Tag: IT strategy and trend

In the U.S., despite a strong move towards sustainability, organizations have been slow in executing ESG promises, primarily due to a lack of direct leadership involvement and inadequate utilization of cloud technology. European companies, conversely, are ahead in using the cloud to enhance sustainability, with many adopting cloud-based systems for energy management and emissions reporting. The U.S. lags in leveraging the cloud’s full potential for ESG and DEI goals, impacting their performance and credibility in sustainability.

Since the iPhone’s debut in 2007, apps have revolutionized interaction with technology. However, advancements in artificial intelligence, especially generative AI, hint at a post-app era. These innovations may lead to more intuitive, AI-driven interfaces, potentially making standalone apps seem cumbersome. The future may favor technology that anticipates and seamlessly fulfills needs, diminishing the app’s dominance and ushering in a more integrated, AI-centric interaction paradigm.

The growing adoption of edge artificial intelligence (AI) is revolutionizing industries, improving decision-making capacities and tackling logistical complexities. However, challenges remain. Smaller edge devices face computational constraints and require good quality datasets. Connectivity and power supply in remote locations pose additional issues. Despite these obstacles, advancements in generative AI and machine-to-machine learning present promising solutions, paving the way for transformative changes across industries and society alike.

The lack of standardized terminology in data governance can hinder program maturity assessment and improvement plans development. To address this issue, the author proposes definitions for terms like mission and vision, standards, business policies, data policies, data controls, and data controls methods. A successful data governance program should add value to an organization’s products, services, and processes while minimizing costs, complexity, delays, and risks.

Infrastructure and operations (I&O) organizations are transitioning from siloed infrastructure to an infrastructural platform-based approach, improving business outcomes and agility. Adoption of this approach could lead to significant advances through employing artificial intelligence, improvements in energy-efficiency in data centres, and greater effectiveness of I&O teams with specialized AI infrastructures. This fundamental shift is expected to be largely evident by 2028 according to Gartner’s predictions.

After decades of experimenting and innovating, researchers and practitioners have finally developed the right recipe for a successful AI implementation. Now, with the advent of

The pace of technological change is rapidly accelerating, significantly impacting every aspect of life. Key trends include Generative AI becoming culturally aware and more accessible worldwide, advancements in FemTech reshaping women’s healthcare, AI assistants improving developers’ productivity, and a shift in education towards continuous, industry-led skills-based training programs. These advancements promise to democratize technology access, alleviate tasks, address overlooked medical conditions, and better equip the workforce for ever-evolving tech innovation.

Hybrid cloud, a versatile IT solution combining private and public clouds with on-premises infrastructure, is becoming key for next-generation computing. It supports agility, control, security, compliance, cost optimization, business continuity, and innovation. However, challenges include complex implementation, management of multi-cloud environments, visibility across systems and processes, and vendor and cost management. As such, it’s crucial for businesses to carefully evaluate their needs to maximize potential benefits and minimize drawbacks.

Untested AI-generated code can pose significant risks, leading to bugs, crashes, non-compliance, and potential legal complications for organizations. IT leaders need to prioritize monitoring, testing, code reviews, and human oversight. Making testing an integral part of the software development lifecycle, involving stakeholders early, and educating developers through custom training can mitigate these issues. AI-generated code can serve as a starting point, which can be refined by human developers to ensure adherence to standards.

ChatGPT is plugged into a lot of apps and more so with every passing day. People are plugging into ChatGPT in record numbers. Then OpenAI

Application modernization involves updating legacy applications with modern technologies and transformative strategies. This process encounters challenges such as duplication, overlapping capabilities, and lack of alignment with business capabilities. Generative AI is becoming increasingly vital in accelerating modernization programs. Its use ranges from code generation and API mapping to providing insights for security validation, though careful contextual orchestration is required.

Underwater data centers might be part of the solution to the challenges of rising energy usage and environmental impacts of traditional data centers. These innovative structures could rely on the cool ocean depths for energy-efficient cooling while being close to population centers for lower latency. Microsoft’s Project Natick was among the first large-scale experiments, demonstrating some effectiveness in reducing energy usage. Companies like Subsea Cloud are continuing with this approach, but long-term practicality and environmental impacts are yet to be fully understood.

Smart factories are digitized facilities using automation and self-optimization to streamline processes, benefiting business functions such as planning, supply chain logistics, and product development. Their adoption helps yield actionable insights and protects businesses from cyber threats. Suitable for complex manufacturing fields like automotive and electronics, smart factories enhance efficiency and competitiveness. However, their implementation can be complex and expensive. As the global economy becomes more competitive, adopting smart technologies is crucial for businesses to improve efficiency and remain competitive.

OpenAI’s new method, Q*, merges Q-learning and A* Search, aiming to advance Artificial General Intelligence (AGI). Offering powerful language/reasoning capabilities over a multi-step process, it constructs a “tree-of-thoughts” to solve reasoning paths. Utilizing Process Reward Models (PRMs) to assign scores to each reasoning step, Q* leverages self-play and look-ahead planning, mainstream methods in Deep RL. It provides the potential for significant advance in large language model training and usage. However, implementing this requires extensive computing resources and expertise.