{"id":5780,"date":"2025-08-01T01:59:14","date_gmt":"2025-08-01T01:59:14","guid":{"rendered":"https:\/\/www.odbms.org\/blog\/?p=5780"},"modified":"2025-08-01T01:59:15","modified_gmt":"2025-08-01T01:59:15","slug":"on-enterprise-ai-interview-with-stephen-kallianos","status":"publish","type":"post","link":"https:\/\/www.odbms.org\/blog\/2025\/08\/on-enterprise-ai-interview-with-stephen-kallianos\/","title":{"rendered":"On Enterprise AI. Interview with Stephen Kallianos"},"content":{"rendered":"\n
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“I wish more organizations realized how fundamental it is to lay a good foundation for any enterprise AI initiative. That foundation includes a robust data strategy and a unified architecture.”
<\/p>\n<\/blockquote>\n\n\n\n

Q1. What are the responsibilities of a Field CTO?

Stephen Kallianos:<\/strong> As a Field CTO, my core responsibility is to serve as a trusted advisor for enterprise customers, especially at the Senior Architect and C-level. It\u2019s a consulting role, focused on establishing credibility and helping customers and prospects connect their strategic priorities to SingleStore\u2019s unique value proposition. <\/p>\n\n\n\n

The work involves identifying and clearly communicating the best-fit enterprise architectures, leveraging deep expertise in data and AI infrastructure. My role requires me to thoroughly understand customer challenges, align our technical solutions to those needs, and recommend the most effective solutions. <\/p>\n\n\n\n

In addition, I lead the presales function here at SingleStore: running technical discovery, developing tailored demonstrations and proofs of value, qualifying opportunities, and shaping value-based engagements that bridge the gap between technology and business results. Ultimately, my goal is to ensure that our solutions deliver both technical and business impact \u2013 setting organizations up for long-term success in their modernization efforts.<\/p>\n\n\n\n

Q2. What\u2019s something you often hear from customers and prospects?<\/strong><\/p>\n\n\n\n

Stephen Kallianos:<\/strong> Organizations are hungry for applications that can leverage the most recent data for AI-driven insights, but often get bogged down managing separate systems for transactional and analytics workloads \u2014 leading to increased complexity and database sprawl. I regularly hear concerns about inconsistent query performance, missed SLAs for real-time or batch data, and the growing need for flexible deployment options \u2014 be it cloud, on-prem, or hybrid. Most notably, there\u2019s a surge in organizations looking to modernize: they want to drive real business outcomes by reducing operational overhead, simplifying their technology stacks, and future-proofing their data infrastructure to keep pace with rapidly evolving AI requirements and new digital experiences.<\/p>\n\n\n\n

Q3. What are the most commonly shared pain points among customers seeking to implement enterprise AI?<\/strong><\/p>\n\n\n\n

Stephen Kallianos:<\/strong> Customers implementing enterprise AI encounter a few pervasive pain points. Common issues include: navigating data silos and complex integrations, struggling to perform large-scale aggregations efficiently, and dealing with the high costs and poor performance that come with scaling legacy data infrastructure. Meeting real-time data requirements for AI workloads is a particular challenge, especially when data resides in multiple, disparate databases. Legacy architectures often fail to deliver the query performance and SLAs necessary for AI use cases, leading to a pressing need to modernize and consolidate systems.<\/p>\n\n\n\n

Q4. Do you see GenAI being used in the enterprise? How? <\/strong><\/p>\n\n\n\n

Stephen Kallianos:<\/strong> Absolutely. Enterprises are rapidly adopting generative AI (GenAI<\/a>). They\u2019re integrating large language models (LLMs<\/a>) into their AI architectures for a range of scenarios from analytics and customer support to productivity tools and operations. We’re seeing production deployments in areas like enterprise search (retrieving contextually relevant records and documents), AI-powered personal assistants and co-pilots, workflow automation, developer productivity tools (text-to-SQL, code recommendations), and even advanced analytics for fraud detection and data enrichment<\/p>\n\n\n\n

Q5. What do you wish more organizations knew when it comes to adopting enterprise AI?<\/strong><\/p>\n\n\n\n

Stephen Kallianos:<\/strong> I wish more organizations realized how fundamental it is to lay a good foundation for any enterprise AI initiative. That foundation includes a robust data strategy and a unified architecture.

Relying on siloed or hastily patched-together systems makes it almost impossible to achieve the simplicity, security, or scale needed for AI to succeed in production. The best results come from adopting a single platform that handles analytics, machine learning, and operational workloads \u2014 streamlining architecture and lowering risk. Ultimately, AI projects succeed when technical outcomes are tightly aligned to clear business value \u2014 not when technology is adopted for its own sake.<\/p>\n\n\n\n

Q6. How can organizations better align their technical solutions with the organizational goals?<\/strong><\/p>\n\n\n\n

Stephen Kallianos:<\/strong> In my experience, the best way organizations can align technical solutions with their business goals is for the organization and the database vendor to hold a workshop to clarify and align on both the desired business outcomes and technical requirements. Mutually qualifying opportunities up front \u2014 ensuring there\u2019s clarity and genuine need \u2014 helps avoid wasted effort later. Together, you can frame what success looks like and define concrete criteria, creating a North Star for architecture, implementation, and measuring results. Having hands-on proof-of-value phases (using real data and involving cross-functional teams) is key to validating that proposed solutions actually deliver the anticipated outcomes, and is an approach I use extensively when leading presales and customer workshops.<\/p>\n\n\n\n

Q7. SingleStore recently unveiled a new version of the database, and it contains a lot of upgrades. In your opinion, which 2-3 things are most valuable to customers? Why?<\/strong><\/p>\n\n\n\n

Stephen Kallianos:<\/strong> For me, several features from the latest SingleStore release<\/a> stand out as particularly remarkable. These include the major upgrades we made to Flow<\/a>, Iceberg<\/a>, Aura<\/a>, and our developer experience.<\/p>\n\n\n\n

The first is about ingest and data integration. With SingleStore Flow<\/a> (our no-code solution for data migration and continuous change data capture) now natively embedded in our Helios managed service, customers can orchestrate data movement into SingleStore directly within the cloud platform, making the process far more streamlined. Data ingestion is now much simpler and more flexible, and moving data from heterogeneous sources like Snowflake, Postgres, SQL Server, Oracle, and MySQL is easier than ever.\u00a0<\/p>\n\n\n\n

This is part of our overall \u201cSingleConnect\u201d experience that allows customers to incorporate more and richer data sources into SingleStore. Adding Flow into SingleStore Helios\u00ae<\/sup><\/a> further strengthens our ability to integrate from diverse environments, reducing integration friction and enabling real-time analytics and AI use cases without the pain of traditional ETL complexities.<\/p>\n\n\n\n

We\u2019ve also done a lot to enhance our Apache Iceberg ecosystem. For customers using data lakehouses with Apache Iceberg, there\u2019s now a speed layer that offers high-performance, low-latency data interaction on top of Iceberg-managed storage. Improved bi-directional integration allows for easier, faster data exchange with external Iceberg tables, so real-time applications can finally tap into lakehouse architectures with the latency and interactivity they require.<\/p>\n\n\n\n

In the area of AI and serverless compute, we upgraded our Aura Container Service. Aura brings together vector search, analytics, function-as-a-service, and GPU<\/a>-accelerated workloads in a single containerized environment. Already optimized for running AI\/ML and containerized workloads, Aura now offers support for cloud functions (lambda-style serverless functions). This unlocks the ability to build data APIs, agents, and inference endpoints for embeddings or other ML tasks, all within a managed, scalable environment. Coupled with performance enhancements like multi-value indexes for JSON, automatic query re-optimization, and improved cross-workspace branching and disaster recovery, these upgrades drive higher reliability and enterprise scalability.<\/p>\n\n\n\n

We\u2019re always thinking about the people doing the work, so we made substantial improvements to the developer experience, with enhancements to our AI-powered query builder assistant (SQrL), deeper integrations with GitHub, notebook scheduling\/versioning, better pipeline and billing visibility, and a more powerful multi-tab SQL editor. All of these improvements make building, monitoring, and scaling AI and data applications faster and more seamless.<\/p>\n\n\n\n

Collectively, these advances eliminate bottlenecks, simplify integration, and provide the speed, flexibility, and full-lifecycle support today\u2019s enterprise AI and analytics apps demand.<\/p>\n\n\n\n

Q8. Which is more prominent, on prem or cloud? With security and privacy being big concerns these days, are people talking about returning on-prem?<\/strong><\/p>\n\n\n\n

Stephen Kallianos:<\/strong> Our strategic advantage is that we are truly hybrid \u2014 with the most versatile offering across SaaS (Helios), Bring Your Own Cloud (BYOC), and self-managed solutions. We provide maximum flexibility for customers to deploy anywhere, on any cloud, allowing them to meet their specific business, technical, and regulatory needs. We’re seeing continued momentum around our managed cloud service, Helios\u2014driven by a desire for operational simplicity, scalability, and innovation. But our BYOC and self-managed (private cloud) solutions are also going strong. This flexibility means customers can mix and match approaches: leveraging Helios for fully managed simplicity, BYOC for deployment control, or self-managed options for maximum security and privacy. Ultimately, we empower customers to modernize on their terms, run workloads wherever they need, and never have to compromise on control, compliance, or agility.<\/p>\n\n\n\n

Q9. What\u2019s next for the industry, and what is SingleStore doing to help meet those needs?<\/strong><\/p>\n\n\n\n

Stephen Kallianos:<\/strong> The next evolution in our industry is all about convergence \u2014 bringing together analytical, transactional, and AI workloads on unified data platforms. Today, less than 1% of enterprise data is being used for enterprise AI, so the opportunity is immense. There’s a heightened focus on delivering real-time intelligence, integrating AI natively, and eliminating data silos, alongside surging demand for seamless integration with data lakes, warehouses, and GenAI\/LLM platforms. SingleStore is innovating aggressively by expanding serverless compute, adding integrated AI and ML functions, launching AI co-pilots, enabling direct LLM integration, and introducing the Aura platform that I mentioned earlier. These advances are designed to enable customers to build the next generation of data-driven and AI-powered applications \u2014 unlocking more value from their data and making enterprise AI real for the business.<\/p>\n\n\n\n

………………………………………<\/p>\n\n\n\n

\"\"<\/a><\/figure>\n\n\n\n

Stephen Kallianos<\/strong>, Americas Field CTO, SingleStore.<\/p>\n\n\n\n

With deep expertise in data-driven strategies and cloud-based innovation, Stephen Kallianos<\/strong> is the Americas Field CTO at SingleStore. In this role, he combines his SingleStore expertise and industry knowledge to drive a collaborative approach towards helping customers align solutions with business goals.<\/em><\/p>\n\n\n\n

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<\/p>\n","protected":false},"excerpt":{"rendered":"

“I wish more organizations realized how fundamental it is to lay a good foundation for any enterprise AI initiative. That foundation includes a robust data strategy and a unified architecture.” Q1. What are the responsibilities of a Field CTO? Stephen Kallianos: As a Field CTO, my core responsibility is to serve as a trusted advisor […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[1811,1807,1809,1810,1697,1808],"_links":{"self":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts\/5780"}],"collection":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/comments?post=5780"}],"version-history":[{"count":13,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts\/5780\/revisions"}],"predecessor-version":[{"id":5794,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts\/5780\/revisions\/5794"}],"wp:attachment":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/media?parent=5780"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/categories?post=5780"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/tags?post=5780"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}