University of Kwazulu-Natal

Ai At Scale Ibm

Bonisiwe Shabane
-
ai at scale ibm

Accelerating AI integration across your enterprise can generate positive business growth. 90% of corporate AI initiatives are struggling to move beyond test stages. Organizations are maturing in Data Science, but still fail to integrate and scale Advanced Analytics and AI/ML into every day, real-time decision making – hence they cannot reap the value of AI. An accelerated digital transformation will be required for the new world of remote work and AI/ML can be leveraged to achieve this more quickly. And they result in more efficient business operations, more compelling customer experiences and more insightful decision-making. Enterprises can capture significant gains across the value chain with AI, but organizations have to do it right from the very beginning or run the risk of accruing fines, penalties, errors, corrupted results and...

Companies that are strategically scaling AI report nearly 3X the return from AI investments compared to companies pursuing siloed proof of concepts. IBM Services has end-to-end capabilities to drive value from AI. Drive sustainable enterprise-wide innovation with scalable AI/ML models, that are environmentally friendly, actionable, reusable, and scalable, which are not just one-off science experiments. IBM services for AI at scale aims at scaling current AI engagements and applications towards an enterprise setup. It consists of multiple pillars, which are building up the overall offering: We start with a vision to establish and scale trustworthy AI and data as key business strategy components for competitive advantage.

We base it on a measurement framework to generate genuine AI value that you and your clients can trust. We advise and work collaboratively with your team to build a tailored operating model. We understand that each organization is different, and what works for one won’t work for another. For example, a federated model instead of a non-federated model. We then work side by side with you to develop a pipeline of initiatives that produces measurable business value through the harvesting of AI assets by scalable and connected teams. As Indian enterprises move rapidly from AI pilots to production-scale agentic systems, the conversation around Responsible AI is shifting.

What was once viewed as an ethical checkbox is now a core business and regulatory requirement. AI agents today do more than automate tasks; they fetch data, interact with systems, trigger workflows, and influence decisions that carry financial, legal, and operational consequences. In this environment, accountability, transparency, and human oversight are no longer optional design choices. To understand how enterprises can embed trust into autonomous systems without slowing innovation, CiOL spoke with Vishal Chahal, Vice President, IBM India Software Labs, on how IBM is operationalising Responsible AI for the agentic... Accountability has to be proportionate to the risk profile of the solution. Responsible AI must be embedded at the design stage itself.

As a platform provider, we build the foundational capabilities. But creators, solution designers, and enterprises deploying these agents all have defined responsibilities. They provide the context, memory, and governance inputs that shape agent behaviour. Boost your brand and generate demand with media programs. Read through guides, explore resource hubs, and sample our coverage. Register for an upcoming webinar and track which industry events our analysts attend.

Listen to our podcast, Behind the Numbers for the latest news and insights. Learn more about our mission and how EMARKETER came to be. ARMONK, N.Y., May 6, 2025 /PRNewswire/ -- Today at the company's annual THINK event, IBM (NYSE: IBM) is unveiling new hybrid technologies that break down the longstanding barriers to scaling enterprise AI – enabling... IBM estimates that over one billion apps will emerge by 2028, putting pressure on businesses to scale across increasingly fragmented environments. This requires seamless integration, orchestration and data readiness. A new IBM CEO study shows that business leaders expect the growth rate of AI investments to more than double over the next two years, with most actively adopting AI agents and preparing to...

Yet their pace of investments has led to disconnected technology – and only 25% of AI initiatives have achieved the ROI they expected. IBM is combining hybrid technologies, agent capabilities and deep industry expertise from IBM Consulting to help businesses operationalize AI. "The era of AI experimentation is over. Today's competitive advantage comes from purpose-built AI integration that drives measurable business outcomes," said Arvind Krishna, Chairman and CEO, IBM. "IBM is equipping enterprises with hybrid technologies that cut through complexity and accelerate production-ready AI implementations." AI is now the primary growth engine for data centers in the United States and is projected to be one of several drivers that will grow in supply and increase power capacity from about...

That capacity is larger than the entire power demand of California today1“Current and forecasted demand,” California ISO, accessed December 2, 2025.—and it’s completely reshaping the nation’s data center infrastructure. This article is a collaborative effort by Chhavi Arora, Marc Sorel, and Pankaj Sachdeva, with Arjita Bhan, Jess He, Nicholas Shaw, Riya Garg, and Shriya Ravishankar, representing views from McKinsey’s Technology, Media, and Telecommunications... Hyperscalers are expected to capture about 70 percent of the forecast capacity in the US market through owned or leased options,2“AI power: Expanding data center capacity to meet growing demand,” McKinsey, October 29, 2024. and their infrastructure decisions will define how the broader data center ecosystem evolves. AI compute today is primarily split between two workload types: training and inferencing. Both workloads are rapidly shaping hyperscaler strategies and are driving a paradigm shift in site selection, power strategy, and architectural design across hyperscalers’ portfolios.

Training workloads are driving the need for large-scale, high-density campuses with advanced mechanical, electrical, and plumbing (MEP) systems and specialized hardware integration patterns. Meanwhile, AI inference workloads are accelerating site build-outs in greater metro and surrounding areas that are optimized for low round-trip time, high network interconnectivity, and energy efficiency. What’s more, our research finds that inferencing workloads are projected to make up a little more than half of AI workloads by 2030, causing hyperscalers to reconsider their design approach and the location of... And energy constraints are changing the way hyperscalers think about the market and ways to build faster. This article explores how these trends are reshaping hyperscaler strategies, outlining five key shifts in how they are choosing to scale, meet surging AI demand, and optimize capacity. It also examines how hyperscalers are balancing greenfield expansion and brownfield retrofits, as well as how capital is being redeployed across the data center value chain to sustain this rapid growth.

ARMONK, N.Y., March 18, 2025 /PRNewswire/ -- IBM (NYSE: IBM) today announced new collaborations with NVIDIA (NASDAQ: NVDA), including planned new integrations based on the NVIDIA AI Data Platform reference design to help enterprises... As part of today's news, IBM is planning to launch a content-aware storage capability for its hybrid cloud infrastructure offering, IBM Fusion; intends to expand its watsonx integrations; and is introducing new IBM Consulting... A 2024 IBM report found that more than three in four executives surveyed (77 percent) say generative AI is market-ready, up from just 36 percent in 2023. With this push to put AI into production comes an increased need for compute and data-intensive technologies. The collaboration between IBM and NVIDIA will enable IBM to provide hybrid AI solutions that take advantage of open technologies and platforms while also supporting data management, performance, security, and governance. Leveraging the NVIDIA AI Data Platform reference architecture, these new solutions are the latest in the IBM and NVIDIA collaboration to build enterprise infrastructure for AI:

"IBM is focused on helping enterprises build and deploy effective AI models and scale with speed," said Hillery Hunter, CTO and General Manager of Innovation, IBM Infrastructure. "Together, IBM and NVIDIA are collaborating to create and offer the solutions, services and technology to unlock, accelerate, and protect data – ultimately helping clients overcome AI's hidden costs and technical hurdles to monetize... "AI agents need to rapidly access, fetch and process data at scale, and today, these steps occur in separate silos," said Rob Davis, vice president, Storage Networking Technology, NVIDIA. "The integration of IBM's content-aware storage with NVIDIA AI orchestrates data and compute across an optimized network fabric to overcome silos with an intelligent, scalable system that drives near real-time inference for responsive AI...

People Also Search

Accelerating AI Integration Across Your Enterprise Can Generate Positive Business

Accelerating AI integration across your enterprise can generate positive business growth. 90% of corporate AI initiatives are struggling to move beyond test stages. Organizations are maturing in Data Science, but still fail to integrate and scale Advanced Analytics and AI/ML into every day, real-time decision making – hence they cannot reap the value of AI. An accelerated digital transformation wi...

Companies That Are Strategically Scaling AI Report Nearly 3X The

Companies that are strategically scaling AI report nearly 3X the return from AI investments compared to companies pursuing siloed proof of concepts. IBM Services has end-to-end capabilities to drive value from AI. Drive sustainable enterprise-wide innovation with scalable AI/ML models, that are environmentally friendly, actionable, reusable, and scalable, which are not just one-off science experim...

We Base It On A Measurement Framework To Generate Genuine

We base it on a measurement framework to generate genuine AI value that you and your clients can trust. We advise and work collaboratively with your team to build a tailored operating model. We understand that each organization is different, and what works for one won’t work for another. For example, a federated model instead of a non-federated model. We then work side by side with you to develop ...

What Was Once Viewed As An Ethical Checkbox Is Now

What was once viewed as an ethical checkbox is now a core business and regulatory requirement. AI agents today do more than automate tasks; they fetch data, interact with systems, trigger workflows, and influence decisions that carry financial, legal, and operational consequences. In this environment, accountability, transparency, and human oversight are no longer optional design choices. To under...

As A Platform Provider, We Build The Foundational Capabilities. But

As a platform provider, we build the foundational capabilities. But creators, solution designers, and enterprises deploying these agents all have defined responsibilities. They provide the context, memory, and governance inputs that shape agent behaviour. Boost your brand and generate demand with media programs. Read through guides, explore resource hubs, and sample our coverage. Register for an u...