Why the Gap Between an AI Translation Demo and Enterprise Production Is Wider Than Most Organizations Realize

Guest: Olga Beregovaya, VP of AI at Smartling
Host: Seth Earley, CEO at Earley Information Science
Published on: June 17, 2026

In this episode, Seth Earley speaks with Olga Beregovaya, VP of AI at Smartling, who brings 25 years of experience across every major evolution in natural language processing - from rules-based systems through statistical models, neural translation, and now LLMs. They explore why plugging into a commercial model at token-level pricing is not a translation strategy, how brand voice fractures at 300,000 employees, why information architecture is just as essential for language pipelines as it is for retrieval, and what it actually takes to deliver consistent, on-brand, multilingual content at enterprise scale. Olga shares candid and specific insights on language complexity, the human-in-the-loop imperative, and why the organizations that are finally succeeding with AI have stopped treating it as art for art's sake.

Key Takeaways:
The price of a commercial model's tokens is not the cost of enterprise AI translation - data integrity, pipeline architecture, linguistic assets, and human review are the real cost drivers.

Brand voice fractures the moment every employee can generate content autonomously - a Fortune 10 company discovered it had 300,000 voices overnight after deploying a co-pilot tool.

Information architecture is equally essential for language pipelines as for retrieval - nested HTML tags, tokenization failures, and unstructured content break translation before the model ever sees the text.

LLMs unlocked context that neural machine translation never had - resolving pronouns, disambiguating terminology, and working at document level instead of sentence by sentence.

The assumption that AI translation works equally across all languages is one of the most dangerous misconceptions in the space - morphological complexity, writing systems, and training data representation vary enormously.

Human review is not optional even in fully automated pipelines - it is how models learn, how ground truth is established, and how brand consistency is maintained over time.

The organizations now succeeding with AI translation have moved from implement-and-fail to measured deployment - defining use cases, respecting prerequisites, and matching tooling to actual requirements.

Insightful Quotes:
"Yes, you can totally consume your million tokens at a super low price point, but what exactly are you buying for this money? Everybody can totally produce a translation or generate copy, but is it going to represent your brand? That's a different question." - Olga Beregovaya

"He installed a co-pilot tool and said, it's great, except my company has 300,000 employees and now my company has 300,000 voices. That's not necessarily what I was prepared for in different countries." - Olga Beregovaya

"If you want your models to evolve, and if you want your models to learn, you obviously need somewhere for these models to learn from - and this is where human review comes in. It is always twofold: guaranteeing the quality to your customers, and helping your models evolve." - Olga Beregovaya

Tune in to discover why AI translation at enterprise scale requires far more than a model and an API key - and what the organizations getting it right have built that their competitors have not.

Links
LinkedIn: https://www.linkedin.com/in/olga-beregovaya-04b5/
Website: https://www.smartling.com

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• Earley Information Science
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Why Supply Chain Visibility Is One of the Most Consequential and Underestimated Applications of AI in the Enterprise

Guest: Ilya Levtov, Founder and CEO at Craft.co

Host: Seth Earley, CEO at Earley Information Science

Published on: June 1, 2026

In this episode, Seth Earley speaks with Ilya Levtov, Founder and CEO of Craft.co, a supplier intelligence platform that uses AI and knowledge graphs to give enterprises and government agencies visibility into their full supply networks. They explore why most organizations believe they have adequate supply chain visibility when they do not, why a simple risk score will always mislead, and how cross-correlating data streams surfaces risks that no human - and no generic LLM - would ever find alone. Ilya shares candid and specific insights on building knowledge graphs for mission-critical infrastructure, why only one percent of enterprise knowledge exists inside today's LLMs, and how the give-to-get model is turning supply chain intelligence into a shared strategic asset.

Key Takeaways:

• Most enterprises believe their top-supplier relationships give them adequate visibility - but the middle and long tail of a supply network, which can run to 20,000 or 30,000 suppliers, remains almost entirely opaque.
• Supply chain is a misnomer - it is a complex, multi-dimensional network where companies are simultaneously suppliers, customers, and competitors to each other.
• A simple risk score is not meaningful and not actionable; supplier risk is deeply contextual and requires human judgment to weigh cost, probability, and consequence together.
• Cross-correlating data streams reveals hidden risks that no single source can surface - including correlations between employee morale and cybersecurity vulnerability that have proven highly predictive.
• Only approximately one percent of enterprise knowledge exists inside today's LLMs - which is exactly why a specialized knowledge graph grounded in proprietary data is essential before applying AI.
• AI has compressed analyst work on a supplier report from eight hours to under 30 minutes - but the decision of what to do with those findings still requires human judgment and always will.
• The give-to-get model and supplier passporting allow enterprises to share intelligence across a shared supply network without compromising their own competitive position.

Insightful Quotes:

"Only 1% of enterprise knowledge approximately exists inside the LLMs today. Companies don't want to give all of their data to the LLMs. Data providers don't want to give it for free either. That's why you need a specialized approach - leverage the power of the models on your own data set and on your knowledge graph." - Ilya Levtov

"A financially vulnerable supplier becomes a target for adversarial capital - entities coming in from unfriendly nations looking to survive. You're connecting two different data sets, connecting entities, and getting to a very significant risk insight you need to act on before it becomes a problem for your enterprise." - Ilya Levtov

"Organizations compete on their knowledge - knowledge of customers, knowledge of solutions, knowledge of supply chains, knowledge of routes to market. Those are competitive advantages. You do not want those inside an LLM. That is why doing this in a way that is internal and proprietary is so important." - Seth Earley

Tune in to discover why supply chain visibility is one of the most important and most underestimated applications of AI in the enterprise today - and what it actually takes to build intelligence at the scale the problem demands.

Links

LinkedIn: https://www.linkedin.com/in/ilya-levtov/

\Website: https://www.craft.co

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

Why Centralization Is the Wrong Foundation for AI - and What Organizations Need to Build Instead

Guest: Todd Barr, CEO at Axonis.ai
Host: Seth Earley, CEO at Earley Information Science
Published on: May 13, 2026

In this episode, Seth Earley speaks with Todd Barr, CEO of Axonis.ai, a company spun out of a government defense integrator that is bringing federated AI and decision intelligence to high-consequence enterprise workflows. They explore why the demo-to-production gap is one of the most costly misconceptions in enterprise AI today, why centralization was built for business intelligence and not for AI, and what it really means to send your AI to your data rather than the other way around. Todd shares a candid and direct perspective on decision artifacts, AI cost exposure, the risks of vendor lock-in, and why enterprises that give away how they make decisions may be giving away the most valuable thing they own.

Key Takeaways:

The demo-to-production gap is a form of malpractice - polished AI demos built on curated data create executive expectations that production reality cannot meet.

Centralized data infrastructure was built for business intelligence, not AI - it is optimized for reporting, not reasoning or prediction.

The premise of agentic AI is decentralization - if agents have to wait for data to be synced and centralized before acting, the architecture is working against itself.

Data resists centralization for three distinct reasons: technical constraints, regulatory and compliance requirements, and organizational politics.

Decision artifacts - cryptographically sealed records of data used, model applied, and reasoning followed - turn AI-assisted decisions into auditable, improvable corporate assets.

Enterprises now face a clear choice: pay in tokens, pay in vendor lock-in, or invest in owning their own AI infrastructure through open source models.

How an organization makes decisions is its most proprietary asset - giving that context to a third-party AI platform may be the most consequential thing enterprises are doing right now without fully understanding it.

Insightful Quotes:
"The misconception is really the gap between prototype and reality, and that's where a lot of these things are falling down right now. Getting people excited about something they can't have is almost malpractice." - Todd Barr

"Centralization is almost a fallacy in itself. Whenever you are using data you are changing it, enriching it, doing something with it. It is a fractal nature of data that defies the whole concept of centralization." - Seth Earley

"If I'm an enterprise, what do I own in this day and age? I own how I make decisions. Which data I use to make those decisions. If we are just going to give that away, that is like giving our brain away." - Todd Barr

Tune in to discover why the most important AI infrastructure decision an enterprise can make right now is not which model to use - but whether they are building a foundation they actually own.

Links
LinkedIn: / tbarr
Website: https://axonis.ai

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book