INBOX INSIGHTS, December 11, 2024: Organizational Health, AI Models - Trust Insights Marketing Analytics Consulting

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Why Organizational Health Outshines Tech Trends Every Time

I just finished another conversation where a client was panicking about not having the latest AI tool. After walking them through their actual needs, I had that familiar aha moment: we’re still chasing shiny objects instead of focusing on what really matters.

I’ve been guilty of this too. Last year, I spent weeks obsessing over a new analytics platform that promised to revolutionize everything. Spoiler alert: the revolution didn’t quite materialize. What did make a difference? The time we spent helping our team understand and embrace the changes.

The Only Constant Is Change—And Health Is Your Competitive Advantage

Real talk: The companies crushing it this year weren’t the ones with the biggest tech budgets. They were the ones who had their organizational house in order.

Quick example: One of my favorite clients spent the first half of 2024 resisting the urge to jump on every new trend. Instead, they invested in weekly team connections and clear communication. When a major industry shift hit in Q3, guess who adapted faster than their “more advanced” competitors? Yep, them. Their secret weapon? A healthy organization.

What Does “Healthy” Look Like?

Let’s look at it through the lens of the 5P Framework. You can get your version here.

Purpose

Remember when everyone was scrambling to launch an AI chatbot? The healthiest companies stopped and asked, “How does this actually help us serve our purpose?” Perhaps you need to redirect your budget to improve your customer support system instead.

People

Let’s say your team is struggling with new collaboration tools, but no one wants to admit it. If your leadership team shares their own challenges, you’re setting the tone and creating a safe space. That one moment of vulnerability transformed their entire culture into one where “I don’t know” became okay to say.

Process

Sometimes we get so excited about new trends that we don’t stop to ask if we should. Introducing new things into an existing process can get messy and complicated. It’s okay to say not now, or even no.

Platform

Oftentimes, the best tech stack isn’t the newest—it’s the one your team actually uses. Stop and do a tech stack audit and ask some basic questions:

Does this serve our purpose?
Does our team use it confidently and consistently?
Does it make work better or just different?

Do this audit, trim that fat, and then see if it makes a difference in your output.

Performance

I used to be a metrics junkie. I wanted to see all the numbers all the time. But numbers don’t tell the whole story. The healthiest organizations now track things like team well-being, collaboration effectiveness, and adaptability. (Yes, you can measure these!)

What Actually Worked in 2024

What did we learn in 2024?

AI found its place—not as our replacement, but as a tool for skilled teams.
Relationships beat tools – the companies thriving in our AI-driven industry? The ones who never stopped focusing on human connections.
Simple > Fancy – teams that simplified their tech stacks are positioned to outperform those who kept adding more. Why? They can be more agile and more adaptive. Don’t overcomplicate it for your teams.

Building Resilience for 2025

Something new and “revolutionary” will pop up next year that everyone will insist you must have. Instead of chasing trends, focus on:

Making learning a daily habit, not a checkbox. Invest in yourself and your team’s continual learning.
Building real communication muscles. Make sure that your “open door policy” isn’t just lip service. Make an effort to be honest and transparent. Create a space where feedback is helpful, not hurtful.
Creating cultural resilience. Change is inevitable. How you handle it within your organization will make or break your culture.

Tech trends are exciting. They promise to solve all our problems. But, after another year in the trenches, I know this: the organizations that thrive aren’t the ones with the most tools. They’re the ones with the strongest foundations.

Let’s make 2025 the year we focus on what really matters: building organizations where people thrive, purpose drives decisions, and change is an opportunity, not a threat.

And yes, we can still get excited about new tech—maybe after we’ve had our coffee.

What are you most looking forward to in 2025? Reach out and tell me, or come join the conversation in our free Slack Group, Analytics for Marketers.

– Katie Robbert, CEO

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The 12 Days of AI Use Cases

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In this week’s Data Diaries, let’s build on the 5P framework Katie reminded us of in the cold open. When it comes to AI tools, you have a bounty of choices; on the model “app store” of the world, Hugging Face, there are over a million different AI models to choose from, and virtually all of them are free of cost.

With that much choice, how do you decide what to use?

This is relevant because as the world changes, as governments change, there are a number of folks who would like more control over what data is given to AI, especially third party services, as well as concerns about reliability and availability.

To no one’s surprise, choosing an AI model can be done using the Trust Insights 5P framework, starting with Purpose and Process.

Purpose: What tasks is the AI model supposed to accomplish? Many models are specialized towards a specific task, or perform certain tasks better than others.
People: Do you have the technical skills necessary to implement certain types of models? If you’re working on Apple hardware, for example, your people should know what MLX is.
Process: How will people use the models? What tasks will they be performing, and how granular is your documentation for those tasks?
Platform: Based especially on your purpose and performance, what models best fit your goals? You’ll need to take into account things like computational power required.
Performance: What will be your benchmarks for success? You’ll have many, from accomplishing the task overall to granular performance metrics like tokens per second and time to first token.

It’s very easy to get lost in the dizzying array of techno-jargon around AI models, with obscure-sounding terms like iMatrix quants and GGUF vs vLLM. All of that is important, but it comes well after working through the 5Ps broadly.

Let’s review the current state of models quickly so that you have a sense of at least what’s out there. This is just a tiny fraction of all the different options.

Foundation/Frontier Language Models

These are the best in class, and either are hosted by someone else or require substantial investment in hardware to operate.

OpenAI o1: great at complex thinking
Anthropic Claude Sonnet 3.5: great at writing and content creation
Google Gemini 1.5 Exp 1206: great at handling huge datasets
Meta Llama 3.1 405B: great for general purpose running in your own hardware

Language Models for Individual Use

Meta Llama 3.3 70B: If you have a beefy laptop (think fully-loaded MacBook Pro M4 Max) or a server, this is the current best in class for following instructions carefully
Alibaba Qwen QwQ 32B: A midrange reasoning model that runs on a gaming-class laptops and can reason well
Alibaba Qwen 2.5 Coder 32B: The current best in class for writing code, also runs on a gaming-class laptop
Cydonia Magnum 22B: A creative writing model that is very fluent at writing and bad at everything else
Mistral Small 22B: A solid general purpose model for midrange computers
Mistral Nemo 12B: A surprisingly good, small model for lower-end computers

Image Generation Models

Black Forest Labs Flux-1 Dev: The current best in class image creation model you can run on your own hardware

Transcription Models

Distil-Whisper Large V3: The fastest distilled version of OpenAI’s Whisper model, 6 times faster than OpenAI’s version, 49% smaller, and same accuracy. Great if you want to process a lot of audio without racking up big bills

Bear in mind that these models are all engines; just as you don’t drive down the road sitting on an engine block, neither do you use these models without infrastructure around them. Another time, we’ll talk about how to put these models into production.

Here’s a roundup of who’s hiring, based on positions shared in the Analytics for Marketers Slack group and other communities.

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