OpenAI’s ChatGPT is setting records for fast consumer application growth. Many similar models to GPT-3.5 are available, paving the way for next-gen AI applications, reliant on AI foundation models for language (AI-FMLs). Enterprises can benefit from these models across domains and work functions, but careful selection and consideration of ecosystem capabilities are essential.
Google Cloud Next showcased AI advancements four months ahead of schedule to compete with AWS and Microsoft Azure. Google highlighted Gemini for enterprise customers, but also emphasized AI integrations, infrastructure, data/analytics, and application development. The event also addressed low-code, FinOps, security, Workspace, and new AI device integrations. Many features were highlighted, but some areas lacked significant announcements.
Artificial intelligence has both identified and caused bias in healthcare. Technical errors and human decisions contribute to AI bias, affecting care decisions and drug development. To mitigate bias, diverse stakeholders should validate algorithms, foster transparency, and monitor for bias and performance disparities. Organizations must proactively correct biases and maintain ethical AI use.
An INSEAD survey of 1,200 alumni from Europe, Asia, and North America reveals that corporate decision-makers and employees are embracing generative AI. 90% see gen AI as an efficiency booster, but a major concern is its potential misuse. Most believe it will benefit employees more than top leaders. The survey also gives insight into AGI development timelines and regional attitudes towards AI.
Public-private partnerships (PPPs) are essential for addressing global challenges. These collaborations leverage complementary competencies, create value, and tackle complex problems. Successful partnerships, like the 17-year alliance between dsm-firmenich and the World Food Programme, require long-term commitment, strategic alignment, and trust. PPPs are crucial for innovatively addressing global issues.
In 2024, application modernization ranks among the top IT investment priorities. The complexity of modernization programs, characterized by evolving technology and a diverse IT landscape, poses significant risks. CIOs must ensure strategic alignment, build robust business cases, learn from past experiences, and prioritize talent reskilling to maximize value and minimize risk.
Many organizations face challenges with legacy systems, causing cost control issues and hampering agility. To address this, experts recommend meticulous assessment of current technology, gradual modernization, and alignment of IT with business goals. Additionally, leveraging AI and machine learning can drive efficiencies and improve customer and employee experiences. This approach requires strategic investments and collaboration for successful implementation.
Data due diligence is crucial for acquiring or evaluating a business. It involves assessing available data to make informed decisions, ensuring data quality, security, and usability. Inadequate due diligence can lead to financial losses, legal issues, and harm to brand reputation. Best practices include setting clear goals, documenting the process, and using technology for accuracy and efficiency.
The COVID-19 crisis prompted a rise in remote work and edge/IoT tech adoption, challenging traditional disaster recovery (DR) plans. Decentralized sites lack dedicated IT support and may not perform regular backups. Companies should assess and update their DR plans to include edge networks and cloud services, and collaborate closely with IT vendors for comprehensive resiliency.
AI TRiSM is a framework for managing AI, focusing on trust, risk, and security. Enterprises face challenges in AI adoption due to skills shortage and reliance on vendors. AI TRiSM aims to ensure accuracy, security, compliance, and explainability. It addresses AI model operations, security, maintenance, and privacy, with a need for broader adoption.
Quantum computing, utilizing qubits instead of traditional bits, promises revolutionary capabilities in cybersecurity, biology, and finance. To prepare for this quantum shift, businesses should retrain talent, embrace quantum-ready infrastructure, and recognize that general-purpose quantum computers are still years away. Early investment in quantum computing offers a competitive advantage.
The global public cloud market is set to grow significantly in the coming years, reaching a projected $679 billion in 2024. AWS’s Cloud Value Framework emphasizes the business value of cloud computing, utilizing key performance indicators (KPIs) to track progress and ensure ROI. Careful selection and tracking of KPIs are crucial for successful cloud migration and ongoing performance evaluation.
AI technology has transformed modern life with voice commands and image generation. However, the demand for power and resources poses sustainability challenges. Researchers are striving to make AI greener, but industry involvement remains limited. Solutions include efficient algorithms, renewable energy use, and consumer pressure. AI’s impact on the energy grid requires ethical and regulatory considerations.
Software-defined vehicles (SDVs) are drastically transforming the automotive industry by shifting from hardware-based to software-centric platforms. Key benefits include customer-centric features through software updates, efficient processing and communication, reduced costs, and improved safety systems. However, security concerns and the need for generative AI (GenAI) training for autonomous driving systems remain prominent challenges.
