AI in 2024: What Actually Worked and What’s Coming Next

The hype around AI was deafening in 2024. But looking past the noise, some real, practical developments emerged that are changing how enterprises work. Let me break down what actually mattered.

Super Agents: From Buzzword to Business Value

Remember when everyone was building basic chatbots? We've moved way beyond that. At Skcript, we took this challenge head-on with S1 EDGE - our edge computing platform that transforms how enterprises deploy AI agents. It's not just about running models; it's about running them intelligently, securely, and cost-effectively at the edge.

What's different now is that these agents can:

• Ground their responses in your company's actual documents
• Interface with multiple internal tools
• Make reasoned decisions about when to escalate to humans
• Maintain security and compliance standards

This isn't just automation – it's intelligent assistance that actually delivers ROI.

The Hardware Story Nobody's Talking About

While everyone focused on Nvidia's dominance, something more interesting happened in the background. Edge computing and efficient inference engines are changing the game. Apple showed us what's possible with on-device AI, and now enterprises are following suit.

The market is finally getting competitive. AMD, Intel, and innovative startups are bringing new solutions that could make AI deployment more cost-effective. For businesses, this means more options and better economics.

Open Source: The Dark Horse of 2024

The open source AI ecosystem exploded this year. Meta's Llama 3 and IBM's Granite series proved you don't need black-box proprietary systems to get enterprise-grade results. More importantly, they showed you can have both openness and safety.

What's Coming in 2025

Based on what we're seeing in production environments:

1. Efficient Scale: The focus is shifting from bigger models to smarter deployment. Companies are getting better results with smaller, more focused models.

2. Real Integration: AI systems will talk to each other better. The days of isolated AI experiments are ending.

3. Practical Multimodal: Processing text, images, and video together will become standard for business applications.

4. Edge Intelligence: More AI processing will happen on local devices, improving privacy and speed.

Related: Why Unstructured Data is the Hidden Gem in Your AI Strategy: A CEO's Perspective

What You Should Do Now

If you're running an enterprise tech stack:

1. Start with specific, high-value problems instead of trying to "AI everything"
2. Build governance into your AI systems from day one
3. Look for solutions that integrate with your existing tools
4. Focus on measuring actual business impact, not just technical metrics
5. Look into automating your manual document data processing with AI

The Reality Check

Here's what we learned from working with Fortune 500 companies this year: success with AI isn't about having the fanciest models. It's about thoughtful implementation and clear business objectives.

The most successful projects we saw weren't the ones with the biggest budgets or the most advanced technology. They were the ones that started with clear problems and built solutions that actually fit their organizations.

Looking Forward

2024 was when AI got real for enterprise. The technology matured, the tools improved, and companies figured out how to actually use this stuff. 2025 will be about scaling what works and finding new ways to create value.

The question isn't whether to use AI anymore. It's how to use it effectively.

Let's talk about how these trends could impact your business. I'm always interested in hearing about new challenges and sharing what we've learned.

Want to discuss more? Drop me a line at [email protected]