The CES AI House at CES 2025 brought together leaders from across the AI ecosystem to discuss the trends, challenges, and opportunities shaping the future of artificial intelligence. With multiple panels spanning topics like generative AI, large language models (LLMs), and AI investment strategies, the event offered a comprehensive look at how AI is transforming industries. GMI Cloud was proud to be featured in two of these panels, contributing its expertise on both enterprise AI adoption and the strategies driving AI investments.

These discussions revealed actionable insights for startups, enterprises, and investors navigating the rapidly evolving AI landscape. Let’s dive into the key highlights from these standout panels.

Accelerating Global AI Investment

Moderated by Denise K. Záles (CIO of Incrediwear), the panel included Tony Kim (BlackRock Fundamental Equities), Moyi Dang (Coinfeeds), Alex Yeh (GMI Cloud), Warren Packard (AI Fund), and Tien Wong (IronGate Capital). Their perspectives provided a roadmap for navigating AI’s complex but promising landscape.

Here are the key insights from the session

The Evolving AI Investment Landscape

AI has become a horizontal technology, touching nearly every industry, but the investment strategies that underpin it remain rooted in scalability and differentiation.

• Tony Kim described AI’s dual role: “Labor accounts for the largest share of enterprise spending, and AI agents, co-pilots, and other tools are poised to transform this space by reducing costs while creating entirely new ways of working.”
• Warren Packard likened AI’s transformative potential to electricity: “We pair subject-matter experts with cutting-edge AI to build businesses that disrupt industries at scale.”
• Both panelists emphasized that vertical-specific applications represent the biggest opportunities for innovation and investment.

Enterprise AI: Overcoming Challenges to Unlock Potential

Enterprises are often held back by systemic barriers when deploying AI. Security, data silos, and scaling infrastructure are top concerns.

• Alex Yeh stressed the critical importance of security: “For enterprises, security is everything—especially for sensitive data still stored on-premise. Without robust systems, deploying AI at scale becomes a significant risk.”
• Enterprises often struggle with fragmented data across departments. “To extract insights, businesses must break down silos and adopt IAM systems that ensure only the right employees can access the right information,” Alex explained.
• Tien Wong noted the role of AI agents in enterprises: “These systems can increase productivity by automating repetitive tasks, but their deployment requires clear guidelines to avoid ethical or regulatory pitfalls.”

GMI Cloud plays a vital role in addressing these challenges by helping enterprises design scalable, secure solutions tailored to their needs.

Startups: Speed, Cost, and Usability Drive Success

Startups are moving at lightning speed to capitalize on AI’s potential, but success hinges on three key factors: speed, cost, and ease of use.

• Moyi Dang shared how Coinfeeds helps investment funds analyze massive datasets with AI: “Our tools allow startups and funds to uncover patterns in hours instead of weeks. Speed and scalability are game-changers.”
• Startups don’t have the time or resources to manage complex infrastructure. “They need tools that are simple to integrate and easy to deploy,” Moyi said.
• Alex Yeh echoed this, emphasizing the importance of agility: “Promising a product a year from now isn’t enough when your competitors are launching today. Scalable GPU usage and intuitive APIs are critical for startups to succeed.”

Data Diversity and Localized AI Models

The panel agreed that diverse datasets are crucial for developing impactful AI solutions. Without them, applications risk missing the mark in industries like healthcare or consumer products.

• Warren Packard underscored this point: “You can’t advance medicine with data that only represents a narrow segment of the population. AI must reflect the diversity of its users.”
• Alex Yeh added, “Localized LLMs are critical for accuracy. Ask a global AI model about the best ramen shop in Kyoto, and it might not know. Tailoring models for specific geographies ensures relevance.”

The future also lies in synthetic data, as Tony Kim pointed out: “The vast majority of human-created data has already been consumed. Advances will increasingly rely on synthetic datasets and reasoning models designed for specific domains.”

What Lies Ahead for AI Investment?

As the conversation turned to the future, the panelists outlined two major trends: consolidation at the infrastructure level and innovation at the application layer.

• Alex Yeh noted, “Infrastructure requires immense capital. The players are already set, and we’ll soon see a wave of mergers and acquisitions.”
• While large players dominate foundational infrastructure, Tony Kim highlighted opportunities for startups: “The application layer is ripe for innovation, with vertical-specific solutions redefining industries.”

AI’s dual focus on cost efficiency and breakthroughs will continue to drive the market. As Tony observed, “AI isn’t just about saving money—it’s about creating entirely new markets that didn’t exist before.”

Takeaways from This Session

This panel revealed a wealth of insights into the rapidly evolving AI landscape:

• For enterprises: Address systemic challenges like security and data silos to unlock AI’s full potential.
• For startups: Focus on speed, cost, and usability to gain a competitive edge in crowded markets.
• For investors: Look for vertical-specific applications that address real-world problems and have clear paths to scalability.

As Alex Yeh concluded, “The future of AI lies in enabling businesses to harness its power securely and effectively. The real winners will be those who innovate while keeping an eye on practical adoption.”

The panel session can be watched in its entirety here.

The Race for Generative AI and LLM Supremacy

Featuring Sandy Carter (Women of AI and Web3), Stephanie Buckner (Altair), Ray Wang (Constellation Research), Hassan Sawaf (AI Explain), and Yujing Qian (GMI Cloud). Here we highlight the panel’s key observations on the current market landscape, the ongoing transformation of businesses, and expert insights into emerging trends and the future direction of the industry.

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Generative AI: A Market Poised for Growth

The panel shared staggering statistics on AI adoption:

• 77% of companies are exploring LLMs for production use.
• The generative AI market is projected to hit $1.3 trillion by 2030.
• By 2025, 90% of enterprises plan to use generative AI.

These numbers highlight the transformative potential of AI. Ray Wang illustrated practical examples, such as Otter.ai streamlining workflows through meeting summaries, and AI’s increasing presence in HR, regulatory compliance, and code generation. "The point of AI," Ray emphasized, "is not more AI, but better decisions."

Transforming Business: Insights from Stephanie Buckner

Stephanie Buckner brought a wealth of experience from Altair, emphasizing how AI adoption is uneven across industries. “A huge concern is around control and governance,” she explained, pointing to the challenges of ensuring that the right people access the right information while avoiding issues like hallucinations in AI outputs.

She also highlighted the workforce transformation brought by AI, stating, “Many skillsets today won’t be needed in the future, but new skillsets will emerge.” AI isn’t just replacing jobs; it’s creating opportunities for reskilling and redefining roles.

From Vision to Execution: Yujing Qian’s Approach

As a leader at GMI Cloud, Yujing Qian offered a pragmatic perspective. He urged businesses to avoid overambitious projects and instead start small:

• Identify low-risk, high-ROI projects like co-pilot workflows or customer service enhancements.
• Focus on integrating AI into compliance-heavy areas or leveraging customer data for insights.
• Tackle data issues first, whether through improving quality or resolving privacy and encryption challenges.

He observed that many enterprises face a significant ‘gap between what they’re doing and what they want to achieve.’ Often, companies begin their AI journeys with an ambitious laundry list of goals, only to find the path to execution more complex than anticipated. To succeed, he recommended focusing on low-risk, high-revenue projects as a starting point, which provide quick wins and build momentum.

From there, enterprises can tackle pressing challenges like data quality, compliance, and GPU resource allocation—critical components of any scalable AI strategy. Platforms like GMI Cloud play a pivotal role in bridging this gap, offering the infrastructure and expertise businesses need to scale their AI initiatives efficiently and cost-effectively.

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AI Agents: The Future of Business Operations

Hassan Sawaf highlighted the rise of AI agents, virtual employees capable of augmenting human workflows. Sharing an example from the insurance industry, he described how AI agents can manage claims processing during disasters, taking on administrative tasks while freeing human agents to focus on customer interactions.

“The bottleneck isn’t perfect data,” Hassan noted. “It’s having the right data to serve a specific problem. Start fast, start small.”

He also introduced the concept of microagents, which act as bodyguards for AI solutions, ensuring compliance, security, and unbiased operations.

Data as the Cornerstone: Stephanie’s Focus on Foundations

Returning to the theme of data, Stephanie reiterated that data quality is more important than AI modeling. “Most data today is segmented into silos,” she explained. For businesses to succeed, they must unify their data and build a robust foundation before diving into complex modeling.

Decision Automation: Ray Wang’s Perspective

Ray Wang brought attention to decision-making automation, where AI enables companies to build decision maps and pinpoint areas where human judgment remains critical. He claims that automation of decision-making is a key purpose for AI. He posed a pivotal question for enterprises: “Are you production-ready? Who assumes liability when something goes wrong?” This focus on accountability underscores the importance of robust implementation strategies especially in industries like

 finance and healthcare where even 85% accuracy isn’t good enough.

When discussing how AI can drive ROI, Ray emphasized that businesses should approach AI projects as not standalone initiatives but rather integrated in overall business projects in critical areas such as supply chain management, marketing, or cybersecurity. 

The Road Ahead: Future Trends

Yujing closed the session with a forward-looking perspective:

• Experiment with smaller AI models to solve core problems at a lower cost.
• Invest in infrastructure to scale AI initiatives while reducing operational expenses.
• Embrace AI agents, which are poised to solve established business problems by acting as virtual employees with functional core capabilities.

He emphasized the importance of companies retaining ownership of their data, cautioning that "the battle for LLMs is cool, but datasets are equally important."

Key Takeaways from this Session

The session revealed three critical factors for AI success:

1. Start Small: Focus on achievable goals with clear ROI before scaling.
2. Data Quality: Build on a solid data foundation to maximize AI’s potential.
3. Execution Matters: Implementation separates leaders from laggards—embrace the challenge of scaling AI to production.

Conclusion

The CES AI House panel showcased how generative AI and LLMs are reshaping industries. From tackling data silos to deploying AI agents, the insights shared by these industry leaders are a blueprint for companies aiming to leverage AI effectively.

As Yujing Qian eloquently summarized, “The key to success lies in tailored solutions that reduce costs, enhance ROI, and deliver real value.”

For businesses looking to embark on their AI journey, the message was clear: start small, experiment boldly, and invest in infrastructure that scales with ambition. The future of AI isn’t just about innovation—it’s about making smarter decisions, faster.

The panel can be watched in its entirety here.

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