Generative AI is no longer just about experiments and proofs of concept; it’s about scaling responsibly.
Yet many executives are still asking the same questions: What makes GenAI enterprise-ready? What tools and practices reduce risk? And what kind of talent is required to bring it all together?
Let’s take a look at the most common questions business leaders like you are asking, and what the answers mean in practice.
What role does LLMOps play?
LLMOps is emerging as the backbone of enterprise GenAI. Just as DevOps reshaped software delivery, LLMOps brings operational rigor to large language models. It covers:
- Versioning for prompts and models so experimentation doesn’t create chaos.
- Monitoring for latency, hallucinations, and bias to ensure outputs remain reliable.
- Access controls and usage limits to prevent runaway costs and security gaps.
- Deployment pipelines that automate retraining and rollbacks when issues arise.
The result is that GenAI shifts from a lab project to a predictable service. Without LLMOps, scaling quickly becomes risky or financially unsustainable.
Want to move beyond pilots? Tenth Revolution Group can help you find trusted technology talent with the LLMOps expertise to scale GenAI safely.
How does RAG 2.0 improve reliability?
One of the biggest challenges with early GenAI pilots was accuracy. Models often generated responses that looked convincing but weren’t grounded in fact. Retrieval-augmented generation (RAG) addressed this by injecting enterprise data into responses, but first-generation RAG had limitations.
RAG 2.0 brings more advanced techniques, including hierarchical chunking, hybrid search, and multi-hop retrieval. These improvements reduce noise and help AI tap into the right information with greater precision. For businesses, this makes GenAI viable for use cases where reliability is non-negotiable, such as compliance queries, customer service, or legal research.
Need data engineers who can deliver RAG-ready pipelines? Tenth Revolution Group connects you with trusted technology talent to make enterprise retrieval accurate and compliant.
What are agentic workflows—and why do they matter?
Agentic workflows take GenAI a step further. Instead of simply providing answers, AI systems can now act on behalf of users by integrating with APIs and business systems. Imagine:
- Customer service agents that not only respond but also issue refunds or rebook tickets.
- Finance assistants that pull market data, run analysis, and generate reports automatically.
- HR copilots that screen applications, cross-check credentials, and schedule interviews.
These workflows move AI into the operational core of the business, creating tangible productivity gains. But they also raise new questions about governance, oversight, and accountability. Agentic AI can be transformative, but only if it is deployed with clear guardrails.
What should executives prioritize in 2026?
For leaders, the priority is to treat GenAI as a strategic capability rather than a series of experiments. That means:
- Building LLMOps into your AI teams to ensure cost control, observability, and governance.
- Investing in RAG 2.0 pipelines so models have reliable access to enterprise knowledge.
- Piloting agentic workflows in low-risk areas before scaling them into mission-critical domains.
- Embedding compliance and monitoring from day one to build trust with regulators and customers.
