Trading Futures: A Bullish Vision for GenAI in the Enterprise
Intel Capital recently had the privilege of hosting our Customer Connect Event, an engaging forum focused on the transformative role of AI in the enterprise. In partnership with NYSE and Deloitte, the event took place at the historic New York Stock Exchange — providing the perfect backdrop for a day of deep insights and forward-thinking dialogue. Thought leaders from academia, industry, and leading enterprises gathered to explore how AI is reshaping business landscapes, sparking new ideas, and forging meaningful connections.
From exploring the parallels between the NYSE’s evolution and AI’s enterprise revolution to sharing real-world examples of AI in action, the event offered a forward-looking lens into the profound ways AI is reshaping industries. Discussions centered on actionable strategies — rethinking data infrastructure, aligning AI investments with business goals, and leveraging generative AI to optimize processes — all while keeping an eye on scalability and sustainable growth.
The New York Stock Exchange stands as a timeless symbol of both tradition and innovation, embodying a storied history of economic evolution. Once characterized by the iconic chaos of traders on the floor and the rhythmic clatter of the original stock ticker, the NYSE has embraced technological advancements to modernize its operations. Today, most trading occurs electronically, with algorithms and high-speed networks replacing the human hustle of the past, demonstrating how technology can transform longstanding systems without erasing their legacy.
This evolution mirrors what enterprises are experiencing with AI today. Just as the NYSE transitioned from analog to digital, businesses today are leveraging AI to modernize operations, moving from manual data analysis to AI-driven insights.
Generative AI, for instance, is automating complex tasks like drafting contracts or responding to customer RFIs, enabling companies to operate more efficiently. Both transformations highlight the delicate balance of preserving foundational practices while embracing innovation to meet modern demands.
Data Fuels AI
In the race to harness the full potential of AI, data stands as the ultimate differentiator. AI’s value is only as good as the data that fuels it, making the ability to manage, secure, and optimize data a critical edge. Yet, challenges persist—80% of enterprise data resides on-premises, far from the cloud-native environments where many AI models thrive. This misalignment creates friction in making data AI-ready, requiring enterprises to rethink their infrastructure and move compute closer to where the data lives.
Products like MinIO’s AIStor bridge this gap by delivering cloud-on-prem AI workloads. These platforms provide the accessibility, performance, and security needed to transform raw data into actionable insights, all while maintaining control over sensitive enterprise data.
Follow the Money
Enterprises must strategically align AI investments with initiatives that deliver measurable business outcomes, ensuring maximum impact and value creation. The path to AI excellence involves minimizing compute waste and prioritizing front-end data processing to ensure AI efforts are not only effective but also sustainable. Solutions like Fortanix’s Data Security Manager address compliance and trust concerns, while platforms like Anyscale’s LLM Suite optimize resource allocation, enabling enterprises to scale AI initiatives without overspending. By balancing immediate ROI with future scalability, enterprises can demonstrate AI's long-term value to stakeholders. Leveraging the power of cloud technologies responsibly—focusing on efficiency and innovation—ensures that AI drives meaningful and sustainable growth.
A hybrid approach—combining custom in-house solutions with integrated full-stack applications—has emerged as a winning strategy for enterprises. The decision on which approach to take depends on the specific use case. For example, tailored applications may warrant a solution like SambaNova Suite, a tightly integrated full-stack LLM-as-a-Service approach for greater control and flexibility, with benefits ranging from efficiency and performance to time to value. Vendors such as LILT, the enterprise language platform for multimodal content translation and generation, have immediate impact on productivity and cost-efficiency demonstrating tangible results to management. By strategically choosing the right approach for each use case, enterprises can maximize the value and impact of their AI initiatives.
Let’s Not Forget Our History
As enterprises push toward generative AI, and it continues to capture headlines, it's important to reflect on the AI technologies that have already proven their value. Machine learning has a long track record of success in enterprise applications, and there are times when simpler, more efficient methods are not only sufficient but preferable. For example, decision trees and gradient-boosting models are still widely used for predictive analytics in finance and manufacturing, where interpretability and speed often outweigh the need for complex models. In applications like customer churn prediction, traditional ML can deliver high accuracy with significantly lower computational demands than deep learning. Additionally, classic machine learning methods are often more scalable when dealing with small or structured datasets. As Professor Ravid Shwartz Ziv highlighted during the event, by understanding when machine learning is “good enough," enterprises can avoid unnecessary complexity and focus on cost-effective, sustainable AI initiatives. This ensures they leverage the right tools for the right problems.
To fully unlock the potential of generative AI, enterprises must embrace the dual challenge: driving innovation while optimizing resources. By leveraging data efficiently, balancing short-term ROI with long-term scalability, and tailoring AI strategies to specific business needs, companies can ensure sustainable growth in an increasingly AI-driven world. As the AI revolution accelerates, enterprises must act with both urgency and foresight. Whether modernizing operations, refining data strategies, or embracing generative AI, the opportunities are boundless for those ready to lead. At Intel Capital, we’re here to support that journey.
