Richard Sonnenblick and Lee Rehwinkel of Planview discuss their transition to Amazon Bedrock for a multi-agent AI system while sharing valuable implementation and user experience lessons.

Topics Include:

• Introduction to Planview's 18-month journey creating an AI co-pilot.
• Planview builds solutions for strategic portfolio and agile planning.
• 5,000+ companies with millions of users leverage Planview solutions.
• Co-pilot vision: AI assistant sidebar across multiple applications.
• RAG used to ingest customer success center documents.
• Tracking product data, screens, charts, and tables.
• Incorporating industry best practices and methodologies.
• Can ingest customer-specific documents to understand company terminology.
• Key benefit: Making every user a power user.
• Key benefit: Saving time on tedious and redundant tasks.
• Key benefit: De-risking initiatives through early risk identification.
• Cost challenges: GPT-4 initially cost $60 per million tokens.
• Cost now only $1.20 per million tokens.
• Market evolution: AI features becoming table stakes.
• Performance rubrics created for different personas and applications.
• Multi-agent architecture provides technical and organizational scalability.
• Initial implementation used Azure and GPT-4 models.
• Migration to AWS Bedrock brought model choice benefits.
• Bedrock allowed optimization across cost, benchmarking, and speed dimensions.
• Added AWS guardrails and knowledge base capabilities.
• Lesson #1: Users hate typing; provide clickable options.
• Lesson #2: Users don't like waiting; optimize for speed.
• Lesson #3: Users take time to trust AI; provide auditable answers.
• Question about role-based access control and permissions.
• Co-pilot uses user authentication to access application data.
• Question about subscription pricing for AI features.
• Need to educate customers about AI's value proposition.
• Question about reasoning modes and timing expectations.
• Showing users the work process makes waiting more tolerable.

Participants:

• Richard Sonnenblick - Chief Data Scientist, Planview
• Lee Rehwinkel – Principal Data Scientist, Planview

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Ryan Steeb shares DTEX Systems’ strategic approach to implementing generative AI with AWS Bedrock, reducing risk while focusing on meaningful customer outcomes.

Topics Include:

• Introduction of Ryan Steeb, Head of Product at DTEX Systems
• Explanation of insider risk challenges
• Three categories of insider risk (malicious, negligent, compromised)
• How DTEX Systems is using generative AI
• Collection of proprietary data to map human behavior on networks
• Three key areas leveraging Gen AI: customer value, services acceleration, operations
• How partnership with AWS has impacted DTEX's AI capabilities
• Value of AWS expertise for discovering AI possibilities
• AWS Bedrock providing flexibility in AI implementation
• Collaboration on unique applications beyond conventional chat assistants
• AWS OpenSearch as a foundational component
• Creating invisible AI workflows that simplify user experiences
• The path to monetization for generative AI
• Three approaches: direct pricing, service efficiency, operational improvements
• Second and third-order effects (retention, NPS, reduced churn)
• How DTEX prioritizes Gen AI projects
• Starting with customer problems vs. finding problems for AI solutions
• Business impact prioritization framework
• Technical capability considerations
• Benefits of moving AI solutions to AWS Bedrock
• Fostering a culture of experimentation and innovation
• Adopting Amazon's "working backwards" philosophy
• Balancing customer-driven evolution with original innovation
• Time machine advice: start experimenting with Gen AI earlier
• Importance of leveraging peer groups and experts
• Future outlook: concerns about innovation outpacing risk mitigation
• Security implications of Gen AI adoption
• Participation in the OpenSearch Linux Foundation initiative
• Final thoughts on the DTEX-AWS partnership

Participants:

• Ryan Steeb – Head of Product, DTEX Systems

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From cost management to practical implementation, Sage's Amaya Souarez shares invaluable insights on building AI-powered business tools that deliver measurable value to customers.

Topics Include:

• Amaya Souarez introduced as EVP Cloud Services at Sage
• Overview of Sage: offers accounting, finance, HR and payroll tech for small businesses
• Company emphasizes human values alongside technology development
• Amaya oversees core cloud services and operations across 200+ products
• Sage Co-Pilot announced as new AI assistant – helping automate invoicing and cash flow management
• Common misconceptions with Generative AI
• AI solutions aren’t always solution to every problem
• Compares AI hype to previous blockchain enthusiasm
• Emphasizes starting with clear use cases before implementation
• Difference between task-based and reporting-based use cases
• Partnering with AWS to build accounting-specific language models
• Different accounting terminology varies by country
• Using AWS Bedrock and Lex for a domain-specific language model development
• Multiple AI models may be needed for single solution
• Customer feedback drives project funding decisions
• AI development integrated into regular product roadmaps
• Focus on reducing cost per user for AI features
• Success story: reducing 20-hour task to 5 minutes
• Tracks AI usage costs per customer interaction
• Early Gen AI hype caused confusion in the market
• Plans to make domain-specific models available via API
• Will offer language models on AWS Marketplace
• Emphasizes practical AI application over blind implementation

Participants:

• Amaya Souarez - EVP Cloud Services and Operations, Sage

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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

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

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/

Suresh Vasudevan, CEO of Sysdig, discusses the evolving challenges of cloud security incident response and the need for new approaches to mitigate organizational risk.

Topics Include:

• Cybersecurity regulations mandate incident response reporting.
• Challenges of cloud breach detection and response.
• Complex cloud attack patterns: reconnaissance, lateral movement, exploit.
• Rapid exploitation - minutes vs. days for on-prem.
• Importance of runtime, identity, and control plane monitoring.
• Limitations of EDR and SIEM tools for cloud.
• Coordinated incident response across security, DevOps, executives.
• Criticality of pre-defined incident response plans.
• Increased CISO personal liability risk and mitigation.
• Documenting security team's diligence to demonstrate due care.
• Establishing strong partnerships with legal and audit teams.
• Covering defensive steps in internal communications.
• Sysdig's cloud-native security approach and Falco project.
• Balancing prevention, detection, and response capabilities.
• Integrating security tooling with customer workflows and SOCs.
• Providing 24/7 monitoring and rapid response services.
• Correlating workload, identity, and control plane activities.
• Detecting unusual reconnaissance and lateral movement behaviors.
• Daisy-chaining events to identify potential compromise chains.
• Tracking historical identity activity patterns for anomaly detection.
• Aligning security with business impact assessment and reporting.
• Adapting SOC team skills for cloud-native environments.
• Resource and disruption cost concerns for cloud agents.
• Importance of "do no harm" philosophy for response.
• Enhancing existing security data sources with cloud context.
• Challenges of post-incident forensics vs. real-time response.
• Bridging security, DevOps, and executive domains.
• Establishing pre-approved incident response stakeholder roles.
• Maintaining documentation to demonstrate proper investigation.
• Evolving CISO role and personal liability considerations.
• Proactive management of cyber risk at board level.
• Developing strong general counsel and audit relationships.
• Transparency in internal communications to avoid discovery risks.
• Security teams as business partners, not just technicians.
• Sysdig's cloud security expertise and open-source contributions.

Participants:

· Suresh Vasudevan – CEO, Sysdig

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