Solving Global Challenges, One Innovation at a Time

Artificial Intelligence (AI) is no longer a futuristic concept, but a powerful force reshaping industry worldwide. The 2024 Global Trends in AI report by WEKA reveals how AI is not only driving innovation and competition but also addressing critical challenges like outdated infrastructure and resource scarcity. The rise of generative AI is particularly exciting, with nearly 88% of organizations exploring its potential to revolutionize workflows, products, and customer experiences.

• A Creative Revolution: With nearly 88% of organizations exploring its potential, generative AI is poised to reshape workflows, products, and customer experiences.
• AI as a Solution Provider: From legacy infrastructure modernization to environmental sustainability, AI is emerging as a key solution to global challenges.
• AI-Driven Growth: As AI continues to mature, organizations are leveraging its power to gain a competitive edge and drive sustainable growth.

AI is not just about implementation; it's about strategic impact. While AI has historically been seen as a cost-cutting tool, like in robotic process automation, its potential is now expanding beyond mere cost reduction. While cost savings remain a significant benefit, organizations are increasingly combining cost-cutting with strategic objectives to maximize the value of AI.

What are the Key Trends in Enterprise AI?

1. AI is Now a Strategic Priority for Enterprises

AI is rapidly becoming an indispensable tool for businesses across industries. Its adoption rate is accelerating, with organizations recognizing AI as a core competency. This shift is driven by the technology's ability to automate tasks, enhance decision-making, and unlock new opportunities for innovation.

• Maturing AI Landscape: Last year, organizations were primarily experimenting with AI or had limited deployments. However, this year, most organizations are now widely implementing AI and recognizing its significant contribution to their business.
• Key Investment Drivers: Organizations are primarily investing in AI to improve product or service quality and drive revenue growth. Additionally, they are focused on enhancing operational efficiency through increased workforce productivity, optimized IT processes, and accelerated innovation.

2. Scaling AI Faces Legacy Data Challenges

AI initiatives frequently encounter obstacles due to weak data foundations. Traditional data architectures hinder widespread implementation.

• Scaling AI remains difficult: Organizations face significant challenges in expanding the scope of their AI projects. On average, companies have 10 projects in the trial phase, 16 with limited deployment, and only six deployed at scale.
• Quality Data Hinders AI Scaling: Data quality poses the most significant challenge to transitioning AI projects into production. Project teams often struggle not with identifying relevant data, but with its accessibility. Organizations find it difficult to establish a cohesive, integrated data foundation for their projects.
• Modernizing data architectures is crucial for success: Given these challenges, it's unsurprising that the majority of respondents (35%) identify storage and data management as the primary infrastructure obstacles hindering AI deployments, surpassing compute (26%), security (23%), and networking (15%).

3. Generative AI is the New Frontier of AI

Generative AI has taken the AI world by storm. Many businesses are already reaping the rewards and are set to further solidify their lead.

• Generative AI is the top priority: A whopping 88% of companies are exploring generative AI, far surpassing other AI applications like prediction models, classification, expert systems, and robotics. As businesses recognize its potential, they're allocating more budget to generative AI.
• Rapid adoption of generative AI: Despite its recent emergence, 24% of organizations have already integrated generative AI into their operations. Only a small fraction (11%) isn’t investing in it, and most are actively working to scale up their generative AI capabilities.
• Early adopters gain a competitive edge: Organizations that have already integrated generative AI plan to further increase their investments, aiming for it to account for 47% of their total AI budget in the next year. These early adopters are experiencing significant benefits across the board, such as increased innovation, faster product development, and quicker time-to-market. This advantage is likely to grow as other organizations are still in the early stages of their generative AI journey.

4. GPU Shortages Hinder AI Deployment

A significant challenge for organizations is securing access to GPUs, the powerful processors crucial for running AI models.

• GPU scarcity: Nearly half of the surveyed organizations prioritize access to AI accelerators when making infrastructure decisions. Additionally, 30% identify GPU availability as a major hurdle in deploying AI models.
• Regional disparities: The issue is particularly acute in regions like Asia-Pacific, especially India, where 38% of organizations cite limited access to GPUs as a top challenge.
• Cloud solutions as a lifeline: To overcome these limitations, 46% of organizations are turning to hyperscaler public clouds for model training. Moreover, 32% are increasingly relying on specialized GPU cloud providers to access the necessary computing power.

5. Balancing AI Innovation with Environmental Responsibility

While the environmental impact of AI remains a significant concern for many organizations, it hasn't deterred their investment in AI projects. As sustainability becomes increasingly important, there's a growing opportunity to mitigate AI's carbon footprint.

• Environmental concerns: A substantial number of organizations (64%) are worried about the energy consumption and carbon emissions associated with AI and machine learning projects.
• Sustainable practices: Organizations are taking steps to address these concerns, such as investing in energy-efficient hardware and software, optimizing data infrastructure, and carefully scoping AI projects.
• Prioritizing sustainability: While sustainability is a factor influencing AI decisions, it's often not the primary driver. Cost savings, operational efficiency, and security typically take precedence. However, as AI is increasingly used to improve energy efficiency and reduce emissions, its role in sustainability efforts is growing.

Key Drivers of AI Infrastructure Decisions in 2024

In the AI revolution, it’s not just about the technology; it’s about the foundation that supports it. Security, performance, and flexibility are the pillars holding up 2024’s AI infrastructure.

Security, performance, and flexibility are the core pillars of AI infrastructure. However, a broader range of factors, including access to AI accelerators, reliability, data privacy, sustainability, and scalability, also play crucial roles. While factors like customer support and data locality may be less significant, they still contribute to the overall AI ecosystem.

1. Top Priorities:

• Security (47%)
• Access to AI accelerators (44%)
• Reliability and availability (42%)
• Flexibility (39%)
• Operating costs (38%)
• Data privacy and governance (38%)

2. Moderate Priorities:

• Sustainability (37%)
• Scalability (35%)
• Access to AI/ML tools and frameworks (33%)

3. Niche Concerns:

• Customer support (31%)
• Up-front costs (28%)
• Data locality/sovereignty (26%)

4. Lesser Priorities:

• Developer communities (21%)
• Geographic distribution (20%)
• Latency (17%)
• Vendor lock-in (15%)

How Can Your Business Leverage AI for Success?

AI adoption has significantly accelerated in 2024, primarily driven by the advancements in generative AI. Businesses are increasingly focused on leveraging AI to enhance product and service quality, and boost revenue.

However, challenges persist. Many organizations struggle with scaling AI initiatives and balancing sustainable operations.

Five Key Recommendations for Businesses:

• Prioritize Data Infrastructure: Build a strong data foundation to support large-scale AI projects.
• Invest Wisely in Generative AI: Allocate resources strategically to maximize the benefits of generative AI, focusing on infrastructure and decision-making.
• Leverage Generative AI for IT Efficiency: Automate routine tasks and improve decision-making to streamline IT operations.
• Embrace Sustainability: Adopt sustainable practices in AI development and deployment, such as optimizing infrastructure and project scopes.
• Create a Comprehensive AI Strategy: Develop a holistic AI strategy that considers a diverse range of AI technologies and use cases.

For more info about Weka, email us at: marketing.communication@infracom-tech.com 

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Written By : Marketing Communication ICT | Published : 17/Dec/2024