In 2023, few foresaw the impact AI would have. Now, experts predict 2024 AI trends: LLMs leading to AGIs, stronger cyber defense vs. crafty attackers, multi-modal AI, generative AI advances, disruptive data analysis, AI in shopping, deepfakes proliferation, professional content creation, AI in software engineering, and AI governance strengthening. These trends will shape the future of AI in IT and business.
AI and data science professionals are in high demand due to the boom in AI models. Job roles like AI-adjacent talent, prompt engineers, LLM and NLP engineers, and emerging AI positions are on the rise. The industry is evolving, and employees with AI skills will become more valuable as AI continues to change job roles.
A new report titled “Top 10 Trends In Infrastructure” highlights the crucial nature of technology infrastructure for enterprises worldwide. Key trends include AI-optimized infrastructure facing chip shortages, hyperscalers targeting specialized infrastructure, mainstream adoption of serverless technology shifting towards the edge and IoT, and the growing importance of sustainability initiatives in shaping infrastructure demands. These trends are essential for technology infrastructure leaders to consider as they plan for the future.
Artificial Intelligence (AI) is revolutionizing industries by automating tasks that required human intelligence. This change is spawning innovation and reshaping business operations. An effective AI strategy helps organizations navigate AI implementation complexities, aligning technology with business goals. Such strategies involve deep data analysis, process optimization, or enhancing customer experiences. A robust AI plan ensures adoption aligns with business objectives, providing a roadmap for addressing challenges, building capabilities, and integrating AI strategically and responsibly into the organization. Those embracing AI now are set to thrive, adapting swiftly to technological advancements and optimizing processes for growth.
Hybrid cloud has emerged as a preferred IT infrastructure, offering interoperability and portability for on-demand data access. Building and managing such an environment comes with challenges. To maximize value, leaders must strategize around core objectives. It involves integrating public, private, and on-premises infrastructure, utilizing orchestration tools for unified deployment and management. Key to this strategy is aligning with business needs, determining cloud migration necessities, assessing workloads, and choosing appropriate cloud partners, all while ensuring robust security and compliance.
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.
