Box's Chief Product Officer Diego Dugatkin discusses how the enterprise content management platform is leveraging AI through partnerships with AWS Bedrock and continuing to innovate for their customers.

Topics Include:

• Introduction of Diego Dugatkin as Box's Chief Product Officer
• Box provides cloud content management for enterprise customers
• Focus on Intelligent Content Management
• Box serves 115,000 customers including 70% of Fortune 500
• Company manages approximately one exabyte of enterprise data
• Box expanding product portfolio to offer more customer value
• Partnership with AWS Bedrock for AI implementation announced
• Collaboration with Anthropic for LLM technology integration
• Box offers neutral approach letting customers choose preferred LLMs
• Common misconceptions about generative AI capabilities and limitations
• Generative AI helps accelerate contract analysis and classification processes
• Box Hubs enables content curation and multi-document queries
• Success measured through hub creation and query accuracy metrics
• Long-term AWS partnership continues expanding with new technologies
• Amazon is major Box customer while Box uses AWS
• API integration important for third-party developer implementations
• AI development exceeding speed expectations in efficiency improvements
• Challenges remain in defining AI agent roles and capabilities
• Content strategy crucial for deploying intelligent content management
• Companies must prepare for AI agents in workplace
• Flexibility in tech stack recommended over single-vendor approach
• Next 12-24 months will see accelerated industry changes
• Box maintains innovative culture through intrapreneurship approach
• Company regularly hosts internal and external hackathons
• Focus on maintaining integrated platform while acquiring companies
• Partnership between Box and AWS continues growing stronger

Participants:

• Diego Dugatkin – Chief Product Officer, Box

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Through case studies of Graviton implementation and GPU integration, Justin Fitzhugh, Snowflake’s VP of Engineering, demonstrates how cloud-native architecture combined with strategic partnerships can drive technical innovation and build business value.

Topics Include:

• Cloud engineering and AWS partnership
• Traditional databases had fixed hardware ratios for compute/storage
• Snowflake built cloud-native with separated storage and compute
• Company has never owned physical infrastructure
• Applications must be cloud-optimized to leverage elastic scaling
• Snowflake uses credit system for customer billing
• Credits loosely based on compute resources provided
• Company maintains cloud-agnostic approach across providers
• Initially aimed for identical pricing across cloud providers
• Now allows price variation while maintaining consistent experience
• Consumption-based revenue model ties to actual usage
• Performance improvements can actually decrease revenue
• Company tracked ARM's move to data centers
• Initially skeptical of Graviton performance claims
• Porting to ARM required complete pipeline reconstruction
• Discovered floating point rounding differences between architectures
• Amazon partnership crucial for library optimization
• Graviton migration took two years instead of one
• Achieved 25% performance gain with 20% cost reduction
• Team requested thousands of GPUs within two months
• GPU infrastructure was new territory for Snowflake
• Needed flexible pricing for uncertain future needs
• Signed three to five-year contracts with flexibility
• Team pivoted from building to fine-tuning models
• Partnership allowed adaptation to business changes
• Emphasizes importance of leveraging provider expertise
• Recommends early engagement with cloud providers
• Build relationships before infrastructure needs arise
• Maintain personal connections with provider executives

Participants:

• Justin Fitzhugh – VP of Engineering, Snowflake

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In this AWS panel discussion, Naveen Rao, VP of AI of Databricks and Vijay Karunamurthy, Field CTO of Scale AI share practical insights on implementing generative AI in enterprises, leveraging private data effectively, and building reliable production systems.

Topics Include:

• Sherry Marcus introduces panel discussion on generative AI adoption
• Scale AI helps make AI models more reliable
• Databricks focuses on customizing AI with company data
• Companies often stressed about where to start with AI
• Board-level pressure driving many enterprise AI initiatives
• Start by defining specific goals and success metrics
• Build evaluations first before implementing AI solutions
• Avoid rushing into demos without proper planning
• Enterprise data vastly exceeds public training data volume
• Customer support histories valuable for AI training
• Models learning to anticipate customer follow-up questions
• Production concerns: cost, latency, and accuracy trade-offs
• Good telemetry crucial for diagnosing AI application issues
• Speed matters more for prose, accuracy for legal documents
• Cost becomes important once systems begin scaling up
• Organizations struggle with poor quality existing data
• Privacy crucial when leveraging internal business data
• Role-based access control essential for regulated industries
• AI can help locate relevant data across legacy systems
• Models need organizational awareness to find data effectively
• Private data behind firewalls most valuable for AI
• Customization gives competitive advantage over generic models
• Current AI models primarily do flexible data recall
• Next few years: focus on deriving business value
• Future developments in causal inference expected post-5 years
• Complex multi-agent systems becoming more important
• Scale AI developing "humanity's last exam" evaluation metric
• Discussion of responsibility and liability in AI decisions
• Companies must stand behind their AI system outputs
• Existing compliance frameworks can be adapted for AI

Participants:

• Naveen Rao – VP of AI, Databricks
• Vijay Karunamurthy – Field CTO, Scale AI
• Sherry Marcus Ph.D. - Director, Applied Science, AWS

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/