The recipe for RAG: How cloud services enable generative AI outcomes across industries

According to research from IBM, about 42 percent of enterprises surveyed have AI in use in their businesses. Of all the use cases, many of us are now extremely familiar with natural language processing AI chatbots that can answer our questions and assist with tasks such as composing emails or essays. Yet even with widespread adoption of these chatbots, enterprises are still occasionally experiencing some challenges. For example, these chatbots can produce inconsistent results as they’re pulling from large data stores that might not be relevant to the query at hand.

Thankfully, retrieval-augmented generation (RAG) has emerged as a promising solution to ground large language models (LLMs) on the most accurate, up-to-date information. As an AI framework, RAG works to improve the quality of LLM-generated responses by grounding the model on sources of knowledge to supplement the LLM’s internal representation of information. IBM unveiled its new AI and data platform, watsonx, which offers RAG, back in May 2023.

In simple terms, leveraging RAG is like making the model take an open book exam as you are asking the chatbot to respond to a question with all the information readily available. But how does RAG operate at an infrastructure level? With a mixture of platform-as-a-service (PaaS) services, RAG can run successfully and with ease, enabling generative AI outcomes for organizations across industries using LLMs.

How PaaS services are critical to RAG

Enterprise-grade AI, including generative AI, requires a highly sustainable, compute- and data-intensive distributed infrastructure. While the AI is the key component of the RAG framework, other “ingredients” such as PaaS solutions are integral to the mix. These offerings, specifically serverless and storage offerings, operate diligently behind the scenes, enabling data to be processed and stored more easily, which provides increasingly accurate outputs from chatbots.

Serverless technology supports compute-intensive workloads, such as those brought forth by RAG, by managing and securing the infrastructure around them. This gives time back to developers, so they can concentrate on coding. Serverless enables developers to build and run application code without provisioning or managing servers or backend infrastructure.

If a developer is uploading data into an LLM or chatbot but is unsure of how to preprocess the data so it’s in the right format or filtered for specific data points, IBM Cloud Code Engine can do all this for them—easing the overall process of getting correct outputs from AI models. As a fully managed serverless platform, IBM Cloud Code Engine can scale the application with ease through automation capabilities that manage and secure the underlying infrastructure.

Additionally, if a developer is uploading the sources for LLMs, it’s important to have highly secure, resilient and durable storage. This is especially critical in highly regulated industries such as financial services, healthcare and telecommunications.

IBM Cloud Object Storage, for example, provides security and data durability to store large volumes of data. With immutable data retention and audit control capabilities, IBM Cloud Object Storage supports RAG by helping to safeguard your data from tampering or manipulation by ransomware attacks and helps ensure it meets compliance and business requirements.

With IBM’s vast technology stack including IBM Code Engine and Cloud Object Storage, organizations across industries can seamlessly tap into RAG and focus on leveraging AI more effectively for their businesses.

The power of cloud and AI in practice

We’ve established that RAG is extremely valuable for enabling generative AI outcomes, but what does this look like in practice?

Blendow Group, a leading provider of legal services in Sweden, handles a diverse array of legal documents—dissecting, summarizing and evaluating these documents that range from court rulings to legislation and case law. With a relatively small team, Blendow Group needed a scalable solution to aid their legal analysis. Working with IBM Client Engineering and NEXER, Blendow Group created an innovative AI-driven tool, leveraging the comprehensive capabilities of  to enhance research and analysis, and streamlines the process of creating legal content, all while maintaining the utmost confidentiality of sensitive data.

Utilizing IBM’s technology stack, including IBM Cloud Object Storage and IBM Code Engine, the AI solution was tailored to increase the efficiency and breadth of Blendow’s legal document analysis.

The Mawson’s Huts Foundation is also an excellent example of leveraging RAG to enable greater AI outcomes. The foundation is on mission to preserve the Mawson legacy, which includes Australia’s 42 percent territorial claim to the Antarctic and educate schoolchildren and others about Antarctica itself and the importance of sustaining its pristine environment.

With The Antarctic Explorer, an AI-powered learning platform running on IBM Cloud, Mawson is bringing children and others access to Antarctica from a browser wherever they are. Users can submit questions via a browser-based interface and the learning platform uses AI-powered natural language processing capabilities provided by IBM watsonx Assistant to interpret the questions and deliver appropriate answers with associated media—videos, images and documents—that are stored in and retrieved from IBM Cloud Object Storage.

By leveraging infrastructure as-a-service offerings in tandem with watsonx, both the Mawson Huts Foundation and Blendow Group are able to gain greater insights from their AI models by easing the process of managing and storing the data that is contained within them.

Enabling Generative AI outcomes with the cloud

Generative AI and LLMs have already proven to have great potential for transforming organizations across industries. Whether it’s educating the wider population or analyzing legal documents, PaaS solutions within the cloud are critical for the success of RAG and running AI models.

At IBM, we believe that AI workloads will likely form the backbone of mission-critical workloads and ultimately house and manage the most-trusted data, so the infrastructure around it must be trustworthy and resilient by design. With IBM Cloud, enterprises across industries using AI can tap into higher levels of resiliency, performance, security, compliance and total cost of ownership.

Source: ibm.com

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Immutable backup strategies with cloud storage

Cyberthreats, once a mostly predictable risk limited to isolated incidents, are now pervasive. Attackers aided by advancements in AI and global connectivity are continually seeking out vulnerabilities in security defenses so they can access critical infrastructure and customer data. Eventually, an attack will compromise an administrative account or a network component, or exploit a software vulnerability, ultimately gaining access to production infrastructure. These inevitable attacks are why having immutable offsite backups for both application and customer data is critical to achieving a swift recovery, minimizing downtime and limiting data loss.

In an era characterized by digital interconnectedness, businesses must confront an ever-evolving array of cyberthreats, which present formidable challenges in defending against attacks. Some of the common challenges that enterprises face when protecting data are:

◉ Maintaining data integrity and privacy amid the threat of potential data breaches and data leaks.

◉ Managing IT budgets while dealing with increased cyberthreats and regulatory compliance.

◉ Dealing with strains on resources and expertise to implement robust data protection measures, which leave businesses vulnerable to data loss and cyberattacks.

◉ Contending with new complexities of managing and securing sensitive data from massive information producing workloads like IoT, AI, mobile and media content workloads.

Use backups to protect your data

Backups serve as a foundational element in any robust data protection strategy, offering a lifeline against various threats, from cyberattacks to hardware failures to natural disasters. By creating duplicates of essential data and storing them in separate locations, businesses can mitigate the risk of permanent loss and ensure continuity of operations in the face of breaches or unforeseen catastrophes. Backups provide a safety net against ransomware attacks, enabling organizations to restore systems and data to a pre-incident state without succumbing to extortion demands.

Additionally, backups offer a means of recovering from human errors, such as accidental deletion or corruption, thereby preventing potentially costly disruptions and preserving valuable intellectual property and customer information. In essence, backups function as a fundamental insurance policy, offering peace of mind and resilience in an increasingly volatile digital landscape.

Workloads and patterns that benefit from a comprehensive backup strategy

There are some typical scenarios where having a backup strategy proves particularly useful.

Cloud-native workloads:

• Applications that use virtual machines (VMs), containers, databases or object storage in AWS, Microsoft® Azure and other clouds should have a backup strategy. Storing these backups in a separate cloud environment such as the IBM Cloud® provides the best isolation and protection for backups.
• Top cloud service providers: AWS, Microsoft Azure, IBM Cloud, Google Cloud and Oracle.

Virtual machines:

• Most organizations run some applications in virtual environments either on premises or in the cloud. These virtual machines must be backed up to preserve their storage, configuration and metadata, ensuring rapid application recovery in the case of cyberattacks or disaster scenarios.
• Key virtualization technologies: VMware®, Microsoft® Hyper-V, Red Hat® and Nutanix.

Enterprise applications and infrastructure:

• Enterprise applications and infrastructure support critical business workloads and workforce collaboration. Ensuring quick application and data recovery in the case of cyberattacks is mission critical to avoid top line business impact.
• Critical enterprise applications: Microsoft® Suite, Oracle Database, SAP and other database technologies.

SaaS applications:

• Many customers are not aware of their responsibilities for backing up their data in SaaS applications. Even though they have SaaS, they can benefit from a backup solution that can prevent customer data loss if the SaaS service is compromised.
• Common enterprise SaaS applications: Microsoft 365, Salesforce, ServiceNow and Splunk.

Back up data to the cloud for enhanced data protection

Effective disaster recovery (DR) practices mandate keeping usable business-critical backups offsite and immutable. Traditionally, this was achieved by sending backups to tape libraries in offsite locations. However, managing tape libraries became operationally complex due to the need to ensure that backups remained available for restoration in disaster scenarios. Restoring from tape libraries can also be slow and cumbersome, failing to meet recovery timelines crucial for critical application workloads.

Cloud storage offers a compelling offsite alternative to traditional tape backups. IBM Cloud® Object Storage is a fully managed cloud storage service with built-in redundancy, security, availability and scalability that is highly resilient to disaster events, ensuring data availability when needed. Accessible through APIs over the internet, cloud storage simplifies operational recovery procedures, which results in faster recovery times and lower data loss risks in cyberattack scenarios.

How IBM Cloud Object Storage protects backups

IBM Cloud Object Storage is a versatile and scalable solution that is crucial for storing and protecting data backups. It is used by clients across a wide range of industries and workloads to store hundreds of petabytes of backup data. Four of the top five US banks use IBM Cloud Object Storage to protect their data.

Clients can develop their native data backup solutions targeting IBM Cloud Object Storage or opt for industry-leading data protection tools such as Veeam, Storage Protect, Commvault, Cohesity, Rubrik, and others natively supporting backups to IBM Cloud Object Storage.

Key benefits of using IBM Cloud Object Storage for backups

Immutable data protection: Native immutability features help prevent backups from being modified or deleted during the retention window. Immutability provides the ultimate data protection against ransomware by blunting its ability to overwrite backup data with encryption.

Reduced disaster recovery time: Because your backup data is stored in a secured and separate environment, you can be confident that the backups will remain unaffected by cyberattacks on production environments. These unaffected backups make it easier to restore data and recover quickly.

Lower cost of backing up: Object storage is a fully managed storage service available at very low costs, allowing organizations to keep backup operational costs low while ensuring continued protection.

Resilience and availability: IBM Cloud Object Storage is a globally accessible service backed by redundant storage zones and network technologies, so your backups always remain available.

IBM Cloud Object Storage’s robust architecture ensures durability, scalability and cost-effectiveness, making it suitable for organizations of all sizes. Moreover, its immutability feature adds an extra layer of protection by preventing accidental or malicious alterations to backup data, thus ensuring data integrity and compliance with regulatory requirements. This feature, combined with IBM’s stringent security measures and extensive data protection capabilities, makes IBM Cloud Object Storage a trusted choice for businesses looking to secure their backup data reliably. By using IBM Cloud Object Storage, organizations can mitigate risks, streamline backup processes, and maintain peace of mind by knowing their critical data is securely stored and protected against any unforeseen events.

Source: ibm.com

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Types of central processing units (CPUs)

What is a CPU?

The central processing unit (CPU) is the computer’s brain. It handles the assignment and processing of tasks and manages operational functions that all types of computers use.

CPU types are designated according to the kind of chip that they use for processing data. There’s a wide variety of processors and microprocessors available, with new powerhouse processors always in development. The processing power CPUs provide enables computers to engage in multitasking activities. Before discussing the types of CPUs available, we should clarify some basic terms that are essential to our understanding of CPU types.

Key CPU terms

There are numerous components within a CPU, but these aspects are especially critical to CPU operation and our understanding of how they operate:

• Cache: When it comes to information retrieval, memory caches are indispensable. Caches are storage areas whose location allows users to quickly access data that’s been in recent use. Caches store data in areas of memory built into a CPU’s processor chip to reach data retrieval speeds even faster than random access memory (RAM) can achieve. Caches can be created through software development or hardware components.
• Clock speed: All computers are equipped with an internal clock, which regulates the speed and frequency of computer operations. The clock manages the CPU’s circuitry through the transmittal of electrical pulses. The delivery rate of those pulses is termed clock speed, which is measured in Hertz (Hz) or megahertz (MHz). Traditionally, one way to increase processing speed has been to set the clock to run faster than normal.
• Core: Cores act as the processor within the processor. Cores are processing units that read and carry out various program instructions. Processors are classified according to how many cores are embedded into them. CPUs with multiple cores can process instructions considerably faster than single-core processors. (Note: The term “Intel® Core™” is used commercially to market Intel’s product line of multi-core CPUs.)
• Threads: Threads are the shortest sequences of programmable instructions that an operating system’s scheduler can independently administer and send to the CPU for processing. Through multithreading—the use of multiple threads running simultaneously—a computer process can be run concurrently. Hyper-threading refers to Intel’s proprietary form of multithreading for the parallelization of computations.

Other components of the CPU

In addition to the above components, modern CPUs typically contain the following:

• Arithmetic logic unit (ALU): Carries out all arithmetic operations and logical operations, including math equations and logic-based comparisons. Both types are tied to specific computer actions.
• Buses: Ensures proper data transfer and data flow between components of a computer system.
• Control unit: Contains intensive circuitry that controls the computer system by issuing a system of electrical pulses and instructs the system to carry out high-level computer instructions.
• Instruction register and pointer: Displays location of the next instruction set to be executed by the CPU.
• Memory unit: Manages memory usage and the flow of data between RAM and the CPU. Also, the memory unit supervises the handling of cache memory.
• Registers: Provides built-in permanent memory for constant, repeated data needs that must be handled regularly and immediately.

How do CPUs work?

CPUs use a type of repeated command cycle that’s administered by the control unit in association with the computer clock, which provides synchronization assistance.

The work a CPU does occurs according to an established cycle (called the CPU instruction cycle). The CPU instruction cycle designates a certain number of repetitions, and this is the number of times the basic computing instructions will be repeated, as enabled by that computer’s processing power.

The three basic computing instructions are as follows:

• Fetch: Fetches occur anytime data is retrieved from memory.
• Decode: The decoder within the CPU translates binary instructions into electrical signals, which engage with other parts of the CPU.
• Execute: Execution occurs when computers interpret and carry out a computer program’s set of instructions.

Basic attempts to generate faster processing speeds have led some computer owners to forego the usual steps involved in creating high-speed performance, which normally require the application of more memory cores. Instead, these users adjust the computer clock so it runs faster on their machine(s). The “overclocking” process is analogous to “jailbreaking” smartphones so their performance can be altered. Unfortunately, like jailbreaking a smartphone, such tinkering is potentially harmful to the device and is roundly disapproved by computer manufacturers.

Types of central processing units

CPUs are defined by the processor or microprocessor driving them:

• Single-core processor: A single-core processor is a microprocessor with one CPU on its die (the silicon-based material to which chips and microchips are attached). Single-core processors typically run slower than multi-core processors, operate on a single thread and perform the instruction cycle sequence only once at a time. They are best suited to general-purpose computing.
• Multi-core processor: A multi-core processor is split into two or more sections of activity, with each core carrying out instructions as if they were completely distinct computers, although the sections are technically located together on a single chip. For many computer programs, a multi-core processor provides superior, high-performance output.
• Embedded processor: An embedded processor is a microprocessor expressly engineered for use in embedded systems. Embedded systems are small and designed to consume less power and be contained within the processor for immediate access to data. Embedded processors include microprocessors and microcontrollers.
• Dual-core processor: A dual-core processor is a multi-core processor containing two microprocessors that act independently from each other.
• Quad-core processor: A quad-core processor is a multi-core processor that has four microprocessors functioning independently.
• Octa-core: An octa-core processor is a multi-core processor that has eight microprocessors functioning independently.
• Deca-core processor: A deca-core processor is an integrated circuit that has 10 cores on one die or per package.

Leading CPU manufacturers and the CPUs they make

Although several companies manufacture products or develop software that supports CPUs, that number has dwindled down to just a few major players in recent years.

The two major companies in this area are Intel and Advanced Micro Devices (AMD). Each uses a different type of instruction set architecture (ISA). Intel processors use a complex instruction set computer (CISC) architecture. AMD processors follow a reduced instruction set computer (RISC) architecture.

• Intel: Intel markets processors and microprocessors through four product lines. Its premium, high-end line is Intel Core. Intel’s Xeon® processors are targeted toward offices and businesses. Intel’s Celeron® and Intel Pentium® lines are considered slower and less powerful than the Core line.
• Advanced Micro Devices (AMD): AMD sells processors and microprocessors through two product types: CPUs and APUs (which stands for accelerated processing units). APUs are CPUs that have been equipped with proprietary Radeon™ graphics. AMD’s Ryzen™ processors are high-speed, high-performance microprocessors intended for the video game market. Athlon™ processors was formerly considered AMD’s high-end line, but AMD now uses it as a basic computing alternative.
• Arm: Although Arm doesn’t actually manufacture equipment, it does lease out its valued, high-end processor designs and/or other proprietary technologies to other companies who do make equipment. Apple, for example, no longer uses Intel chips in Mac® CPUs but makes its own customized processors based on Arm designs. Other companies are following this example.

Related CPU and processor concepts

Graphics processing unit (GPUs)

While the term “graphics processing unit” includes the word “graphics,” this phrasing does not truly capture what GPUs are about, which is speed. In this instance, its increased speed is the cause of accelerating computer graphics.

The GPU is a type of electronic circuit with immediate applications for PCs, smartphones and video game consoles, which was their original use. Now GPUs also serve purposes unrelated to graphics acceleration, like cryptocurrency mining and the training of neural networks.

Microprocessors

The quest for computer miniaturization continued when computer science created a CPU so small that it could be contained within a small integrated circuit chip, called the microprocessor. Microprocessors are designated by the number of cores they support.

A CPU core is “the brain within the brain,” serving as the physical processing unit within a CPU. Microprocessors can contain multiple processors. Meanwhile, a physical core is a CPU built right into a chip, but which only occupies one socket, thus enabling other physical cores to tap into the same computing environment.

Output devices

Computing would be a vastly limited activity without the presence of output devices to execute the CPU’s sets of instruction. Such devices include peripherals, which attach to the outside of a computer and vastly increase its functionality.

Peripherals provide the means for the computer user to interact with the computer and get it to process instructions according to the computer user’s wishes. They include desktop essentials like keyboards, mice, scanners and printers.

Peripherals are not the only attachments common to the modern computer. There are also input/output devices in wide use and they both receive information and transmit information, like video cameras and microphones.

Power consumption

Several issues are impacted by power consumption. One of them is the amount of heat produced by multi-core processors and how to dissipate excess heat from that device so the computer processor remains thermally protected. For this reason, hyperscale data centers (which house and use thousands of servers) are designed with extensive air-conditioning and cooling systems.

There are also questions of sustainability, even if we’re talking about a few computers instead of a few thousand. The more powerful the computer and its CPUs, the more energy will be required to support its operation—and in some macro-sized cases, that can mean gigahertz (GHz) of computing power.

Specialized chips

The most profound development in computing since its origins, artificial intelligence (AI) is now impacting most if not all computing environments. One development we’re seeing in the CPU space is the creation of specialty processors that have been built specifically to handle the large and complex workloads associated with AI (or other specialty purposes):

• Such equipment includes the Tensor Streaming Processor (TSP), which handles machine learning (ML) tasks in addition to AI applications. Other products equally suited to AI work are the AMD Ryzen Threadripper™ 3990X 64-Core processor and the Intel Core i9-13900KS Desktop Processor, which uses 24 cores.
• For an application like video editing, many users opt for the Intel Core i7 14700KF 20-Core, 28-thread CPU. Still others select the Ryzen 9 7900X, which is considered AMD’s best CPU for video editing purposes.
• In terms of video game processors, the AMD Ryzen 7 5800X3D features a 3D V-Cache technology that helps it elevate and accelerate game graphics.
• For general-purpose computing, such as running an OS like Windows or browsing multimedia websites, any recent-model AMD or Intel processor should easily handle routine tasks.

Transistors

Transistors are hugely important to electronics in general and to computing in particular. The term is a mix of “transfer resistance” and typically refers to a component made of semiconductors used to limit and/or control the amount of electrical current flowing through a circuit.

In computing, transistors are just as elemental. The transistor is the basic building unit behind the creation of all microchips. Transistors help comprise the CPU, and they’re what makes the binary language of 0s and 1s that computers use to interpret Boolean logic.

The next wave of CPUs

Computer scientists are always working to increase the output and functionality of CPUs. Here are some projections about future CPUs:

• New chip materials: The silicon chip has long been the mainstay of the computing industry and other electronics. The new wave of processors (link resides outside ibm.com) will take advantage of new chip materials that offer increased performance. These include carbon nanotubes (which display excellent thermal conductivity through carbon-based tubes approximately 100,000 times smaller than the width of a human hair), graphene (a substance that possesses outstanding thermal and electrical properties) and spintronic components (which rely on the study of the way electrons spin, and which could eventually produce a spinning transistor).
• Quantum over binary: Although current CPUs depend on the use of a binary language, quantum computing will eventually change that. Instead of binary language, quantum computing derives its core principles from quantum mechanics, a discipline that has revolutionized the study of physics. In quantum computing, binary digits (1s and 0s) can exist in multiple environments (instead of in two environments currently). And because this data will live in more than one location, fetches will become easier and faster. The upshot of this for the user will be a marked increase in computing speed and an overall boost in processing power.
• AI everywhere: As artificial intelligence continues to make its profound presence felt—both in the computing industry and in our daily lives—it will have a direct influence on CPU design. As the future unfolds, expect to see an increasing integration of AI functionality directly into computer hardware. When this happens, we’ll experience AI processing that’s significantly more efficient. Further, users will notice an increase in processing speed and devices that will be able to make decisions independently in real time. While we wait for that hardware implementation to occur, chip manufacturer Cerebras has already unveiled a processor its makers claim to be the “fastest AI chip in the world” (link resides outside ibm.com). Its WSE-3 chip can train AI models with as many as 24 trillion parameters. This mega-chip contains four trillion transistors, in addition to 900,000 cores.

CPUs that offer strength and flexibility

Companies expect a lot from the computers they invest in. In turn, those computers rely upon having a CPUs with enough processing power to handle the challenging workloads found in today’s data-intensive business environment.

Organizations need workable solutions that can change as they change. Smart computing depends upon having equipment that capably supports your mission, even as that work evolves. IBM servers offer strength and flexibility, so you can concentrate on the job at hand. Find the IBM servers you need to get the results your organization relies upon—both today and tomorrow.

Source: ibm.com

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T-Mobile unlocks marketing efficiency with Adobe Workfront

With 109 million customers and counting, “uncarrier” T-Mobile is one of the top mobile communications providers in the U.S. The company always puts the customer first, which it achieves by delivering the right experiences and content to the right customers at the right time. But with different sub-brands and business units, T-Mobile’s marketing and content workflows were complex—and often inefficient and disconnected.

Executive visibility is key for T-Mobile

To ensure the best customer experience, T-Mobile’s C-suite participates in all overarching marketing strategy and marketing campaign decisions. However, when critical decisions were pending, manual workflows and disjointed tools made it nearly impossible for senior leadership to see everything in one system or retrieve information efficiently.

The marketing operations team knew they needed to create a more seamless work management system to support its content supply chain.

“We realized leadership didn’t have the right information at their fingertips to make decisions in the moment. We knew we needed to pull together a leadership dashboard to show all of the given campaigns in real-time.” Ilona Yeremova, Head of Marketing Tools, Operations and Analytics Team, T-Mobile

Like many other large companies with complex marketing organizations, T-Mobile turned to Adobe Workfront to streamline its content supply chain, help connect teams, plan and prioritize content creation, ensure compliance, and manage assets and customer data.

Scaling Adobe Workfront activation throughout the organization

T-Mobile started implementing Adobe Workfront on the creative side of the house. One of its 25 groups, T-Studios, was using Workfront, but it was siloed from the other 24 groups. “We quickly realized that work management has to happen centrally within the organization. Data has to connect, people need to connect and collaborate, and we need to start talking in the same language,” Yeremova said.

T-Mobile did an inventory of the things they really wanted to accomplish with customer-focused content marketing efforts, and evaluated how they could orchestrate a seamless customer journey across the platform in a way that would aid in that delivery. They started with the customer in mind and then walked it back to the technology, applications, and processes. The key questions they asked themselves were:

• What are those journeys we’re trying to orchestrate?
• How are we trying to talk to those customers?
• What are the trigger points?
• How does that all come to life in a connected way in Adobe Workfront?

When T-Mobile first started using Adobe Workfront five years ago, it was a basic project management system for 60 employees. Today, it is regarded internally as a transformational business technology used by 6000+ employees as part of a content marketing strategy to achieve business objectives. Overall, T-Mobile has realized a 47% increase in its productivity on the marketing side since optimizing Adobe Workfront, without adding any additional headcount.

IBM helps unlock more value from Adobe Workfront

Once T-Mobile achieved a more mature state with Adobe Workfront, they wanted to better understand the ROI realized with Adobe Workfront and how to connect with other platforms. That’s when T-Mobile turned to IBM.

“IBM, a primary partner for content supply chain strategy and enablement, helped augment the T-Mobile team in a very seamless way,” said Yeremova. “[They are helping us] accelerate the onboarding of teams and connecting platforms.”

IBM drove change management for several departments, including the Technology Customer Experience (TCX) and Career Development teams, two of the largest groups at T-Mobile, both of whom were previously operating in Smartsheets.

“[We brought in IBM as an] outside party for change management because internally there’s just so much passion and inertia and you’ve got to take the passion out of it,” Yeremova said.

In addition to change management, IBM conducted a Value Realization assessment of Workfront for the two groups and found that the career development team realized a 90% decrease in time spent manually setting up and managing projects and 93% decrease in time creating or managing reports. The TCX team saved 11 hours a week by eliminating unnecessary meetings and improving automated workflows. T-Mobile now has all 25 marketing groups operating in Workfront, an effort for which IBM has onboarded and assisted with configurations. 

Yeremova says, “It’s all iterative. Tomorrow is going to be different than today. T-Mobile now has a fairly robust environment that is Adobe-centric, and everything is integrated within the platform.”

Looking forward to an AI-powered future

T-Mobile strongly believes that creating the right guardrails and building a strong foundation with Adobe Workfront has helped them prepare for the innovation that is happening today, as well as the AI-powered future of tomorrow.

“We are very diligent about governing the platform we have. And it’s critical for us to have clean data in the system.” Ilona Yeremova, Head of Marketing Tools, Operations and Analytics Team, T-Mobile.

As her team ingests data, they are constantly studying it and verifying it – because if your data is stale, nothing else will be accurate.

T-Mobile is currently focused onunifying taxonomies across the enterprise. Yeremova says, “The team did a lot of work and [the creative taxonomies] are like an A plus now – but next up, we’re focused on unifying taxonomies in the whole marketing organization and then even looking upwards to the enterprise.”

The combination of a mature work management strategy and a focus on change management, governance, and clean data sets T-Mobile up nicely to supercharge Workfront with new features and generative AI capabilities. “If you’re not onboard with AI, you’ll be left behind,” Yeremova says. IBM is currently helping T-Mobile evaluate different use cases where they can leverage generative AI, like enabling sales reps to make recommendations more quickly to customers in its more than 6,000+ retail stores.

“We’re going to be much quicker at doing things and it’s exciting to envision that future where folks are happy doing more of the purposeful work and less of the manual busy work,” Yeremova said. “I watch how much administrative stuff that my team does, and I know that there’s a better way to do it. If we can have GenAI technologies like IBM® watsonx™ do some of those repetitive, mundane tasks for us, I bet we’ll incrementally gain that benefit of more meaningful work. My team is small but mighty and we are incredibly lucky to have partnership from our Adobe and IBM teams.”

Source: ibm.com

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5 SLA metrics you should be monitoring

In business and beyond, communication is king. Successful service level agreements (SLAs) operate on this principle, laying the foundation for successful provider-customer relationships.

A service level agreement (SLA) is a key component of technology vendor contracts that describes the terms of service between a service provider and a customer. SLAs describe the level of performance to be expected, how performance will be measured and repercussions if levels are not met. SLAs make sure that all stakeholders understand the service agreement and help forge a more seamless working relationship.

Types of SLAs

There are three main types of SLAs:

Customer-level SLAs

Customer-level SLAs define the terms of service between a service provider and a customer. A customer can be external, such as a business purchasing cloud storage from a vendor, or internal, as is the case with an SLA between business and IT teams regarding the development of a product.

Service-level SLAs

Service providers who offer the same service to multiple customers often use service-level SLAs. Service-level SLAs do not change based on the customer, instead outlining a general level of service provided to all customers.

Multilevel SLAs

When a service provider offers a multitiered pricing plan for the same product, they often offer multilevel SLAs to clearly communicate the service offered each level. Multilevel SLAs are also used when creating agreements between more than two more parties.

SLA components

SLAs include an overview of the parties involved, services to be provided, stakeholder role breakdowns, performance monitoring and reporting requirements. Other SLA components include security protocols, redressing agreements, review procedures, termination clauses and more. Crucially, they define how performance will be measured.

SLAs should precisely define the key metrics—service-level agreement metrics—that will be used to measure service performance. These metrics are often related to organizational service level objectives (SLOs). While SLAs define the agreement between organization and customer, SLOs set internal performance targets. Fulfilling SLAs requires monitoring important metrics related to business operations and service provider performance. The key is monitoring the right metrics.

What is a KPI in an SLA?

Metrics are specific measures of an aspect of service performance, such as availability or latency. Key performance indicators (KPIs) are linked to business goals and are used to judge a team’s progress toward those goals. KPIs don’t exist without business targets; they are “indicators” of progress toward a stated goal.

Let’s use annual sales growth as an example, with an organizational goal of 30% growth year-over-year. KPIs such as subscription renewals to date or leads generated provide a real-time snapshot of business progress toward the annual sales growth goal.

Metrics such as application availability and latency help provide context. For example, if the organization is losing customers and not on track to meet the annual goal, an examination of metrics related to customer satisfaction (that is, application availability and latency) might provide some answers as to why customers are leaving.

What SLA metrics to monitor

SLAs contain different terms depending on the vendor, type of service provided, client requirements, compliance standards and more and metrics vary by industry and use case. However, certain SLA performance metrics such as availability, mean time to recovery, response time, error rates and security and compliance measurements are commonly used across services and industries. These metrics set a baseline for operations and the quality of services provided.

Clearly defining which metrics and key performance indicators (KPIs) will be used to measure performance and how this information will be communicated helps IT service management (ITSM) teams identify what data to collect and monitor. With the right data, teams can better maintain SLAs and make sure that customers know exactly what to expect.

Ideally, ITSM teams provide input when SLAs are drafted, in addition to monitoring the metrics related to their fulfillment. Involving ITSM teams early in the process helps make sure that business teams don’t make agreements with customers that are not attainable by IT teams.

SLA metrics that are important for IT and ITSM leaders to monitor include:

1. Availability

Service disruptions, or downtime, are costly, can damage enterprise credibility and can lead to compliance issues. The SLA between an organization and a customer dictates the expected level of service availability or uptime and is an indicator of system functionality.

Availability is often measured in “nines on the way to 100%”: 90%, 99%, 99.9% and so on. Many cloud and SaaS providers aim for an industry standard of “five 9s” or 99.999% uptime.

For certain businesses, even an hour of downtime can mean significant losses. If an e-commerce website experiences an outage during a high traffic time such as Black Friday, or during a large sale, it can damage the company’s reputation and annual revenue. Service disruptions also negatively impact the customer experience. Services that are not consistently available often lead users to search for alternatives. Business needs vary, but the need to provide users with quick and efficient products and services is universal.

Generally, maximum uptime is preferred. However, providers in some industries might find it more cost effective to offer a slightly lower availability rate if it still meets client needs.

2. Mean time to recovery

Mean time to recovery measures the average amount of time that it takes to recover a product during an outage or failure. No system or service is immune from an occasional issue or failure, but enterprises that can quickly recover are more likely to maintain business profitability, meet customer needs and uphold SLAs.

3. Response time and resolution time

SLAs often state the amount of time in which a service provider must respond after an issue is flagged or logged. When an issue is logged or a service request is made, the response time indicates how long it takes for a provider to respond to and address the issue. Resolution time refers to how long it takes for the issue to be resolved. Minimizing these times is key to maintaining service performance.

Organizations should seek to address issues before they become system-wide failures and cause security or compliance issues. Software solutions that offer full-stack observability into business functions can play an important role in maintaining optimized systems and service performance. Many of these platforms use automation and machine learning (ML) tools to automate the process of remediation or identify issues before they arise.

For example, AI-powered intrusion detection systems (IDS) constantly monitor network traffic for malicious activity, violations of security protocols or anomalous data. These systems deploy machine learning algorithms to monitor large data sets and use them to identify anomalous data. Anomalies and intrusions trigger alerts that notify IT teams. Without AI and machine learning, manually monitoring these large data sets would not be possible.

4. Error rates

Error rates measure service failures and the number of times service performance dips below defined standards. Depending on your enterprise, error rates can relate to any number of issues connected to business functions.

For example, in manufacturing, error rates correlate to the number of defects or quality issues on a specific product line, or the total number of errors found during a set time interval. These error rates, or defect rates, help organizations identify the root cause of an error and whether it’s related to the materials used or a broader issue.

There is a subset of customer-based metrics that monitor customer service interactions, which also relate to error rates.

◉ First call resolution rate: In the realm of customer service, issues related to help desk interactions can factor into error rates. The success of customer services interactions can be difficult to gauge. Not every customer fills out a survey or files a complaint if an issue is not resolved—some will just look for another service. One metric that can help measure customer service interactions is the first call resolution rate. This rate reflects whether a user’s issue was resolved during the first interaction with a help desk, chatbot or representative. Every escalation of a customer service query beyond the initial contact means spending on extra resources. It can also impact the customer experience.

◉ Abandonment rate: This rate reflects the frequency in which a customer abandons their inquiry before finding a resolution. Abandonment rate can also add to the overall error rate and helps measure the efficacy of a service desk, chatbot or human workforce.

5. Security and compliance

Large volumes of data and the use of on-premises servers, cloud servers and a growing number of applications creates a greater risk of data breaches and security threats. If not monitored appropriately, security breaches and vulnerabilities can expose service providers to legal and financial repercussions.

For example, the healthcare industry has specific requirements around how to store, transfer and dispose of a patient’s medical data. Failure to meet these compliance standards can result in fines and indemnification for losses incurred by customers.

While there are countless industry-specific metrics defined by the different services provided, many of them fall under larger umbrella categories. To be successful, it is important for business teams and IT service management teams to work together to improve service delivery and meet customer expectations.

Benefits of monitoring SLA metrics

Monitoring SLA metrics is the most efficient way for enterprises to gauge whether IT services are meeting customer expectations and to pinpoint areas for improvement. By monitoring metrics and KPIs in real time, IT teams can identify system weaknesses and optimize service delivery.

The main benefits of monitoring SLA metrics include:

Greater observability

A clear end-to-end understanding of business operations helps ITSM teams find ways to improve performance. Greater observability enables organizations to gain insights into the operation of systems and workflows, identify errors, balance workloads more efficiently and improve performance standards.

Optimized performance

By monitoring the right metrics and using the insights gleaned from them, organizations can provide better services and applications, exceed customer expectations and drive business growth.

Increased customer satisfaction

Similarly, monitoring SLA metrics and KPIs is one of the best ways to make sure services are meeting customer needs. In a crowded business field, customer satisfaction is a key factor in driving customer retention and building a positive reputation.

Greater transparency

By clearly outlining the terms of service, SLAs help eliminate confusion and protect all parties. Well-crafted SLAs make it clear what all stakeholders can expect, offer a well-defined timeline of when services will be provided and which stakeholders are responsible for specific actions. When done right, SLAs help set the tone for a smooth partnership.

Understand performance and exceed customer expectations

The IBM® Instana® Observability platform and IBM Cloud Pak® for AIOps can help teams get stronger insights from their data and improve service delivery.

IBM® Instana® Observability offers full-stack observability in real time, combining automation, context and intelligent action into one platform. Instana helps break down operational silos and provides access to data across DevOps, SRE, platform engineering and ITOps teams.

IT service management teams benefit from IBM Cloud Pak for AIOps through automated tools that address incident management and remediation. IBM Cloud Pak for AIOps offers tools for innovation and the transformation if IT operations. Meet SLAs and monitor metrics with an advanced visibility solution that offers context into dependencies across environments.

IBM Cloud Pak for AIOps is an AIOps platform that delivers visibility into performance data and dependencies across environments. It enables ITOps managers and site reliability engineers (SREs) to use artificial intelligence, machine learning and automation to better address incident management and remediation. With IBM Cloud Pak for AIOps, teams can innovate faster, reduce operational cost and transform IT operations (ITOps).

Source: ibm.com

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Mastering budget control in the age of AI: Leveraging on-premises and cloud XaaS for success

As organizations strive to harness the power of AI while controlling costs, leveraging anything as a service (XaaS) models emerges as a strategic approach. In this blog, we’ll explore how businesses can use both on-premises and cloud XaaS to control budgets in the age of AI, driving financial sustainability without compromising on technological advancement.

Embracing the power of XaaS

XaaS encompasses a broad spectrum of cloud-based and on-premises service models that offer scalable and cost-effective solutions to businesses. From software as a service (SaaS) to infrastructure as a service (IaaS), platform as a service (PaaS) and beyond, XaaS enables organizations to access cutting-edge technologies and capabilities without the need for upfront investment in hardware or software.

Harnessing flexibility and scalability 

One of the key advantages of XaaS models is their inherent flexibility and scalability, whether deployed on premises or in the cloud. Cloud-based XaaS offerings provide organizations with the agility to scale resources up or down based on demand, enabling optimal resource utilization and cost efficiency. Similarly, on-premises XaaS solutions offer the flexibility to scale resources within the organization’s own infrastructure, providing greater control over data and security.

Maintaining cost predictability and transparency 

Controlling budgets in the age of AI requires a deep understanding of cost drivers and expenditure patterns. XaaS models offer organizations greater predictability and transparency in cost management by providing detailed billing metrics and usage analytics. With granular insights into resource consumption, businesses can identify opportunities for optimization and allocate budgets more effectively.

Outsourcing infrastructure management 

Maintaining and managing on-premises infrastructure for AI workloads can be resource-intensive and costly. By leveraging both cloud-based and on-premises XaaS offerings, organizations can offload the burden of infrastructure management to service providers. Cloud-based XaaS solutions provide scalability, flexibility and access to a wide range of AI tools and services, while on-premises XaaS offerings enable greater control over data governance, compliance and security.

Accessing specialized expertise 

Implementing AI initiatives often requires specialized skills and expertise in areas such as data science, machine learning and AI development. XaaS models provide organizations with access to a vast ecosystem of skilled professionals and service providers who can assist in the design, development and deployment of AI solutions. This access to specialized expertise enables businesses to accelerate time-to-market and achieve better outcomes while controlling costs.

Facilitating rapid experimentation and innovation 

In the age of AI, rapid experimentation and innovation are essential for staying ahead of the competition. XaaS models facilitate experimentation by providing businesses with access to a wide range of AI tools, platforms and services on demand. This enables organizations to iterate quickly, test hypotheses and refine AI solutions without the need for significant upfront investment. Embracing a culture of experimentation helps businesses drive innovation while minimizing financial risk.

Managing budgets effectively 

As organizations navigate the complexities of AI adoption and strive to control budgets, leveraging both on-premises and cloud XaaS models emerges as a strategic imperative. By embracing the flexibility, scalability, cost predictability and access to expertise provided by XaaS offerings, businesses can optimize costs, drive innovation and achieve sustainable growth. Whether deployed on premises or in the cloud, XaaS serves as a catalyst for success, empowering organizations to unlock the full potential of AI while maintaining financial resilience in an ever-evolving business landscape. 

IBM solutions 

Master your AI budget with IBM Storage as a Service and Flexible Capacity on Demand for IBM® Power®. Whether on premises in your data center or in the IBM Cloud®, you can provision, budget and get the same customer experience from these IBM offerings.

Source: ibm.com

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Prioritizing operational resiliency to reduce downtime in payments

The average lost business cost following a data breach was USD 1.3 million in 2023, according to IBM’s Cost of a Data Breach report. With the rapid emergence of real-time payments, any downtime in payments connectivity can be a significant threat. This downtime can harm a business’s reputation, as well as the global financial ecosystem.

For this reason, it’s paramount that financial enterprises support their resiliency needs by adopting a robust infrastructure that is integrated across multiple environments, including the cloud, on prem and at the edge.

Resiliency helps financial institutions build customer and regulator confidence

Retaining customers is crucial to any business strategy, and maintaining customer trust is key to a financial institution’s success. We believe enterprises that prioritize resilience demonstrate their commitment to providing their consumers with a seamless experience in the event of disruption.

In addition to maintaining customer trust, financial enterprises must maintain regulator trust as well. Regulations around the world, such as the Digital Operational Resilience Act (DORA), continue to grow. DORA is a European Union regulation that aims to establish technical standards that financial entities and their critical third-party technology service providers must implement in their ICT systems by 17 January 2025.

DORA requires financial institutions to define the business recovery process, service levels and recovery times that are acceptable for their business across processes, including payments. Traditionally, this has caused covered institutions to evaluate their cybersecurity protection measures.

To meet customer and regulator demands, it is critical that financial institutions are proactive and strategic about creating a cohesive strategy to modernize their payments infrastructure with resiliency and compliance at the forefront.

How IBM helps clients address resiliency in payments

As the need for operational resilience grows, enterprises increasingly adopt hybrid cloud strategies to store their data across multiple environments including the cloud, on prem and at the edge. By developing a workload placement strategy based on the uniqueness of a financial entity’s business processes and applications, they can optimize the output of these applications to enable the continuation of services 24/7.

IBM Cloud® remains committed to providing our clients with an enterprise-grade cloud platform that can help them address resiliency, performance, security and compliance obligations. IBM Cloud also supports mission-critical workloads and addresses evolving regulations around the globe.

To accelerate cloud adoption in financial services, we built IBM Cloud for Financial Services®, informed by the industry and for the industry. With security controls built into the platform, we aim to help financial entities minimize risk as they maintain and demonstrate their compliance with their regulators.

With approximately 500 industry practitioners across the globe, the expertise of the IBM Payments Center® provides clients with guidance on their end-to-end payments’ modernization journey. Also, clients can use payments as a service, including checks as a service, which can help give them access to the benefits of a managed, secured cloud-based platform that can scale up and down to meet changing electronic payment and check volumes.

IBM’s swift connectivity capabilities on IBM Cloud for Financial Services enable resiliency and use IBM Cloud multizone regions to help keep data secured and enable business continuity in case of advanced ransomware or cyberattacks.

IBM® can help you navigate the highly interconnected payments ecosystem and build resiliency. Partner with us to reduce downtime, protect your reputation and maintain the trust of your customers and regulators.

Source: ibm.com

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Agility, flexibility and security: The value of cloud in HPC

In today’s competitive business environment, firms are confronted with complex, computational issues that demand swift resolution. Such problems might be too intricate for a single system to handle or might require an extended time to resolve. For companies that need quick answers, every minute counts. Allowing problems to linger for weeks or months is not feasible for businesses determined to stay ahead of the competition. To address these challenges, enterprises across various industries, such as those in the semiconductor, life sciences, healthcare, financial services and more, have embraced high-performance computing (HPC).

With HPC, enterprises are taking advantage of the speed and performance that comes with powerful computers working together. This can be especially helpful amid a steadily growing push to build AI on a larger and larger scale. While analyzing massive amounts of data might feel impossible, HPC enables the use of high-end computational resources that can perform many computations rapidly and in parallel to help businesses get insights faster. At the same time, HPC is used to help businesses bring new products to market. It is also used to better manage risks and more, which is why an increasing number of enterprises are adopting it.

The role of cloud in HPC

Most commonly, enterprises that run workloads with surges in activity are finding that they exceed the compute capacity available on-premises. This is an example of where cloud computing can augment on-premises HPC to transform the business’s approach to HPC with cloud resources. Cloud can help address peaks in demand during product development cycles, which might last from a short duration to a longer duration, and enable organizations to get access to the resources and capabilities that they might not have a need for around the clock. Businesses using HPC from the cloud can take advantage of the benefits of greater flexibility, enhanced scalability, better agility, improved cost efficiencies and more.

Cadence uses IBM Cloud HPC

Cadence is a global innovator in electronic design automation (EDA) with over 30 years of computational software experience. It has helped companies across the world design electronic products that drive today’s emerging technology, including chips. The growing demand for more chips, along with the company’s incorporation of AI and machine learning into its EDA processes means that their need for compute power is at an all-time high. For organizations in the EDA industry like Cadence, solutions that enable workloads to seamlessly shift between on premises and the cloud, while also allowing for differentiation from project to project, are key.

Cadence uses IBM Cloud® HPC with IBM Spectrum® LSF as the workload scheduler to support the development of chip and system design software, which requires innovative solutions, powerful compute resources and advanced security support. By using IBM Cloud HPC, Cadence reports improved time-to-solution, performance enhancements, cost reductions and streamlined workload management.

Additionally, Cadence understands firsthand that moving to the cloud can require new knowledge and capabilities that not every company possesses. The Cadence Cloud comprehensive portfolio aims to help customers across the world use the possibilities of the cloud with Cadence Managed Cloud Service as a turnkey solution ideal for start-ups and small and medium customers, and with the customer-managed cloud option known as Cloud Passport to enable Cadence tools for large enterprise customers. Cadence is dedicated to giving its customers an easy path to the cloud by connecting them with knowledgeable service providers, such as IBM®, whose platforms can be used to deploy Cadence tools in cloud environments. For enterprises that want to drive innovation at scale, the Cadence Cloud Passport model can deliver access to cloud-ready software tools for use on IBM Cloud.

Taking a hybrid cloud approach to HPC

Traditionally, HPC systems were built on-premises. However, the large models and large workloads that exist today are often not compatible with the hardware that most companies have on premises. Given the high up-front costs of obtaining GPUs, CPUs and networking, as well as those of building the data center infrastructures needed to efficiently run compute at scale, many companies have used cloud infrastructure providers that have already made massive investments in their hardware. To realize the full value of public cloud and on-premises infrastructures, many organizations are adopting a hybrid cloud architecture that is focused on the mechanics of transforming portions of a company’s on-premises data center into private cloud infrastructure.

By adopting a hybrid cloud approach to HPC where cloud and on premises are used together, organizations can use the strengths of both, allowing organizations to achieve the agility, flexibility and security required to meet their demands. For example, IBM Cloud® HPC can help organizations flexibly manage compute-intensive workloads on-premises. With security and controls built into the platform, IBM Cloud HPC also allows organizations to consume HPC as a fully managed service while helping them address third- and fourth-party risks.

Looking ahead

By using hybrid cloud services through platforms like IBM Cloud HPC, enterprises can solve many of their most difficult challenges. As organizations continue to embrace HPC, they should consider how a hybrid cloud approach can complement traditional on-premises HPC infrastructure deployments.

Source: ibm.com

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Streamlining digital commerce: Integrating IBM API Connect with ONDC

In the dynamic landscape of digital commerce, seamless integration and efficient communication drive the success of buyers, sellers and logistics providers. The Open Network for Digital Commerce (ONDC) platform stands as a revolutionary initiative to streamline the digital commerce ecosystem in India. When coupled with the robust capabilities of IBM API Connect, this integration presents a game-changing opportunity for buyers, sellers and logistics partners to thrive in the digital marketplace. Let’s delve into its benefits and potential impact on business.

Introduction to ONDC and IBM API Connect 

The ONDC platform, envisioned by the Government of India, aims to create an inclusive and interoperable digital commerce ecosystem. It facilitates seamless integration among various stakeholders—including buyers, sellers, logistics providers and financial institutions —fostering transparency, efficiency and accessibility in digital commerce. 

IBM API Connect is a comprehensive API management solution that enables organizations to create, secure, manage and analyze APIs throughout their lifecycle. It provides capabilities for designing, deploying and consuming APIs, thereby putting up secure and efficient communication between different applications and systems. 

Benefits for buyers and sellers apps 

1. Enhanced integration: Integration with IBM API Connect allows buyers and sellers apps to seamlessly connect with the ONDC platform, enabling real-time data exchange and transaction processing. This makes for smoother operations and improved user experience for buyers and sellers alike. 

2. Expanded services: Buyers and sellers apps can leverage the ONDC platform’s wide range of services, including inventory management, order processing and payment solutions. Integration with IBM API Connect enables easy access to these services, empowering apps to offer comprehensive solutions to their users. 

3. Improved efficiency: By automating processes and streamlining communication, the integration enhances the overall efficiency of buyers and sellers apps. Tasks such as inventory updates, order tracking and payment reconciliation can be performed seamlessly, reducing manual effort and minimizing errors. 

4. Better data insights: IBM API Connect provides advanced analytics capabilities that enable buyers and sellers apps to gain valuable insights into customer behavior, market trends and inventory management. By leveraging these insights, apps can optimize their operations, personalize user experiences and drive business growth.

Impact on business and logistics 

1. Operational efficiency: The integration of IBM API Connect with the ONDC platform streamlines operations for buyers, sellers and logistics partners, reducing costs and improving productivity. Automated processes and real-time data exchange enable faster order fulfilment and smoother logistics operations. 

2. Customer experience boost: Seamless communication between buyers and sellers apps and the ONDC platform translates into a better customer experience. From faster order processing to accurate inventory information, customers benefit from a more efficient and transparent shopping experience. 

3. Business growth: By leveraging the integrated capabilities of IBM API Connect and the ONDC platform, buyers and sellers apps can expand their reach, attract more customers and increase sales. The ability to offer a seamless and comprehensive shopping experience gives apps a competitive edge in the market. 

4. Logistics optimization: Logistics providers can also benefit from this integration by gaining access to real-time shipment data, optimizing delivery routes and improving inventory management. This leads to faster delivery times, reduced transportation costs and enhanced customer satisfaction. 

Enhanced integration with IBM API Connect

The integration of IBM API Connect with the ONDC network platform represents a significant advancement in the digital commerce ecosystem. Buyers, sellers and logistics partners stand to benefit from enhanced integration, expanded services, improved efficiency and valuable data insights.

As businesses embrace this integration, they can expect to see tangible impacts on operational efficiency, customer experience and overall business growth. By leveraging the combined capabilities of IBM API Connect and the ONDC platform, stakeholders can navigate the complexities of digital commerce with confidence and unlock new opportunities for success. 

Source: ibm.com

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How an AI Gateway provides leaders with greater control and visibility into AI services

Generative AI is a transformative technology that many organizations are experimenting with or already using in production to unlock rapid innovation and drive massive productivity gains. However, we have seen that this breakneck pace of adoption has left business leaders wanting more visibility and control around the enterprise usage of GenAI.

When I talk with clients about their organization’s use of GenAI, I ask them these questions:

• Do you have visibility into which third-party AI services are being used across your company and for what purposes?
• How much is your company cumulatively paying for LLM subscriptions, including signups by teams and individuals, and are those costs predictable and controllable?
• Are you able to address common vulnerabilities when invoking LLMs, such as the leakage of sensitive data, unauthorized user access and policy violations?

These questions can all be answered if you have an AI Gateway.

What is an AI Gateway? 

An AI gateway provides a single point of control for organizations to access AI services via APIs in the public domain and brokers secured connectivity between your different applications and third-party AI APIs both within and outside an organization’s infrastructure. It acts as the gatekeeper for data and instructions that flow between those components. An AI Gateway provides policies to centrally manage and control the use of AI APIs with your applications, as well as key analytics and insights to help you make decisions faster on LLM choices. 

Announcing AI Gateway for IBM API Connect 

Today IBM is announcing the launch of AI Gateway for IBM API Connect, a feature of our market-leading and award-winning API management platform. This new AI gateway feature, generally available by the end of June, will empower customers to accelerate their AI journey. When this feature launches in June, you will be able to get started with centrally managing watsonx.ai APIs, with the ability to manage additional LLM APIs planned for later this year. 

Key benefits of AI Gateway for IBM API Connect 

1. Faster and more responsible adoption of GenAI: Centralized, controllable self-service access to enterprise AI APIs for developers. 

2. Insights and cost management: Address unexpected or excessive costs for AI services through limiting the rate of requests within a certain duration and by caching AI responses, use built-in analytics and dashboards to get visibility into enterprise-wide use of AI APIs. 

3. Governance and compliance: By funneling LLM API traffic through the AI Gateway, you can centrally manage the use of AI services through policy enforcement, data encryption, masking of sensitive data, access control, audit trails and more, in support of your compliance obligations. 

Take the next step 

Learn how to complement watsonx.ai and watsonx.gov with AI Gateway for IBM API Connect by visiting our webpage or requesting a live demo to see it in action.

Source: ibm.com

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