AWS for Software Companies Podcast

In a keynote address from re:Invent, McKinsey & Company's Lareina Yee shares fascinating data, trends and best practices on AI adoption, the future of skillsets, and leadership insights that are needed for AI transformation at scale.Topics Include:Over 80% of companies have adopted AI in at least one business function currently.Despite heavy investment, 62% of companies remain in experimental or pilot phases with AI.Only 7% of organizations have achieved full-scale AI implementation, up from 2% earlier this year.Agentic AI has proliferated rapidly across functions from knowledge management to manufacturing in one year.Between 45% and 5% of companies have implemented AI agents across different business functions today.AI's productivity potential represents $4.4 trillion in economic value beyond just cost savings opportunities.Innovation ranks as the number one goal for AI investments, ahead of cost reduction priorities.Employee satisfaction, customer satisfaction, and competitive differentiation drive AI adoption alongside revenue growth and cost.High AI performers view implementation as total enterprise transformation, not just technology deployment projects.Leading companies spend 4.9 times more budget on AI investments compared to average performing organizations.Traditional software stacks evolved to SaaS, now transforming into AI-ready tech stacks within one generation.Job outlook remains mixed: 32% expect losses, 13% expect increases, 43% see no major change.Since 2023, significant skill shifts show increased demand for software development and business intelligence capabilities.AI fluency has increased seven times as the most sought-after skill across all job types.AI fluency means using AI in everyday work, not building models or creating large language models.Skills like driving records, coaching, customer service, and management remain harder to automate with current AI.Transactional, data-driven repetitive tasks like inventory management and invoicing face highest automation exposure currently.Historical technology revolutions like electricity created six to eight jobs for every one job displaced.New roles like prompt engineering emerge, requiring skills like effective questioning rather than technical coding.Participants:Lareina Yee - Director of Technology Research, McKinsey & CompanySee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

AWS Principal Solutions Architect Wallace Printz explains how agents are reshaping SaaS business models, pricing strategies, and technical architectures.Topics Include:Wallace Printz discusses agentic workloads transforming SaaS with largest AWS customersNew interaction models include generative UI, voice agents, and proactive workAgents extending SaaS products to interact with external systems and businessesVirtual teammates enabling cross-department collaboration and upskilling non-expert users effectivelyMonetization strategies evolving as predictable costs become variable with agentsThree patterns: dedicated agents, shared agents, and multi-tenant personalized agentsMulti-tenant agents enable hyper-personalized experiences using individual tenant context enrichmentAgent-centric business strategy requires real assessment beyond AI hype cycleAgent orchestration complexity grows with multiple specialized agents interacting togetherTenant isolation requires JWT tokens and AWS Bedrock Agent Core identityCost-per-tenant management needs LLM throttling, tiering, and unified control planeMulti-tenancy creates sticky personalized experiences; AWS white paper releasing soonParticipants:Wallace Printz - Principal Solution Architect, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Industry leaders from Boomi, Demandbase and Smarsh share hard-won lessons on balancing AI creativity with guardrails, why data quality trumps frameworks, and deploying AI at scale.Topics Include:Three industry leaders share experiences building AI solutions at Boomi, Demandbase, and Smarsh.Smarsh manages trillion communications for financial services, detecting bad actors across multiple channels.Boomi built agent studio, garden, and control tower while spawning 33,000 internal agents.Chris Timmerman used vibe coding to build embeddable Boomi in five months solo.Companies balance creativity with guardrails, starting with IT policies before unleashing innovation.Internal adoption driven by empowering teams to build their own solutions versus top-down.Demandbase saw 70% adoption within six months through grassroots approach and local champions.Measuring success proves challenging, comparable to tracking Excel usage rather than specific KPIs.Companies focus on outcomes like touch-free bug fixes and support metrics versus raw usage.Biggest lesson: Data quality and context determine success more than agentic frameworks.Need scaling framework from low-risk UX improvements to high-risk automation with appropriate guardrails.Industry created fatigue by overpromising; should have started smaller with realistic expectations.Participants:Chris Timmerman – Vice President, Global Services Delivery, BoomiHarshal Dedhia – Vice President of AI, DemandbaseBrandon Carl - Executive Vice President of AI and Product Strategy, SmarshAllison Johnson - AMER Technology Partnerships Leader, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Phill Robinson of Boardwave joins Miguel Alava and Massimo Ghislandi of AWS to share research and actionable strategies for European software companies using cloud infrastructure, AI features, and marketplace leverage to drive unprecedented growth.Topics Include:Boardwave and AWS reveal research on European software companies becoming global innovators.Cloud-first businesses exceed customer expectations at 60% versus 46% for laggards.Boardwave's 2,500 CEO members validate findings: AI companies growing 45% annually.Leaders excel at gathering customer feedback for innovation and implementing AI.Top performers leverage marketplaces and deliver continuous customer experience updates consistently.Cloud adoption is foundational for generative AI and agentic AI to scale.Companies face different challenges depending on their cloud maturity stage currently.Cloud serves as table stakes before companies can capture AI growth opportunities.Benchmarking tool helps identify current position and plan strategic next steps forward.Startups should solve universal problems globally, building painkillers not vitamin products.Intercom scales customer service; Wix transforms efficiency through cultural and engineering mindset.Future requires cloud foundation with AI features; AWS offers comprehensive support programs.Participants:Phill Robinson – Chair & Co-Founder, BoardwaveMiguel Alava – EMEA ISV General Manager, Amazon Web ServicesMassimo Ghislandi -  Head of EMEA Marketing for Software Companies, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Industry leaders from Kore AI, SS&C Blue Prism and AWS reveal what actually works in agentic AI deployment, from contact center automation to employee productivity, with proven strategies for regulated industries.Topics Include:Kore AI and SS&C Blue Prism leaders discuss achievable agentic AI actionsThree deployment areas show real ROI: customer service, employee automation, and process workflowsKore AI handles billions of annual interactions, with 85% focused on contact center operationsSS&C Blue Prism achieved $200 million annual savings using agentic AI across 120 internal use casesThe company processes 6 million transactions monthly consuming 10-12 billion tokens in productionRegulated industries like financial services and healthcare successfully deploy agentic AI with proper guardrailseBay case study demonstrates measurable productivity gains tied directly to AI agent implementationTwo identical pilot programs yielded different results: one tied to business outcomes, one didn'tISVs should stop chasing shiny objects and focus on solving customers' stickiest problems insteadDesign for scale from day one and accept no single vendor solves everything aloneEmployee-facing use cases carry less risk than customer-facing applications for initial AI deploymentsCombining deterministic automation with AI plus governance creates more viable and trustworthy solutionsParticipants:Erik Walton - EVP of WW Sales/Partner Sales, Kore AISatish Shenoy - VP, Global Technology Alliances & AI GTM, SS&C Blue PrismArym Diamond – Head of North America Data & AI Sales, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

CEO Scott Stephenson explains how Deepgram's voice AI technology powers everything from pharmacies to drive-thru ordering and why, after so many years, Voice AI is now ready for prime time.Topics Include:Scott Stephenson introduces Deepgram as an audio AI company building speech productsMajor brands like CVS and Anthropic use Deepgram to power voice agentsCVS handles prescription status calls where 25-40% ask if prescriptions are readyVoice technology now accurately understands diverse accents and speech patterns from callersAutomated systems free pharmacists to focus on their actual jobs insteadJack in the Box uses Deepgram for drive-thru ordering with natural conversationsPrevious McDonald's and Wendy's failures happened because the technology wasn't ready yetVoice AI can handle any task with text input like CRM notesHealthcare companies adopted voice AI faster than expected despite compliance hurdlesStaffing shortages drove hospitals to push through HIPAA and regulatory red tapeFirst misconception: AI will never match human performance in customer interactionsSecond misconception: one product should solve all voice-related business problemsCompanies must strategically decide what to build, partner on, or buyDeepgram's research team controls speech speed and outputs conversational data like timestampsAdoption will feel slow initially but suddenly be everywhere within three yearsParticipants:Scott Stephenson – Co-Founder & CEO, DeepgramSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Uri Cohen reveals how Elastic transformed from managing 50,000 complex clusters to building a seamless serverless platform that eliminates operational overhead while scaling globallyTopics Include:Johan Broman of AWS hosts Uri Cohen who leads Elastic's platform products teamUri shares his nine-year journey at Elastic from small company to global scaleElasticsearch started 15 years ago, becoming popular for search, logs, and security eventsElastic Cloud launched 2015, but users struggled with shards, nodes, and infrastructure complexityServerless eliminates operational concerns, letting users just ingest and analyze their dataDesign goal: maintain familiar Elasticsearch experience while removing all infrastructure management burdenChose complete architectural redesign over retrofitting auto-scaling to existing infrastructureNew architecture uses S3 persistence with lightweight routing layer serving 50,000+ clustersCell-based design limits blast radius and improves multi-tenancy across 40+ global regionsLearned S3 API costs can explode unexpectedly without careful request pattern optimizationAI transforms security workflows: 10,000 alerts become 3 actionable attack summaries automaticallyWeekly continuous deployment enables faster innovation delivery without waiting for version releasesParticipants:Uri Cohen – Vice President of Product Management, Platform, ElasticJohan Broman – EMEA ISV Head of Solutions Architecture, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

SS&C Blue Prism's VP reveals how they achieved $200M annual savings and $600M revenue growth by deploying 3,000 AI agents, processing 6 million documents monthly as their own first customer.Topics Include:SS&C Blue Prism evolved from RPA leader to agentic automation provider over 25 yearsServes 22,000 clients in regulated industries like financial services, healthcare, manufacturing, and retailOffers AI agents, governance gateway, and secure enterprise chat leveraging AWS BedrockAs "customer zero," they deployed 3,000 agents processing 6 million documents monthlyGenerated $200M annual savings and $600M revenue growth using their own technologyFinancial services client unlocked unstructured document processing previously impossible with traditional automationHealthcare client's AI processes MRIs more accurately than human radiologistsKey lesson: Focus on business outcomes first, not just implementing AI everywhereCritical insight: Plan for scale on day one, not after pilots succeedAWS Marketplace streamlined purchasing, especially in challenging Latin American marketsFuture vision: B2A economy where agents negotiate parking, shopping, and services autonomouslyPredicts agent-to-agent communication will revolutionize healthcare monitoring and wealth managementParticipants:Satish Shenoy – Global Vice President, Technology Alliances and GenAI GTM, SS&C Blue PrismSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Three enterprise AI leaders from Archer, Demandbase, and Highspot reveal how top companies are implementing AI responsibly while navigating data privacy, bias prevention, and regulatory compliance challenges.Topics Include:AWS Security GM Brian Shadpour hosts three AI leaders discussing responsible enterprise deploymentDemandbase's Umberto Milletti explains tenant-based models ensuring first-party customer data remains confidentialHighspot's Oliver Sharp uses behavior-specific feedback frameworks to eliminate bias in sales assessmentsReal-time AI evaluation proves challenging when assessing dynamic sales conversations and customer interactionsCompanies create "second-party data" networks where customers opt-in to share insights collectivelyOpen-source models gain traction but require significant expertise for enterprise-grade implementationEU AI Act mandates human oversight, reshaping how companies design AI systems globallyArcher's Kayvan Alikhani extends identity management principles from web applications to AI agentsUnattended AI agents performing tasks autonomously create new security and accountability challengesHuman-in-the-loop oversight remains essential, especially for high-stakes decisions affecting customersFuture challenge: Determining when AI accuracy justifies removing costly human oversightEnterprise data hygiene becomes critical as AI systems need clean, reviewed internal dataParticipants:Kayvan Alikhani - Global Head of Engineering- Emerging Solutions, Archer Integrated Risk ManagementUmberto Milletti - Chief R&D Officer, DemandbaseOliver Sharp - Co-Founder & Chief AI Officer, HighspotBrian Shadpour - General Manager, Security, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

SnapLogic CTO Jeremiah Stone reveals how they evolved from open-source to AI-powered integration platform, doubled AI adoption with one UX change, and delivers measurable enterprise ROI.Topics Include:SnapLogic CTO shares their decade-long journey building AI-powered integration with AWS partnership.SnapLogic drives "human cost of integration to zero" for thousands of global companies.Started as open-source project, pivoted to cloud in 2015 with AWS infrastructure.Began AI workloads in 2018, predicting next steps in integration workflows using models.Became AWS Bedrock launch partner, completely reinventing their product for generative AI era.SnapLogic lives through transformations first, then credibly helps ISV customers do same.Helped Adobe migrate entire CRM from Salesforce to Microsoft over single weekend.Built normalized data architecture using S3, Iceberg, Glue for analytics-ready enterprise data.SnapGPT copilot converts plain language prompts into complete integration pipelines in minutes.Live demo shows generating Salesforce-to-Redshift pipeline with filters using natural language commands.Small UX tweak adding helpful header doubled monthly active users of SnapGPT.Changed legal agreements in 2017 to capture metadata, enabling AI features years later.Agent Creator delivers ROI across customers: Inspirant, Core Plus, AstraZeneca use cases.SnapLogic's own finance team cut order reconciliation from 40 hours monthly to 90 minutes.Key lessons: governance first, understand business impact, use AWS native patterns consistently.Participants:Jeremiah Stone – Chief Technical Officer, SnapLogicOlawale Oladehin – Managing Director, NAMER Technology Segments, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Vice President of Solution Engineering Chris Timmerman reveals how Boomi's integration platform evolved into a no-code AI agent builder on AWS, serving 22,000 enterprises while solving the 95% failure rate in AI production deployments.Topics Include:Chris Timmerman shares his 9-year journey from Field CTO to VP Solution Engineering at BoomiBoomi connects cloud and on-premise systems, helping enterprises move data seamlessly since early 2000sThe platform serves 22,000+ customers, from order-to-cash processes to complex M&A integrationsNew CEO Steve Lucas pivoted Boomi toward generative AI when ChatGPT emergedBoomi approaches AI three ways: internal automation, product enhancement, and customer enablementAI Agent Studio lets users build agents on AWS Bedrock without writing codeAgent Garden marketplace allows partners to share specialized agents for Salesforce, NetSuite, and moreChris reveals 95% of enterprise AI projects fail to reach production due to data issuesAWS partnership since 2018 provides infrastructure plus hands-on engineering collaboration for problem-solvingHackathons with AWS engineers generate excitement and innovative solutions for customer challengesChris advises new AWS partners: "Don't be afraid to ask for help" and be transparentFuture vision: Partner with market leaders like AWS rather than reinvent foundational AI frameworksParticipants:Christopher Timmerman – Vice President, Solution Engineering, BoomiSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Learn how Anyscale's Ray platform enables companies like Instacart to supercharge their model training while Amazon saves heavily by shifting to Ray's multimodal capabilities.Topics Include:Ray originated at UC Berkeley when PhD students spent more time building clusters than ML modelsAnyscale now launches 1 million clusters monthly with contributions from OpenAI, Uber, Google, CoinbaseInstacart achieved 10-100x increase in model training data using Ray's scaling capabilitiesML evolved from single-node Pandas/NumPy to distributed Spark, now Ray for multimodal dataRay Core transforms simple Python functions into distributed tasks across massive compute clustersHigher-level Ray libraries simplify data processing, model training, hyperparameter tuning, and model servingAnyscale platform adds production features: auto-restart, logging, observability, and zone-aware schedulingUnlike Spark's CPU-only approach, Ray handles both CPUs and GPUs for multimodal workloadsRay enables LLM post-training and fine-tuning using reinforcement learning on enterprise dataMulti-agent systems can scale automatically with Ray Serve handling thousands of requests per secondAnyscale leverages AWS infrastructure while keeping customer data within their own VPCsRay supports EC2, EKS, and HyperPod with features like fractional GPU usage and auto-scalingParticipants:Sharath Cholleti – Member of Technical Staff, AnyscaleSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Trellix's Director of Strategy Zak Krider reveals how they automated tedious security tasks like event parsing and threat detection using Amazon Bedrock's multi-model approach, achieving 100% accuracy while eliminating bottlenecks in their development lifecycle.Topics Include:Trellix merged FireEye and McAfee Enterprise, combining two decades of cybersecurity AI expertiseProcessing thousands of daily security events revealed traditional ML's weakness: overwhelming false positivesTwo years ago, they integrated generative AI to automate threat investigation workflowsAmazon Bedrock's multi-model access enabled rapid testing and "fail fast, learn fast" methodologyBuilt custom cybersecurity testing framework since public benchmarks don't reflect domain-specific needsAgentic AI now autonomously investigates threats across dark web, CVEs, and telemetry dataAWS NOVA builds investigation plans while Claude executes detailed threat research analysisLaunched "Sidekick" internal tool with agents mimicking human developer onboarding processesChose prompt engineering over fine-tuning for flexibility, cost-effectiveness, and faster iterationAutomated security rule generation across multiple languages that typically require unicorn developersAchieved 100% accuracy in automated event parsing, eliminating tedious manual SOC workKey lesson: don't default to one model; test and mix for optimal resultsParticipants:Zak Krider - Director of Strategy & AI, TrellixSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Honeycomb's VP of Marketing Shabih Syed reveals why traditional observability is dead and how AI-powered tools are transforming the way engineers debug production systems, with real examples.Topics Include:Observability is how you understand and troubleshoot your production systems in real-timeShabih's 18-year journey: developer to product manager to marketing VP shares unique perspectiveAI coding assistants are fundamentally changing how fast engineers ship code to productionCustomer patience is gone - one checkout failure means losing them foreverOver 90% of engineers now "vibe code" with AI, creating new complexityObservability costs are spiraling - engineers forced to limit logging, creating debugging dead-endsHoneycomb reimagines observability: meeting expectations, reducing complexity, breaking the cost curveMajor customers like Booking.com and Intercom already transforming with AI-native observabilityMCP server brings production data directly into your IDE for real-time AI assistanceCanvas enables plain English investigations to find "unknown unknowns" before they become problemsAnomaly detection helps junior engineers spot issues they wouldn't know to look forStatic dashboards are dead - AI-powered workflows are the future of system observationParticipants:Shabih Syed - VP Product Marketing, Honeycomb.io See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Discover how Siteimprove partnered with AWS to build an AI system processing 100 million accessibility checks monthly, making the web usable for 1.3 billion people with disabilities worldwide. Topics Include:AWS and Siteimprove partnered to solve digital accessibility at massive scale using AI.Digital accessibility ensures 1.3 billion people with disabilities can use web content effectively.Deep semantic understanding is needed to verify if content truly matches its descriptions.Siteimprove processes 75 million webpages across government, healthcare, and education sectors daily.The challenge required AWS infrastructure beyond just AI models for cost-effective scaling.Their platform unifies accessibility checks with SEO, analytics, and content performance tools.Business requirements included enterprise security, multi-region support, and flexible pricing models.They built three processing patterns: interactive conversations, overnight batch, and high-priority async.The AI Accelerator framework separates business logic from model adapters for easy expansion.Intelligent routing sends simple checks to Nova micro, complex ones to Nova Pro.Production system now processes over 100 million accessibility checks monthly using Bedrock Batch.Key lessons: cross-region inference reduces latency, prompt optimization crucial, special characters increase hallucination. Participants:Hamed Shahir - Director of AI, SiteimproveDavid Kaleko - Senior Applied Scientist, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Archer's Global Head of Engineering reveals how they're using Amazon Bedrock to help enterprises avoid billions in regulatory fines by transforming complex compliance laws into actionable AI-powered workflows.Topics Include:James Griffith, VP Engineering at Archer, leads development for risk and compliance solutionsArcher helps enterprises navigate the complex world of regulatory compliance beyond outdated spreadsheetsSince 2009, banks alone have been fined $342 billion by regulators worldwideEven "deregulated" Texas added 1,100 new laws in just one legislative sessionRegulatory data exists online but is overwhelming—too much for humans to processArcher built an AI pipeline: ingesting regulations, extracting obligations, and generating compliance controlsAmazon Bedrock eliminated the need to build ML infrastructure or hire specialized teamsModel interchangeability let them switch between Claude and Llama with just clicksBuilt-in guardrails prevented users from misusing AI without custom security developmentFrom initial vision to working product took just six months using BedrockDifferent AI models deploy globally, adapting to each country's unique regulatory stanceEngineers experiment safely with AI using Bedrock, preparing the team for the futureParticipants:James Griffith – Global Head of Engineering, ArcherSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Arctic Wolf's Dean Teffer reveals how they transformed security operations by processing one trillion daily alerts with AI, and shares hard-won lessons from operationalizing AI in production SOC environments Topics Include:Arctic Wolf processes one trillion security alerts daily across 10,000 global customersSecurity operations remained stubbornly human-mediated due to constantly evolving threats and infrastructure complexityDean explains why platformizing data creates a virtuous cycle enabling AI automationTraditional ML models couldn't handle SOC's situational complexity, leading to LLM adoptionArctic Wolf's unique advantage: direct access to 1000+ SOC analysts for continuous feedbackAWS partnership began with governance concerns about data privacy and model training"Centaur Chess" approach: AI-human teams consistently outperform either alone in cybersecurityThree-generation AI evolution: from personal use to prompt engineering to expert-tuned modelsThree-day AWS hackathon achieved breakthroughs that would've taken months independentlySOC analysts actively shaped AI responses through iterative feedback during live operationsObservability proved critical: tracking performance, quality metrics, and response times for continuous improvementMeasurable impact achieved: automated alert orientation dramatically increased analyst efficiency and response quality Participants:Dean Teffer - VP of AI/ML, Arctic WolfAswin Vasudevan - Senior ISV Solution Architect, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Learn how DataStax transformed customer feedback into a hybrid search solution that powers Fortune 500 companies through their partnership with AWS.Topics Include:AWS and DataStax discuss how quality data powers AI workloads and applications.DataStax built on Apache Cassandra powers Starbucks, Netflix, and Uber at scale.Their TIL app collects outside-in customer feedback to drive product development decisions.Hybrid search and BM25 kept trending in customer requests for several months.Customers wanted to go beyond pure vector search, not specifically BM25 itself.Research showed hybrid search improves accuracy up to 40% over single methods.ML-based re-rankers substantially outperform score-based ones despite added latency and cost.DataStax repositioned their product as a knowledge layer above the data layer.Developer-first design prioritizes simple interfaces and eliminates manual data modeling headaches.Hybrid search API uses simple dollar-sign parameters and integrates with Langflow automatically.AWS PrivateLink ensures security while Graviton processors boost efficiency and tenant density.Graviton reduced total platform operating costs by 20-30% with higher throughput.Participants:Alejandro Cantarero – Field CTO, AI, DataStaxRuskin Dantra - Senior ISV Solution Architect, AWS, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Qlik's Field CTO for Generative AI Ryan Welsh reveals why 95% of enterprise AI projects fail and shares the three proven strategies the successful 5% use to deliver real business value from their AI investments.Topics Include:Qlik's Field CTO reveals why 95% of AI projects fail despite massive investmentsMIT research shows shocking failure rates, but 5% are achieving real business valueFirst major pitfall: Bad data foundations doom even the most sophisticated AI modelsSecond problem: Companies use generative AI when predictive models would work betterThird issue: Unnecessary complexity - AI projects disconnected from business outcomesSuccess secret #1: Ground AI in trusted enterprise data and user contextSome LLMs struggle at specific tasks like claims processing despite passing medical examsSuccess secret #2: Let AI learn from users while keeping data governance intactSuccess secret #3: Embed AI directly into existing workflows like SalesforceAgentic AI shifts from reactive Q&A to proactive systems that execute across platformsCase study: Lintek reduced churn 10% and saved millions using these principlesYour AI choices today will lock in your trajectory for years to comeParticipants:Ryan Welsh – Field CTO – Generative AI, QlikSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at aws.amazon.com/isv/

Rapid7's Vice President of Data and AI Laura Ellis shares how they built an AI-first cybersecurity platform by investing in AI platform AND data infrastructure simultaneously.Topics Include:Rapid7 processes massive cybersecurity data across exposure management, threat detection, and managed SOC.84% of security analysts want to quit due to data overload burnout.Challenge: investing in AI platform AND data infrastructure simultaneously, not sequentially.Built security data lake with AWS, unified IDs, and standardized schemas across products.Used traditional machine learning for 10 years before generative AI emerged.Generative AI raised questions about business impact; agentic AI enables full automation.Chose AWS for scale, model marketplace flexibility, and true partnership on capacity.Co-development incubator with SOC team proved critical: equal responsibility, full-time collaboration.Launched alert triage automation, SOC assistant chatbot, and incident report generation tools.Built AI platform with guardrails after pen testers generated cookie recipes costing money.One agentic feature initially cost-estimated at $140 million before optimization and guidance.Future: more AI features, granular customer configuration, and bring-your-own-model capabilities.Participants:Laura Ellis – Vice President, Data & AI, Software Engineering, Rapid7See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/