Dr. Ryan Ries is a renowned data scientist with more than 15 years of leadership experience in data and engineering at fast-scaling technology companies. Dr. Ries holds over 20 years of experience working with AI and 5+ years helping customers build their AWS data infrastructure and AI models. After earning his Ph.D. in Biophysical Chemistry at UCLA and Caltech, Dr. Ries has helped develop cutting-edge data solutions for the U.S. Department of Defense and a myriad of Fortune 500 companies.
As Chief AI and Data Scientist for Mission, Ryan has built out a successful team of Data Engineers, Data Architects, ML Engineers and Data Scientists to solve some of the hardest problems in the world utilizing AWS infrastructure.
Mission is a leading managed services and consulting provider born in the cloud, offering end-to-end cloud services, innovative AI solutions, and software for AWS customers. As an AWS Premier Tier Partner, the company helps businesses optimize technology investments, enhance performance and governance, scale efficiently, secure data, and embrace innovation with confidence.
You’ve had an impressive journey—from building AR hardware at DAQRI to becoming Chief AI Officer at Mission. What personal experiences or turning points most shaped your perspective on AI’s role in the enterprise?
Early AI development was heavily limited by computing power and infrastructure challenges. We often had to hand-code models from research papers, which was time-consuming and complex. A major shift came with the rise of Python and open-source AI libraries, making experimentation and model-building much faster. However, the biggest turning point occurred when hyperscalers like AWS made scalable compute and storage widely accessible.
This evolution reflects a persistent challenge throughout AI's history—running out of storage and compute capacity. These limitations caused previous AI winters, and overcoming them has been fundamental to today’s “AI renaissance.”
How does Mission’s end-to-end cloud service model help companies scale their AI workloads on AWS more efficiently and securely?
At Mission, security is integrated into everything we do. We've been the security partner of the year with AWS two years in a row, but interestingly, we don’t have a dedicated security team. That’s because everyone at Mission builds with security in mind at every phase of development. With AWS generative AI, customers benefit from using the AWS Bedrock layer, which keeps data, including sensitive information like PII, secure within the AWS ecosystem. This integrated approach ensures security is foundational, not an afterthought.
Scalability is also a core focus at Mission. We have extensive experience building MLOps pipelines that manage AI infrastructure for training and inference. While many associate generative AI with massive public-scale systems like ChatGPT, most enterprise use cases are internal and require more manageable scaling. Bedrock’s API layer helps deliver that scalable, secure performance for real-world workloads.
Can you walk us through a typical enterprise engagement—from cloud migration to deploying generative AI solutions—using Mission's services?
At Mission, we begin by understanding the enterprise’s business needs and use cases. Cloud migration starts with assessing the current on-premise environment and designing a scalable cloud architecture. Unlike on-premise setups, where you must provision for peak capacity, the cloud lets you scale resources based on average workloads, reducing costs. Not all workloads need migration—some can be retired, refactored, or rebuilt for efficiency. After inventory and planning, we execute a phased migration.
With generative AI, we’ve moved beyond proof-of-concept phases. We help enterprises design architectures, run pilots to refine prompts and address edge cases, then move to production. For data-driven AI, we assist in migrating on-premises data to the cloud, unlocking greater value. This end-to-end approach ensures generative AI solutions are robust, scalable, and business-ready from day one.
Mission emphasizes “innovation with confidence.” What does that mean in practical terms for businesses adopting AI at scale?
It means having a team with real AI expertise—not just bootcamp grads, but seasoned data scientists. Customers can trust that we’re not experimenting on them. Our people understand how models work and how to implement them securely and at scale. That’s how we help businesses innovate without taking unnecessary risks.
You’ve worked across predictive analytics, NLP, and computer vision. Where do you see generative AI bringing the most enterprise value today—and where is the hype outpacing the reality?
Generative AI is providing significant value in enterprises mainly through intelligent document processing (IDP) and chatbots. Many businesses struggle to scale operations by hiring more people, so generative AI helps automate repetitive tasks and speed up workflows. For example, IDP has reduced insurance application review times by 50% and improved patient care coordination in healthcare. Chatbots often act as interfaces to other AI tools or systems, allowing companies to automate routine interactions and tasks efficiently.
However, the hype around generative images and videos often outpaces real business use. While visually impressive, these technologies have limited practical applications beyond marketing and creative projects. Most enterprises find it challenging to scale generative media solutions into core operations, making them more of a novelty than a fundamental business tool.
“Vibe Coding” is an emerging term—can you explain what it means in your world, and how it reflects the broader cultural shift in AI development?
Vibe coding refers to developers using large language models to generate code based more on intuition or natural language prompting than structured planning or design. It’s great for speeding up iteration and prototyping—developers can quickly test ideas, generate boilerplate code, or offload repetitive tasks. But it also often leads to code that lacks structure, is hard to maintain, and may be inefficient or insecure.
We’re seeing a broader shift toward agentic environments, where LLMs act like junior developers and humans take on roles more akin to architects or QA engineers—reviewing, refining, and integrating AI-generated components into larger systems. This collaborative model can be powerful, but only if guardrails are in place. Without proper oversight, vibe coding can introduce technical debt, vulnerabilities, or performance issues—especially when rushed into production without rigorous testing.
What’s your take on the evolving role of the AI officer? How should organizations rethink leadership structure as AI becomes foundational to business strategy?
AI officers can absolutely add value—but only if the role is set up for success. Too often, companies create new C-suite titles without aligning them to existing leadership structures or giving them real authority. If the AI officer doesn’t share goals with the CTO, CDO, or other execs, you risk siloed decision-making, conflicting priorities, and stalled execution.
Organizations should carefully consider whether the AI officer is replacing or augmenting roles like the Chief Data Officer or CTO. The title matters less than the mandate. What’s critical is empowering someone to shape AI strategy across the organization—data, infrastructure, security, and business use cases—and giving them the ability to drive meaningful change. Otherwise, the role becomes more symbolic than impactful.
You’ve led award-winning AI and data teams. What qualities do you look for when hiring for high-stakes AI roles?
The number one quality is finding someone who actually knows AI, not just someone who took some courses. You need people who are genuinely fluent in AI and still maintain curiosity and interest in pushing the envelope.
I look for people who are always trying to find new approaches and challenging the boundaries of what can and can't be done. This combination of deep knowledge and continued exploration is essential for high-stakes AI roles where innovation and reliable implementation are equally important.
Many businesses struggle to operationalize their ML models. What do you think separates teams that succeed from those that stall in proof-of-concept purgatory?
The biggest issue is cross-team alignment. ML teams build promising models, but other departments don’t adopt them due to misaligned priorities. Moving from POC to production also requires MLOps infrastructure: versioning, retraining, and monitoring. With GenAI, the gap is even wider. Productionizing a chatbot means prompt tuning, pipeline management, and compliance…not just throwing prompts into ChatGPT.
What advice would you give to a startup founder building AI-first products today that could benefit from Mission’s infrastructure and AI strategy experience?
When you're a startup, it's tough to attract top AI talent, especially without an established brand. Even with a strong founding team, it’s hard to hire people with the depth of experience needed to build and scale AI systems properly. That’s where partnering with a firm like Mission can make a real difference. We can help you move faster by providing infrastructure, strategy, and hands-on expertise, so you can validate your product sooner and with greater confidence.
The other critical piece is focus. We see a lot of founders trying to wrap a basic interface around ChatGPT and call it a product, but users are getting smarter and expect more. If you're not solving a real problem or offering something truly differentiated, it's easy to get lost in the noise. Mission helps startups think strategically about where AI creates real value and how to build something scalable, secure, and production-ready from day one. So you're not just experimenting, you're building for growth.
Thank you for the great interview, readers who wish to learn more should visit Mission.
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