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The EU has completed a very important initiative by approving one of the worlds first regulations on AI, in an anthropocentric function, protecting fundamental rights and guaranteeing innovation, Valentini continues. It is not easy to master this framework, and AI Pact can also help with the guidance provided by the AI Office.
Aligning ESG and technological innovation At the core of this transformation is the CIO, a pivotal player whose role has expanded beyond managing technological innovation to overseeing how these innovations contribute to ESG goals. It provides CIOs a roadmap to align these technologies with their organizations’ ESG goals.
The Cybersecurity Maturity Model Certification (CMMC) serves a vital purpose in that it protects the Department of Defense’s data. Cybersecurity company Camelot Secure, which specializes in helping organizations comply with CMMC, has seen the burdens of “compliance overload” first-hand through its customers.
Innovator/experimenter: enterprise architects look for new innovative opportunities to bring into the business and know how to frame and execute experiments to maximize the learnings. Data protection and privacy: Ensuring compliance with data regulations like GDPR and CCPA.
IT leaders know the importance of compliance at every level, but the database often gets left behind as other environments are automated for robust protection. This whitepaper emphasizes the importance of robust, auditable, and secure database change management practices for safeguarding organizational compliance.
In 2025, data management is no longer a backend operation. It has become a strategic cornerstone for shaping innovation, efficiency and compliance. This article dives into five key data management trends that are set to define 2025. This reduces manual errors and accelerates insights.
The Middle East is rapidly evolving into a global hub for technological innovation, with 2025 set to be a pivotal year in the regions digital landscape. AI and machine learning are poised to drive innovation across multiple sectors, particularly government, healthcare, and finance.
Business leaders may be confident that their organizations data is ready for AI, but IT workers tell a much different story, with most spending hours each day massaging the data into shape. Theres a perspective that well just throw a bunch of data at the AI, and itll solve all of our problems, he says.
Most AI workloads are deployed in private cloud or on-premises environments, driven by data locality and compliance needs. AI a primary driver in IT modernization and data mobility AI’s demand for data requires businesses to have a secure and accessible data strategy. Cost, by comparison, ranks a distant 10th.
The first is to foster a culture of agility, collaboration, and AI-driven innovation, driven in part by our new Office of AI. Were adopting best-in-class SaaS solutions, a next-generation data architecture, and AI-powered applications that improve decision-making, optimize operations, and unlock new revenue stream opportunities.
Migration to the cloud, data valorization, and development of e-commerce are areas where rubber sole manufacturer Vibram has transformed its business as it opens up to new markets. Data is the heart of our business, and its centralization has been fundamental for the group,” says Emmelibri CIO Luca Paleari.
However, trade along the Silk Road was not just a matter of distance; it was shaped by numerous constraints much like todays data movement in cloud environments. Merchants had to navigate complex toll systems imposed by regional rulers, much as cloud providers impose egress fees that make it costly to move data between platforms.
Enterprise use of artificial intelligence comes with a wide range of risks in areas such as cybersecurity, data privacy, bias and discrimination, ethics, and regulatory compliance. An AI GRC plan allows companies to proactively address compliance instead of reacting to enforcement, Haughian says.
Despite hoisting the Larry OBrien Championship Trophy eluding the team so far, however, the franchise is at the forefront of digital innovation to revolutionize the fan experience. The observability of data and the insights derived will allow us to continually evolve and grow while keeping our fans at the top of our game, he adds.
On October 29, 2024, GitHub, the leading Copilot-powered developer platform, will launch GitHub Enterprise Cloud with data residency. This will enable enterprises to choose precisely where their data is stored — starting with the EU and expanding globally. The key advantage of GHEC with data residency is clear — protection.
Data is the lifeblood of the modern insurance business. Yet, despite the huge role it plays and the massive amount of data that is collected each day, most insurers struggle when it comes to accessing, analyzing, and driving business decisions from that data. There are lots of reasons for this.
In 2025, insurers face a data deluge driven by expanding third-party integrations and partnerships. Many still rely on legacy platforms , such as on-premises warehouses or siloed data systems. Step 1: Data ingestion Identify your data sources. First, list out all the insurance data sources.
These advancements offer immense economic growth and innovation potential, but they also introduce new cybersecurity challenges. New technologies like AI and IoT are coming into play,” he said, underscoring how these innovations are driving transformation across sectors. Huawei takes pride in its compliance,” Malik explained.
It demands a robust foundation of consistent, high-quality data across all retail channels and systems. AI has the power to revolutionise retail, but success hinges on the quality of the foundation it is built upon: data. The Data Consistency Challenge However, this AI revolution brings its own set of challenges.
AI, once viewed as a novel innovation, is now mainstream, impacting just about facet of the enterprise. Over the next 12 months, IT leaders can look forward to even more innovations, as well as some serious challenges. As 2025 dawns, CIOs face an IT landscape that differs significantly from just a year ago.
Do we have the data, talent, and governance in place to succeed beyond the sandbox? These, of course, tend to be in a sandbox environment with curated data and a crackerjack team. They need to have the data, talent, and governance in place to scale AI across the organization, he says. How confident are we in our data?
For instance, AT&T launched a comprehensive reskilling initiative called “Future Ready” to train employees in emerging technologies such as cloud computing, cybersecurity, and data analytics. The compay fostered a culture of innovation by involving employees in the modernization process and addressing their concerns.
The McKinsey 2023 State of AI Report identifies data management as a major obstacle to AI adoption and scaling. Enterprises generate massive volumes of unstructured data, from legal contracts to customer interactions, yet extracting meaningful insights remains a challenge.
By eliminating time-consuming tasks such as data entry, document processing, and report generation, AI allows teams to focus on higher-value, strategic initiatives that fuel innovation. Similarly, in 2017 Equifax suffered a data breach that exposed the personal data of nearly 150 million people.
Allow me, then, to make five predictions on how emerging technology, including AI, and data and analytics advancements will help businesses meet their top challenges in 2025 particularly how their technology investments will drive future growth. Prediction #3: Superior guardrails and governance will spur innovation.
Focused on digitization and innovation and closely aligned with lines of business, some 40% of IT leaders surveyed in CIO.com’s State of the CIO Study 2024 characterize themselves as transformational, while a quarter (23%) consider themselves functional: still optimizing, modernizing, and securing existing technology infrastructure.
Effective IT leadership now demands not only the courage to innovate but also a profound understanding of change management principles. Plus, forming close partnerships with legal teams is essential to understand the new levels of risk and compliance issues that gen AI brings.
Lastly, voluntary frameworks have been proposed by many countries such as Singapore and Japan, to encourage AI innovation. The Law provides a set of frameworks that are as comprehensive as the EU AI Act, with the intention of balancing the need for innovative AI development with the need to safeguard society.
Enterprise applications have become an integral part of modern businesses, helping them simplify operations, manage data, and streamline communication. However, as more organizations rely on these applications, the need for enterprise application security and compliance measures is becoming increasingly important.
To drive change, a reworking of what defines CIO/IT success is needed, with a focus on strategic business goals, innovation, and market differentiation. This data shows that a majority of companies — 62% — are still focused on short-term or functional goals rather than long-term strategic transformation.
To maintain their competitive edge, organizations are constantly seeking ways to accelerate cloud adoption, streamline processes, and drive innovation. Readers will learn the key design decisions, benefits achieved, and lessons learned from Hearst’s innovative CCoE team. This post is co-written with Steven Craig from Hearst.
AI and Machine Learning will drive innovation across the government, healthcare, and banking/financial services sectors, strongly focusing on generative AI and ethical regulation. Data sovereignty and local cloud infrastructure will remain priorities, supported by national cloud strategies, particularly in the GCC.
A modern data and artificial intelligence (AI) platform running on scalable processors can handle diverse analytics workloads and speed data retrieval, delivering deeper insights to empower strategic decision-making. They are often unable to handle large, diverse data sets from multiple sources.
Following that, the completed code of practice will be presented to the European Commission for approval, with compliance assessments beginning in August 2025. Srinivasamurthy pointed out that key factors holding back enterprises from fully embracing AI include concerns about transparency and data security.
For Marc, the certification is not just a compliance checkboxits an affirmation of Cranes commitment to structured, scalable, and resilient systems. Marc offers a bold new blueprint for technology leaders navigating an era where cybersecurity must scale with innovation. Thats where transformation happens.
With data central to every aspect of business, the chief data officer has become a highly strategic executive. Todays CDO is focused on helping the organization leverage data as a business asset to drive outcomes. Even when executives see the value of data, they often overlook governance.
The answer informs how you integrate innovation into your operations and balance competing priorities to drive long-term success. Companies like Qualcomm have to plan and commit well in advance, estimating chip production cycles while simultaneously innovating at breakneck speed. They dont just react to change; they engineer it.
An agentic era needs a platform that brings AI, data, and workflows together, and that should be an open, connected, enterprise-ready platform, said ServiceNows chief innovation officer Dave Wright in a press conference last week. We look at it as distributed intelligence across the enterprise.
Research from Gartner, for example, shows that approximately 30% of generative AI (GenAI) will not make it past the proof-of-concept phase by the end of 2025, due to factors including poor data quality, inadequate risk controls, and escalating costs. [1] AI in action The benefits of this approach are clear to see.
Procurement Takes Too Long, Slowing Innovation The Challenge: Traditional IT procurement cycles average three to six months, delaying critical projects and threatening your organizations competitive edge. Read on to gain insights that can help you procure a strategic advantage with AI. How AI Overcomes 10 Common Procurement Challenges 1.
The data landscape is constantly evolving, making it challenging to stay updated with emerging trends. That’s why we’ve decided to launch a blog that focuses on the data trends we expect to see in 2025. Poor data quality automatically results in poor decisions. That applies not only to GenAI but to all data products.
Technology has shifted from a back-office function to a core enabler of business growth, innovation, and competitive advantage. Senior business leaders and CIOs must navigate a complex web of competing priorities, such as managing stakeholder expectations, accelerating technological innovation, and maintaining operational efficiency.
If data is the new oil, too many CIOs are still stuck building barrels instead of businesses. Despite steady investment in data platforms and governance, many organizations still struggle to extract lasting value from their data. Its a mindset shift: treating data as a product, the way information businesses do.
From prompt injections to poisoning training data, these critical vulnerabilities are ripe for exploitation, potentially leading to increased security risks for businesses deploying GenAI. Data privacy in the age of AI is yet another cybersecurity concern. This puts businesses at greater risk for data breaches.
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