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Salesforce is a vendor of cloud-based software and applications for sales, customer service, marketing automation, ecommerce, analytics, and application development. Based in San Francisco, Calif., its services include Sales Cloud, Service Cloud, Marketing Cloud, Commerce Cloud, and Salesforce Platform. Its subsidiaries include Tableau Software, Slack Technologies, and MuleSoft, among others. The company is undergoing a pivot to agentic AI, increasingly focused on blending generative AI with a range of other capabilities to offer customers the ability to develop autonomous decision-making agents for their service and sales workflows. Salesforce has a market cap of $311 billion, making it the world’s 28th most valuable company by market cap. Here is the latest Salesforce news and analysis: source

Microsoft has also announced the general availability of Fabric Real-Time Intelligence, which provides pro-dev and no-code tools for ingesting high-volume streaming data. Dener Motorsports has been leveraging Real-Time Intelligence to stream data from its race cars during races, giving engineers access to that data in real-time. Microsoft also announced the preview of new capabilities including Fabric events and enhancements to Eventstreams and Eventhouses. The company also announced the general availability of sustainability data solutions in Microsoft Fabric to provide a single place for environmental, social, and governance (ESG) data needs, as well as the general availability of API for GraphQL, an API for accessing data from multiple sources in Fabric with a single query API. Microsoft has also made Azure SQL DB mirroring generally available. Microsoft also previewed a number of additions to Fabric, including Copilot in Fabric for data pipelines in Fabric Data Factory, integration with Esri ArcGIS for advanced spatial analytics, and open mirroring in OneLake, which enables any application or data provider to write change data directly into a mirrored database within Fabric. source

In our real-world case study, we needed a system that would create test data. This data would be utilized for different types of application testing. The requirements for the system stated that we need to create a test data set that introduces different types of analytic and numerical errors. Twelve different scenarios need to be tested against, and the data files need to contain or be able to contain data that will exercise those 12 tests. In addition, the system needs to create different files that mimic the data sets or files customers submit. There can be up to eight different data sets or files. Each record in each file needs to have a correlation ID or primary/foreign key value to match and link across records in the files. These correlation IDs can be kept in a text file that the system will read and assign along with the created output.   Then, the system needs to be able to create different amounts of records per file to mimic the number of transactions in the source system. The output of the system should be able to stress the end user application by producing different-sized test files. The requirement for the output is to be able to create files of 1000, 10,000, 100,000 and 1,000,000,000 records.   Lastly, the system needs to keep track of the number of records in each file, the time it takes to create the output, the time it takes to process, the number of errors created per output test file by the 12 different test types, the number of errors correctly captured by the automated tests and other business-specific metrics. Some of these data points will come from the agentic AI system and some will be generated from the automation testing system.   source

In the quest to reach the full potential of artificial intelligence (AI) and machine learning (ML), there’s no substitute for readily accessible, high-quality data. If the data volume is insufficient, it’s impossible to build robust ML algorithms. If the data quality is poor, the generated outcomes will be useless. Data silos, lack of standardization, and uncertainty over compliance with privacy regulations can limit accessibility and compromise data quality, but modern data management can overcome those challenges. By partnering with industry leaders, businesses can acquire the resources needed for efficient data discovery, multi-environment management, and strong data protection. To fully leverage AI and analytics for achieving key business objectives and maximizing return on investment (ROI), modern data management is essential. The power of modern data management Modern data management integrates the technologies, governance frameworks, and business processes needed to ensure the safety and security of data from collection to storage and analysis. It enables organizations to efficiently derive real-time insights for effective strategic decision-making. Some of the key applications of modern data management are to assess quality, identify gaps, and organize data for AI model building. It’s also useful in countering the pressing IT talent shortage, in many cases providing the deep and broad expertise that few organizations can maintain in house. Partnering for greater value generation SAS and Intel customers have found that the strengths of each company – SAS’s advanced analytics and Intel’s high-performance computing – are magnified through their “better together” approach. Together, they offer complementary tools and services to achieve data discovery, gain access to real-time insights, implement multi-environment data management, and embed data protection at the chip level. “Tasks such as data analysis, machine learning, and predictive analytics require high performance, which Intel’s latest processors provide,” noted Bruno Domingues, CTO for Intel’s financial services industry practice. “The faster data is processed, the quicker actionable insights can be generated.” And that processing speed need not be hampered by the quest for perfection. The goal of modern data management is not to make data pristine. “It’s impossible,” says Shadi Shahin, Vice President of Product Strategy at SAS. “Trying to clean the data and make it perfect is not going to work. Understanding the use of the data is critical – it must be fit for purpose.” Achieving ROI from AI requires both high-performance data management technology and a focused business strategy. Organizations that are determined to control costs, minimize risk, and maximize productivity in their execution of an AI strategy should start small, leverage state-of-the-art technology, and work with trusted partners. Getting trusted results There’s no need for any organization to rely on traditional data management, data prep, and algorithms. “You can get value out of data much faster,” notes Shahin, “whether through recommendation engines, automated machine learning pipelines, or other modern techniques designed to solve legacy problems.” Together, SAS and Intel accelerate the journey to value realization. “You can start quickly and show value quickly,” adds Shahin. “You don’t need a multiyear project to show value in your data.” Check out this webinar to learn more tips and strategies for building a data foundation for AI-driven business growth. source

Generative AI (GenAI) is rapidly emerging as a game changer for enterprises, but turning its potential into measurable value remains a significant challenge. According to a recent IDC study (Future Enterprise Resiliency and Spending Survey, Wave 4, IDC, April 2024), companies are conducting an average of 37 GenAI proofs of concept (POCs), with only five advancing to production. Of those, just three are considered successful. This stark contrast between experimentation and execution underscores the difficulties in harnessing AI’s transformative power. To bridge this gap, CIOs and technology leaders must not only identify the barriers but also adopt strategic approaches to improve the success rate and deliver real business value from GenAI initiatives. Let’s discuss the barriers and solutions for them. Data privacy and compliance issues Failing: Mismanagement of internal data with external models can lead to privacy breaches and non-compliance with regulations. Solution: Implement robust data governance frameworks and ensure compliance with regulations like GDPR and CCPA. Use anonymization and encryption techniques to protect sensitive data. Key takeaway: Prioritize data privacy and compliance to build trust and avoid legal repercussions. Bias and hallucinations Failing: GenAI models often produce biased or inaccurate outputs, leading to misinformation and potential legal issues. Solution: Regularly audit and retrain models using diverse and representative data sets. Implement bias detection and mitigation tools. Key takeaway: Continuous monitoring and updating of AI models are essential to minimize bias and improve accuracy. Provide transparency back to the original data source to allow verification of information. High costs Failing: The infrastructure and computational costs for training and running GenAI models are significant. Solution: Optimize models for efficiency, leveraging cloud-based solutions. But don’t forget to assess whether a private cloud option or a small language model will address your concerns. Key takeaway: Cost management strategies are crucial for sustainable AI deployment. We’ve already seen people struggle with cloud budgets; we are seeing a similar pattern with GenAI. Integration challenges Failing: Integrating AI into existing systems can be technically and operationally challenging. Solution: Develop a clear integration road map, invest in middleware solutions, and ensure cross-functional collaboration. Key takeaway: A well-planned integration strategy can smooth the transition and maximize AI benefits. Scalability issues Failing: AI solutions that work in controlled environments may struggle to scale effectively in real-world conditions. Solution: Conduct thorough scalability testing and use modular architectures to facilitate easier scaling. Key takeaway: Scalability should be a core consideration from the outset to ensure long-term success. Lack of clear use cases Failing: Difficulty in identifying specific business needs that GenAI can address. Solution: Engage stakeholders to identify pain points and opportunities where AI can add value. Pilot projects can help validate use cases. Key takeaway: Clear, well-defined use cases are essential for demonstrating AI’s value. Look for super use cases that address multiple opportunities rather than point solutions. Trust and oversight Failing: Lack of transparency and explainability in AI models can erode trust among users and stakeholders. Solution: Use explainable AI (XAI) techniques and maintain clear documentation of AI decision-making processes. Key takeaway: Transparency and explainability are key to building and maintaining trust in AI systems. Intellectual property risks Failing: GenAI can inadvertently use copyrighted material, leading to legal complications. Solution: Implement strict content sourcing policies and use AI tools that can verify the originality of generated content. Key takeaway: Protecting intellectual property is essential to avoid legal issues and maintain ethical standards. Conclusion GenAI offers transformative possibilities, but unlocking its true value demands more than just enthusiasm; it requires strategy, foresight, and resilience. To move from potential to impact, organizations must confront its unique challenges head-on with well-thought-out solutions. By zeroing in on critical lessons and proactively managing risks, businesses can not only mitigate the pitfalls but also position themselves to fully capitalize on the immense power of GenAI, driving innovation and delivering sustained value. Learn more about IDC’s research for technology leaders OR subscribe today to receive industry-leading research directly to your inbox. International Data Corporation (IDC) is the premier global provider of market intelligence, advisory services, and events for the technology markets. IDC is a wholly owned subsidiary of International Data Group (IDG Inc.), the world’s leading tech media, data, and marketing services company. Recently voted Analyst Firm of the Year for the third consecutive time, IDC’s Technology Leader Solutions provide you with expert guidance backed by our industry-leading research and advisory services, robust leadership and development programs, and best-in-class benchmarking and sourcing intelligence data from the industry’s most experienced advisors. Contact us today to learn more. Daniel Saroff is group vice president of consulting and research at IDC, where he is a senior practitioner in the end-user consulting practice. This practice provides support to boards, business leaders, and technology executives in their efforts to architect, benchmark, and optimize their organization’s information technology. IDC’s end-user consulting practice utilizes IDC’s extensive international IT data library, robust research base, and tailored consulting solutions to deliver unique business value through IT acceleration, performance management, cost optimization, and contextualized benchmarking capabilities. source

Saudi Arabia has announced a 100 billion USD initiative aimed at establishing itself as a major player in artificial intelligence, data analytics, and advanced technology. The program, known as Project Transcendence, marks a significant push by the Kingdom to develop a robust AI ecosystem that can rival leading tech hubs, including neighbouring United Arab Emirates and other global technology centers. This unprecedented investment will focus on building state-of-the-art data centers, supporting startups, and expanding AI infrastructure to drive both domestic growth and international competitiveness. As part of Saudi Arabia’s Vision 2030 plan, this AI project underscores the country’s commitment to economic diversification away from oil, aiming to become a global tech leader within the next decade. Project Transcendence is expected to channel investments into critical areas needed to create a thriving AI industry. These include data center expansion, tech startups, workforce development, and partnerships with leading technology firms. Sources familiar with the project have indicated that Saudi Arabia intends to recruit top AI talent from around the world, invest in R&D, and incentivize foreign companies to establish a footprint in the Kingdom. source

Mohanasevlan Jeyapalan, Senior VP, PMO at Expo City Dubai, commented on the evolution of cybersecurity from being a technical concern to a priority for board members, emphasizing that data quality is a risk that now demands executive-level attention. “Cybersecurity took a lot of time to come to board members, and data quality is a risk that needs to be discussed,” Jeyapalan stated. This shift marks a crucial step in aligning cybersecurity with broader business goals, ensuring that executive leaders are actively engaged in securing data quality and managing risks. AI-Driven Insights for Predictive Risk Management Another theme that resonated with panellists was the use of AI and analytics for predictive risk management. The ability to analyze large datasets to predict and prioritize threats empowers companies to take preemptive action, enhancing resilience against cyber incidents and other disruptions. In particular, AI can help security teams make more accurate predictions about where and when a threat may arise, enabling companies to allocate resources more effectively. Anoop Kumar, Head of Information Security Governance, Rish and Compliance at GulfNews, Al Nisr Publishing also reflected this sentiment: “We need to make sure the risk is managed properly.” This proactive approach to managing risk with AI-driven insights is essential for companies seeking to not only respond to threats but anticipate and mitigate them before they materialize. source

As we navigate through 2024 and gear up for 2025, we are witnessing a pivotal moment in technological evolution where two transformative forces—5.5G (also referred to as 5G-Advanced or 5G-A) networks and artificial intelligence (AI)—are converging to reshape our digital landscape. The commercial launch of 5.5G, coinciding with an extraordinary multiplying of AI applications, marks a watershed moment in technological history. With over three million AI-capable applications now developed globally, surpassing the total number of traditional apps available in app stores, we are entering an entirely new paradigm: the Mobile AI era. This convergence represents far more than the sum of its parts. While previous technological advances have incrementally improved our capabilities, the marriage of 5.5G and AI promises to fundamentally transform how we interact with technology, conduct business, and structure our societies. The implications of this transformation extend far beyond the telecommunications industry, touching every sector of the global economy and reshaping the fabric of our digital interactions. With the emergence of Mobile AI, we need to rethink our understanding of connectivity and computing power. Traditional boundaries between device capabilities and network infrastructure are blurred to create an ecosystem where intelligence is ubiquitous and seamlessly integrated. This new era brings forth unprecedented forms of human-machine interaction, spawning intelligent services previously confined to science fiction. The structural changes in traffic models and data consumption patterns we observe are the initial indicators of a more profound transformation underway. Telecoms as the foundation of the Mobile AI era Telecommunications carriers stand at the heart of this revolution, positioned as the crucial enablers of this new technological shift. Their evolution from traditional service providers to sophisticated technology companies (Techcos) represents a fundamental reimagining of their role in the digital ecosystem. Modern consumers increasingly demand services that are not only real-time and on-demand but also intelligent and predictive, expecting seamless integration across platforms and personalized experiences. Leading carriers worldwide have already demonstrated the remarkable potential of AI service capabilities on live 5.5G networks, showcasing applications that span personal use, smart homes, transportation systems, and enterprise operations. If telecommunication carriers are the heart, the integration of 5.5G or 5G-A technology is the soul that unleashes AI’s full potential in mobile environments. This provides the essential combination of high bandwidth, low latency, and massive connection density required to support advanced AI applications at scale. The sophistication of 5.5G networks enables real-time processing of complex AI algorithms, facilitating everything from autonomous vehicles to smart manufacturing systems. The recent development of “Mobile going AI,” where new service and business models are transforming mobile internet services, and “AI going Mobile”, where new mobile services like smart vehicles and robots create new momentum for both society and the mobile industry, particularly as 5.5G networks demonstrate their capability to support diversified connections, experiences, and services needed to address new requirements coming from AI agents, smart vehicles, and embodied intelligence. As the rapid development of these technologies accelerates the Fourth Industrial Revolution, AI emerges as a principal driver of global economic growth, transforming multiple markets simultaneously while creating new opportunities and disrupting traditional business models. Ammar Tobba, VP, Public Affairs & Communications, Middle East & Central Asia, Huawei Huawei Connecting underserved areas and driving digital economies The deployment of advanced 5G technologies, particularly Fixed Wireless Access (FWA), plays a transformative role in developing regional digital economies. These technologies are instrumental in bridging the digital divide and achieving meaningful connectivity goals, particularly in underserved areas where traditional infrastructure deployment has been challenging. The impact of this technological democratization extends far beyond simple internet access, enabling new forms of digital participation and economic opportunity. The impact of 5.5G or 5G-A technology spans multiple sectors, each with its transformative potential. In healthcare, it enables remote surgery and real-time patient monitoring. In education, it facilitates immersive learning experiences and personalized instruction at scale. In industrial applications, it powers smart factories and autonomous systems. The technology’s enhanced machine learning capabilities support extended reality applications, industrial IoT implementations, and precision agriculture initiatives, directly contributing to sustainable development goals while driving economic growth. The GCC region stands at the forefront of global 5G deployment, positioning itself to fully exploit 5.5G commercialization and its transformative use cases. This leadership is particularly significant given the proven correlation between ICT industry development and national economic growth. According to Huawei’s Global Digitalization Index (GDI), a one-US-dollar investment in digital transformation yields an impressive 8.3-US-dollar return in a country’s digital economy. This research, covering 77 countries representing 93% of global GDP and 80% of the world’s population, demonstrates the crucial role of ICT investment in building robust digital economies. The path to realizing the full potential of Mobile AI requires unprecedented collaboration among ecosystem stakeholders. Public-private partnerships are crucial in accelerating adoption and ensuring equitable access to these transformative technologies. Sharing knowledge, insights, and experiences among key players is essential for inspiring the next generation of digital transformation and achieving regional digital ambitions. Moving forward, it’s clear that the convergence of 5.5G and AI is a fundamental shift in how we approach digital transformation and sustainable development. The Mobile AI era promises to deliver not only economic benefits but also social and environmental improvements through more efficient resource utilization and intelligent system optimization. source

It’s unsurprising to see legacy providers shifting their business models from perpetual software licensing to subscription-based pricing. Some do it with a measure of grace. But lately, some licensees of virtual desktops and applications have been confronted with abrupt changes and even forced to accept and pay for unwanted features. However, there are ways for alienated customers to protect their best interests. This past year has been rife with complaints over a substantial licensing change by Citrix after it was acquired by private equity firms and merged with TIBCO Software. Those changes remarkably parallel a playbook that VMware customers experienced in the wake of their vendor being acquired by Broadcom. For those paying close attention, substantive changes were foreshadowed in 2023 when it was quietly noted that perpetual software maintenance licenses would not be renewed upon expiration. Even those paying attention back then have been hit with what they consider even more egregious changes, and some are citing licensing cost increases of 300% or more. Specifically, some organizations have griped that while they asked for renewal terms six months or more before their deadlines, those requests went into a dark hole until as little as 30 days remained on their current agreement. That’s too late for an organization with substantial numbers of users—or even just a few—to evaluate and prepare for a switch if they so desire. When they receive a replacement, er, “renewal” proposal, surprises abound. First, separately licensed products are now “features” within a universal license. For most, that means paying for shelfware they’ll never use while absorbing sometimes astounding price hikes from what they were accustomed to. Some organizations are coming away from licensing discussions convinced they can only obtain 3-year or 5-year agreements! So much for pay-as-you-go subscription models and winning your customer’s trust every day. Moreover, many are being shifted to new support models while being gaslighted the changes are in their best interests. Only select customers—by invitation only—are being offered platform licenses and support. Most are being shifted toward channel partners. Not only that, but channel partners handling 2,000 or fewer licenses have themselves been shifted to a third-party provider. How to protect your interests If you’ve yet to receive a renewal offer from your vendor or channel partner, there’s no time to lose. If you already agreed to a one-year license, prepare for your next renewal date. First, start planning immediately to ease the transition to desktop-as-a-service (DaaS). The writing is on the wall for terminated support of legacy applications, no matter who the vendor is. So, there’s no time to get ahead of the game like the present. Develop a timeline for your next renewal date. Here are some steps to plan for: Demand your sales representative answer your renewal questions and keep demanding until you get an informed response. Take advantage of modeling and cost estimator tools that can help IT model configurations and better understand resource needs and budget impacts for a shift from legacy VDI to DaaS. Determine what virtualized applications to modernize, migrate, or retire–this is something all organizations should be doing no matter their circumstances, as it likely will save money now and ease a transition whenever it comes. Determine the cloud service that can best suit your DaaS needs for the long term, including ease of integration and collaboration with third-party tools and services. Prepare users for upcoming changes to interfaces and processes. Involve them early in the process to both gain feedback and demonstrate the expected benefits. Find the best consolidated management tools that can help enterprises and MSPs manage scaling, security, and updates. Large vendors are continuously looking to cut costs, and that’s not likely to change. Support is costly, whether it’s continuing maintenance of older products or hand-holding customers that vendors no longer view as premium. The more you can control your situation, the better off you’ll be. For more insights into DaaS, read Gartner’s Magic Quadrant report, notably its overview of Microsoft Azure Desktop. source