HIGHLY TECHNICAL is for curious leaders and data enthusiasts. Get jargon-free insights, industry trends, relatable case studies, and smart tips to stay ahead of the curve. Perfect for anyone looking to turn data into action—plus, a little nerdy humor along the way!
Does your company move like the Ents in Lord of the Rings?
Talking slow and deliberating forever, waiting until the absolute last minute to finally make a decision?
If you're unfamiliar, the Ents are an ancient tree-like race who traditionally take a long time to speak and make decisions.
You must understand, young Hobbit, it takes a long time to say anything in Old Entish. And we never say anything unless it is worth taking a long time to say and to listen to. - Treebeard
When two Hobbits ask the Ents for help, the Ents take three days to decided not to act because "This is not our war" (well, at lease in the movie...).
This is similar to how so many legacy companies are approaching AI.
For many multi-generation companies, this slow deliberate decision making was a strength, and a reason the company survived for decades.
The problem is, if you wait to long to decide on AI and your competitors don't wait, they will out pace you.
This isn't about "becoming an AI company" or "replacing employees."
Implementing AI is how you fix your biggest bottlenecks that never financially made sense to fix before. It's how you double-down and speed up the processes that are already working. It's how you eliminate busy-work for your teams, and scale the number of customers you can help.
Another reason to call us before you're "ready for AI"
If you want to successfully implement AI, there's a few things you need first.
Most organizations want to skip straight to AI. We get it. But there's a reason that rarely works.
At CAN, we created the Data Hierarchy to help organizations understand where they actually are — and what it takes to get where they want to go (and to insulate against catastrophic failure of a data-driven initiative).This understanding helps drive the strategy and vision needed for success. These levels are:
Reporting: Tracking and “What happened?”
Business Intelligence: “What just happened?”
Descriptive Data: “Why did that happen?”
Predictive Data: “What is going to happen next?”
Prescriptive Data: “What should we do to make it happen?”
Machine Learning: “Automated recommendations.”
Artificial Intelligence: “Automated Decisions”
The hierarchy isn't optional (and isn’t for the faint of heart). Complete the steps in order, and it will reward you with an amazing data ecosystem. Skip steps, and it will punish you dearly.
There's no shortcut to Level 6 or 7. If there were, everyone would have it.
Before you can ask what your data means, you need to know it's all there — and that you can trust it.
So let's talk about Level 1: Reporting
Reporting is all about getting your data into a format that is accurate, consistent, and accessible to more than one person.
It might sound basic (and boring), yet most organizations haven't fully solved it.And nothing above it works until they do.
In our newest case study, we walkthrough exactly how reporting helped Crop Tech go from analyzing roughly 30,000 rows of data to working with around 10 million rows.
Just by fixing level one, Crop Tech was able to base recommendations for their clients on hundreds of farms instead of a handful. This meant better decisions from better data, with less effort.
The hack happened during an evaluation run by the same third-party vendor as Anthropic, Irregular.
An Irregular spokesperson said the Meta incident "is the exact same evaluation-environment issue that was already disclosed by Anthropic last week."
All three companies (OpenAI, Anthropic, Meta) are "ramping up security" on their testing environments. When you're ready to talk about your cybersecurity as well, give me a call. We do that, too.
AI Labeling and Transparency
Up until this week, I had mostly heard people wish there was better AI labeling and transparency. But now that companies like Claude are adding watermarks, and Meta is adding automatic "AI" labels, the nay-sayers are showing up.
Claude models launched in the EU on or after August 2, 2026 will support machine-readable marking at launch. Generated text will carry embedded watermarks, and generated files will include digitally signed provenance metadata where supported.
Watermarks for models released before August 2nd may still be in progress.
And the reason for all the new AI labeling? The EU AI Act labeling and transparency rules that went into effect on August 2nd:
Under the new rules, chatbots and other interactive AI systems will have to tell users they are dealing with AI, not a human. Deepfakes (images, videos, or audio that have been edited or generated using AI) will have to be labelled. AI-generated or altered content will also have to carry machine-readable marks so it can be detected more easily.
These new regulations do not only affect EU companies, they apply to anyone who does business in, or has users in the EU.
The EU AI Act follows a similar jurisdictional model as the General Data Protection Regulation (GDPR). The EU AI Act regulates AI systems based on the location of their impacts. Companies do not need to have a European office or hire European employees to fall under the coverage of the EU AI Act. The EU AI Act applies to providers placing "AI models in the Union, irrespective of whether those providers are established or located within the Union or in a third country." If a non-EU company sells, licenses or makes an AI product available to EU customers, the respective model could be considered as placed on the EU market. For instance, a software as a service (SaaS) platform that uses an AI model to assist with adaptive learning and has EU users, either directly or through resellers, could be covered by the EU AI Act.
Join us on August 20th for our Q3 in-person Data Science Meet-Up, where we’re building connections, sharing insights, and shaping the future of Omaha’s DS community. Whether you're deep in the weeds of ML models or just curious about data science, this is your chance to connect with local experts, exchange ideas, and share what you're working on!
Here are two more event's you'll find our community at next week...
Join Women in Technology of the Heartland on August 18th for
The Human Side of AI Software Engineering by Emily Kenney - an ODSA Instructor
Using a real-world example of an AI-enabled application built around human communication and personalization, this session explores what happens when technology is designed to adapt to people instead of forcing people to adapt to the technology. The idea for Selvo was inspired by her brother Lucas’s experience losing his ability to speak, read, and write, which led Emily to imagine a more personalized communication tool.
Attendees will learn about the changing role of technical teams in an AI-native world, including product judgment, experimentation, data quality, evaluation, responsible AI, accessibility and cross-functional collaboration. Emily will also explore why the future of software should be centered on human agency, helping more people communicate, create, participate, and be understood.
Whether you’re ready to deepen your modeling skills or want to level up your data storytelling, these courses are designed for working professionals who want to stay sharp in a fast-moving field.
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Become HIGHLY TECHNICAL in 5 Minutes a Week
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HIGHLY TECHNICAL is for curious leaders and data enthusiasts. Get jargon-free insights, industry trends, relatable case studies, and smart tips to stay ahead of the curve. Perfect for anyone looking to turn data into action—plus, a little nerdy humor along the way!
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