This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
The United Arab Emirates has taken a bold step by becoming the first country to officially use AI to help draft, review, and update its laws. The system will keep suggesting legal updates as things change and new data comes in. With the global AI legal tech market set to grow from $1.2 billion in 2024 to $3.5
In a world where business, strategy and technology must be tightly interconnected, the enterprise architect must take on multiple personas to address a wide range of concerns. These include everything from technical design to ecosystem management and navigating emerging technology trends like AI.
AI agents are valuable across sales, service, marketing, IT, HR, and really all business teams, says Andy White, SVP of business technology at Salesforce. An AI briefer could inform a sales pipeline review process, for instance, or an AI trainer could simulate customer interactions as part of an onboarding program, he adds.
Data architecture definition Data architecture describes the structure of an organizations logical and physical data assets, and data management resources, according to The Open Group Architecture Framework (TOGAF). An organizations data architecture is the purview of data architects. Curate the data.
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.
In todays economy, as the saying goes, data is the new gold a valuable asset from a financial standpoint. A similar transformation has occurred with data. More than 20 years ago, data within organizations was like scattered rocks on early Earth.
Thats why tech leaders need solutions now, not months from now. Thats an eternity in tech terms ; by the time a deal is signed, market conditions may have changed, new competitors emerged, or the solution itself evolved. See also: How AI is empowering tech leaders and transforming procurement. )
This transformation requires a fundamental shift in how we approach technology delivery moving from project-based thinking to product-oriented architecture. They require fundamentally reimagining how we approach enterprise architecture and technology delivery. The stakes have never been higher.
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.
Artificial Intelligence (AI), a term once relegated to science fiction, is now driving an unprecedented revolution in business technology. The Nutanix State of Enterprise AI Report highlights AI adoption, challenges, and the future of this transformative technology. Nutanix commissioned U.K.
But CIOs need to get everyone to first articulate what they really want to accomplish and then talk about whether AI (or another technology) is what will get them to that goal. Otherwise, organizations can chase AI initiatives that might technically work but wont generate value for the enterprise. What ROI will AI deliver?
Cities like Samarkand, Constantinople and Alexandria became gravitational hubs, attracting merchants, culture and commerce due to their strategic locations. 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.
“The platform brings together guidance and new practical resources which sets out clear steps such as how businesses can carry out impact assessments and evaluations, and reviewingdata used in AI systems to check for bias, ensuring trust in AI as it’s used in day-to-day operations,” the government said in a statement.
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.
Technology continues to advance at a furious pace. When addressed properly , application and platform modernization drives immense value and positions organizations ahead of their competition, says Anindeep Kar, a consultant with technology research and advisory firm ISG. Is your organization overdue for an IT systems update?
As systems scale, conducting thorough AWS Well-Architected Framework Reviews (WAFRs) becomes even more crucial, offering deeper insights and strategic value to help organizations optimize their growing cloud environments. This time efficiency translates to significant cost savings and optimized resource allocation in the review process.
However, in todays era of rapid technological advancement and societal shifts, especially over the past five years, relying solely on traditional approaches is no longer enough to stay competitive. Success depends on understanding data needs, measuring ROI, fostering organizational AI fluency and partnering with ethically aligned ecosystems.
Once the province of the data warehouse team, data management has increasingly become a C-suite priority, with data quality seen as key for both customer experience and business performance. But along with siloed data and compliance concerns , poor data quality is holding back enterprise AI projects.
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.
To be a platform business, you need a network, demand, supply, data, and a customer experience that differentiates. Instead, we own the mode of connection between OEMs, technology brands, vendors, and hundreds of thousands of resellers. What were the technical considerations moving from a distribution model to a platform?
Virtually every company relied on cloud, connectivity, and security solutions, but no technology organization provided all three. Justin Giardina, CTO at 11:11 Systems, notes that the company’s dedicated compliance team is also a differentiator. At 11:11 Systems, we go exceptionally deep on compliance,” says Giardina. “At
Sovereign AI refers to a national or regional effort to develop and control artificial intelligence (AI) systems, independent of the large non-EU foreign private tech platforms that currently dominate the field. This ensures AI decisions align with local social values, reducing the risk of bias, discrimination, or misinterpretation of data.
Increasingly, however, CIOs are reviewing and rationalizing those investments. The reasons include higher than expected costs, but also performance and latency issues; security, data privacy, and compliance concerns; and regional digital sovereignty regulations that affect where data can be located, transported, and processed.
And it’s not just an employee perception issue, says Sriram Nagaswamy, executive vice president for technology at supply chain visibility platform vendor FourKites. Launching AI projects for show can lead to doubts about the technology, even though AI has great potential in supply chain management and other areas, he adds.
The status of digital transformation Digital transformation is a complex, multiyear journey that involves not only adopting innovative technologies but also rethinking business processes, customer interactions, and revenue models. Business is too dependent on technology as a key driver for both business value and differentiation.
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.
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.
Agentic AI systems require more sophisticated monitoring, security, and governance mechanisms due to their autonomous nature and complex decision-making processes. Durvasula also notes that the real-time workloads of agentic AI might also suffer from delays due to cloud network latency.
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.
The G7 AI code of conduct: Voluntary compliance In October 2023 the Group of Seven (G7) countries agreed to a code of conduct for organizations that develop and deploy AI systems. However, notably absent from the code is any form of enforcement or penalty; compliance is completely voluntary.
A single business task can involve multiple steps, use multiple agents, and call on multiple data sources. In fact, recent research and red team reports about frontier language models show that theyre capable of deceit and manipulation, and can easily go rogue if they work from contradictory instructions or bad data sets.
The right tools and technologies can keep a project on track, avoiding any gap between expected and realized benefits. 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.
Standard maintenance for ECC is due to end on December 31, 2027, while the extended maintenance for on-premises SAP ERP systems is set to expire at the end of 2030. The goal, said Kramer, is to reduce risks, security vulnerabilities, and compliance challenges tied to outdated systems. A strong data management plan is also important.
Most CIOs and CTOs are bullish on agentic AI, believing the emerging technology will soon become essential to their enterprises, but lower-level IT pros who will be tasked with implementing agents have serious doubts. There arent a lot of places to learn about agents now, other than hands-on experience, he adds.
Noting that companies pursued bold experiments in 2024 driven by generative AI and other emerging technologies, the research and advisory firm predicts a pivot to realizing value. Forrester said most technology executives expect their IT budgets to increase in 2025. Others won’t — and will come up against the limits of quick fixes.”
Verisk (Nasdaq: VRSK) is a leading strategic data analytics and technology partner to the global insurance industry, empowering clients to strengthen operating efficiency, improve underwriting and claims outcomes, combat fraud, and make informed decisions about global risks.
At scale, upholding the accuracy of each financial event and maintaining compliance becomes a monumental challenge. With advancement in AI technology, the time is right to address such complexities with large language models (LLMs). Amazon Bedrock has helped democratize access to LLMs, which have been challenging to host and manage.
However, as AI insights prove effective, they will gain acceptance among executives competing for decision support data to improve business results.” By 2027, 70% of healthcare providers will include emotional-AI-related terms and conditions in technology contracts or risk billions in financial harm.
This is where the integration of cutting-edge technologies, such as audio-to-text translation and large language models (LLMs), holds the potential to revolutionize the way patients receive, process, and act on vital medical information.
With security, many commercial providers use their customers data to train their models, says Ringdahl. For instance, you might have to pay more to ensure the data isnt being used for training, and might potentially be exposed to the public. Plus, some regions have data residency and other restrictive requirements.
So while the company, of course, wants to be robust for developers, Vo says it is even more focused on brands that lack technical resources or domain expertise. The company, he said, is working to simplify an otherwise complicated process with multiple bank partners, data and card vendors.
Sometimes it actually creates more work than it saves due to legal and compliance issues, hallucinations, and other issues. These technologies can produce more content that everyone needs to consume and be aware of,” says Anita Woolley, professor at Carnegie Mellon University. Hold off,” says Ross.
A successful agentic AI strategy starts with a clear definition of what the AI agents are meant to achieve, says Prashant Kelker, chief strategy officer and a partner at global technology research and IT advisory firm ISG. In addition, can the business afford an agentic AI failure in a process, in terms of performance and compliance?
CIOs must tie resilience investments to tangible outcomes like data protection, regulatory compliance, and AI readiness. According to Salesforces Perez, even though AI brings much opportunity, it also introduces complexity for CIOs, including security, governance, and compliance considerations.
We organize all of the trending information in your field so you don't have to. Join 49,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content