The future of sales in the AI ​​era: Diversification and Dispersion

Over the past few years, AI has dramatically changed how businesses approach sales. But instead of moving towards a single, all-encompassing AI model, a new trend is emerging: diversified and distributed AI systems where multiple specialized agents collaborate to optimize the sales process.

 

This isn't just a technological shift; it's a shift in how we design AI systems for businesses.

When "LLM wrapper" is no longer enough

In February 2026, Darren Mowry, Google's Vice President of Global Startups, warned that two types of AI startups were losing ground: companies that built only the user interface layer on LLMs, and platforms that bundled multiple LLMs into a single API. According to him, the market was losing patience with these models.

The facts also prove this. In Google's Atoms and Accel accelerator programs, about 70% of AI startups are eliminated because they are only superficial "wrappers." The startups that are selected all have one thing in common: they build specialized models for specific industries or problems.

 

This reveals a clear trend: AI is shifting from a "one-tool, all-in-one" model to an ecosystem of many specialized tools.

Each AI technology is suited to a specific problem.

Over the past decade, AI has not developed in a single direction, but rather through multiple parallel breakthroughs.

In the early 2010s, CNNs helped computers see and recognize images. Later, deep reinforcement learning helped AI make decisions in complex environments. And most recently, LLM helps computers understand and create natural language.

Each technology excels in a specific area. CNN excels in vision, reinforcement learning in decision-making, and LLM in language and reasoning.

The important thing is: no technology is a one-size-fits-all solution.

LLM isn't always the best solution.

Currently, many businesses use LLM for most tasks. However, LLM is not always the optimal choice.

For example, in B2B sales, employees are constantly making decisions: when to follow up, who to contact, whether to offer a discount or not. These decisions have long-term effects, and results may only become apparent months later.

This is a sequential decision-making problem — an area where reinforcement learning is more suitable than LLM.

LLMs can write excellent follow-up emails, but deciding which emails to send and when is a completely different matter. This is precisely where the "LLM wrapper" model often overlooks it.

From single-model to AI agent networks

The new trend is to build systems consisting of multiple specialized AI agents, rather than a single large model.

For example, in sales, a business might have:

• Deal dynamics analyst
• Agent monitors customer behavior.
• Price sensitivity analysis agent
• The agent predicts the likelihood of closing the deal.

Each agent performs a separate task, then they coordinate with each other to create a more intelligent system.

This is how the software industry has evolved in the past, moving from monolithic applications to microservices architecture. AI is also following a similar path.

 

The role of LLM in multi-agent systems

In the new architecture, the LLM still plays an important role, but it is not the sole center.

LLM will take on the task of reasoning and breaking down the problem. For example, when evaluating a deal, LLM can break it down into steps: stakeholder analysis, price assessment, and comparison with historical data. Then, specialized agents will handle each part, while the orchestration system will coordinate the entire process. This creates a much more flexible and powerful AI system than a standalone model.

This trend has led to a new concept: businesses powered by AI agents.

In such a model, humans and AI work in parallel. AI can detect data patterns that humans don't recognize, while humans provide context and intuition that AI lacks. This combination creates a new competitive advantage. It's not that AI replaces humans, but rather that it's a collaboration between humans and AI.

What should businesses do right now?

If you're implementing AI for sales or business processes, consider these points:

• If it's a language-related task, LLM is a good choice. Writing emails, summarizing calls, and analyzing content are all suitable.
• If it's a classification or recognition problem, a specialized model should be considered.
• If it's a long-term decision-making problem, reinforcement learning might be a better solution.

More importantly, businesses need to think about the overall architecture. How will the agents coordinate with each other? How will data be managed between systems? This is the direction of AI development in businesses.

The future of AI is not a single model that does everything. Instead, it's an ecosystem of multiple specialized agents working together in a distributed architecture. AI in sales will also follow this direction.