Artificial intelligence has become one of the core technologies of today's business operations. From data centers to conference rooms, AI applications are everywhere, driving innovation in new products and services. However, to realize the full potential of AI, companies must align and invest in strategy, talent, architecture, and data management to ensure smooth implementation.
How companies are responding to the challenge: balancing foundational and emerging technologies
Today, many companies have adopted a "best of both worlds" strategy, investing in both foundational and emerging technologies. This balanced approach is proven to help companies unlock their growth potential and ensure maximum value from their technology. While AI is leading the way, companies need to address challenges such as technology scaling, data management, and energy consumption to achieve greater breakthroughs.
From siloed technology to converged solutions
As the needs of industries continue to change, traditional stand-alone technologies are being replaced by integrated, connected business solutions. Artificial intelligence is currently one of the most talked-about technologies, but there are still some challenges to overcome before it can be applied to a larger scale. For example, unlocking the potential of new technologies such as space interfaces, next-generation chipsets, quantum computing, etc., will present tremendous opportunities for various industries.
Three key trends: the future of AI
According to Deloitte's 2024 Technology Trends Report, AI will see significant changes in the next 18 to 24 months.
Spatial computing: Interaction from plane to stereotype
Traditional human-computer interaction methods mostly rely on two-dimensional screens, but with the development of AI, voice and gesture operations are becoming new ways of interaction. Spatial computing technology provides a more intuitive and immersive experience by combining data with real-world scenarios. With the addition of AI, spatial computing is not limited to training tools, and more and more enterprises are beginning to use this technology to conduct real-time data analysis and intelligent adjustments, thereby improving overall business efficiency.
Miniaturized models drive AI at scale
At present, many enterprises are choosing to solve specific tasks with "small and specialized" AI models, rather than using huge hyperscale models. Miniaturized AI models can not only respond more flexibly to different scenarios, but also reduce energy consumption and improve security. For example, some businesses are developing multiple small models that can work together to solve different tasks and provide more specialized services. This trend may lead to a shift in AI applications from "big models" to "small but fine" distributed models.
Figure: Deloitte: Artificial intelligence leads the future (Source: WV News)
AI materialization: Making PCs and the Internet of Things smarter
AI is no longer relying solely on cloud computing, and more and more hardware manufacturers are embedding AI models in PCs and edge devices, allowing devices to process data locally without relying on remote servers. This not only improves the responsiveness of the device, but also saves energy, especially in Internet of Things (IoT) devices, where the application of AI makes medical devices, robotics, and other fields smarter and more efficient.
The technological revolution and AI are reshaping the IT sector
Artificial intelligence is not only revolutionizing business processes, but is also beginning to change the way enterprise IT departments operate. AI-based automation enhances the ability of code writing, software testing, and technical staff to do their jobs. This means that enterprise IT will no longer be simply technical support, but will become a key part of strategic decision-making.
At the same time, the introduction of AI may also make the IT architecture of enterprises more flexible and intelligent, helping companies better coordinate workloads and optimize resource allocation. This new type of "intelligent IT" will redefine the technical support model of enterprises and become the driving force for enterprise innovation.
The challenge: quantum computing and cybersecurity
With the continuous development of quantum computing technology, current cybersecurity measures will face severe challenges. Quantum computing is able to decrypt the security defenses of current encryption technologies, which poses a huge threat to the privacy and data protection of enterprises. To address this challenge, countries across the globe are stepping up with new encryption standards to ensure data security. Businesses need to prepare in advance to adapt to the arrival of the quantum era.
The convergence of AI and legacy systems: from complex to simplified
Introducing AI technology into the core architecture of an enterprise can bring about profound changes in systems and processes. While AI can simplify the user experience, achieving this goal requires a complex technology architecture to support it. Businesses still rely on traditional custom systems (such as ERP and cloud solutions), and the addition of AI has the potential to change the core content of these systems, and even redefine the way data flows and transacts.
