Future Of Work: Context And Memory

Hi there,

Happy release day! Today we dive into the future of AI in the enterprise. Tune in here 🎧

I sat down with Aman Singh, CTO and Co-founder of Contextual AI, to explore how enterprise AI is evolving from demos to production-grade systems. Aman’s insights reveal a future where language models are orchestrators, enterprise systems are safeguarded against hallucination, and the enterprise AI ecosystem diverges from its consumer counterpart.

The conversation surfaced three transformative trends that will define enterprise AI over the next five years:

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My 5 Year Outlook:

• LLMs Are The Universal Interface For Orchestrating Specialized Models And Agents

  Humans use language to communicate making LLMs our natural first engagement layer for AI.

• Grounded Language Models (GLMs) and RAG Will Eliminate AI Hallucinations in the Enterprise

  The enterprise won’t accept misguided answers, luckily new technology is grounded AI replies in their data.

• Consumer AI Consolidates Vs Enterprise AI Proliferates

  Enterprise AI is going down a very different path to consumer.

Curious? Read on as I unpack each below 👇🏼

LLMs Are The Universal Interface For Orchestrating Specialized Models And Agents

Large Language Models (LLMs) are evolving into the universal interface through which humans interact with complex AI systems. Because natural language is the most intuitive, accessible medium for human interaction, LLMs are perfectly positioned to become the primary UX layer — enabling users to instruct, query, and coordinate a diverse array of specialized models and agents.

In this vision, LLMs are not just processing language; they are interpreting human intent and delegating tasks to other systems, including retrieval agents, world models, and domain-specific AIs. As Aman Singh explained, the power of LLMs lies in their ability to act as “the planner” — understanding user requests, breaking them down into sub-tasks, and routing those tasks to the right agents.

Companies like Adept are already building agent ecosystems that respond to natural language instructions. Meanwhile, Contextual AI is developing systems where LLMs act as orchestration layers, retrieving contextual data, coordinating agents, and optimizing performance through feedback loops. This paradigm is especially critical for enterprise environments where context, accuracy, and specialization are essential.

The future enterprise stack will increasingly rely on LLMs to serve as the connective tissue between human users and a diverse array of AI tools. Whether it’s accessing a specialized world model, deploying a retrieval system, or orchestrating a workflow across multiple agents, the LLM will be the central interface managing it all.

In short, LLMs are becoming the conductor of the AI orchestra — where agents and models are the instruments, and language is the baton.

Grounded Language Models (GLMs) and RAG Will Eliminate AI Hallucinations in the Enterprise

One of the biggest pain points for enterprises deploying AI is the problem of hallucinations. In consumer AI, minor inaccuracies are acceptable; a chatbot that suggests the wrong movie or provides a slightly incorrect fact can be forgiven. But for enterprises making financial decisions, legal evaluations, or critical diagnostics, mistakes are catastrophic.

Contextual AI’s approach is radically different from generic LLMs like GPT-4. By implementing Grounded Language Models (GLMs) — systems that only respond when the necessary context is available — Aman’s team is creating AI that understands its own limitations. The RAG 2.0 framework provides a retrieval system that is not only context-aware but continually optimized through feedback loops.

The value of these safeguards is already evident in Contextual’s work with Qualcomm, where their models are tuned to respect privacy rules, document attribution, and domain-specific expertise. Aman explained that this architecture doesn’t just prevent hallucinations; it offers a clear audit trail, allowing enterprises to trace an answer back to its source.

Going one step further is then prioritizing the top expertise in the organization so that their knowledge, often tribal in nature, is surfaced ahead of others. For Contextual AI this is their re-ranker system but it can be seen as a wider movement across the enterprise as they model their data to better represent their organizational structure.

Consumer AI Consolidates Vs Enterprise AI Proliferates

What is becoming clear is that AI in the enterprise can no longer be viewed via the same lens as consumer-facing AI like Chat-GPT. Over the coming years there is a divergent path that points to consumer AI being increasingly consolidated amongst the dominant players who have distribution, data access, compute, and brand: OpenAI, Google, LLaMa. As all of the internet data corpus is leveraged data doesn’t become the clear differentiator rather product and distribution is.

But in the enterprise, the opposite is happening. Each company has unique processes, proprietary data, and specialized needs. According to Aman, Contextual AI is building custom agents designed to work within these specific contexts, allowing enterprises to deploy AI models that fit their workflows, rather than forcing their workflows to fit a general-purpose model.

The proliferation of verticalized AI solutions is already visible. Companies like Harvey are building LLMs specifically for legal workflows, while others like Glean AI are developing accounting-specific agents. This specialization will continue to drive fragmentation in the enterprise AI landscape.

The consequence? While consumer AI consolidates under a few massive brands, enterprise AI will diversify into a rich ecosystem of tailored, purpose-built solutions designed for specific industries and companies.

Overall what surprises me most is how practical and near-term these changes are. We’re not talking about far-off AGI scenarios — we’re seeing concrete shifts in how enterprises design their AI systems today. Those that embrace specialization, context, and precision will emerge as the leaders of this new era.

Time to shine!