Yashodha Bhavnani, Head of AI at Box, reveals Box's vision for intelligent content management that transforms unstructured data into actionable insights.

Topics Include:

• Yashodha Bhavnani leads AI products at Box.
• Box's mission: power how the world works together.
• Box serves customers globally across various industries.
• Works with majority of Fortune 500 companies.
• AI agents will join workforce for repetitive tasks.
• Workflows like hiring will become easily automated with AI.
• Content will work for users, not vice versa.
• Customers demand better experiences with generative AI.
• Box calls this shift "intelligent content management."
• 90% of enterprise content is unstructured data.
• AI thrives on unstructured data.
• Current content systems are unproductive and unsecured.
• AI can generate insights from scattered company knowledge.
• AI extracts metadata automatically from documents like contracts.
• Automated workflows triggered by AI-extracted data.
• Box provides enterprise-grade AI connected to your content.
• AI follows same permissions as the content itself.
• Customer data never used to train AI models.
• AI helps classify sensitive data to prevent leaks.
• Box offers choice of AI models to customers.
• AI is seamlessly connected with customer content.
• Administrators control AI deployment across their organization.
• Partnership with AWS Bedrock brings frontier models to Box.
• Box supports customers using their own custom models.
• Box preparing for AI agents to join workforce.
• Introduced "AI Units" for flexible pricing.
• Basic AI included free with Business Plus tiers.
• Both horizontal and vertical multi-agent architectures planned.
• Working toward agent-to-agent communication protocols.

Participants:

• Yashodha Bhavnani - VP of Product Management, AI products, Box

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

Tech leaders from RingCentral, Zoom and AWS discuss how generative AI is transforming business communications while balancing challenges & regulatory concerns in this rapidly evolving landscape.

Topics Include:

• Introduction of panel on generative AI's impact on businesses.
• How to transition AI from prototypes to production.
• Understanding value creation for customers through AI.
• Introduction of Khurram Tajji from RingCentral.
• Introduction of Brendan Ittleson from Zoom.
• How generative AI fits into Zoom's product offerings.
• Zoom's AI companion available to all paid customers.
• Zoom's federated approach to AI model selection.
• RingCentral's new AI Receptionist (AIR) launch.
• How AIR routes calls using generative AI capabilities.
• AI improving customer experience through sentiment analysis.
• The disproportionate value of real-time AI assistance.
• Economics of delivering real-time AI capabilities.
• Real-time AI compliance monitoring in banking.
• Value of preventing regulatory fines through AI.
• Voice cloning detection through AI security.
• Democratizing AI access across Zoom's platform.
• Monetizing specialized AI solutions for business value.
• Challenges in taking AI prototypes to production.
• Importance of selecting the right AI models.
• Privacy considerations when training AI models.
• Maintaining quality without using customer data for training.
• Co-innovation with customers during product development.
• Scaling challenges for AI businesses.
• Case study of AI in legal case assessment.
• Ensuring unit economics work before scaling AI applications.
• Zoom's approach to scaling AI across products.
• Importance of centralizing but federating AI capabilities.
• Breaking down data silos for effective AI context.
• Navigating evolving regulations around AI.
• EU AI Act restrictions on emotion inference.
• Balancing regulations with customer experience needs.
• Future of AI agents interacting with other agents.
• How AI enhances human connection by handling routine tasks.
• Impact of AI on company valuations and M&A activity.

Participants:

• Khurram Tajji – Group CMO & Partnerships, RingCentral
• Brendan Ittleson – Chief Ecosystem Officer, Zoom
• Sirish Chandrasekaran – VP of Analytics, AWS

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CEO Joe Kim shares how Sumo Logic has implemented generative AI to democratize data analytics, leveraging AWS Bedrock's multi-agent capabilities to dramatically improve accuracy.

Topics Include:

• Introduction of Joe Kim, CEO of Sumo Logic.
• Question: Overview of Sumo Logic's products and customers?
• Sumo Logic specializes in observability and security markets.
• Company leverages industry-leading log management and analytics capabilities.
• Question: How has generative AI entered this space?
• Kim's background is in product, strategy and engineering.
• Non-experts struggle to extract value from complex telemetry data.
• Generative AI provides easier interface for interacting with data.
• Question: How do you measure success of AI initiatives?
• Focus on customer problems, not retrofitting AI everywhere.
• Launched "Mo, the co-pilot" at AWS re:Invent.
• Mo enables natural language queries of complex data.
• Mo suggests visualizations and follow-up questions during incidents.
• Question: What challenges did you face implementing AI?
• Team knew competitors would eventually implement similar capabilities.
• Single model approach topped out at 80% accuracy.
• Multi-agent approach with AWS Bedrock achieved mid-90% accuracy.
• Bedrock offered security benefits and multiple model capabilities.
• Question: How was working with the AWS team?
• Partnered with Bedrock team and tribe.ai for implementation.
• Partners helped avoid pitfalls from thousands of prior projects.
• Question: What advice for other software leaders?
• Don't implement AI just to satisfy board pressure.
• Identify problems without mentioning generative AI first.
• Innovation should come from listening to customers.
• Question: Future plans with AWS partnership?
• Moving toward automated remediation beyond just analysis.
• Question: Has Sumo Logic monetized generative AI?
• Changed pricing from data ingestion to data usage.
• New model encourages more data sharing without cost barriers.

Participants:

• Joe Kim – Chief Executive Officer, Sumo Logic

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

CTO Arun Kumar discusses how Socure leverages AWS and generative AI to collect billions of data points each day in order to combat sophisticated online fraud at scale.

Topics Include:

• Introduction of Arun Kumar, CTO of Socure
• What does Socure specialize in?
• KYC and anti-money laundering checks
• Mission: eliminate 100% fraud on the internet
• Fraud has increased since COVID
• Socure blocks fraud at entry point
• Works with top banks and government agencies
• CTO responsibilities include product and engineering
• Focus on increasing efficiency through technology
• Two goals: internal efficiency and combating fraud
• Countering tools like FraudGPT on dark web
• Measuring success through reduced human capital needs
• Fraud investigations reduced from hours to minutes
• Improved success rates in uncovering fraud rings
• Detecting multi-hop connections in fraud networks
• Question: Who's winning - fraudsters or AI?
• It's a constant "cat and mouse game"
• Creating a fraud "red team" similar to cybersecurity
• Partnership details with AWS
• Amazon Bedrock provides multiple LLM options
• Building world's largest identity graph with Neptune
• Real-time suspicious activity detection
• Blocking account takeovers through phone number changes
• Success story: detecting deepfake across 3,000 IDs
• Collecting hundreds of data points per identity
• Challenges: adding selfie checks and liveness detection
• Future strategy: 10x-100x performance improvements
• Creating second and third-order intelligence signals
• Internal efficiency applications of generative AI
• AI-powered sales tools and legal document review

Participants:

• Arun Kumar – Chief Technical Officer, Socure

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

Oron Noah of Wiz outlines how organizations evolve their security practices to address new vulnerabilities in AI systems through improved visibility, risk assessment, and pipeline protection.

Topics Include:

• Introduction of Oron Noah, VP at Wiz.
• Wiz: largest private service security company.
• $1.9 billion raised from leading VCs.
• 45% of Fortune 100 use Wiz.
• Wiz scans 60+ Amazon native services.
• Cloud introduced visibility challenges.
• Cloud created risk prioritization issues.
• Security ownership shifted from CISOs to everyone.
• Wiz offers a unified security platform.
• Three pillars: Wiz Cloud, Code, and Defend.
• Wiz democratizes cloud security for all teams.
• Security Graph uses Amazon Neptune.
• Wiz has 150+ available integrations.
• Risk analysis connects to cloud environments.
• Wiz identifies critical attack paths.
• AI assists in security graph searches.
• AI helps with remediation scripts.
• AI introduces new security challenges.
• 70% of customers already use AI services.
• AI security requires visibility, risk assessment, pipeline protection.
• AI introduces risks like prompt injection.
• Data poisoning can manipulate AI results.
• Model vulnerabilities create attack vectors.
• AI Security Posture Management (ASPM) introduced.
• Four key questions for AI security.
• AI pipelines resemble traditional cloud infrastructure.
• Wiz researchers found real AI security vulnerabilities.
• Wiz AI ASPM provides agentless visibility.
• Supports major AI services (AWS, OpenAI, etc.).
• Built-in rules detect AI service misconfigurations.

Participants:

• Oron Noah – VP Product Extensibility & Partnerships, Wiz

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

Ruslan Kusov of SoftServe presents how their Application Modernization Framework accelerates ISV modernization, assesses legacy code, and delivers modernized applications through platform engineering principles.

Topics Include:

• Introduction of Ruslan Kusov, Cloud CoE Director at SoftServe
• SoftServe builds code for top ISVs
• Success case: accelerated security ISV modernization by six months
• Healthcare tech company assessment: 1.6 million code lines in weeks
• Business need: product development acceleration for competitive advantage
• Business need: intelligent operations automation
• Business need: ecosystem integration and "sizeification" to cloud
• Business need: secure and compliant solutions
• Business need: customer-centric platforms with personalized experiences
• Business need: AWS marketplace integration
• Distinguishing intentional from unintentional complexity
• Platform engineering concept introduction
• Self-service internal platforms for standardization
• Applying platform engineering across teams (GenAI, CSO, etc.)
• No one-size-fits-all approach to modernization
• SAMP/SEMP framework introduction
• Core components: EKS, ECS, or Lambda
• Modular structure with interchangeable components
• Case study: ISV switching from hardware to software products
• Four-week MVP instead of planned ten weeks
• Six-month full modernization versus planned twelve months
• Assessment phase importance for business case development
• Calculating cost of doing nothing during modernization decisions
• Healthcare customer case: 1.6 million code lines assessed
• Benefits: platform deployment in under 20 minutes
• Benefits: 5x reduced assessment time
• Benefits: 30% lower infrastructure costs
• Benefits: 20% increased development productivity with GenAI
• Integration with Amazon Q for developer productivity
• Closing Q&A on security modernization and ongoing management

Participants:

• Ruslan Kusov – Cloud CoE Director, Softserve

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

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

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

Executives from DataRobot, LaunchDarkly and ServiceNow share strategies, actions and recommendations to achieve profitable growth in today's competitive SaaS landscape.

Topics Include:

• Introduction of panelists from DataRobot, LaunchDarkly & ServiceNow
• ServiceNow's journey from service management to workflow orchestration platform.
• DataRobot's evolution as comprehensive AI platform before AI boom.
• LaunchDarkly's focus on helping teams decouple release from deploy.
• Rule of 40: balancing revenue growth and profit margin.
• ServiceNow exceeding standards with Rule of 50-60 approach.
• Vertical markets expansion as key strategy for sustainable growth.
• AWS Marketplace enabling largest-ever deal for ServiceNow.
• R&D investment effectiveness through experimentation and feature management.
• Developer efficiency as driver of profitable SaaS growth.
• Competition through data-driven decisions rather than guesswork.
• Speed and iteration frequency determining competitive advantage in SaaS.
• Balancing innovation with early customer adoption for AI products.
• Product managers should adopt revenue goals and variable compensation.
• Product-led growth versus sales-led motion: strategies and frictions.
• Sales-led growth optimized for enterprise; PLG for practitioners.
• Marketplace-led growth as complementary go-to-market strategy.
• Customer acquisition cost (CAC) as primary driver of margin erosion.
• Pricing and packaging philosophy: platform versus consumption models.
• Value realization must precede pricing and packaging discussions.
• Good-better-best pricing model used by LaunchDarkly.
• Security as foundation of trust in software delivery.
• LaunchDarkly's Guardian Edition for high-risk software release scenarios.
• Security for regulated industries through public cloud partnerships.
• GenAI security: benchmarks, tests, and governance to prevent issues.
• M&A strategy: ServiceNow's 33 acquisitions for features, not revenue.
• Replatforming acquisitions into core architecture for consistent experience.
• Balancing technology integration with people aspects during acquisitions.
• Trends in buying groups: AI budgets and tool consolidation.
• Implementing revenue goals in product teams for new initiatives.

Participants:

• Prajakta Damle – Head of Product / SVP of Product, DataRobot
• Claire Vo – Chief Product & Technology Officer, LaunchDarkly
• Anshuman Didwania – VP/GM, Hyperscalers Business Group, ServiceNow
• Akshay Patel – Global SaaS Strategist, Amazon Web Services

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

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

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

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

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