Why AGI Might Take Over your CPQ Tasks

Configure, Price, Quote (CPQ) has always been more than just clicking through a set of predefined options. At its heart, CPQ is a reasoning engine that balances engineering constraints, commercial trade-offs, and customer needs in real-time. This complexity makes CPQ one of the most natural enterprise applications for advanced AI, and ultimately for Artificial General Intelligence (AGI).

CPQ is Already a Reasoning Platform

The basic question of CPQ is deceptively simple: given what the customer wants, what configuration can we offer that is valid, competitive, and profitable? Behind that question lies a web of logic. A 650 hp engine only works with certain axle packages. A Highline cab excludes the low chassis height. A construction body requires a PTO. Today, consultants and engineers spend enormous amounts of time encoding such dependencies into symbolic models - tables, rules, and logic trees.

An AGI-level system could learn these constraints directly from product documentation, engineering specs, and historical quote data. Instead of painstakingly entering every dependency, the knowledge could be absorbed and maintained automatically.

Natural Language as a New Foundation

One of the biggest limitations of current CPQ systems is their reliance on brittle, rule-based logic. Rules must be updated constantly as products evolve, and the error risk grows with every manual change.

AGI offers a different approach. Instead of rigid rules, it can use natural language. Imagine an AI reading a truck configuration handbook and understanding that electric retarders only work with electric drivetrains, or that hybrid powertrains exclude manual transmissions. More importantly, it can explain those decisions clearly. Instead of an error message like “rule violation,” a sales rep would hear: “You chose an electric retarder with a diesel engine. That’s not compatible, since the retarder draws power directly from the drivetrain. A hydraulic retarder would be a valid alternative.”

This kind of explanation bridges the gap between engineering and sales in a way rules alone never could.

Bounded Domains Make CPQ Achievable for AGI

The idea of AGI solving everything is still far off. But CPQ lives in a bounded domain. The universe of trucks, MRI machines, or industrial equipment is large, but finite. This makes it a tractable problem for AGI. Within those limits, AGI can reliably suggest configurations, validate compliance, and generate well-reasoned recommendations.

Unlike open-ended tasks such as scientific research or creative writing, CPQ has a clear set of constraints and a structured product universe. That makes it one of the first places where AGI-level reasoning can become consistently useful.

From Bottlenecked Experts to Curators of Intelligence

In traditional CPQ projects, knowledge bottlenecks are common. A handful of engineers or product managers often hold the keys to valid configurations, and consultants spend months encoding their expertise into rules.

AGI changes this equation. Product data becomes reasoning-ready, not just rule-ready. Experts shift from being rule maintainers to becoming curators of product intelligence. They design structured anchors - modules, variants, SKUsand write narratives that explain trade-offs. The AI handles the interpretation, explanation, and scaling.

This turns expert knowledge from a bottleneck into a resource that scales instantly across teams, geographies, and languages.

From Static Forms to Conversational Companions

Perhaps the biggest shift is not in how configurations are validated, but in how people interact with CPQ. The old world of CPQ is form-based. Users fill in questions one by one, navigating a static decision tree. It is like reading a book: linear, predefined, and limited.

The new world will feel more like a seminar with the author. Instead of filling in forms, users will ask follow-up questions, receive pros and cons explained in real time, and be offered several configuration alternatives that balance different trade-offs such as performance, cost, or sustainability.

This makes CPQ dynamic. It becomes a sales companion, capable of conversation, reasoning, and persuasion - not just validation.

When Might AGI Arrive?

Timelines for AGI are debated. Some leading researchers and executives believe it could appear as early as the late 2020s, perhaps by 2027. Others suggest a horizon around 2030, while more conservative voices place it closer to mid-century.

The precise year is uncertain, but the direction is clear. AGI is coming faster than many expected, and CPQ is one of the enterprise domains most likely to benefit early. The combination of bounded product complexity, clear business value, and heavy reasoning requirements makes it a natural candidate.

What Early AGI Will Mean for CPQ

When AGI does reach usable form, the way we work with CPQ logic will be completely different. Some things will remain the domain of explicit rules: safety-critical dependencies, legal compliance, and hard technical constraints. Those must always be transparent and testable. Other things will shift to AI reasoning: explanations, trade-off analysis, customer dialogues, and sales justification.

This will reshape the very foundation of CPQ. Specifications of modules, variants, and features will no longer be built only for rigid validation. They will need to be redesigned as reasoning-ready datasets - structured enough for symbolic checks but rich enough in narrative to fuel AI-driven conversations.

The Coming Rehaul of Logic and Interaction

The impact of AGI on CPQ is not simply about speed. It changes the nature of the work itself. Instead of consultants building vast rule trees, CPQ teams will focus on curating product intelligence - data and narratives that an AI can use to reason with, explain, and adapt.

In practical terms, this means that CPQ will move away from form-based interactions toward conversational, exploratory experiences. A sales team will no longer tick boxes. They will talk with the system, explore trade-offs, and receive recommendations that feel more like dialogue than data entry.

In the past, CPQ was like reading a book. In the future, it will be like having a seminar with the author.

Conclusion

AGI will not eliminate CPQ. Instead, it will transform it into something more powerful. Rules and constraints will remain where they matter most, but the broader process of configuration, explanation, and persuasion will be driven by AI reasoning. Product data will need a major rehaul to support this new balance.

The end result is a CPQ system that does not just configure - it thinks alongside you, explains trade-offs, and adapts to the conversation. That is the future of CPQ in the age of AGI.