INBOX INSIGHTS, January 15, 2025: Considerations for Local AI, Data Analysis with AI - Trust Insights Marketing Analytics Consulting

INBOX INSIGHTS: Considerations for Local AI, Data Analysis with AI (2025-01-15) :: View in browser

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Four Considerations for Local AI Models

I’ve been thinking a lot about the future of AI implementation in businesses. After spending the last few years helping companies navigate their AI transformations, I’ve noticed a significant shift in how organizations are thinking about their AI infrastructure.

Imagine you’re building your dream house. You could rent everything—the furniture, the appliances, even the artwork on the walls. That’s kind of like using cloud-based AI models. It’s convenient, sure, but you’re constantly paying rent, and you don’t have control over when things might change or disappear. Now imagine owning everything instead. That’s what local AI models offer: more control, potentially lower long-term costs, and the ability to customize everything to your exact needs.

Through my work with various organizations – from scrappy startups to Fortune 500 companies – I’ve identified four crucial reasons why local AI models deserve serious consideration. This isn’t just theoretical; these insights come from real-world implementations, complete with their successes and (let’s be honest) their face-palm moments.

If you want to hear more about local AI models, tune in to the In-Ear Insights podcast.

Before we dive in, though, let’s be clear: this isn’t about jumping on the latest tech bandwagon. It’s about making strategic decisions that align with your business needs, regulatory requirements, and long-term goals. I’ve seen too many companies rush into AI implementations without considering all the angles. Trust me, cleaning up those messes isn’t fun for anyone.

Data Privacy: The Non-Negotiable Factor

Not long ago, a client accidentally sent PII (Personally Identifiable Information) to a public API. Talk about a heart-stopping moment! Thankfully, we caught it quickly, but it reinforced why data privacy isn’t just a checkbox – it’s a fundamental requirement for any business.

With local AI models, your sensitive data stays exactly where it belongs: within your infrastructure. No more wondering if your proprietary information is being used to train someone else’s models. Honestly, the terms of current service agreements can be pretty vague.

Sustainability: The Hidden Environmental Impact

If one of your 2025 goals is sustainability, consider building a local AI model. Local AI models can significantly reduce your carbon footprint by:

Minimizing data transfer energy costs
Optimizing processing for your specific needs
Reducing redundant computations
Allowing for more efficient hardware utilization

Costs: The Numbers Don’t Lie

Many companies are looking for ways to reduce costs in 2025. While local AI models require upfront investment, they often make more financial sense in the long run.

Consider these cost factors:

No per-token charges
Predictable infrastructure costs
Reduced data transfer fees
Better optimization for your specific use cases

Business Continuity: Because Downtime Isn’t an Option

Remember that global IT outage last July? Yeah, that wasn’t fun for anyone. Running local AI models gives you control over your destiny (or at least your uptime).

Benefits include:

Independence from third-party service availability
Faster response times
Better integration with existing systems
Custom failover and redundancy options

All that said, local AI models aren’t for everyone. You need the right infrastructure, team, and use cases to make them work. But if you’re dealing with sensitive data, high computation needs, or strict regulatory requirements, it’s worth exploring.

I’d love to hear your thoughts on this. Have you had any experiences (good or bad) with local AI models? Drop a comment below or reach out – I’m always eager to learn from others in the trenches!

Pro tip: Start small with a pilot project. Test a non-critical model locally and gather real-world data before making bigger moves. Trust me, your future self will thank you for this approach!

Are you considering using local models for AI? Reply to this email to tell me, or come join the conversation in our free Slack Group, Analytics for Marketers.

– Katie Robbert, CEO

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss using generative AI to analyze your data. You’ll discover how to leverage AI to understand your marketing data better. You’ll learn powerful techniques to identify trends and patterns in your data, even if you’re not a data scientist. You’ll learn to use the 5Ps framework to ask the right questions to solve the right problem. You’ll gain insights into building a repeatable process so you aren’t starting from scratch every time you analyze your data. Tune in now to unlock the power of AI for your marketing analysis!

Watch/listen to this episode of In-Ear Insights here »

Last time on So What? The Marketing Analytics and Insights Livestream, we looked at the barebones of setting up local AI models. Catch the episode replay here!

On this week’s So What?, we’re going to walk through how to analyze data with generative AI. Are you following our YouTube channel? If not, click/tap here to follow us!

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In this week’s Data Diaries, let’s continue our theme of analyzing data with generative AI. There are three major considerations when it comes to whether generative AI is a good fit for your data analysis needs.

1. Is the data made for the strengths of generative AI?

Generative AI, and large language models in particular, are optimized for predicting the next item in a sequence, like the next word in a sentence. Some data lends itself especially well to this; any language-based task like talking to an Ideal Customer Profile or developing a corporate strategy based on earnings call transcripts is well suited for generative AI.

Conversely, non-language tasks like spatial understanding, sensory information, computation and mathematics are tasks that today’s models are not well suited for. Mathematics doesn’t work on the principle of what number appears next to another number most frequently. It works on symbolic and computational logic, and that’s not what generative AI is good at. If you have a lot of this data, you need to process it outside of generative AI first.

2. Does the data need to be aggregated or granulated?

Aggregated data is useful for when you want generative AI to see the big picture, such as what we did with our 2025 Marketing Trends report. We took a huge corpus of text and had generative AI distill down the corpus into trends. That plays to the strength of the models.

Granulated data is when you want to perform operations on small pieces of text that add up to a lot, like rows of a table. You don’t want to lump all the rows together; instead you want to process them piece by piece, such as analyzing the sentiment of customer feedback. Granulated data is harder for generative AI to process and reliably return individual results well without additional infrastructure.

3. Does the data require statefulness?

Statefulness means that what we’re doing requires keeping the data in memory over long periods of time. Generative AI language models are stateless; when you open up a new chat in Claude, Gemini, or ChatGPT, for the most part they have no knowledge of previous conversations. You have to provide any prior knowledge (and there are exceptions like Claude/ChatGPT projects that can read previous stored chats, but those have limits) every time you start up generative AI.

For some datasets, this is computationally very expensive to do. In those instances, stateful data that persists from session to session may be better suited for a database that connects to AI, rather than feeding it to AI directly, a process called Retrieval Augmented Generation.

4. Does the data need cleaning and processing?

One of the things we talked about on this week’s podcast is how generative AI is generally bad at math. This is inherent to the architecture of the system itself, so model makers have had to build workarounds to get around that issue. But math isn’t the only problem – dirty data is also problematic.

Remember that every word you put into generative AI affects how it generates results, how it comes up with answers. If you have text that’s rife with misspellings, with irrelevant data, with garbage and spam, then AI is going to perform less well. Ideally the data that goes into generative AI has been cleaned and processed at least somewhat, to remove the worst offenders.

Generative AI is a powerful tool, but it’s not a magic wand. When you know the strengths and weaknesses of the system and the data it works best with, you’ll get the best results out of it.

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Imagine a world where your marketing strategies are supercharged by the most cutting-edge technology available – Generative AI. Generative AI has the potential to save you incredible amounts of time and money, and you have the opportunity to be at the forefront. Get up to speed on using generative AI in your business in a thoughtful way with our workshop offering, Generative AI for Marketers.

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