Considerations for Local AI

This content was originally featured in the January 15th, 2025 newsletter found here: INBOX INSIGHTS, January 15, 2025: Considerations for Local AI, Data Analysis with AI

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

Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.