write to sghosh@martechseries.com] 

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IPFone Unveils The ChatGPT-Enabled ‘IPFoneMate’ for Enhanced Webex Interactions

IPFone a pioneer in cloud communication services, is excited to announce the launch of “IPFoneMate,” the first Webex bot fully integrated with OpenAI’s ChatGPT. This groundbreaking tool leverages OpenAI’s ChatGPT technology, tailor-made to support organizations of various sizes in managing essential customer support, troubleshooting, and training tasks. IPFoneMate sets a new benchmark in customer service quality and significantly improves the customer experience.

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The OpenAI/ChatGPT bot is specifically customized for each business or industry, drawing on the company’s own resources such as learning centers, manuals, presentations, sales collateral, and other relevant documentation.

Key Applications:

• Customer Support: Equipped to deliver prompt and accurate responses to customer queries, the bot ensures high-quality and efficient customer service.
• Advanced Troubleshooting: IPFoneMate adeptly identifies and addresses common issues, providing detailed guidance and support.
• Employee Training: The bot offers comprehensive training on the latest products and services tailored to specific industries or companies.

“Integrating AI-driven solutions like ChatGPT with Webex can provide businesses with a significant competitive advantage. This integration enhances communication and support for customers and clients, particularly by offering instantaneous responses to inquiries, thereby significantly enhancing the customer experience,” stated Damian Chmielewski, CEO of IPFone.

AiThority Interview Insights: AiThority Interview with Ramsey Masri, Chief Executive Officer at Ceres Imaging

The introduction of IPFoneMate is a testament to IPFone’s ongoing commitment to innovation in cloud communications. This includes services such as UCaaS, Webex, Contact Center, Internet, SD-WAN, and more. With over 25 years of industry experience, IPFone continues to lead in offering advanced solutions to meet contemporary business challenges.

 Latest AiThority Interview Insights : AiThority Interview with Itamar Kandel, Chief Executive Officer at Vista.ai

 [To share your insights with us, please write to sghosh@martechseries.com] 

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MyHeritage, the leading global family history service, announced  the release of two groundbreaking features that mark the next frontier in family history research: AI Record Finder and AI Biographer. AI Record Finder revolutionizes genealogy like ChatGPT revolutionized searching the internet: it is an interactive, intelligent, free-text chat to help the user locate relevant historical records about a person of interest in MyHeritage’s vast database of 20 billion records. AI Biographer automatically compiles a rich narrative about an individual’s life using information from historical records that match the person, creating a Wikipedia-like biography about anyone. Narratives are enriched with relevant historical context using AI and are easy to share. MyHeritage is the only service to offer such groundbreaking features for family history, and the first to leverage conversational AI for searching historical records. The two features are integrated, allowing users to generate an AI Biography™ for individuals they find using AI Record Finder. AI Biographies may also be generated directly for individuals in family trees on MyHeritage.

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“We’re constantly pushing the boundaries of genealogy to reinvent the way people can discover their family history as we implement a bold vision for genealogy in the 21^st century”

AI Record Finder

Until now, searching for historical records on online genealogy platforms like MyHeritage has been very similar to using a regular internet search engine. One entered names and other terms into dedicated fields in a search form, and the search engine returned a large number of search results. Then, it was necessary to comb through the results to discover relevant information. AI Record Finder transforms this experience by enabling users to converse with an AI assistant in a chat to quickly find records about their ancestors, relatives, or other deceased individuals. Users can still use the traditional search engine on MyHeritage, but AI Record Finder™ adds an additional chat mode that increases the chances that users may be able to find elusive records they have never found before, thanks to the power of AI.

The chat is like an interview with a friendly concierge that the user can converse with in one of two modes: casual or formal. AI Record Finder processes the information the user enters, and understands what additional details are necessary to help narrow down the search results. It guides the user by asking the relevant questions according to the context and information provided by the user, to find the most relevant records about the person the user is searching for. Once located, the records can be reviewed and the details saved to the user’s family tree. AI Record Finder™ includes a seamless user interface, where historical records that are found appear directly within the chat.

AI Biographer

AI Biographer™ creates a rich Wikipedia-like biography summarizing a person’s life. This is especially useful for creating biographies about the billions of individuals who were not famous, and therefore do not appear in Wikipedia. An AI Biography can be created from historical records found via AI Record Finder™ and for deceased individuals within a user’s family tree on MyHeritage. AI Biographer utilizes MyHeritage’s acclaimed matching technologies to curate historical records and family tree profiles that pertain to the selected individual. All information from the pertinent records is then compiled into an biography that is enriched with photos and scanned documents, and in some cases, additional information from the web. The resulting biography includes the person’s immediate family, describes the main events of their life, and includes rich historical context and the origins of their surname. Each biography is a unique narrative that can be shared with family and friends, and saved for posterity. Facts listed in AI Biographies include footnotes and source citations, and link to the records from which they were obtained. Any inconsistencies within the information listed are noted. AI Biographies are saved as PDF files that are emailed to the user.

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When created from the user’s family tree, an AI Biography is added to the family tree as a media item and tagged with the individual’s name, so that it is accessible through the MyHeritage mobile app and Family Tree Builder desktop software. The biography is included whenever the family tree is exported in GEDCOM format, ensuring that the enriched biographical information remains an integral part of the family tree. Biographies can easily be regenerated whenever new information becomes available. Additional entry points for generating an AI Biography such as from MyHeritage’s traditional form-based search engine, and from family tree profile pages, will be added soon.

“We’re constantly pushing the boundaries of genealogy to reinvent the way people can discover their family history as we implement a bold vision for genealogy in the 21^st century,” said Gilad Japhet, Founder and CEO of MyHeritage. “AI Record Finder is a disruptive feature that simplifies the way people can find information about their ancestors by making the search easier and more intuitive. AI Biographer curates the details about a person’s life into a compelling story. Not all our ancestors were famous, but they all deserve to be remembered! Together, these cutting-edge features strengthen MyHeritage’s position as the industry leader for innovative genealogy and continue our mission to make family history easier, more accessible, and more fun for everyone.”

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[To share your insights with us, please write to sghosh@martechseries.com]

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With barely a few days left in 2023, I am offering my views on the adoption and impact of generative AI in legal industry in 2024, in the context of the knowledge management function.

Generative AI will deliver value to those who know how to use it, the tool doesn’t understand your legal function

Whilst generative AI is becoming synonymous with AI, the reality is that the latter isn’t a single technology but a cluster of different technologies. Generative AI applications, on the other hand, specifically use natural language processing on data provided to deliver results and outcomes in response to precise requests. The generative AI application doesn’t understand the legal concepts underlying the questions that it is asked or the words/output that it produces. So, legal expertise within the context of matter strategy and complexity is needed to sanity-check the outputs the generative AI tool throws up.

Consequently, generative AI will deliver value and convenience only to those who know how to use it, and even help fine-tune thought processes to achieve the desired outcome. For instance, the same question can be asked in multiple ways, and generative AI will respond in different ways. Likewise, lawyers can receive different outputs to the same questions, depending on the documents that they are ‘authorized’ to access in the firm. The tool is processing natural language, after all!

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Microsoft will enable generative AI adoption, but compliance and confidentiality will be showstoppers

In the near term, the first ‘showstopper’ for meaningful use of generative AI will be compliance.  A lawyer using generative AI (be that co-pilot or its adaptive version) to create a report for sharing with clients or third parties needs to ensure that the document complies with all the applicable data protection and data privacy regulations. In addition to enabling generative AI adoption through the provision of a co-pilot, Microsoft will play a role in ensuring that data security and residency aren’t compromised.

Microsoft’s security policy for Azure ensures that data residing in individual organizations’ cloud tenants is not shared externally.

So, it will be incumbent on law firms to only use data that is within their own cloud tenant, much like it was in previous years where data resided within firms’ networks.

The second showstopper is going to be data confidentiality. At an individual user level, people will only have access to documents they are authorized to view, based on the principles of need-to-know security. This means that individuals using the firm’s generative AI tool will only surface results based on the data they are authorized to access. In some cases, the tool may produce output based on a completely different set of documents to another individual. This will possibly impact the value of generative AI in terms of contextual accuracy, currentness, and suitability.

Without a trusted knowledge source, your generative AI tool will deliver limited value

Document management systems will be the logical place for law firms to embark on their AI journey, which, BTW for many firms, have already started.

For instance, AI tools are embedded in document management systems to assist with document classification, automatic filing of emails and documents based on user behaviors, and even interrogating the application for information by asking specific questions. Note here that for this kind of broad AI adoption, there is no need to “train” the tool for it to deliver the desired results.

To adopt generative AI and ensure that the tool is “trained” on the most accurate, authorized, and current data, firms will need to create a central, curated repository of trusted data – i.e., the knowledge management system. This will ensure the best and most useful output. To illustrate, a lawyer would be able to instruct the firm’s generative AI tool to create an 800 words abstract based on a 100-page M&A contract, that refers to participants from the US and Germany and pertains to the New York jurisdiction – and be assured that the right data has been used to create the output.

AiThority Interview with Snyk’s CMO Jonaki Egenolf

It isn’t the ‘rise of the machine’ in legal

Finally, despite the hype surrounding generative AI, we aren’t heading towards a ‘rise of the machine’ scenario in legal. There are many issues to iron out – from hallucinations and security to ethics, data management, and regulation – which means that human intellect is very much indispensable. It’s true that AI technology more broadly has a lot to offer in the form of support, convenience, and efficiency – and that over time generative AI will become more accurate. But natural language processing is exactly that – i.e., language processing. Generative AI engines know what letters or words to put one after the other, but it does not understand the words or concepts and needs detailed instruction and high-quality data to deliver value.

[To share your insights with us, please write to sghosh@martechseries.com]

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SnapLogic Chief Scientist Greg Benson reveals his predictions on Generative AI, and the challenges and opportunities it presents in 2024.

Prediction 1 – GenAI won’t take your job, but it might change it.

In 2024, Generative AI won’t lead to mass job displacements and redundancies, as many early sensationalist reports might have suggested. GenAI won’t completely replace experts in any field, as although models have access to an insurmountable amount of information, users still have to articulate concepts well enough to get the right answers – thus, expertise and human input and, more importantly, human review always will be necessary.

Collaboration with GenAI is a trend we can expect to continue in 2024, as businesses look to capitalize even further on its benefits, reaping the rewards of increased productivity and quality of content creation. This means adopting even more GenAI tools and encouraging even more use of them; the goal being not to replace workers, but instead assist what they do.

Prediction 2 – Now that we’ve invented GenAI, the next step is understanding it.

Next year, we can expect to see businesses attempt to improve the consistency of output from Generative AI. Currently, there is no set rule book for achieving great results with GenAI; there are tips and tricks you can deploy for better or faster results, of course, but overall the process is largely trial and error.

Interacting with GenAI in its current iteration is like a science experiment – you come up with a hypothesis and continue to test different manners of prompts until it produces the result you’re looking for. In the future, the focus of experimentation will be on figuring out how we evaluate the responses it gives us and using that data to inform prompts further.

Companies that want to apply GenAI to their products will need to think about how they carry forward and evolve prompts that can improve results directly. Qualitative and quantitative improvements can only be brought about by reevaluating their approach to AI application and development.

Prediction 3 –Expect an onslaught of GenAI tools and GenAI startups.

In 2024, we’re going to see another year of the AI market expanding, with more variety as GenAI startups try to find their niche among the masses.

Rather than consolidation, more GenAI solutions will continue to pop up in different industries. Of course, there will be a lot of attempts that don’t get traction, or just don’t work, but this won’t deter the wave of opportunistic entrepreneurs and businesses who look to capitalize on the GenAI wave.

There’s already the start of a huge race on the hardware side too: companies such as Google and AWS are building their own AI hardware in addition to NVIDIA, which is worth watching. If successful, these advances in hardware could lead to another explosion in how large language models are trained, as currently, it takes a lot of human input, money, and effort to build from the ground up.

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Prediction 4 – GenAI regulation is essential to adoption.

Regulating GenAI will be a huge focus for governing bodies and business leaders in 2024. Earlier this year, calls were heard for a pause in AI development from numerous visionaries, but this isn’t realistic as the fundamental technology is increasingly available through open-source models available on Hugging Face. Rather than focusing on halting development, creating clear regulations, guidelines and best-use practices will be necessary to ensure partnership with AI will move forward safely and securely.

Like any other technology, defining the boundaries that keep safety in mind will allow for leveraging the benefits without sacrificing progress. We can liken this to all manners of tools and equipment that need to be regulated; for example, we don’t stop ourselves from building cars that go fast, but we do put speed limits in place to ensure safety. Internationally, governments will draw their attention first to the areas of regulation that present the greatest impact on citizens, including frontier AI.

From an industry perspective, the GenAI applications and most helpful cases will emerge as front runners for wider business use cases. Understanding the risks, challenges, and security issues potentially imposed by these tools will be vital for businesses to understand exactly when and how these tools need to be regulated internally.

Likewise, companies hoping to leverage GenAI will have to communicate to customers exactly how it’s used and how it complies with current and future regulation requirements.

Prediction 5 – GenAI and Legacy technology: Why the key to modernization may reside in GenAI tools.

After a year of GenAI practice, legacy businesses are starting to understand that GenAI interest is not just driven by ‘hype’, and instead could be truly transformative for their sector. Therefore, in 2024, we can expect even more traditional businesses to deploy the technology to help evolve legacy systems and modernize their technology stack.

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Typically, traditional companies are not amenable to change or agile enough to adopt the latest in new technology. Many companies are tied to legacy software due to a combination of outdated procurement processes, familiarity, or concerns about data loss or disruption, making modernization inaccessible. The key here is that GenAI can assist with migrating off old code bases and technology stacks to modern programming languages and platforms.

However, GenAI could bridge this gap by allowing companies previously locked into legacy systems to access a more modern workforce’s knowledge and work practices. GenAI also makes some modern tools far more user-friendly, and therefore more likely to be deployed across businesses.

Prediction 6 – AI and the question of originality

Next year we’ll see the average person become more adept at using AI, both in business and in their personal lives. Students will also interact with GenAI at a greater scale.

On the one hand, ChatGPT and others can be great personal tutors to help students understand concepts. On the other hand, ChatGPT can be used to generate solutions to problems. I tell my students that they can use GenAI to help them as they are learning, but they must turn in their own original work. The problem is that it is extremely tempting to have GenAI provide the answers, perhaps just partially. In addition, since the answers are coming from a computer program and not another student, it distances students from the notion they are cheating. So far, for my classes in computer systems, it has been fairly easy to determine if a student has turned in GenAI solutions because they don’t follow the conventions in code that I’ve taught in class and require in student solutions.

How GenAI is used in classrooms is very much a work in progress. At the moment there’s still no best practice model – even at my University, we have workshops about AI, but no succinct policy. Beyond the classroom, there is the larger question of intellectual property and how GenAI is trained on internet-accessible creations and works available in digital form. We will see this play out in all industries and the courts in 2024.

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Prediction 7 – Universities will begin to teach prompt engineering

In 2024, universities will teach prompt engineering as a minor field of study and through certificate programs. Prompt engineering for GenAI is a skill already augmenting domain experts, similar to how computing has augmented other domains. The successful use of large language models (LLMs) relies heavily on giving the models the right prompts. When looking to fill the role of a prompt engineer, the task becomes finding a domain expert who can formulate a question with examples in a specific domain, a skill critical for today’s IT

professionals to refine to successfully implement LLMs. Given this, universities will introduce new academic focus areas to address the growing demand for professionals with specific skills required to build the next generation of GenAI applications.

Currently, SnapLogic is the leader in generative integration. As a pioneer in AI-led integration, the SnapLogic Platform accelerates digital transformation across the enterprise and empowers everyone to integrate faster and easier.

Whether you are automating business processes, democratizing data, or delivering digital products and services, SnapLogic enables you to simplify your technology stack and take your enterprise further. Thousands of enterprises around the globe rely on SnapLogic to integrate, automate, and orchestrate the flow of data across their business.

[To share your insights with us, please write to sghosh@martechseries.com]

The post SnapLogic Chief Scientist Reveals GenAI Predictions for 2024 appeared first on AiThority.

New Capability Filters Generative AI to Harden Enterprise Defenses

DNSFilter announced  the expansion of its protective DNS software with a new Generative AI category. DNSFilter’s enterprise-grade defense provides organizations of all sizes the ability to secure their network against harmful threats such as malware, botnet, and phishing in order to reduce corporate risk.  Enterprise security teams who manage and secure tech infrastructure need to address PII protection, and GenAI tools have introduced a new threat to that guarded corporate information.

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As Generative AI rapidly gains traction in the business world, many companies have rushed to implement this emerging technology without a deep understanding of the cybersecurity implications. Threat actors are now taking advantage of this new attack surface.

The DNSFilter team saw the proliferation of GenAI sites first-hand in their network data; there was a 279% increase in GenAI sites accessed on the DNSFilter network between January 2023 and August 2023 when looking only at the top seven GenAI sites of the year (including OpenAI’s ChatGPT).

To help address corporate security challenges, DNSFilter scans every domain its customers access to identify zero-day threats and prevent access before malicious actors or malware infiltrate the network. Now, organizations can block generative AI sites at the corporate level to comply with their newly written GenAI policies.

With a dedicated Generative AI category, security teams no longer have to manage and maintain a list of Generative AI applications and manually add to a block list. Users in the education sector can keep Generative AI tools off public and owned networks, helping to combat plagiarism. Similar offerings on the market are only able to block a small percentage of Generative AI-related domains.

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Ken Carnesi, CEO and co-founder, DNSFilter, said: “With the growth of AI over the last year, it has become critical that organizations have visibility and control over what Generative AI tools employees are accessing. DNSFilter has a powerful machine learning-based categorization engine, supplemented by third-party feeds, and we are always innovating new ways to use this data to protect our customers. AI is now a top concern within our customer base, and this category is a direct result of our product team listening to their worries and responding. Our Generative AI category gives our customers greater visibility, control, and peace of mind.”

Read: AI and Machine Learning Are Changing Business Forever

[To share your insights with us, please write to sghosh@martechseries.com]

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The IT firm’s recent research outlines six foundational responsible AI guidelines that organizations can adapt to reflect their own culture, values, and needs.

The rapid adoption of AI technologies across organizations worldwide is prompting IT and business leaders to establish clear guardrails that will define and guide the deployment of AI technology. Recognizing this challenge, Info-Tech Research Group has released a new research blueprint with six key principles titled Develop Responsible AI Guiding Principles. Implementing AI without a well-structured strategy and guiding principles for responsible AI use could lead to adverse business outcomes.

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The firm’s blueprint is designed to be a comprehensive guide to help organizations shape their AI principles, forming the bedrock of their future policies and governance practices. By adopting this proactive approach, organizations can ensure responsible and effective adoption of AI technology, mitigating risks while maximizing benefits.

“For many previous technology revolutions, business and technology leaders had time to plan the approach and control the implementation,” says Travis Duncan, research director at Info-Tech Research Group. “In the case of AI, the horse had already left the barn before most organizational leaders could even begin to grasp the full complexities of opportunity and risk inherent in the technologies. Before leadership could get policies and governance in place, nontechnical staff were exploring the productivity gains in ChatGPT and other similar tools, and technical staff were either formally or informally deploying AI models.”

This exponential advancement in AI technology is further emphasized by Info-Tech’s research, which shows the importance of establishing AI principles as an initial step. However, the real challenge emerges when integrating these principles into the daily operations of both technical and nontechnical staff. Engaging key stakeholders and communicating the importance of AI principles is essential to bridge this gap, ensuring that everyone is aligned with the organization’s vision for AI deployment.

“Complicating things further, the technology is evolving so rapidly from week to week that traditional policy and governance approaches are lacking,” explains Duncan. “This pace has made responsible AI practices essential. Organizations need to quickly understand the new risks that AI-based solutions can introduce. Within this, responsible AI guiding principles are foundational, providing a framework to set safeguards for technical and nontechnical staff when working with AI technologies.”

Info-Tech explains in the newly published resource that with the right set of AI guiding principles, organizations can effectively navigate the complexities of risk. The firm outlines the following six AI principles for responsible use, derived from insights from practitioners and industry frameworks. These principles can help guide IT leaders in addressing the challenges associated with AI:

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1. Evaluate Essential Inputs: Research and analyze industry standards, regulatory frameworks and guidelines, corporate principles, and other key considerations to ensure the approach meets legal requirements and employs best practices.
2. Engage Key Stakeholders: Engage executives and other key stakeholders to ensure their understanding and participation in the process of defining and ratifying principles.
3. Draft Responsible Al Principles: Ensure key stakeholders weigh inputs and other considerations to help identify the core principles that will guide the development of responsible Al for the organization.
4. Test & Validate Principles: Socialize the principles in pilots and select use cases to validate their appropriateness and effectiveness.
5. Put Principles Into Practice: Educate and train technical and nontechnical staff on the importance of Al principles and integrate them into the design, development, deployment, usage, and governance of Al technologies.
6. Monitor and update: Update and revise the principles regularly to ensure their relevance and effectiveness over time.

These principles will contribute to heightened risk awareness and more robust oversight. Moreover, they play a crucial role in facilitating more informed and effective decision-making processes within organizations, ultimately leading to more responsible and successful AI deployments.

Info-Tech Research Group emphasizes that the benefits of adopting responsible AI principles are multifaceted. By implementing these principles, organizations can improve the trust level of their AI applications, thereby boosting greater adoption among users and customers.

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[To share your insights with us, please write to sghosh@martechseries.com]

The post Setting the Standard for AI: Info-Tech Research Group Publishes Six Principles for Responsible Use appeared first on AiThority.

Generative AI in marketing is a disruptive force– every aspect of marketing is permanently reshaped by the novel technology. According to a survey of 1800 global CMOs and marketing executives, organizations are allocating a significant portion of their budgets to include Generative AI in marketing technology stacks. More than two-thirds (76%) of organizations are ready to fully embrace generative AI in marketing to unlock new revenue opportunities, enhance customer experiences (CX), and gain a competitive edge in the marketplace. In 2023, generative AI or gen AI has forced organizations to embrace a culture of innovation and experimentation, with compliance, ethics, and governance at the center, with their Marketing Technology tools and solutions. Capgemini’s report titled, “Generative AI and the Evolving Role of Marketing: A CMO’s Playbook” highlights the game-changing impact of Generative AI in marketing efforts and the future of generative AI-enabled marketing campaigns in 2024.

No other technology has proliferated our lives faster than gen AI. For instance, ChatGPT reached 100 million users within two months. AI has made an impact on every business function within an organization. Sales, Marketing, Customer Service, HR, IT, Finance, and Operations departments heavily rely on ChatGPT-like tools and capabilities to harness the power of data analytics for automation-driven real-time decision-making. Marketing departments, in particular, are at the forefront of generative AI adoption for selective applications in website development, content marketing, video and image generation, chatbots, and search.

Why do Marketers Love Generative AI in Marketing Tasks?

Generative AI, by definition, can learn from a wide range of data points, applied to specific tasks and situations. According to Hubspot, marketers use AI to create better and faster content. 35% of marketers use AI for research, generate new ideas, and write better copies. 48% of marketers consider AI-powered inspiration as a major benefit of using Generative AI in Marketing. 49% of marketers feel AI content is more personalized, while 50% believe AI enhances the quality of their copy.

The pattern-derived learning of AI models such as LLMs helps users to generate texts, speech, videos, images, and in some cases, neural sentiments.

Today, Gen AI tools can perform creative tasks for various operations. In some cases, tools like Bing, BARD, Synthesia, DALL-E2, Midjourney, and ChatGPT can even outperform human efforts. Marketers love using gen AI tools for the obvious reason that they can be easily integrated with the existing Marketing Technologies for email, content management, CX, and so on.

According to Capgemini, the Marketing function is rapidly embracing Gen AI despite their organizations adopting a “wait-and-watch” approach in 2023.

Top Use Cases of Gen AI in Marketing

With Gen AI adoption, technology investments in the Marketing and Sales functions have soared significantly in 2023. The state of implementation of generative AI in marketing highlights the importance of time-saving capabilities in content generation, personalization, and financial opportunities. According to Deloitte, 82% of early AI adopters have already gained financially from their AI investments. In the next two years, 90% of global marketing leaders will increase the use of gen AI. Despite open talks related to the biases, hallucinations, ethics, and accountability of gen AI tools, marketing leaders are optimistic about their AI-focused martech investments for B2B and B2C sectors.

Top Applications of Gen AI in Marketing

Designing and Content Generation

Marketing teams are using Gen AI tools for designing and generating content for their websites, emails, newsletters, surveys, social media posts, messengers, chats, and videos. In most omnichannel campaigns, AI has replaced design teams. In 2024, we could find generative AI tools proliferating deeper into multiple channels to engage with customers with augmented and mixed reality content. Regardless of the channels they choose, marketing teams will be able to deliver a personalized customer experience.

Marketing Analytics

Generative AI in marketing functions can transform the way teams leverage analytics for measuring performance and impact. Platforms such as Google Cloud and Adobe Experience Cloud help marketers create advanced audiences and segmentation to power superior customer experiences for omnichannel campaigns. Data-driven marketing insights can increase CLTV with prioritization of customer sentiments, ad spends, and real-time recommendations for upsells/ cross-sells.

Conversational AI for Voice and Chatbots

Marketing teams use conversation AI technologies for their voice and chatbot-based customer experience management solutions. Generative AI in marketing using voice and chatbot technologies helps in creating an intuitive customer service and support workflow. These can understand complex customer queries and generate relevant responses with little or no intervention from human marketers. As gen AI tools get more advanced through self-learning models, marketing teams can expect to improvise on their ability to improve customer interactions and reduce the friction in customer journeys.

Event Planning and Marketing

AI-borne events are the future of B2B marketing. Webinars, virtual conferences, and product launches could all see a dash of generative AI in the coming months. At AiThority.com, we covered events organized by Salesforce, Adobe, Cisco, Intel, AWS, NVIDIA, and others — these events showcased their AI themes with powerful AI-generated messaging and content. Using AI for events planning and marketing reduces production time with personalized content generation and automation techniques. For marketers, events ROI can be quickly scaled by driving registrations and engagements through AI and bot-based interactions.

According to Capgemini’s CMO’s Playbook on generative AI in Marketing, here are the top use cases:

• Data Analysis
• Personalization and CX
• Campaign creation
• SEO
• Image and video generation
• Text content creation
• Brand avatars
• New product development
• Metahumans
• Customer service management
• Brand metrics and sentiment analysis

Other areas where gen AI in marketing is making the biggest splash are shown below:

Marketers are not pausing on their AI investments despite challenges related to transparency and copyrights. Almost 80% of organizations have allocated a budget for gen AI integrations or plan to do so in the next six months. Leaders believe gen AI initiatives will lead to brand enhancement, cost efficiency, innovation, time optimization, and improved customer experience. However, as 70% of enterprises are potentially exposed to ethical challenges and legal issues, there is still much work to be done.

For example, less than half of companies (42%) are prepared to address copyright infringement cases for AI-generated content. 

Our team spoke to Eliza Nevers, SVP of Product at Lotame about AI’s role in marketing and advertising. Eliza said, “Brands and agencies remain cautious when it comes to handing over large portions of their operations to artificial intelligence. But, that doesn’t mean they aren’t looking to master the tech and seek future advantages (and efficiencies). Therefore, companies — particularly agencies — will scramble in 2024 to upskill talent with help from the right partners. Meanwhile, media buying agencies will focus on training specific AI models to get ahead, as more ad buying is likely to be executed and optimized by machines over time. We’ll continue to see the rise of AI consultants as a competitor to agencies, which will likely drive agencies to expand their services in this realm to better compete, and serve clients.”

Likewise, Nicola Kinsella, SVP of global marketing at Fluent Commerce said, “AI had quite the year in 2023, dominating the headlines with major analyst firms predicting its significant impact over the years to come. But to be successful in 2024 and beyond, AI will be forced to rely on the very sources many fear the technology will replace: people and data. Retail data is highly complex and dynamic with siloed information that is constantly in flux, whether it’s consumer buying behaviors, delayed shipments, product shortages, or labor demands. Teams equipped with retail order and inventory data management systems will play a major role next year to help produce and maintain clean, accurate, and accessible data needed for businesses to take full advantage of AI.”

Generative AI and Machine Learning: B2C versus B2B Campaigns

When it comes to incorporating Gen AI in marketing strategies, marketers in the B2B sectors are only slightly behind their B2C counterparts. However, the gaps will close between the two with B2B marketers testing and experimenting with Gen AI more actively than B2Cs.

The budget for generative AI in marketing technology investments has grown significantly in 2023. According to Capgemini, surveyed organizations have earmarked 62% of their martech budget for AI-focused initiatives. These efforts highlight the evolution of generative AI in marketing and customer support. In B2C organizations, CMOs are assuming much larger roles and taking greater accountability for revenue growth and profit-making. Most B2C CMOs are keenly investing in Martech platforms such as SaaS-based Marketing Clouds, CDPs, Customer Journey Platforms, and Analytics.

Future of AI in Marketing Technology: What CMOs should Prepare for

The report mentions that gen AI in marketing will augment human creativity, and not replace it.

Challenges with Generative AI in Marketing

Accepting hard facts and trustworthiness about generative AI in marketing are the top challenges for CMOs in 2023. Within six months, organizations that believed the benefits of generative AI outweighed the costs and risks decreased significantly. In April, 74% of organizations agreed generative AI benefits outweigh costs and risks; by October, this number dwindled to 57%. Despite challenges, CMOs could continue to leverage gen AI in their marketing technology strategies for assistance with long-term brand building, and enhanced customer service.

66% of organizations use gen AI for innovation in new channels in addition to the reduction in marketing costs.

To prove a point, Capgemini mentions the AI-generated marketing campaigns owned by Coca-Cola, Spotify, Wendy, Nestle, JP Morgan Chase, Heinz, Mercedes, Casper, and others.

Apart from the obvious risks and compliance-related challenges, organizations agreed that finding a balance between generative AI and human creativity tests their strategies. On average, 55% of organizations stated that balancing gen AI efforts with human creativity is an ongoing challenge in 2023. Experts believe that expecting machines to 100% match human emotions and build a deep emotional connection is beyond AI’s capabilities at the moment.

By the end of the decade, gen AI could create new opportunities for CMOs to augment human creativity through advanced analytics, intuitive decision-making, emotional conversations, and context understanding. By 2030, most organizations could be differentiated from each other as “AI innovators” or “fence-sitters.”

[To share your insights with us, please write to sghosh@martechseries.com]

The post State of Implementation of Generative AI (Gen AI) in Marketing appeared first on AiThority.

AI ready data and models to redefine and simplify customer experiences in financial services

BUSINESSNEXT, a universe of composable enterprise solutions for BFSI announces the launch of GPTNEXT, a generative AI platform built around the Large Language Models (LLMs). GPTNEXT can leverage GPT4 APIs with a proprietary Generative AI masking service to mask Personal Identifiable Information (PII) data overcoming the crucial data security concerns faced by BFSI. In addition, it also has a Private LLM offering fine-tuned for BFSI needs, leveraging Open Source LLMs like Meta’s Llama2 and many more. With ready and trained models for the banking, financial services, and Insurance industry (BFSI), GPTNEXT utilizes domain-specific Gen AI models to suggest effective actions at every stage of customer journey and interaction, empowering businesses to maximize every opportunity. GPTNEXT aligns with BUSINESSNEXT’s vision of autonomous financial services and marks a revolutionary leap towards how banks and insurance companies engage with their customers.

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Key business use cases driven by GPTNEXT:

• Frontline Sales Copilot
• Synthetic Credit Data generation
• Contact Centre Assistant
• Customer Authentication, Fraud Prevention, AML Compliance & Reporting
• Customer Interaction and sentiments Analysis
• Semantic Knowledge Base Search
• Automated email response generation with personalized communication and targeted messaging
• Holistic Summary of every customer across heterogenous touch points
• Personalized cross-sell offers/ Next best product recommendation.
• Customer Churn Prevention via diagnostics and predictive analytics
• Loan Processing, AI-Driven Information Extraction and streamlining workflows:
• Customer behaviour insights, competitive analysis, and market predictions
• Account Planning Recommendations & Personalized Account Prioritization
• Mitigates LLM hallucinations and data toxicity through LLM Grounding
• Boost efficiencies with its auto-prompt generation ability and dedicated bots for customer support

Speaking about GPTNEXT, Sushil Tyagi, Executive Director at BUSINESSNEXT, said, “GPTNEXT represents a significant step forward in leveraging generative AI for the BFSI sector. By combining the power of AI ready data and proprietary AI models, we aim to equip financial institutions with information quality, accuracy, action, process transparency, and speed to push forward intelligent customer interactions. With the added benefit of a unique data masking service, GPTNEXT enables financial institutions to offer an AI-enhanced value proposition, elevating customer trust through a strong focus on data security, privacy, and regulatory compliance.”

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GPTNEXT harnesses the capabilities of machine learning, deep learning, transformers, and natural language processing to deliver actionable insights, automate tasks, and provide large-scale personalized experiences. It leverages LLM grounding techniques to ensure contextualization and accuracy. This platform goes further by enabling intelligent insights through a comprehensive view of customer accounts and impactful call script generation, enhancing user productivity and efficiency. Additionally, each instance of account handling generates a repository of knowledge articles for future reference by financial institutions, contributing to an ongoing cycle of transformative customer experiences. The Forrester Wave™ Financial Services CRM, 2023 report recognised BUSINESSNEXT as a ‘Leader’ for its purpose fit CRMNEXT platform. BUSINESSNEXT was rated the highest for its strong Vision and Roadmap for financial services CRM.

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[To share your insights with us, please write to sghosh@martechseries.com]

The post BUSINESSNEXT Unveils GPTNEXT: Revolutionizing Customer Experiences with Generative AI appeared first on AiThority.