Executive summary
AI is evolving from a mere productivity support tool into a new operational interface between people, companies and markets. The question is no longer just where to use AI, but how organizations must restructure to operate in an environment where agents intermediate discovery, decision-making, transactions, service and execution.
In this context, the concept of B2A — business-to-agents — is gaining momentum. This is not a replacement for B2B or B2C, but a new layer of economic and operational interaction. Instead of designing solely for human buyers or traditional enterprises, organizations now need to design for agents that represent intent, compare alternatives, interpret context and execute actions. In certain sectors, this movement is already being described as a new competitive channel, where structured data, APIs, context and execution capabilities become as vital as brand, interface and traditional distribution.
This shift demands a deeper overhaul than what most of the market currently treats as "AI adoption." It is less about experimenting with isolated tools and more about preparing architecture, data, platforms, governance and operating models for a new market dynamic. Less about incremental automation; more about structural transformation.
On the consumer side, the signals are already emerging. Recent research indicates that a significant portion of consumers is already comfortable delegating tasks to agents, including scheduling, support, shopping and benefit optimization. This suggests that agent mediation will not be restricted to the back office; it is poised to become part of the customer experience itself.
On the corporate side, however, readiness remains limited. While AI adoption has spread, operational maturity remains low. Most companies still operate in reactive modernization cycles, hampered by fragmented legacy systems, dispersed data and a poor ability to turn pilots into scalable capabilities. In a survey published by Thoughtworks with IDC, only 12% of surveyed organizations reached a truly continuous, AI-driven operating model.
The central thesis is simple: the next competitive advantage won't come just from adopting AI first. It will come from building companies that are legible, interoperable and actionable by agents, without losing strategic coherence, governance, or business value.
1. From AI as a tool to AI as an economic agent
For years, the corporate AI conversation focused on productivity, analytics, point automation and decision support. That cycle was relevant but insufficient to explain the current moment. What is changing now is that AI is ceasing to be just a tool plugged into existing processes; it is beginning to take an active role in task coordination, contextual usage, system interaction and multi-step workflow execution.
This shift repositions AI within the firm and the market. It is not just about accelerating known tasks; it is about creating new forms of mediation between intent and execution. In other words: less about answering questions, more about interpreting goals, navigating constraints, triggering capabilities and delivering results.
This transition makes the B2A concept essential. It names a shift already underway: agents are moving beyond "assistants" to become practical intermediaries in the relationship between companies, customers, products, services and operations.
2. B2A as a new market layer
B2A does not replace B2B or B2C; it adds a new layer to existing logic. Instead of thinking only of companies selling to companies or consumers, it now makes sense to think of companies operating for agents that represent consumers, employees, teams, or other organizations.
In practice, this means an increasing share of discovery, comparison, recommendation, purchasing, negotiation and execution may pass through agents. Instead of a person opening an app, manually searching, comparing options and checking availability, an agent can execute much of this journey — provided it has access to structured information, clear rules and reliable interfaces.
This redefines what it means to compete. In many contexts, the battle moves from the human interface to the machine-readability layer. It becomes less about winning attention and more about winning interpretability, trust, availability and execution capacity within agentic ecosystems.
3. The consumer side is already in motion
There is a common tendency to treat agents as an exclusively corporate topic linked to internal efficiency. This is a short-sighted view. The movement is also advancing at the edge, where consumers are beginning to accept the idea of delegating daily activities to intelligent systems.
Recent data highlights this shift. According to Salesforce, 39% of consumers are already comfortable with agents scheduling appointments for them. Nearly a quarter are comfortable with agents shopping on their behalf. Furthermore, 70% would use agents to optimize loyalty points, 67% to handle exchanges and returns and 66% to buy items when prices drop.
These numbers matter less as exact forecasts and more as signals of changing expectations. The customer hasn't disappeared, but how they interact with brands is increasingly filtered through a digital representation layer. This means customer experience is no longer just human UX; it now includes UX for agents.
4. The biggest bottleneck isn’t intent — it’s readiness
Most organizations understand that AI matters. The problem is that few have made the transition from adoption to readiness. This is evident in the gap between the progress of pilots and the low capacity to scale value.
Legacy architectures were designed for linear processes, siloed systems and predictable interactions. Agentic environments require a different foundation: shared context, cross-capability orchestration, reliable data access, permission controls, runtime governance, observability and clear human intervention mechanisms. Without this, a company can launch pilots but cannot operate with consistency.
This discrepancy also appears in modernization initiatives. Despite widespread AI adoption, most organizations remain stuck in reactive, intermittent cycles. The Thoughtworks/IDC study shows that approximately 90% follow this pattern, while only 12% have reached a truly continuous, AI-driven stage. The result is predictable: much experimentation, but little ability to turn AI into a durable operating model.
5. Preparing the enterprise for B2A
Preparing for B2A requires an agenda that goes far beyond deploying models or copilots. The challenge is institutional, not just technological.
Legacy: Not because legacy systems are inherently bad, but because many were structured for a world where integration, real-time context and intelligent automation were not prerequisites. Where there is excessive fragmentation and low interoperability, legacy ceases to be just a technical debt — it becomes a strategic barrier.
Platform: B2A requires reusable, composable and governable capabilities. This includes robust APIs, clear services, capability discovery, explicit rules, access control and an orchestration layer capable of securely connecting agents, data, systems and business flows.
Data products: Companies will only be relevant to agents if they are understandable by agents. This depends on structured, updated, reliable and semantically useful data. The "AI-ready" debate points exactly here: unified access to information, integration of current and historical data and real-time activation capacity.
Governance: The greater the operational autonomy of agents, the greater the need to define boundaries, roles, audit trails and oversight mechanisms. The question shifts from "What can AI do?" to "What should it be allowed to do, in which contexts, and under whose responsibility?"
Business-tech alignment: In a B2A scenario, technology doesn't just support operations; it shapes the way you compete. This requires a much tighter relationship between strategy, product, data, architecture and operations. Fewer handoffs, more joint capability design.
6. The risk of false transformations
One of the biggest mistakes in this cycle is treating any AI layer as synonymous with transformation. In many cases, we are simply seeing the automation of existing inefficiency.
Some organizations use AI to accelerate poorly designed processes, multiply unintegrated interfaces, or increase complexity without creating corresponding value. This isn't transformation; it is sophisticated dysfunction.
Therefore, the innovation agenda must be refocused. It’s less about putting AI everywhere and more about reviewing processes, eliminating unnecessary friction and building capabilities that make economic sense. Less about incremental novelty; more about clarity of impact.
7. New languages for new capabilities
When structural change occurs, it demands a new vocabulary. This isn't just jargon; it’s about building organizational understanding.
Terms like agent readiness, AgentOps, continuous modernization, data products, machine-readable business and agent-facing interfaces help name capabilities that previously didn't need to be explicit. Naming them correctly allows for better prioritization, investment and alignment across the organization. B2A is a label for a new way of reading the market, operations and enterprise architecture.
8. What this agenda can unlock
Companies that prepare for a B2A context won't just gain efficiency. They will unlock new forms of relationship, distribution and execution.
They will reduce friction between intent and action, making service and operations more responsive. They will transform data into real operational capacity rather than just insights. They will create products prepared to be discovered and triggered in agent-mediated ecosystems. Ultimately, architecture will stop being a "technical topic" and become a central pillar of strategy.
Conclusion
The next frontier of business transformation will be less about AI as an isolated feature and more about the ability to reorganize the company to operate in agent-mediated markets.
B2A offers a useful framework for this transition. It makes visible a shift already taking shape: agents are moving from support tools to becoming active participants in discovery, decision-making and the relationship between companies and their audiences.
The challenge is organizational, strategic and operational. It requires continuous modernization, proper platforms, reliable data, robust governance and, above all, a willingness to redesign processes and business models rather than just automating the status quo.
The question is no longer "How do we use AI?" It is now: "How do we prepare the company to be relevant, legible and actionable in a world increasingly operated by agents?"