AWS for Software Companies Podcast

Latest Episodes:

Ep127: Enabling AI Acceleration at Scale - How Celonis Leverages Amazon Bedrock Aug 04, 2025

Show notes

Industry leaders from Celonis and AWS explain why 2025 marks the inflection point for agentic AI and how early adopters are gaining significant competitive advantages in efficiency and innovation.

Topics Include:

AWS's Cristen Hughes and Celonis's Jeff Naughton discuss AI agent transformation
Andy Jassy declares AI agents will fundamentally change how we work
Three key trends make AI agents practical: smarter models, longer tasks, cheaper costs
AI now beats humans on complex benchmarks for the first time ever
Claude 3.7 cracked graduate-level reasoning where humans previously dominated completely
AI evolved from brief interactions to managing sustained multi-step complex workflows
Processing costs plummeted 99.7% making enterprise-grade AI economically viable at scale
We're transitioning from 2023's adaptation era to 2025's human-AI collaboration era
By 2028, AI will suggest actions to humans rather than vice versa
Agents are autonomous software that plan, act, and reason independently with minimal intervention
Agent workflow: receive human request, create plan, execute actions, review, adjust, deliver
Four agent components: brain (LLM), memory (context), actions (tools), persona (role definition)
AWS offers three building approaches: ready-made solutions, managed platform, DIY development
Key enterprise applications: software development acceleration, customer care automation, knowledge work optimization
Manual processes like accounts payable offer huge transformation opportunities through intelligent automation
Deep process analysis is critical before deploying agents for maximum effectiveness
Celonis pioneered process mining to help enterprises understand their actual workflow realities
Companies are collections of interacting processes that agents need proper context to navigate
Process intelligence provides agents with placement guidance, data feeds, monitoring, and workflow direction
Celonis-AWS partnership demonstrates order management agents that automatically handle at-risk situations

Participants:

Jeff Naughton – SVP and Fellow, Celonis
Cristen Hughes – Solutions Architecture Leader, ISV, North America, Amazon Web Services

Further Links:

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

Ep126: Using AWS to Transform Customer Interactions with Glia Aug 01, 2025

Show notes

Justin DiPietro, Co-Founder & Chief Strategy Officer of Glia, shares how they are leveraging AI to enhance the customer experience in the highly regulated world of financial institutions.

Topics Include:

Glia provides voice, digital, and AI services for customer-facing and internal operations
Built on "channel-less architecture" unlike traditional contact centers that added channels sequentially
One interaction can move seamlessly between channels (voice, chat, SMS, social)
AI applies across all channels simultaneously rather than per individual channel
700 customers, primarily banks and credit unions, 370 employees, headquartered in New York
Targets 3,500 banks and credit unions across the United States market
Focuses exclusively on financial services and other regulated industries
AI for regulated industries requires different approach than non-regulated businesses
Traditional contact centers had trade-off between cost and quality of service
AI enables higher quality while simultaneously decreasing costs for contact centers
Number one reason people call banks: "What's my balance?" (20% of calls)
Financial services require 100% accuracy, not 99.999% due to trust requirements
Uses AWS exclusively for security, reliability, and future-oriented technology access
Real-time system requires triple-hot redundancy; seconds matter for live calls
Works with Bedrock team; customers certify Bedrock rather than individual features
Showed examples of competitors' AI giving illegal million-dollar loans at 0%
"Responsible AI" separates probabilistic understanding from deterministic responses to customers
Uses three model types: client models, network models, and protective models
Traditional NLP had 50% accuracy; their LLM approach achieves 100% understanding
Policy is "use Nova unless" they can't, primarily for speed benefits

Participants:

Justin DiPietro – Co-Founder & Chief Strategy Officer, Glia

Further Links:

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

Ep125: Bridging the gap between requirements and budget - Better data while still controlling costs Jul 30, 2025

Show notes

Ed Bailey, Field CISO at Cribl, shares how Cribl and AWS are helping customers rethink their data strategy by making it easier to modernize, reduce complexity, and unlock long-term flexibility.

Topics Include:

Ed Bailey introduces topic: bridging gap between security data requirements and budget
Companies face mismatch: 10TB data needs vs 5TB licensing budget constraints
Data volumes growing exponentially while budgets remain relatively flat year-over-year
IT security data differs from BI: enormous volume, variety, complexity
Many companies discover 600+ data sources during SIEM migration projects
50% of SIEM data remains un-accessed within 90 days of ingestion
Complex data collection architectures break frequently and require excessive maintenance
Teams spend 80% time collecting data, only 20% analyzing for value
Data collection and storage are costs; analytics and insights provide business value
Poor data quality creates operational chaos requiring dozens of browser tabs
SOC analysts struggle with context switching across multiple disconnected systems
Traditional vendor approach: "give us all data, we'll solve problems" is outdated
Data modernization requires sharing information widely across organizational business units
Data maturity model progression: patchwork → efficiency → optimization → innovation
Data tiering strategy: route expensive SIEM data vs cheaper data lake storage
SIEM costs ~$1/GB while data lakes cost ~$0.15-0.20/GB for storage
Compliance retention data should go to object storage at penny fractions
Decouple data retention from vendor tools to enable migration flexibility
Cribl platform offers integrated solutions: Stream, Search, Lake, Edge components
Customer success: Siemens reduced 5TB to 500GB while maintaining security effectiveness

Participants:

Edward Bailey – Field CISO, Cribl

Further Links:

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

Ep124: Powering Enterprise AI - How Our AI Journey Evolved featuring Jamf Jul 28, 2025

Show notes

Sam Johnson, Chief Customer Officer of Jamf, discusses the implementation of AI built on Amazon Bedrock that is a gamechanger in helping Jamf’s 76,000+ customers scale their device management operations.

Topics Include:

Sam Johnson introduces himself as Chief Customer Officer from Jamf company
Jamf's 23-year mission: help organizations succeed with Apple device management
Company manages 33+ million devices for 76,000+ customers worldwide from Minneapolis
Jamf has used AI since 2018 for security threat detection
Released first customer-facing generative AI Assistant just last year in 2024
Presentation covers why, how they built it, use cases, and future plans
Jamf serves horizontal market from small business to Fortune 500 companies
Challenge: balance powerful platform capabilities with ease of use and adoption
AI could help get best of both worlds - power and simplicity
AI also increases security posture and scales user capabilities significantly
Customers already using ChatGPT/Claude but wanted AI embedded in product
Built into product to reduce "doorway effect" of switching digital environments
Created small cross-functional team to survey land and build initial trail
Rest of engineering organization came behind to build the production highway
Team needed governance layer with input from security, legal, other departments
Evaluated multiple providers but ultimately chose Amazon Bedrock for three reasons
AWS team support, large community, and integration with existing infrastructure
Uses Lambda, DynamoDB, CloudWatch to support the Bedrock AI implementation
AI development required longer training/validation phase than typical product features
Released "AI Assistant" with three skills: Reference, Explain, and Search capabilities

Participants:

Sam Johnson – Chief Customer Officer, Jamf

Further Links:

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

Ep123: Signal from the Noise - How SecurityScorecard leverages AI to Power Global Threat Detection Jul 25, 2025

Show notes

Mark Stevens, SVP, Channels and Alliances, discusses how SecurityScorecard's strategic partnership with AWS enables them to scale their security solutions through cloud infrastructure, marketplace integration, and co-sell programs

Topics Include:

SecurityScorecard founded 10 years ago to understand third-party vendor security posture
Company has grown to 3,000 enterprise customers and 200+ partners globally
Evolved from ratings to "supply chain detection and response" over last year
Supply chain threats have doubled, creating extended attack surfaces for companies
Many organizations don't know their vendor count or vulnerabilities within supply chains
SecurityScorecard provides visibility into attack surfaces and management tools for control
Generative AI is central to their ecosystem, leveraging AWS Bedrock extensively
They scan the entire internet every two days at massive scale
Have scored 12 million companies with security scorecards to date
All workloads run on AWS cloud infrastructure as their primary platform
AWS partnership provides necessary scale for managing hundreds of thousands of vendors
Case study: Identified vendor misconfigurations that could shut down 1,000 locations
Own massive 10-year data lake with tens of millions of companies
New managed service combines AI automation with human analysts for support
Large organizations cannot fully automate supply chain security management yet
Quality threat intelligence data now valuable to SOC teams, not just risk
Third-party risk management and SOC teams are slowly converging for better security
AWS marketplace integration provides frictionless customer experience and larger deals
Co-sell programs with AWS enterprise sales teams create effective flywheel motion
Future expansion includes identity management, response actions, and internal signal management

Participants:

Mark Stevens – SVP, Channels and Alliances, SecurityScorecard

Further Links:

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

Ep122: Securing the Software Supply Chain - How Sonatype Protects Developers in the Age of AI Jul 23, 2025

Show notes

Chief Product Development Officer Mitchell Johnson discusses how Sonatype protects enterprise developers from malicious open source components while keeping them productive through AI.

Topics Include:

Sonatype provides software supply chain solutions for enterprises using open source components
They serve large enterprises, government agencies, and critical infrastructure providers globally
Main challenge: keeping developers productive while maintaining secure software supply chains
Cybercrime and supply chain attacks are massive, growing industries threatening developers
AI adoption is happening faster than expected, profoundly changing development workflows
Bad actors evolved from waiting for vulnerabilities to creating malicious components
Malicious open source components specifically target developer and DevOps toolchains
Sonatype's security research team uses AI/ML to analyze every open source component
They can predict and block malicious components before entering customer environments
AWS partnership helps Sonatype meet customers where they want to do business
Partnership focuses on go-to-market alignment, not just technical integration
AWS sales teams should be treated as extensions of your own sales organization
Understanding AWS sales structure and incentives is crucial for successful partnerships
AI development is following same pattern as open source adoption twenty years ago
"Shadow AI" parallels the earlier "shadow IT" trend with open source software
AI speeds up code generation but security review processes haven't kept pace
Developers need a "Hippocratic Oath" - taking responsibility for AI-generated code output
Within 24 months, professionals not skilled in AI will struggle to stay relevant
Sonatype's culture encourages curiosity, experimentation, and accepts failure as part of innovation
Their core mission: help developers focus on innovation, not security chores

Participants:

Mitchell Johnson – Chief Product Development Officer, Sonatype

Further Links:

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

Ep121: Ethical Hackers and AI Agents: The Future of Vulnerability Management with HackerOne Jul 21, 2025

Show notes

Founder and CTO Alex Rice discusses how HackerOne uses generative AI to automate security workflows and prioritizing accuracy over efficiency to achieve end-to-end outcomes.

Topics Include:

HackerOne uses ethical hackers and AI to find vulnerabilities before criminals
White hat hackers stress test systems to identify security weaknesses proactively
Generative AI plays a huge role in HackerOne's security operations
Security teams struggle with constant toil of finding and fixing vulnerabilities
AI helps minimize toil through natural language interfaces and automation
Both good and bad actors have access to generative AI tools
Success requires measuring individual task inputs and outputs, not just aggregates
Breaking down workflows into granular tasks reveals measurable AI improvements
HackerOne deployed "Hive," their AI security agent to reduce customer toil
Initial focus was on tasks where AI clearly outperformed humans
Started with low-hanging fruit before tackling more complex strategic workflows
Accuracy is the primary success metric, not just efficiency or speed
Security requires precision; wrong fixes create bigger problems than inefficiency
Customer acceptance and reduced time to remediation are north star metrics
Humans remain the source of truth for validation and feedback loops
Break down human jobs into granular AI tasks using systems thinking
Build specific agents for individual tasks rather than entire job roles
Keep humans accountable for end-to-end outcomes to maintain customer trust
AWS Bedrock chosen for security, confidentiality, and data separation requirements
Moving from efficiency improvements to entirely new AI-enabled capabilities

Participants:

Alex Rice – Founder & CTO/CISO, HackerOne

Further Links:

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

Ep120: Asana and Amazon Q - Co-Innovating with AWS Generative AI Services Jul 17, 2025

Show notes

Spencer Herrick, Principal AI Product Manager of Asana and Oliver Myers of AWS demonstrate how their integration allows Asana's AI workflows to access enterprise data from Amazon Q Business, enabling seamless cross-application automation and insights.

Topics Include:

Oliver Myers leads Amazon Q Business go-to-market, Spencer Herrick manages Asana AI products.
Session focuses on end user productivity challenges with generative AI technology implementations.
End users face technology overload with doubled workplace application usage over five years.
Data silos prevent getting maximum value from generative AI across fragmented enterprise systems.
Workers spend 53% of time on "work about work" instead of strategic contributions.
Ideal experience needs single pane of glass with cross-application insights and actions.
Amazon Q Business launched as managed service with 40+ enterprise data connectors.
Connectors maintain end-user permissions from source systems for enterprise security compliance.
QIndex feature enables ISVs to access Q Business data via API calls.
End users get answers enriched with multiple data sources without switching applications.
Asana's work graph connects all tasks, projects, and portfolios to company goals.
Phase 1 AI focused on narrow solutions like smart status updates.
Phase 2 aimed for AI teammate capabilities requiring extensive contextual knowledge.
AI Studio launched as no-code workflow automation builder within Asana platform.
Q integration allows AI Studio to access cross-application context beyond Asana boundaries.
SmartChat enhanced with Q can answer "what should I work on today?" holistically.
Users returning from PTO can quickly understand goal risks across data sources.
AI Studio workflows automate feature request processing across Asana, Drive, Slack, email.
Partnership eliminates silos while maintaining enterprise security and permission controls.
Integration creates connected ecosystem enabling true cross-application AI automation and insights.

Participants:

Spencer Herrick - Principal AI Product Manager, Asana
Oliver Myers - Worldwide Head of Business Development, Amazon Web Services

Further Links:

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

Ep119: Process Intelligence in the Age of AI – A New Era of Business Automation with Celonis Jul 16, 2025

Show notes

Chief Product Officer Dan Brown explains how Celonis creates digital twins of business processes to power AI agents that automate operational improvements.

Topics Include:

Dan Brown introduces Celonis as the thought leader in process mining for over a decade.
Celonis serves largest global companies across all industries seeking operational improvements.
Companies have process diagrams but actual operations differ significantly from documentation.
Celonis creates digital twins of business processes by analyzing system data flows.
Process intelligence reveals how work actually happens versus how companies think it happens.
Platform enables process normalization, improvement assessment, and automated corrective actions.
Celonis vision: making processes work better for people, companies, and the planet.
Process intelligence provides visibility into current operations and improvement strategies.
Great AI requires great data, but most companies only have static views.
Process intelligence graph shows real-time flow of orders, invoices, and opportunities.
Agentic AI requires four capabilities: sensing, planning, executing, and governing operations.
Process intelligence enables real-time detection of conformance problems and deviations.
AWS partnership leverages Bedrock for agentic AI and infrastructure for data processing.
Data ingestion, organization, and enrichment are core to process intelligence value.
AI agents now handle process deviations with increasing autonomy and sophistication.
Heavy equipment manufacturer uses agents to coordinate with third-party vendors automatically.
Agents text and email vendors to confirm delivery dates, reducing manual work.
Implementation challenges include data quality, conservative adoption, and governance concerns.
Companies should start with achievable use cases and expand gradually across domains.
Future involves enterprise-wide process visibility powering automated applications and continuous improvement.

Participants:

Dan Brown – Chief Product Officer, Celonis

Further Links:

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

Ep118: Revolutionizing Customer Experience through Generative AI with Automation Anywhere, Qlik and Vectra.ai Jul 14, 2025

Show notes

AWS partners Automation Anywhere, Qlik, and Vectra.ai discuss revolutionizing customer experience through generative AI, sharing real-world implementations in automation, analytics, and cybersecurity applications.

Topics Include: