Meta's commitment to artificial intelligence is not a recent development; it has been a decade-long investment that has positioned the company at the forefront of AI innovation.

Meta has not only recently started investing in AI; we have been doing it for over 10 years.

Llama: Meta's open source language model

Meta’s open-source large language model, Llama, is a key part of the company's AI strategy. By releasing Llama to the public, Meta enables developers around the world to create and refine specialized models.

So far, over 200,000 derivative models have been created. This open-source philosophy—also behind technologies like React and PyTorch—lowers barriers to innovation and accelerates AI adoption across industries.

Shopify’s Lava

One notable implementation of Llama is Lava, developed by Shopify to streamline e-commerce. Small businesses often neglect full product descriptions, which reduces discoverability.

Lava uses AI to generate tags and features automatically, improving product visibility and conversion.

Advantage plus campaigns

Marketers know how difficult it can be to select the right audience for advertising. However, platforms (such as Meta) already have all the necessary data. And there are technologies that can automatically target advertising with precision.

Meta has integrated AI into its advertising infrastructure with Advantage Plus Campaigns, achieving a 9% lift in conversions. This system leverages AI to optimize across campaign goals, improving targeting and delivery.

Tip: Try Advantage Plus in Meta to automate the selection of the right target audience for your specific needs.

Here’s one example shared by Akash:

When using an Advantage Plus manual campaign, I took the opportunity to test it myself. I received some credits in Meta to try out the experience, since I work on the ads team.

In my testing, I found that, especially for small or growing businesses, audience expertise can be a challenge. So I spend a lot of time figuring out, 'Oh, what should be my targeting? What should this be?' I end up burning more money than I gain.

And then imagine a company like Meta, which is sitting on such a huge knowledge base, where it knows if you are a D2C company or if you are an automobile company, what type of audience, and what kind of location suits you best. If I think from that perspective, as an individual, I'm wasting my time and effort thinking about things that can be automated. Advantage Plus does that job so that I can focus more on my outcome.

AI-Powered Creative Tools

Meta also offers tools that enhance ad content creation:

• Image Expansion adjusts visuals to different formats automatically.
• Text Generation creates ad copy and headlines.
• Background Enhancement improves static imagery for better engagement.

Breaking traditional constraints

Artificial intelligence is redefining long-standing business limitations. Traditionally, businesses were forced to choose between quality, speed, and cost—achieving all three was seen as unrealistic.

Today, this paradigm is changing. The rapid accessibility of AI technologies has made it possible to prototype advanced tools with minimal resources and time investment. For example, what once required weeks of engineering work can now be built using affordable APIs in a matter of days.

I was trying to build a bot for my own purpose, and I hardly spent $10 to $20 on API costs with GPT. Within a week, I had a prototype ready. If I had tried to build such a bot five years ago, it would have required at least 20 to 25 man-days of effort.

Industry-specific AI models

The future of AI is moving toward specialization. While most businesses today rely on generic, off-the-shelf solutions, industry-specific models are gaining traction. According to Gartner research, only around 1% of businesses currently use tailored AI models, but that number is expected to reach 50% by 2027.

These models are designed for precise use cases—such as fraud detection in banking or travel agents in tourism—making them more effective than general-purpose systems.

The rise of agentic AI

One of the most forward-looking trends is the development of agentic AI—systems composed of multiple AI agents working collaboratively. Rather than a single assistant handling tasks, these agents each specialize in a particular domain and communicate with one another under the guidance of a manager agent.

I’ve tried to create a system of AI agents designed to help me manage jet lag during international travel. Instead of relying on a single AI assistant, I developed specialized agents that focused on various aspects such as sleep patterns, fitness training, and dietary considerations. These agents communicated with one another under the guidance of a "manager agent" to develop comprehensive recommendations.

This approach mirrors human collaboration, where experts from different fields combine knowledge to solve complex problems. It also opens up new possibilities for managing multi-step workflows and increasing productivity.

Transforming business communication through WhatsApp

AI has become central to how businesses communicate with customers, especially through messaging platforms like WhatsApp. In regions like India, Southeast Asia, and Latin America, WhatsApp is often the primary business interface.

“WhatsApp-first” companies use AI agents to enable 24/7 support. These agents can retrieve catalog items, answer FAQs, and even process transactions. A small AI label in chats ensures transparency by indicating when responses are generated by AI.

Meta’s integration of AI with physical products is evident in devices like Ray-Ban smart glasses. These glasses combine video capture with object recognition, enhancing user interactions with the environment.

First-party data and privacy

In partnership with Bïrch, Meta has recently launched the Signals Gateway, a solution designed to support and accelerate the adoption of first-party data collection.

Traditionally, tools like the Facebook Pixel operated as third-party scripts—JavaScript snippets loaded from domains like facebook.com onto advertiser websites. For example, when a user visits a site such as loopearplugs.com, the embedded Pixel would send data back to Facebook, classifying it as third-party tracking from the browser’s perspective.

This approach is increasingly seen as problematic due to privacy concerns and browser-level restrictions. To address this, Meta is transitioning to a model where all tracking scripts and data exchanges occur within the business’s own domain. In the new setup, interactions and data exchanges happen solely between the user and the website they are visiting (e.g., loopearplugs.com), ensuring compliance with privacy regulations like GDPR and reinforcing user trust. Consent mechanisms remain in place, but the technical implementation now operates entirely as first-party.

First-party data is  the future of digital marketing infrastructure. While more advanced frameworks such as data clean rooms are also on the horizon, the immediate priority is helping businesses move away from third-party tracking toward more secure, privacy-centric practices.

The road ahead

Meta's AI strategy is built around practical utility: solving real-world business problems and augmenting human capabilities. As AI tools become more specialized and collaborative, their impact will deepen across industries.

From open-source language models to agentic workflows and messaging-first strategies, Meta is shaping a future where AI is not a luxury but an integral part of business operations. The AI revolution is not coming—it’s already here.

Key takeaways

• AI is mature, not hypothetical: A decade of R&D and infrastructure spending makes today’s AI both scalable and robust.
• Open Source accelerates innovation: Tools like Llama enable customized applications without expensive licensing.
• Messaging is the new interface: Platforms like WhatsApp are now central to commerce in many regions.
• AI democratizes creativity: Automated tools make high-quality content creation accessible to all marketers.

The future is agentic: Multi-agent systems will handle complex workflows, freeing humans to focus on strategy and input.