CIO CIO

“I think acceptance is at the heart of all these things,” he says, “but after that, you have to make some decisions.” Schadler says CIOs can take steps to lessen the discomfort (and risk) of such situations — first by recognizing they’re not alone. “Bring in those rational voices to bolster your own ability to shape the investments you’re going to make,” Schadler says, adding that privacy, risk, and security officers are often partners here. So are third parties, such as service providers and consulting firms, because they typically bring a wealth of experiences and lessons-learned. “Bring them in as advisors to you and the business, to step into the conversation and to problem-solve,” Schadler says. And learn to stretch as an executive. “Change your comfort calibration to take on risk that doesn’t violate corporate principles but that stretches your team’s ability to execute,” he advises. 8. Collaboration remains elusive Despite the need for cooperation and collaboration to succeed, Kellie Romack, chief digital information officer at ServiceNow, says, “There are still too many people working in silos.” “When I talk to industry peers, silos are one of the biggest challenges and typically the main reason things go off course. They happen when people try to move fast without taking time to connect the dots, or big initiatives get planned and funded through individual departments without collaboration,” she explains. CIOs can counter that by serving “as the connective fiber for transformation.” “We need to know everything about the business units we serve, so we can have the big-picture view and be that unifier,” she says. “For example, it’s important for CIOs to work with their CFO to understand the finance and budgeting roadmap, determine where tech can solve business problems, and identify other stakeholders who should play a role and give critical input.” source

Beyond Bank Australia is one of the largest customer-owned banks in Australia and one of the leading B Corps in the country. That certification measures your company’s social and environmental impact, both of which we’re super proud of, but it’s also deeply rooted in our culture. Beyond Bank has a real focus on customers who are the members and owners of the bank. All the money that’s made goes back into the bank, which is a different slant compared to say, the big banks or large corporates that have shareholders they have to pay dividends to.  We particularly focused on communities and, last year, we partnered with more than 5,000 community organizations around Australia. We work with them on some of Australia’s most pressing issues, from housing affordability to helping all Australians have stronger levels of financial well-being. We’re a very relationship-led bank.  As the CIO of Beyond Bank, I’m accountable for all aspects of technology, across the business: banking, tech and data and business intelligence. Everything from ‘my computer won’t turn on’ and ‘can you fix this’ type workplace and service desk, to engineering, the building and running systems that power the bank, the branch tech, ATMs, tellers, call centers, the infrastructure and architecture, cyber incident management and the NOC. Basically, the whole lot. It’s a really big, broad role, which I love.  source

The updates announced at AWS’ re:Invent annual developer conference could help CIOs tackle priorities such as streamlining workflows and optimizing IT expenditure. source

The integration of data and AI offerings at AWS and Azure, though, raises important questions about how they will adapt their partnerships with players like Snowflake and Databricks, Gupta said: “These companies, once central to data unification efforts, now face growing competition from the integrated offerings of the two cloud giants.” While many technology vendors appear to be converging on the same unification strategy, Constellation’s Henschen said that AWS is a step ahead of Google, Microsoft, Databricks, and others. Microsoft’s Fabric and Google’s BigQuery have similar AI model development capabilities, he said, but they don’t yet have SageMaker’s inbuilt generative AI development capabilities. On the other hand, Moor Insights and Strategy’s Jason Andersen sees AWS’s intentions with SageMaker differently. While at a high level the new version of SageMaker may resemble Fabric, AWS intends to offer a consistent experience for the entire data life cycle from data to model development, he said, comparable to a developer platform that offers tools to manage the entire software development lifecycle. source

As organizations globally discover new opportunities created by AI, many are investing significantly in GenAI, including as part of their cloud modernization efforts. They are using the considerable power of this fast-evolving technology to tackle the common challenges of cloud modernization, particularly in projects that involve the migration and modernization of legacy applications — a key enabler of digital and business transformation. Many legacy applications were not designed for flexibility and scalability. Making changes to their code can be difficult, and they often can’t be integrated with other processes or applications. The fact that these applications were not born in the cloud makes efforts to update them laborious at best and sometimes impossible. A primary benefit of cloud replatforming is that it gives organizations the flexibility to update, maintain or customize applications with extended functionality aimed at customers and employees. In this context, GenAI can be used to speed up release times. In fact, many organizations save up to 30% of the time from strategy to deployment by taking a modern approach to application modernization. A faster time to market and a better customer experience GenAI “copilots” are well-established in the world of software engineering and will continue to proliferate and evolve. More organizations and vendors are rolling out these coding agents to allow developers to fully automate or offload certain tasks. Increasingly, organizations are also using GenAI in industry cloud deployments and component-based development, as it speeds up modernization and promotes code reusability — writing code in such a way that it may be reused in multiple development contexts with little or no modification required. While this allows developers to build and deploy applications with ease, the value to the business is an improved speed to market and better customer experiences. Services are delivered faster and with stronger security and a higher degree of engagement, and it frees up skilled resources to focus on more strategic endeavors. AI and GenAI optimize cloud architectures and cloud-native applications GenAI is also proving adept at analyzing cloud architectures, suggesting optimal cloud configurations and identifying the most appropriate modernization approaches. More efficient workloads and processes allow organizations to reapply cost savings in other areas of the business. Furthermore, cloud-native applications can be used to apply full-time AI-driven threat hunting, digital forensics and incident responses, as well as faster security orchestration and responses. The result is a more cybersecure enterprise. Get ahead of the megatrend Nearly two-thirds of organizations already see GenAI as a game-changing technology and a megatrend on a par with cloud computing, according to NTT DATA’s Global GenAI Report. The early winners will be those who integrate it into both their culture and their roadmaps for cloud technology and application modernization. Get a copy of Winning big with AI-powered cloud modernization, a white paper published by CIO, NTT DATA and Microsoft. source

The world must reshape its technology infrastructure to ensure artificial intelligence makes good on its potential as a transformative moment in digital innovation. New technologies, such as generative AI, need huge amounts of processing power that will put electricity grids under tremendous stress and raise sustainability questions. But pioneering technologists are working on a potential game changer that goes some way to address these issues: photonics. John Gallant, CIO.com’s Enterprise Consulting Director and Vito Mabrucco, NTT Corp. Chief Marketing Officer, recently engaged in an extensive discussion on exactly how photonics technology could help meet the power demands of AI. Mabrucco first explained that AI will put exponentially higher demands on networks to move large data sets. He said: “We know the current infrastructure that we have can’t possibly support all of the new innovations that are going to result from the very wide and broad and deep implementation of AI.” The demands of AI call for an entirely new approach – a paradigm shift that replaces electronics-based computing and networking with photonics-enabled computing and networking. Photonics addresses a variety of issues and concerns: first, because photonics uses less energy than electronics, it can reduce the amount of power needed to do the same amount of work. This is vitally important because there are legitimate concerns that AI will outpace the capacity of the power grid. Photonics technology also delivers exponentially higher bandwidth rates with lower latency. This enables use cases such as near real-time disaster recovery over photonics-based links in industries like banking and finance, vehicle-to-vehicle communication in an autonomous vehicle scenario, and real-time edge-to-data center connections for robotics applications in factories, or at remote sites in mining or oil and gas industries. How does it work? Mabrucco explained that NTT is working to take fiber-optic technology, which has been used for decades to transmit data over long distances (think undersea cables) and shrink it down for deployment inside computers and networking gear, even down to the chip level. He also says that photonics can “change the paradigm of computing” by enabling a disaggregation of the traditional computing stack. With photonics-based interconnects, organizations will be able to create efficient pools of processing units for specific use cases, such as large language model (LLM) data processing in one location, data storage in another location, and a high-speed link between the two.   NTT takes leadership role NTT is taking a leadership role on a variety of fronts, Mabrucco said. NTT created, alongside Sony and Intel, the IOWN Global Forum. Over 150 leading organizations are involved in it, with the aim of achieving early and successful uses cases that can then be scaled. Mabrucco also encouraged CIOs to get involved in the IOWN Global Forum. NTT has its own internal set of principles that guide its approach to AI. NTT believes that AI should respect human rights and diversity; that it be fair, unbiased and transparent; that it protects personal data; that it be secure; and that it will not only create new business opportunities, but also benefit people and the planet. Speaking of NTT’s AI Charter, Mabrucco said NTT was looking to take a leadership role in AI governance and ethics. It’s clear that the technology firms must understand how this technology will impact if it is to deliver on the promise of a secure and trustworthy AI. View the entire discussion here: source

Generative artificial intelligence (AI) is hot property when it comes to investment, but there’s a pronounced hesitancy around adoption. AI faces a fundamental trust challenge due to uncertainty over safety, reliability, transparency, bias, and ethics. In a recent global survey, 86% of participants said their organizations had dedicated budget to generative AI, but three-quarters admitted to significant concerns about data privacy and security. What makes AI responsible and trustworthy? At the top of the list of trust requirements is that AI must do no harm. Compliance is necessary but not sufficient. Rather, the guiding principle for making AI trustworthy is to align it with societal values. Yet determining what AI should do is challenging. What’s considered right, accurate, and ethical can vary depending on context, use case, industry, country, and culture. AI teams have to figure out what values their organizations want to reflect and what “fair” and “accurate” mean in that context. Governance implications for key gen AI use cases Some key use cases for generative AI include increasing productivity, improving business functions, reducing risk, and boosting customer engagement. A good governance framework makes generative AI not only more responsible but also more effective. “Aligning AI with organizational goals and deploying it responsibly and efficiently ensure long-term productivity benefits,” noted Bruno Domingues, CTO for Intel’s financial services industry practice. “Establishing guardrails based on organizational principles ensures efficient resource allocation, fosters accountability and transparency, and builds trust among stakeholders.” A solid governance structure addresses ethical issues related to AI across the organization. As part of its model, SAS has an AI Oversight committee that might reject a generative AI marketing message as inappropriate, for example. “The committee essentially acts as an additional audit layer, ensuring that AI applications and decisions align with SAS’s ethical standards,” said Josefin Rosén, Trustworthy AI specialist at SAS’ Data Ethics Practice. Structure, policies, and oversight A solid AI governance framework bridges the divide between generative AI’s promise and realization of its benefits, including: Increased productivity due to more distributed decision-making Competitive advantage and market agility resulting from being forward-compliant Improved trust thanks to better accountability in data use Heightened brand value in response to addressing AI’s impact on society and the environment Ability to win and keep top talent who value responsible innovation Partnering for a sustainable future A strong AI governance framework also supports sustainability goals, which require intelligent data management, model development and deployment, and decision monitoring and management. SAS and Intel have forged a partnership that integrates high-performance computing hardware with advanced analytics software to drive sustainability, energy efficiency, and cost-effectiveness. “SAS’s tools enable organizations to analyze and optimize energy consumption, carbon footprints, and operational efficiencies, while Intel’s processors and accelerators deliver the performance needed for these analytics with reduced power consumption,” noted Domingues. The great promise of generative AI to deliver transformative business benefits rests on the willingness of organizations to commit to good governance and ethical AI practices. Those who make that connection will be well positioned as the AI revolution gains steam. Check out this webinar to learn how to unlock the benefits of generative AI – ethically and responsibly. source