The AI conversation has shifted. One model won’t cover every need: different teams demand different strengths. Leaders now face the task of orchestrating multiple LLMs, balancing speed with safety, creativity with compliance. Success comes from governance, IP protection, and building internal systems (sometimes entire GPTs) that turn AI into a strategic capability.
1: Why One LLM Won’t Cut It Anymore
If you’re in a leadership role right now, chances are you’ve had the conversation: “Should we be using AI?” and not long after that comes the harder question: “Which AI should we be using?”
The truth is, that’s no longer the right question. Today, most organisations aren’t choosing one AI model, they’re using several.
Customer support teams might want a low-latency, affordable model that can handle high volumes of chat. Engineering teams need something that’s code-literate and can integrate with their existing tools. Legal teams want precision, transparency, and guardrails. And when sensitive data is involved, risk and compliance teams want full control over what’s going in and what’s coming out.
What’s emerging is a multi-model approach, where different parts of the business rely on different large language models (LLMs) based on the specific job they’re trying to get done. They’re building a system that lets them combine the strengths of several.
The result is a growing number of companies running three or four LLMs in parallel, each purpose-fit to its domain. They treat LLMs like specialists: each with their own strengths, each with their own use case.
2: What Different LLMs Do Best (And Where They Fall Short)
Not every LLM is built for the same thing. And if you use the wrong one for the wrong job, the output might look polished but it won’t be fit for purpose.
Some models are designed for deep reasoning and writing. Others are built for speed, structure, or working inside strict privacy walls. When you break it down, the strengths of each become clearer:
- OpenAI’s GPT-4: strong reasoning and creativity – strategy papers, idea generation, marketing content, complex summaries.
- Claude by Anthropic: safe and measured responses ideal for policy reviews, compliance documentation, and internal reports.
- Google’s Gemini: great for planning, structured tasks, and integrating with productivity tools.
- Meta’s LLaMA and Mistral: open-source, lightweight, highly customisable perfect for privacy-centric internal tools.
- Cohere Command R+: strong in document Q&A and internal knowledge retrieval.
- GitHub Copilot: built for code developers can write, translate, and debug code faster.
- Harvey: legal specialist model – contract reviews, legal research, due diligence.
Each model brings something different to the table. You’re not buying a one-size-fits-all solution, you’re building an ecosystem of specialised tools that work together.
3: How to Run Multiple LLMs Without Creating a Mess
As you bring multiple models into the business, the next challenge is orchestration. Without the right infrastructure, this can quickly become unmanageable with different tools, different APIs, disconnected workflows. That’s where orchestration layers come in.
Leading organisations are now building what’s essentially an “AI control tower”, an internal system that routes each task to the most appropriate model behind the scenes. This means your users don’t need to know which model is being used. They just get the best outcome, every time.
Some organisations build their own orchestration platforms using frameworks like LangChain or LlamaIndex. Others rely on enterprise platforms like AWS Bedrock, Azure OpenAI Service, or Google Vertex AI that allow access to multiple LLMs under one roof. This makes it easy to switch between models depending on the task, or even run them in sequence where needed.
In more advanced use cases, we’re starting to see multi-agent systems being trialled where different LLMs act like teammates. One agent breaks down the request, another researches, another drafts a response, and a final agent checks the result before it’s returned.
The goal here isn’t to have every model doing everything. It’s a coordinated, modular system where each model plays its role and the orchestration framework makes it feel seamless.
4: Governance, IP Protection, and the Rise of Internal LLMs
All of this only works if you can trust the system. And that’s where governance comes in.
The minute you start using AI tools with internal data especially anything sensitive, regulated, or commercially valuable – you need to think seriously about privacy, IP protection, and compliance. And many businesses are now taking this very seriously.
We’re seeing a major move toward private LLM deployments. Rather than sending sensitive data to a public API, organisations are running models in their own environment on-prem, in a private cloud, or through secure vendor agreements. Some fine-tune open-source models in-house. Others sign enterprise contracts with zero data retention and full isolation.
JPMorgan is a prime example, they built their own internal ChatGPT-style tool. Morgan Stanley and PwC took a different approach, integrating enterprise LLMs (GPT-4 and Harvey) with heavy oversight, prompt controls, and human-in-the-loop policies.
Good governance isn’t just about technology. It’s also about culture and accountability. Risk, compliance, legal, and IT aren’t being dragged in at the end, they’re part of the build from the start. That’s how you scale AI with confidence.
5: What Executive Leaders Should Do Now
If you’re making decisions about AI strategy, here’s the shift to keep in mind: the most successful companies aren’t asking “Which model should we use?” They’re asking “How do we build a system where the best model is used for each task and our data, people, and reputation stay protected?”
That mindset opens the door to innovation, flexibility, and real competitive advantage.
As a business leader, you don’t need to be an AI expert. But you do need to ask the right questions:
- Are we matching the right models to the right use cases?
- Have we built the internal architecture to manage multiple LLMs?
- Are our risk and governance teams shaping this journey, not just reacting to it?
- Are we protecting our IP, data, and brand as we scale AI?
- Are we investing in enablement and education so our people know how to use this tech responsibly?
The companies getting this right aren’t locking themselves into one model or one vendor. They’re building systems that evolve with the technology. They’re turning AI from a tactical experiment into a strategic capability with intent, structure, and trust built in from the start.
6: Why More Companies Are Building Their Own GPT Internally
One of the most strategic moves we’re seeing from larger organisations is the decision to build and deploy their own internal GPTs. This isn’t about jumping on the AI bandwagon, it’s about creating a controlled, private environment where the organisation can use the power of LLMs without risking its most sensitive IP.
By hosting their own GPT models internally, whether by fine-tuning open-source models or standing up a secured foundation model, companies can lock down access and ensure nothing leaves the organisation’s walls. It addresses the single biggest concern for legal, compliance and IT leaders: data leakage. No external training, no prompt data sent to the cloud, no dependency on the privacy policies of third-party providers.
What makes this even more powerful is the ability to monitor how employees are actually using the model. Internal teams can analyse usage patterns, identify gaps, and train the LLM to be even more helpful in specific workflows. Imagine an internal GPT that gets smarter about your business every month because it’s being refined by how your people use it, ask it, and rely on it.
By offering an internal GPT, companies give employees a safe and approved alternative to public tools. It builds trust. It creates space for experimentation. And it keeps AI innovation aligned to the company’s values and standards.
More importantly, it gives companies a competitive edge: an AI capability that is not just off-the-shelf, but entirely tailored to their knowledge, processes, and people.
For business leaders looking to reduce risk and increase control, an internal GPT is becoming less of a novelty and more of a strategic imperative.
AI won’t replace Talent Leaders but Talent Leaders who understand AI will outpace the rest. At ATC2025, Marrin will equip you with practical ways to lead this shift, from orchestrating Agents to structuring prompts that work. Join us ATC2025: IMPACT → Check-Out Event
28-29 October 2025 | Melbourne
