Dr. Ryan Ries is a renowned data scientist with over 15 years of data and engineering leadership experience in fast-scale technology companies. Dr. RIES has more than 20 years of experience working with AI for more than 5 years to help customers build their AWS data infrastructure and AI models. After receiving his PhD, Dr. RIES has helped develop cutting-edge data solutions for the U.S. Department of Defense and countless Fortune 500 companies in biophysical chemistry.
As Mission’s chief AI and data scientist, Ryan has built a successful team of data engineers, data architects, ML engineers and data scientists to leverage AWS infrastructure to solve some of the world’s most difficult problems.
Mission is a leading provider of hosting services and consulting born in the cloud, providing end-to-end cloud services, innovative AI solutions and software to AWS clients. As AWS Prime Minister’s Partner, the company helps businesses optimize technology investments, improve performance and governance, scale effectively, ensure data and accept innovation with confidence.
You’ve had an impressive journey – from Daqri building AR hardware to becoming Chief AI Officer at Mission. What personal experiences or turning points best give you a view of the role of AI in the enterprise?
Early AI development was severely limited by computing power and infrastructure challenges. We often have to manually code models from research papers, which is time-consuming and complex. The rise of Python and open source AI libraries has undergone a major shift, making experiments and model building faster. However, the biggest turning point is that when AWS (such as AWS) provides scalable compute and storage space, it happens.
This evolution reflects ongoing challenges throughout AI history – undercarriage and computing power. These restrictions have led to previous AI winters and overcoming them is crucial to today’s “AI revival.”
How does Mission’s end-to-end cloud service model help companies scale their AI workloads more efficiently and securely?
In Mission, security is integrated into everything we do. We have been AWS’s security partner of the year for two consecutive years, but interestingly, we don’t have a dedicated security team. This is because at every stage of development, everyone in the mission will keep it safe in mind. Using AWS-generated AI, customers benefit from using the AWS basic layer, which retains data, including sensitive information such as PII, secure within the AWS ecosystem. This comprehensive approach to ensuring safety is fundamental, not an afterthought.
Scalability is also the core focus of the task. We have extensive experience building MLOPS pipelines to manage AI infrastructure for training and reasoning. While many associations with large public-scale systems like CHATGPT generate AI, most enterprise use cases are internal and require more manageable scaling. BedRock’s API layer helps provide scalable, secure performance to enable real-world workloads.
Through Mission’s services, can you guide us to engage in typical enterprise engagement, from cloud migration to deployment to generate AI solutions?
In the task, we first understand the business needs and use cases of the enterprise. Cloud migration begins with evaluating the current on-premises environment and designing a scalable cloud architecture. Unlike on-premises setup, you have to provide peak capacity here, and the cloud allows you to scale resources based on average workload, thus reducing costs. Not all workloads need to be migrated – some jobs can be retired, refactored, or rebuilt to improve efficiency. After inventory and planning, we performed a phased migration.
With generated AI, we have gone beyond the proof of concept phase. We help businesses design architectures, run pilots to refine tips and solve edge cases, and 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 that the generative AI solution from day one is powerful, scalable, and business-friendly.
The task emphasizes “confidence and innovation”. What does this mean for enterprises that adopt AI at a large scale?
This means having a team with real AI expertise, not just a bootcamp graduate, but an experienced data scientist. Customers can trust us not to try them. Our people understand how models work and how they can be implemented safely. This is how we help businesses innovate without taking unnecessary risks.
You have spanned predictive analytics, NLP and computer vision. Where do you see generated AI bringing the greatest enterprise value today, and where is the hype that outweighs reality?
Generative AI mainly provides important enterprise value through intelligent document processing (IDP) and chatbots. Many businesses strive to scale operations by hiring more people, so the generated AI helps automate repetitive tasks and speed up workflows. For example, IDP reduces insurance application review time by 50% and improves care coordination among patients in healthcare. Chatbots often act as interfaces to other AI tools or systems, enabling companies to effectively automate daily interactions and tasks.
However, the hype surrounding generating images and videos often outweighs the real business uses. Although visually impressive, these technologies have limited practical applications besides marketing and creative projects. Most businesses find it challenging to scale generative media solutions into core operations, making them more novel than basic business tools.
“Vibe encoding” 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 encoding refers to developers using large language models that generate code based on intuition or natural language cues more than structured plans or designs. This is very useful for speeding iteration and prototyping – developers can quickly test ideas, generate boilerplate code, or uninstall repetitive tasks. However, this also often leads to lack of structure, difficult to maintain and can be inefficient or insecure code.
We see a broader shift to a proxy environment where LLM functions like the role played by junior developers and humans more similar to architects or quality inspection engineers – browsing, refining and integrating AI-generated components into larger systems. This collaborative model can be powerful, but only if there is a guardrail. Without proper supervision, Vibe encoding can introduce technical debt, vulnerabilities or performance issues, especially when rushing to production without rigorous testing.
What do you think of the evolving role of AI officials? How should organizations rethink leadership structures because AI becomes the foundation of business strategy?
AI officials can definitely add value, but only when the role of success is successful. Companies often create new C-suite titles without aligning them with existing leadership structures or giving them real authority. If AI officials do not share goals with CTOs, CDOs or other executives, they may risk isolated decisions, conflicting priorities and stagnant execution.
Organizations should carefully consider whether AI officials are replacing or adding roles such as chief data officers or CTOs. Title is smaller than task. It is crucial to empower someone to shape AI strategies (DATA, infrastructure, security and business use cases) throughout the organization and enable them to drive meaningful change. Otherwise, the character will become more symbolic than influence.
You have led an award-winning AI and data team. What qualities do you look for when hiring a high-risk AI role?
The first quality is finding someone who really understands AI, not just someone who takes some courses. You need truly fluent AI and still keep your interest in the curiosity and interest that drives the envelope.
I look for people who are always trying to find new ways and challenge the boundaries of things that cannot be done. This combination of deep knowledge and ongoing exploration is crucial for high-risk AI roles, where innovation and reliable implementation are equally important.
Many businesses are working hard to operate their ML models. What do you think is separating successful teams from those in proof-of-concept purgatory?
The biggest problem is cross-team alignment. The ML team built promising models, but other departments did not adopt them due to inconsistent priorities. From POC to production, MLOPS infrastructure is also required: versioning, retraining and monitoring. For Genai, the gap is even bigger. Production of chatbots means prompting tweaks, pipeline management, and compliance…not just bringing prompts into chat.
What advice do you have today for the founders of building AI-First products that can benefit from Mission’s infrastructure and AI strategy experience?
When you are a startup, it’s hard to attract top AI talent, especially without a established brand. Even with a strong founding team, it is difficult to hire someone with the depth of experience needed to build and scale an AI system correctly. That’s where working with companies like Mission can really change. We can help you move faster by providing infrastructure, strategy, and hands-on expertise so you can verify your product faster and more confidently.
Another key part is focus. We see many founders trying to wrap the basic interface on Chatgpt and call it a product, but users are getting smarter and expect more. If you don’t solve real problems or provide something truly differentiated, it’s easy to get lost in the noise. Mission helps startups think strategically about AI that generates real value and how to build scalable, secure and productive things from day one. So you not only have to experiment, but you can also build growth.
Thank you for your excellent interview and hopefully learn more about the readers should visit Mission.
