Most people discussing agent optimization are talking about basic visibility or commerce mechanics. I’d like to go deeper: data structures, trust, identity, interoperability, and security. I’m not sorry about trying to coin the term BEO. Except, as it turns out after a search, I’m maybe a month or so from not being first with this, (so close!). Though I’d like to extend the concept anyway, based on my own experience building and what I’ve been seeing.

We shouldn’t use AEO, for “agent”, because that’s taken. We need something new because as we all know if there’s no acronym, it really can’t be a technology. And wouldn’t it be nice to have unambiguous names? I mean, “DaaS” can apparently be “Data as a Service” or “Desktop as a Service” and we have plenty of other ambiguous mess. We should try to do better with our naming. Why Bot Engines now? We’ve trudged through the cave-dwelling ancient history of Search Engine Optimization (SEO), evolved into Answer Engine Optimization (AEO), and now we’re managing Generative Engine Optimization (GEO). So, naturally, we need something fresh. I’d like to try out BEO for the win as an idea. It’s the art and science of tuning bots, agents, and autonomous systems to thrive in interconnected digital ecosystems; optimizing them not just for performance, but for discoverability, interoperability, and trustworthiness in a world where machines negotiate, transact, and collaborate on our behalf.

It’s SEO, (though much more), for the agentic era. Just as websites compete for search visibility, bots will need to stand out in marketplaces, integrate with protocols, and build reputations.

The following goes over a fast way to get a jumpstart on a basic competitive analysis using Claude.ai along with the increasingly popular Skill function, complete with a slide deck and an Excel workbook. Spoiler Alert: Just skip down to “How to Install…” if you want to skip the explanations.

The catch: AI can accelerate the first draft, but it can’t replace judgment. Treat the output as a structured hypotheses, not truth. You’ll still need to validate claims, metrics, and positioning with primary sources and customer reality.

• Understanding Your Own Work: When doing strategic analysis, I believe part of understanding is in doing some of the discovery yourself. As great as AI is, it’s increasingly clear we sometimes lose at least a little something when we just have all the work done for us. That being said, the tools can help us get a big jump on things as a starting place.
  
• About Claude Skills: Claude.ai from Anthropic is one of many AI tools we have for generative language tasks. All these tools are bulking up with everything from training models, to agent tools collecting data, project organization and more. “Skills” are structured instruction files that shape how an AI behaves. It’s like an operating runbook for an assistant. They’re especially helpful for repetitive tasks.

I’ve spent some time the past year on an important project, potentially life-saving for many. But if the site went down for a few hours or even a day, no one would die immediately. Maybe some revenue loss, but nothing catastrophic. What about yours? Is it mission or safety critical? Or doing so much business that a half a day is millions in loss? Not to mention the customer service issues?

Major digital outages keep happening. The Internet was designed to survive nuclear war, yet we’ve layered on centralization that increases failure risk. And it seems to be getting worse as new inflection points pile up.

As a product person and retail investor, I’m sensing what I would have thought is an obvious risk with Real World Assets (RWA) tokenization, but don’t see much discussion beyond esoteric finance venues. I’m a believer in blockchain and crypto opportunities. However, I prefer a more thoughtful approach than the breathless crypto maxi hype spew. With that perspective, I’d like to offer a primer for product managers and investors interested in this area. I’m trying for a deeper sense of what’s going on than, “The RWA Tokens Are Coming, Invest Before You Miss It!” Or as Darth Crypto would say, “The FOMO is Strong with This One.” Disclaimer: I’m not a finance person… these are my explorations into this world as a retail investor and digital product builder. I’d just like to help my friends and colleagues with informed choices.

Introduction

Short Answer? Maybe not so much. (At least when talking about the core tech vs. specialized solutions.) This won’t be as much about AI as about category and startup creation in general. AI just happens to provide us with a great developing case to review. (At least in terms of Large Language Models (LLMs) and Generative Pre-trained Transformer (GPT) products.)

Product people need to zoom in and out. There’s so much noise around new AI features, we may forget to take a wider view. While we’re often responsible for detailed product roadmaps, some of us also do strategy and business planning. In that role, we need to think about offerings in terms of long term strategy. Rapid growth in AI LLMs / GPTs provides a case worth study in terms of new category creation. We should keep in mind that AI / ML has been around for decades. What’s interesting now is the LLM / GPT space is a great example of a Blue Ocean strategy that is perhaps only fulfilling its promise in a general way. That is, the rising tide is lifting many boats, but early pioneers might not be able to take margin advantaged positions, which should be a benefit in a new category. (If you buy the book, be sure to get the latest edition.) Even though OpenAI bust this category open, they didn’t get to a winner-take-all or even winner-take-most position.

Why?

Companies are missing the mark with AI projects. The headlines are not great.

• Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept By End of 2025
• Research by the RAND Corporation estimates more than 80% of AI projects fail, which is twice the rate of failure of non-AI projects.
• Deloitte shows some benefits accruing such as uncovering ideas and insights with ROI generally positive, and yet, barriers due to mistakes with real-world consequences.
• BCG’s 2025 survey indicates only “25% of executives are seeing significant value from AI”, with projects vulnerable due to unclear use cases and poor data readiness.

What’s going on? And more to the point; can product people help improve these outcomes?

Perhaps there are efforts to solve tech problems, but not always customer problems. From a Product person’s perspective, it’s time to re-visit basics and use cases. In the add AI scramble, there’s confusion about our shiny new toys. You see it in the drive to do anything with them. You see ridiculous job requirements posted from tech to product asking 5 – 10 years experience deploying AI. (For traditional ML, that may be fair. But they usually mean GPTs/LLMs, which have only been around a handful of years.)

Let’s reconsider our new capabilities from customer use case perspectives with focus on LLMs/GPTs. While also growing, we’ll skip traditional ML as it’s a separate and better understood category. (Things like regression models, decision trees, or clustering algorithms used in fields like finance, logistics, or medical diagnostics, which have established frameworks and decades of practical application.)

Why should we try a look from use case perspectives? Because for those of us that work in Product, instead of panicking figuring out how to check off the “We’re doing AI” into a checkbox in an Annual Report or a marketing piece, it might be useful to have clarity of purpose. We’ll focus on Generative Pre-trained Transformers (GPTs) and the Large Language Models (LLMs) over traditional ML or multi-modal GPTs.