Speed, scale and trustworthy AI on IBM Z with Machine Learning for IBM z/OS v3.2

Recent years have seen a remarkable surge in AI adoption, with businesses doubling down. According to the IBM® Global AI Adoption Index, about 42% of enterprise-scale companies surveyed (> 1,000 employees) report having actively deployed AI in their business. 59% of those companies surveyed that are already exploring or deploying AI say they have accelerated their rollout or investments in the technology. Yet, amidst this surge, navigating the complexities of AI implementation, scalability issues and validating the trustworthiness of AI continue to be significant challenges that companies still face.   

A robust and scalable environment is crucial to accelerating client adoption of AI. It must be capable of converting ambitious AI use cases into reality while enabling real-time AI insights to be generated with trust and transparency.  

What is Machine Learning for IBM z/OS? 

Machine Learning for IBM® z/OS® is an AI platform tailor-made for IBM z/OS environments. It combines data and transaction gravity with AI infusion for accelerated insights at scale with trust and transparency. It helps clients manage their full AI model lifecycles, enabling quick deployment co-located with their mission-critical applications on IBM Z without data movement and minimal application changes. Features include explainability, drift detection, train-anywhere capabilities and developer-friendly APIs. 

Machine Learning for IBM z/OS use cases

Machine Learning for IBM z/OS can serve various transactional use cases on IBM z/OS. Top use cases include:

1. Real-time fraud detection in credit cards and payments: Large financial institutions are increasingly experiencing more losses due to fraud. With off-platform solutions, they were only able to screen a small subset of their transactions. In support of this use case, the IBM z16™ system can process up to 228 thousand z/OS CICS credit card transactions per second with 6 ms response time, each with an in-transaction fraud detection inference operation using a Deep Learning Model.

Performance result is extrapolated from IBM internal tests running a CICS credit card transaction workload with inference operations on IBM z16. A z/OS V2R4 logical partition (LPAR) configured with 6 CPs and 256 GB of memory was used. Inferencing was done with Machine Learning for IBM z/OS running on Websphere Application Server Liberty 21.0.0.12, using a synthetic credit card fraud detection model and the IBM Integrated Accelerator for AI. Server-side batching was enabled on Machine Learning for IBM z/OS with a size of 8 inference operations. The benchmark was run with 48 threads performing inference operations. Results represent a fully configured IBM z16 with 200 CPs and 40 TB storage. Results might vary. 

2. Clearing and settlement: A card processor explored using AI to assist in determining which trades and transactions have a high-risk exposure before settlement to reduce liability, chargebacks and costly investigation. In support of this use case, IBM has validated that the IBM z16 with Machine Learning for IBM z/OS is designed to score business transactions at scale delivering the capacity to process up to 300 billion deep inferencing requests per day with 1 ms of latency.

Performance result is extrapolated from IBM internal tests running local inference operations in an IBM z16 LPAR with 48 IFLs and 128 GB memory on Ubuntu 20.04 (SMT mode) using a synthetic credit card fraud detection model exploiting the Integrated Accelerator for AI. The benchmark was running with 8 parallel threads, each pinned to the first core of a different chip. The lscpu command was used to identify the core-chip topology. A batch size of 128 inference operations was used. Results were also reproduced using a z/OS V2R4 LPAR with 24 CPs and 256 GB memory on IBM z16. The same credit card fraud detection model was used. The benchmark was run with a single thread performing inference operations. A batch size of 128 inference operations was used. Results might vary. 

 

3. Anti-money laundering: A bank was exploring how to introduce AML screening into their instant payments operational flow. Their current end-day AML screening was no longer sufficient due to stricter regulations. In support of this use case, IBM has demonstrated that the IBM z16 with z/OS delivers up to 20x lower response time and up to 19x higher throughput when colocating applications and inferencing requests versus sending the same inferencing requests to a compared x86 server in the same data center with 60 ms average network latency.

Performance results based on IBM internal tests using a CICS OLTP credit card workload with in-transaction fraud detection. A synthetic credit card fraud detection model was used. On IBM z16, inferencing was done with MLz on zCX. Tensorflow Serving was used on the compared x86 server. A Linux on IBM Z LPAR, located on the same IBM z16, was used to bridge the network connection between the measured z/OS LPAR and the x86 server. Additional network latency was introduced with the Linux “tc-netem” command to simulate a network environment with 5 ms average latency. Measured improvements are due to network latency. Results might vary. IBM z16 configuration: Measurements were run using a z/OS (v2R4) LPAR with MLz (OSCE) and zCX with APAR- oa61559 and APAR- OA62310 applied, 8 CPs, 16 zIIPs and 8 GB of memory. x86 configuration: Tensorflow Serving 2.4 ran on Ubuntu 20.04.3 LTS on 8 Skylake Intel® Xeon® Gold CPUs @ 2.30 GHz with Hyperthreading turned on, 1.5 TB memory, RAID5 local SSD Storage.  

Machine Learning for IBM z/OS with IBM Z can also be used as a security-focused on-prem AI platform for other use cases where clients want to promote data integrity, privacy and application availability. The IBM z16 systems, with GDPS®, IBM DS8000® series storage with HyperSwap® and running a Red Hat® OpenShift® Container Platform environment, are designed to deliver 99.99999% availability.

Necessary components include IBM z16; IBM z/VM V7.2 systems or above collected in a Single System Image, each running RHOCP 4.10 or above; IBM Operations Manager; GDPS 4.5 for management of data recovery and virtual machine recovery across metro distance systems and storage, including Metro Multisite workload and GDPS Global; and IBM DS8000 series storage with IBM HyperSwap. A MongoDB v4.2 workload was used. Necessary resiliency technology must be enabled, including z/VM Single System Image clustering, GDPS xDR Proxy for z/VM and Red Hat OpenShift Data Foundation (ODF) 4.10 for management of local storage devices. Application-induced outages are not included in the preceding measurements. Results might vary. Other configurations (hardware or software) might provide different availability characteristics. 

Source: ibm.com

Continue reading

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

Continue reading

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

Continue reading

Why CHROs are the key to unlocking the potential of AI for the workforce

It’s no longer a question of whether AI will transform business and the workforce, but how it will happen. A study by the IBM Institute for Business Value revealed that up to three-quarters of CEOs believe that competitive advantage will depend on who has the most advanced generative AI.

With so many leaders now embracing the technology for business transformation, some wonder which C-Suite leader will be in the driver’s seat to orchestrate and accelerate that change.

CHROs today are perfectly positioned to take the lead on both people skills and AI skills, ushering the workforce into the future. Here’s how top CHROs are already seizing the opportunity. 

Orchestrating the new human + AI workforce 

Today, businesses are no longer only focused on finding the human talent they need to execute their business strategy. They’re thinking more broadly about how to build, buy, borrow or “bot” the skills needed for the present and future.  

The CHRO’s primary challenge is to orchestrate the new human plus AI workforce. Top CHROs are already at work on this challenge, using their comprehensive understanding of the workforce and how to design roles and skills within an operating model to best leverage the strengths of both humans and AI.  

In the past, that meant analyzing the roles that the business needs to execute its strategy, breaking those roles down into their component skills and tasks and creating the skilling and hiring strategy to fill gaps. Going forward, that means assessing job descriptions, identifying the tasks best suited to technology and the tasks best suited to people and redesigning the roles and the work itself.  

Training the AI as well as the people 

As top CHROs partner with their C-Suite peers to reinvent roles and change how tasks get done with AI and automation, they are also thinking about the technology roadmap for skills. With the skills roadmap established, they can play a key role in building AI-powered solutions that fit the business’ needs.  

HR leaders have the deep expertise in training best practices that can inform not only how people are trained for skills, but how the AI solutions themselves are trained.  

To train a generative AI assistant to learn project management, for example, you need a strong set of unstructured data about the work and tasks required. HR leaders know the right steps to take around sourcing and evaluating content for training, collaborating with the functional subject matter experts for that area.  

That’s only the beginning. Going forward, business leaders will also need to consider how to validate, test and certify these AI skills.  

Imagine an AI solution trained to support accountants with key accounting tasks. How will businesses test and certify those skills and maintain compliance, as rigorously as is done for a human accountant getting an accounting license? What about certifications like CPP or Six Sigma? HR leaders have the experience and knowledge of leading practices around training, certification and more that businesses will need to answer these questions and truly implement this new operating model.  

Creating a culture focused on growth mindset and learning 

Successfully implementing technology depends on having the right operating model and talent to power it. Employees need to understand how to use the technology and buy in to adopting it. It is fundamentally a leadership and change journey, not a technology journey.  

Every organization will need to increase the overall technical acumen of their workforce and make sure that they have a basic understanding of AI so they can be both critical thinkers and users of the technology. Here, CHROs will lean into their expertise and play a critical role moving forward—up-skilling people, creating cultures of growth mindset and learning and driving sustained organizational change.  

For employees to get the most out of AI, they need to understand how to prompt it, evaluate its outputs and then refine and modify. For example, when you engage with a generative AI-powered assistant, you will get very different responses if you ask it to “describe it to an executive” versus “describe it to a fifth-grader.” Employees also need to be educated and empowered to ask the right questions about AI’s outputs and source data and analyze them for accuracy, bias and more.  

While we’re still in the early phases of the age of AI, leading CHROs have a pulse on the anticipated impact of these powerful technologies. Those who can seize the moment to build a workforce and skills strategy that makes the most of human talent plus responsibly trained AI will be poised to succeed.

Source: ibm.com

Continue reading

Using generative AI to accelerate product innovation

Generative artificial intelligence (GenAI) can be a powerful tool for driving product innovation, if used in the right ways. We’ll discuss select high-impact product use cases that demonstrate the potential of AI to revolutionize the way we develop, market and deliver products to customers. Stacking strong data management, predictive analytics and GenAI is foundational to taking your product organization to the next level.

1. Addressing customer inquiries with an AI-driven chatbot 

ChatGPT distinguished itself as the first publicly accessible GenAI-powered virtual chatbot. Now, enterprises can adopt the foundational principles of this technology and apply them within their operations, further enriched by contextualization and security. With IBM watsonx™ Assistant, companies can build large language models and train them using proprietary information, all while helping to ensure the security of their data.

Conversational AI solutions can have several product applications that drive revenue and improve customer experience. For instance, an intelligent chatbot can address common customer concerns regarding bill explanations. When customers seek explanations for their bills, a GenAI-powered chatbot can provide them with detailed explanations, including transaction logs for usage and overage charges.

It can also provide new product packages or contract terms that align with a customer’s past usage needs, identifying new revenue opportunities and improving customer satisfaction. Businesses that use IBM watsonx Assistant can expect to see a 30% reduction in customer support costs and a 20% increase in customer satisfaction.

2. Accelerating product modernization 

GenAI has the power to automate manual product modernization processes. GenAI technologies can survey publicly available sources, such as press releases, to collect competitor data and compare the current product mix to competitor offerings. It can also gain an understanding of market advantages and suggest strategic product changes. These new insights can be realized at greater speeds than ever before. 

A key benefit of GenAI is its ability to generate code. Now, a business user can use GenAI tools to develop preliminary code for new product features without as much reliance on technical teams. These same tools can analyze code and identify and fix bugs in the code to reduce testing efforts. 

GenAI solutions such as IBM watsonx™ Code Assistant meet the core technical needs of enterprises. Watsonx Code Assistant can help enterprises achieve a 30% reduction in development effort or a 30% productivity gain. These tools have the potential to revolutionize technical processes and increase the speed of technical product delivery. 

3. Analyzing customer behavior for tailored product recommendations 

With the power of predictive analytics and GenAI, businesses can understand when specific customers are best suited for new products, receive suggestions for the appropriate products, and receive suggested next steps for engaging with the client. For example, if a customer undergoes a major business change such as an acquisition, predictive models trained on previous transactions can analyze the potential need for new products. 

GenAI can then suggest upselling opportunities and write an email to the customer, to be reviewed by the salesperson. This empowers sales teams to increase speed to value while offering customers top-tier service. Using IBM® watsonx.data™, enterprise data can be prepared for various analytical and AI use cases. 

4. Analyzing customer feedback to inform business strategy 

Enterprises have the opportunity to use GenAI to improve customer experience by more readily actioning customer feedback. Through IBM® watsonx.ai™, various industry-leading models are available for different types of summarization. This technology can quickly interpret and summarize large volumes of customer feedback. 

It can then provide suggested product improvements with fleshed-out requirements and user stories, accelerating the speed of responsiveness and innovation. GenAI can pull themes from feedback from lost customers to illuminate trends, suggest new sales strategies, and arm sales teams with business intelligence and pre-scripted follow-ups. 

5. Applying customer segmentation for intelligent marketing 

GenAI has the potential to revolutionize digital marketing by increasing the speed, effectiveness and personalization of marketing processes. Using standard data analytics practices, businesses can identify patterns and clusters within data to enable more accurate targeting of customers. 

Once the clusters are created, GenAI can power automated content creation processes that reach specific customer groups across various platforms. IBM watsonx™ Orchestrate enables the user to automate daily tasks and increase productivity. This tool can create content, connect to different platforms, and send out updates across them at the drop of a hat, saving marketing teams time and money as they deliver solutions. 

This content creation and customer outreach ability is the key differentiator of generative AI and part of what makes these new technologies so exciting. GenAI can take expensive, manual marketing processes and translate them into accelerated, automated processes. 

Source: ibm.com

Continue reading

Building for operational resilience in the age of AI and hybrid cloud

Each year we see the challenges that enterprises face become more complex as they strive to keep up with the latest technologies, such as generative AI, and increasing customer expectations.

For highly regulated industries, these challenges take on an entirely new level of expectation as they navigate evolving regulatory landscape and manage requirements for privacy, resiliency, cybersecurity, data sovereignty and more. Organizations in the financial services, healthcare and other regulated sectors must place an even greater focus on managing risk—not only to meet compliance requirements, but also to maintain customer confidence and trust.

To do this, it’s crucial that enterprises place an emphasis on operational resilience with the aim of maintaining stability, preserving market integrity and protecting confidential data for themselves and their customers.

Prioritizing operational resiliency

In our view, the essence of operational resilience is an assumption that disruption is inevitable, and organizations must have measures in place to be able to absorb and adapt to any shocks. This includes cyber incidents, technology failures, natural disasters and more. With more dependency on technology and third and fourth parties, expectations are increasing for organizations to continue delivering critical business services through a major disruption in a safe and secure manner. This means actively minimizing downtime and closing gaps in the supply chain to remain competitive.

This is different from the long-standing industry practice of disaster recovery where, traditionally, companies would return to normal operations in the several days after an event with defined recovery point objectives and recovery time objectives. Although still an important practice, appetite for conventional disaster recovery approaches is diminishing across industries and especially with regulators. This is evident from emerging regulatory requirements and expectations in UK (Bank of England’s Critical Third-Party regime), Europe (Digital Operational Resilience Act), Australia (APRA CPS-230 Operational Risk Management) and Canada (OSFI – Operational Resilience and Operational Risk Management), etc. Similarly, in the U.S. the Office of the Comptroller of Currency (OCC) also indicated that the Federal Banking Agencies are considering updates to operational resilience frameworks and approaches for critical business services and for third-party services providers. 

As hybrid cloud and generative AI adoption increases, data and applications are everywhere—across multiple clouds and vendors (SaaS/Fintech), on premises and even at the edge. For this reason, it’s more important than ever for enterprises to ensure their cybersecurity and resiliency strategy incorporates their entire IT estate, no matter where it resides.

To do this, enterprises must first prioritize the most critical business services and develop a workload and data placement strategy to determine which applications and data should reside in a certain environment based on its specific security, resiliency and data sovereignty needs. 

According to the 2024 IBM X-Force Threat Intelligence Index, attackers are increasingly shifting from ransomware to malware that is designed to steal information, which reinforces the importance of leveraging technology and approach that provides holistic view and end-to-end protection across your entire IT estate, including your partners.

While partnerships are essential for businesses to remain competitive and tap into new entry points, enterprises must make sure third parties are thinking about security, resiliency and controls in the same way they and their regulators are.

It’s clear trust and security must be at the foundation of decisions about where workloads and data reside—regardless of the industry. But how can an enterprise ensure these priorities remain front and center, especially when working with third and fourth parties?

Taking an industry-specific approach to accelerating digital transformation

Hybrid cloud is now the dominant architecture adopted by enterprises, according to an IBM Study, but critical to hybrid cloud strategy is an industry cloud approach. Over the past few years, IBM Cloud® has continued to innovate on, and made significant enhancements to our enterprise cloud platform designed for regulated industries. This purpose-built approach has enabled clients to take advantage of cloud services, SaaS providers and Fintechs at a consistent level of security, resiliency and compliance to build and deliver world-class solutions for their customers, while managing third- and fourth-party risk. 

Several years ago, we took a strategic step to address the needs of our clients in regulated industries with the first industry-specific cloud platform designed to meet the needs of financial services sector. This includes the highest set of operational, resiliency, cybersecurity and regulatory standards with built-in controls informed by the industry. By meeting the stringent standards for financial services, it can be seamlessly leveraged across other industries including insurance, government, healthcare, manufacturing and telecommunications, allowing for continuous and central management of security and risk management. 

To support clients in their transformation journey, we are continuing our work with key industry organizations to further address risk and allow organizations to leverage the cloud with confidence. One of our premier industry forums is the IBM Financial Services Cloud Council, which now consists of a network of more than 160 CIOs, CTOs, CISOs and Risk and Compliance officers from over 90 financial institutions working together to develop safe, secure and compliant adoption of cloud and Gen AI.

Moreover, we are collaborating with industry leading organizations such as the Cloud Security Alliance to advance hybrid cloud security and Gen AI adoption for enterprises. On-going engagement with regulators around the globe and private-public sector collaboration through organizations such as the U.S. Financial Services Sector Coordinating Council (FSSCC) and engagements with the Financial Stability Board Third-Party Risk group are also important in developing practical and consistent industry-wide approach to common challenges.

Shared understanding and ownership

As enterprises continue to balance the complexities of innovation, risk and resilience, we believe the path forward will be working towards a common, risk-based understanding of the core principles that underpin effective operational resiliency. It’s essential for enterprises to take ownership of their operations and prioritize their actions and investments based on the impact to themselves, their customers and market stability, but this can’t happen in a vacuum. 

At IBM, we are committed to helping clients on this journey. We believe it takes all of us—enterprises, trade organizations, policy makers, regulatory authorities and cloud providers— to work in unison to accomplish the same critical mission: accelerating digital experiences that move the world in a secure, resilient and compliant manner. 

Source: ibm.com

Continue reading

Maximizing business outcomes and scaling AI adoption with a Hybrid by design approach

For established businesses, the debate is settled: a hybrid cloud approach is the right strategic choice.

However, while embracing hybrid cloud might be intrinsic, clients continually seek to derive business value and higher return on investment (ROI) from their investments. According to a study conducted by HFS Research in partnership with IBM Consulting, only 25% of surveyed enterprises have reported solid ROI on business outcomes from their cloud transformation efforts.

The lack of ROI progress can be attributed to several factors, including slow adoption, unrealized use cases and unaddressed cloud sprawl. This is exacerbated by the increasing challenges of platform scaling, skilling, cybersecurity and the explosion of data volumes.

 

Businesses aiming to derive value from business outcomes through their cloud investments and harness the potential of artificial intelligence (AI) must adopt an intentional approach.

At IBM, we provide clear guidance for navigating these challenges and achieving better business outcomes. Adopting a hybrid by design strategy allows organizations to align architectural choices with business priorities across technology, platforms, processes and people, resulting in significantly enhanced outcomes and a higher ROI.

The latest paper from IBM, titled “Maximize the value of hybrid cloud in the generative AI era,” offers clarity for businesses seeking to optimize their hybrid cloud architecture and amplify the impact of generative AI (gen AI).

What does a Hybrid by design approach involve?

Many organizations have adopted cloud in isolated areas, seeking immediate benefits, but inconsistently applying the technology throughout their operations. This complexity and inconsistency increase costs and hinder businesses from meeting demands, goals and outcomes. We view this as a default approach, which characterizes most companies today.

Businesses must recalibrate transformation programs to align more closely with business imperatives and outcomes. They must also recognize how gen AI can amplify the value of hybrid cloud and accelerate digital transformation.

A hybrid by design approach intentionally structures your hybrid, multicloud IT estate to achieve key business priorities and maximize ROI. Organizations can shift from technology dictating business limitations to purposefully built architectures ready for evolving technology landscapes, new business and customer demands, and emerging processes and skill requirements. Driven by an organization’s key business objectives, intentional and consistent architectural decisions enable organizations to fully use hybrid cloud and gen AI to drive business outcomes. 

IBM built a codified framework to help clients strategically focus on decisions that drive the most outcomes for their business, promoting a hybrid by design approach. This framework facilitates the translation of business priorities into key architectural decisions.

We implement this framework by aligning business objectives with a set of decision points across 3 domains: product native by design, technology by design and integration by design. These domains also encompass various design points. Each enterprise situation requires different levels of capabilities to address and achieve its business objectives. This value framework aids in capturing these decision points.

In an AI-centric world, why does this matter for enterprises? 

Gen AI plays a critical role in shaping the digital enterprise, helping to maximize business returns on IT investments. However, wide-scale adoption and deployment of gen AI present challenges. Enterprises must manage vast volumes of data, significant computing power, advanced security architecture across distributed environments and ensure rapid scalability.

An intentional and consistent hybrid architecture design helps enterprises to overcome these challenges, facilitating the successful adoption and scalability of gen AI capabilities to meet business needs.

Current pilots and deployments prove that a hybrid by design approach is required to scale the adoption of gen AI to meet business needs. Gen AI accelerates the execution and value of hybrid cloud by providing enhanced automation with observability, improved cost optimization with automated insights, and heightened security with compliance across the hybrid platform. 

Applying intentional architecture decisions based on business needs amplifies the value of hybrid cloud and AI, driving business outcomes.

Source: ibm.com

Continue reading