Most enterprise AI conversations still center on the obvious stuff: chatbots for customer service, copilots for productivity, image generation for marketing. These are fine, but they’re table stakes at this point.

The more interesting developments are happening in quieter corners of the AI landscape. Here are seven I think deserve more attention from innovation managers.

1. Small Language Models

The race to build bigger models gets the headlines. But small language models - those that can run on-device or on modest infrastructure - are arguably more important for enterprise deployment.

Why this matters: Data residency requirements mean many enterprises can’t send sensitive data to cloud AI services. Latency matters for real-time applications. Cost matters when you’re running inference millions of times daily.

Models like Microsoft’s Phi, Google’s Gemma, and various distilled versions of larger models are getting surprisingly capable. A 7B parameter model running on local infrastructure can now handle many tasks that required 175B parameter cloud models a year ago.

The implication: If your AI use case involves sensitive data or requires low latency, investigate whether small models can meet your needs. The cost and compliance benefits are significant.

2. Synthetic Data Generation

Training AI models requires data. Most organizations don’t have enough good data for their specific use cases. Synthetic data - artificially generated training data - is becoming a practical solution.

The approach: Use large language models or specialized generators to create realistic synthetic data that augments or replaces real training data. For some applications, synthetic data can even outperform real data because you can control its distribution and eliminate biases.

Applications I’ve seen: Generating edge cases for fraud detection systems, creating diverse test scenarios for autonomous vehicles, producing synthetic medical records for healthcare AI development (avoiding privacy issues with real patient data).

The catch: Synthetic data can embed biases from the generation process. Quality control matters. But for organizations data-poor in specific domains, this is increasingly viable.

3. AI for Code Generation Beyond Copilot

GitHub Copilot gets the attention, but the code generation space is broader and more interesting than autocomplete in your IDE.

What I’m watching: AI systems that can generate entire codebases from specifications, automated code review that catches subtle bugs, systems that can migrate legacy code to modern frameworks, tools that generate tests from code (or code from tests).

Cognition’s Devin made headlines as an “AI software engineer,” though the reality is more limited than the hype. More practically, tools like Cursor and Aider are changing how developers interact with code - not replacing them, but significantly accelerating certain types of work.

For R&D leaders: This has implications for team structure and hiring. If AI can handle certain types of programming tasks, what skills become more or less valuable? How does this affect technical debt management?

4. Automated Scientific Discovery

AI systems that can generate and test hypotheses are moving from research demonstrations to practical tools.

The poster child is DeepMind’s work - AlphaFold for protein structure, AlphaTensor for matrix multiplication algorithms, AI systems that discovered new mathematical theorems. But applications are spreading to materials science, drug discovery, and engineering optimization.

What makes this different from traditional computational science is the hypothesis generation component. The AI isn’t just testing human hypotheses faster - it’s proposing new ones.

For R&D-heavy organizations, this could matter. If AI can explore parameter spaces and identify promising directions orders of magnitude faster than human researchers, the nature of R&D work changes.

5. Multimodal Understanding

Large language models can now process and reason about images, audio, video, and text together. This sounds obvious - humans do it naturally - but the enterprise applications are underexplored.

Practical applications: Document processing that understands layout and images, not just text. Video analysis that can summarize meetings or inspect quality on production lines. Interfaces that accept voice, text, or image input interchangeably.

The gap I see: Most enterprises are still treating these modalities separately - text goes to the NLP team, images to computer vision, audio to speech recognition. Multimodal models collapse these distinctions, but organizational structures haven’t caught up.

6. AI Security and Red-Teaming

As AI systems become more capable, so do AI-powered attacks. This creates both risks and opportunities.

The risk side: AI can generate more convincing phishing emails, deepfakes for social engineering, and automated vulnerability discovery. Security teams need to plan for AI-augmented adversaries.

The opportunity side: AI can also strengthen defenses. Automated red-teaming of your own systems, anomaly detection that’s harder to evade, intelligent response to security incidents.

What I’m seeing: A growing market for AI-powered security tools, and increasing demand for expertise in AI safety and robustness. If you’re building AI systems, security testing needs to include adversarial attacks specific to AI (prompt injection, training data extraction, etc.).

7. AI Infrastructure Maturation

The least glamorous but most practically important development: the infrastructure for deploying AI in production is getting better.

Specific improvements: Better observability tools for understanding what AI systems are doing. More sophisticated techniques for managing model versions and rollbacks. Improved frameworks for A/B testing AI systems. Better tooling for monitoring drift and degradation.

Why this matters: Getting AI into production is the hard part. Most AI projects fail not because the model doesn’t work in development, but because the infrastructure for reliable production deployment doesn’t exist.

Organizations building internal AI capabilities should invest in this infrastructure early. It’s not exciting, but it determines whether your AI projects actually deliver value.

The Common Thread

The pattern across these seven trends: AI is becoming more practical, more deployable, more integrated with real business operations.

The early hype cycle was about raw capability - look what AI can do! The current phase is about making those capabilities actually useful - reliable, cost-effective, secure, compliant.

Innovation managers who focus only on the flashy applications will miss the more substantive opportunities. The competitive advantage often comes from applying mature technology more effectively, not from being first to try experimental approaches.

These seven areas deserve a place on your radar, even if they never make the headlines.