Dr. Ryan Ries is a famend knowledge scientist with greater than 15 years of management expertise in knowledge and engineering at fast-scaling expertise firms. Dr. Ries holds over 20 years of expertise working with AI and 5+ years serving to prospects construct their AWS knowledge infrastructure and AI fashions. After incomes his Ph.D. in Biophysical Chemistry at UCLA and Caltech, Dr. Ries has helped develop cutting-edge knowledge options for the U.S. Division of Protection and a myriad of Fortune 500 firms.
As Chief AI and Knowledge Scientist for Mission, Ryan has constructed out a profitable group of Knowledge Engineers, Knowledge Architects, ML Engineers and Knowledge Scientists to resolve a number of the hardest issues on the earth using AWS infrastructure.
Mission is a number one managed providers and consulting supplier born within the cloud, providing end-to-end cloud providers, progressive AI options, and software program for AWS prospects. As an AWS Premier Tier Accomplice, the corporate helps companies optimize expertise investments, improve efficiency and governance, scale effectively, safe knowledge, and embrace innovation with confidence.
You’ve had a powerful journey—from constructing AR {hardware} at DAQRI to changing into Chief AI Officer at Mission. What private experiences or turning factors most formed your perspective on AI’s function within the enterprise?
Early AI improvement was closely restricted by computing energy and infrastructure challenges. We regularly needed to hand-code fashions from analysis papers, which was time-consuming and complicated. A serious shift got here with the rise of Python and open-source AI libraries, making experimentation and model-building a lot sooner. Nevertheless, the most important turning level occurred when hyperscalers like AWS made scalable compute and storage extensively accessible.
This evolution displays a persistent problem all through AI’s historical past—operating out of storage and compute capability. These limitations brought about earlier AI winters, and overcoming them has been elementary to as we speak’s “AI renaissance.”
How does Mission’s end-to-end cloud service mannequin assist firms scale their AI workloads on AWS extra effectively and securely?
At Mission, safety is built-in into all the pieces we do. We have been the safety accomplice of the 12 months with AWS two years in a row, however curiously, we don’t have a devoted safety group. That’s as a result of everybody at Mission builds with safety in thoughts at each section of improvement. With AWS generative AI, prospects profit from utilizing the AWS Bedrock layer, which retains knowledge, together with delicate info like PII, safe throughout the AWS ecosystem. This built-in method ensures safety is foundational, not an afterthought.
Scalability can be a core focus at Mission. We now have intensive expertise constructing MLOps pipelines that handle AI infrastructure for coaching and inference. Whereas many affiliate generative AI with huge public-scale techniques like ChatGPT, most enterprise use instances are inner and require extra manageable scaling. Bedrock’s API layer helps ship that scalable, safe efficiency for real-world workloads.
Are you able to stroll us by means of a typical enterprise engagement—from cloud migration to deploying generative AI options—utilizing Mission’s providers?
At Mission, we start by understanding the enterprise’s enterprise wants and use instances. Cloud migration begins with assessing the present on-premise surroundings and designing a scalable cloud structure. In contrast to on-premise setups, the place it’s essential to provision for peak capability, the cloud enables you to scale assets primarily based on common workloads, decreasing prices. Not all workloads want migration—some could be retired, refactored, or rebuilt for effectivity. After stock and planning, we execute a phased migration.
With generative AI, we’ve moved past proof-of-concept phases. We assist enterprises design architectures, run pilots to refine prompts and handle edge instances, then transfer to manufacturing. For data-driven AI, we help in migrating on-premises knowledge to the cloud, unlocking larger worth. This end-to-end method ensures generative AI options are sturdy, scalable, and business-ready from day one.
Mission emphasizes “innovation with confidence.” What does that imply in sensible phrases for companies adopting AI at scale?
It means having a group with actual AI experience—not simply bootcamp grads, however seasoned knowledge scientists. Prospects can belief that we’re not experimenting on them. Our folks perceive how fashions work and implement them securely and at scale. That’s how we assist companies innovate with out taking pointless dangers.
You’ve labored throughout predictive analytics, NLP, and laptop imaginative and prescient. The place do you see generative AI bringing essentially the most enterprise worth as we speak—and the place is the hype outpacing the fact?
Generative AI is offering important worth in enterprises primarily by means of clever doc processing (IDP) and chatbots. Many companies wrestle to scale operations by hiring extra folks, so generative AI helps automate repetitive duties and velocity up workflows. For instance, IDP has diminished insurance coverage software assessment instances by 50% and improved affected person care coordination in healthcare. Chatbots usually act as interfaces to different AI instruments or techniques, permitting firms to automate routine interactions and duties effectively.
Nevertheless, the hype round generative photographs and movies usually outpaces actual enterprise use. Whereas visually spectacular, these applied sciences have restricted sensible functions past advertising and artistic initiatives. Most enterprises discover it difficult to scale generative media options into core operations, making them extra of a novelty than a elementary enterprise software.
“Vibe Coding” is an rising time period—are you able to clarify what it means in your world, and the way it displays the broader cultural shift in AI improvement?
Vibe coding refers to builders utilizing giant language fashions to generate code primarily based extra on instinct or pure language prompting than structured planning or design. It’s nice for dashing up iteration and prototyping—builders can shortly take a look at concepts, generate boilerplate code, or offload repetitive duties. However it additionally usually results in code that lacks construction, is tough to keep up, and could also be inefficient or insecure.
We’re seeing a broader shift towards agentic environments, the place LLMs act like junior builders and people tackle roles extra akin to architects or QA engineers—reviewing, refining, and integrating AI-generated elements into bigger techniques. This collaborative mannequin could be highly effective, however provided that guardrails are in place. With out correct oversight, vibe coding can introduce technical debt, vulnerabilities, or efficiency points—particularly when rushed into manufacturing with out rigorous testing.
What’s your tackle the evolving function of the AI officer? How ought to organizations rethink management construction as AI turns into foundational to enterprise technique?
AI officers can completely add worth—however provided that the function is about up for fulfillment. Too usually, firms create new C-suite titles with out aligning them to present management constructions or giving them actual authority. If the AI officer doesn’t share objectives with the CTO, CDO, or different execs, you danger siloed decision-making, conflicting priorities, and stalled execution.
Organizations ought to fastidiously contemplate whether or not the AI officer is changing or augmenting roles just like the Chief Knowledge Officer or CTO. The title issues lower than the mandate. What’s essential is empowering somebody to form AI technique throughout the group—knowledge, infrastructure, safety, and enterprise use instances—and giving them the flexibility to drive significant change. In any other case, the function turns into extra symbolic than impactful.
You’ve led award-winning AI and knowledge groups. What qualities do you search for when hiring for high-stakes AI roles?
The primary high quality is discovering somebody who truly is aware of AI, not simply somebody who took some programs. You want people who find themselves genuinely fluent in AI and nonetheless preserve curiosity and curiosity in pushing the envelope.
I search for people who find themselves at all times looking for new approaches and difficult the boundaries of what can and cannot be executed. This mix of deep data and continued exploration is important for high-stakes AI roles the place innovation and dependable implementation are equally necessary.
Many companies wrestle to operationalize their ML fashions. What do you assume separates groups that succeed from those who stall in proof-of-concept purgatory?
The largest challenge is cross-team alignment. ML groups construct promising fashions, however different departments don’t undertake them on account of misaligned priorities. Shifting from POC to manufacturing additionally requires MLOps infrastructure: versioning, retraining, and monitoring. With GenAI, the hole is even wider. Productionizing a chatbot means immediate tuning, pipeline administration, and compliance…not simply throwing prompts into ChatGPT.
What recommendation would you give to a startup founder constructing AI-first merchandise as we speak that would profit from Mission’s infrastructure and AI technique expertise?
While you’re a startup, it is robust to draw prime AI expertise, particularly with out a longtime model. Even with a powerful founding group, it’s exhausting to rent folks with the depth of expertise wanted to construct and scale AI techniques correctly. That’s the place partnering with a agency like Mission could make an actual distinction. We might help you progress sooner by offering infrastructure, technique, and hands-on experience, so you’ll be able to validate your product sooner and with larger confidence.
The opposite essential piece is focus. We see lots of founders making an attempt to wrap a fundamental interface round ChatGPT and name it a product, however customers are getting smarter and anticipate extra. When you’re not fixing an actual drawback or providing one thing actually differentiated, it is simple to get misplaced within the noise. Mission helps startups assume strategically about the place AI creates actual worth and construct one thing scalable, safe, and production-ready from day one. So you are not simply experimenting, you are constructing for development.
Thanks for the nice interview, readers who want to be taught extra ought to go to Mission.
