Foundation models: When they become a commodity, where will enterprise competitive advantage come from?

A powerful model does not equal a powerful business system

It is easy to mistake the capabilities of a large language model for genuine business capability. While generating reports in seconds, producing marketing content, or responding instantly to customer inquiries may demonstrate impressive technical performance, these actions do not necessarily translate into sustainable business value.

Klarna provides a compelling example. The fintech company leveraged general-purpose LLMs to automate approximately two-thirds of its customer service interactions, replacing a significant number of human agents. Yet little more than a year later, the company resumed hiring customer support staff. The issue was not that the model lacked capability, but that the business system had become misaligned with business value creation.

AI relentlessly optimizes the objectives it is given but often overlooks elements that are difficult to quantify, such as handling complex customer intent, maintaining customer relationship, and preserving long-term brand trust. When efficiency becomes the sole metric, AI will optimize for efficiency, even at the expense of service quality.

By contrast, a consumer electronics company in Shenzhen adopted a different approach. Around 70% of standardized customer inquiries were analyzed and handled by AI, while human agents remained responsible for complex and high-value cases. The result was a balance between operational efficiency and customer satisfaction.

The difference did not come from the model. It came from how each company redesigned its business processes and governance around AI.

Agentic AI Is reshaping enterprise organization

AI agents are no longer simple conversational assistants: they are digital workers capable of accessing enterprise systems, executing workflows, completing tasks, operating autonomously at scale, and continuously learning over time.

Unlike human employees, who naturally exercise caution when facing high-risk decisions, AI agents still execute both routine and high-risk actions with the same level of confidence. As a result, configuration errors, permission issues, knowledge contamination, or workflow flaws can quickly propagate across the organization.

This fundamentally changes how enterprises should approach AI governance.

• First, resilience becomes as important as prevention. Organizations should establish recovery mechanisms such as backup environments, staging systems, and one-click rollback capabilities.
• Second, the principle of least privilege should become standard practice. AI agents should only be granted access to the data and systems strictly necessary to complete their assigned tasks.
• Third, compliance must be embedded into AI operations. Enterprises need to comply with regulations such as data localization requirements and privacy laws, while clearly defining which data can be accessed by which models.
• Finally, organizations must prepare for emerging AI-native risks. For example, AI agents may inadvertently execute hidden instructions embedded within internal business documents, bypassing intended operating rules. Continuous monitoring, governance updates, and evolving security controls therefore become essential.

Organizational context is the enterprise’s most valuable AI asset

Rather than simply building an AI application, enterprises should be developing a continuous learning system.

The core of this system is the ability to transform business processes, decision logic, operational knowledge, compliance requirements, and institutional expertise into an organizational context layer that AI agents can access, learn from, and continuously improve.

Unlike the model itself, this context layer compounds over time. Every optimized workflow, every validated decision, and every successful interaction strengthens the enterprise’s AI capabilities, creating an asset that becomes increasingly difficult for competitors to replicate.

A simple question illustrates whether an organization truly owns its AI advantage:

If you replaced your foundation model tomorrow, would your accumulated AI capabilities remain intact?

If the answer is no, then the organization has merely borrowed the model’s capabilities rather than built a competitive advantage of its own.

AI transformation requires rethinking business processes

Today, many enterprise AI initiatives remain focused on accelerating existing workflows. Genuine AI transformation requires redesigning business processes around the capabilities of AI agents, rather than simply supporting human tasks.

Consider enterprise key account management.

Traditionally, relationship managers can only dedicate deep attention to a limited number of strategic customers, while the rest receive exception-based management. Business reviews occur periodically, leaving emerging issues undiscovered until scheduled reviews. Meanwhile, valuable customer insights, relationship history, and decision rationale often exist only in the experience of individual employees, disappearing when they leave.

Using AI merely to generate account reports makes the existing process faster but leaves the underlying operating model unchanged.

An agent-centric redesign looks fundamentally different.

AI agents can monitor every customer with equal depth, rather than focusing only on strategic accounts. They can detect purchasing changes, order anomalies, or communication gaps in real time instead of waiting for monthly reviews. Most importantly, every customer interaction, business decision, and successful practice is captured and incorporated into the organization’s context layer, becoming a shared organizational capability rather than individual knowledge.

The result is not simply greater efficiency, but an entirely new way of operating.
The same principle applies across demand forecasting, pricing optimization, compliance management, supply chain operations, and virtually every enterprise process constrained by human capacity.

Where should enterprises begin?

Building enterprise AI competitiveness is ultimately about redesigning how an organization thinks, makes decisions, and operates. This is not something a model provider can deliver.

The priority for every enterprise is therefore to identify business-critical activities that still rely heavily on human expertise, define the non-negotiable rules that AI must respect, and systematically structure organizational knowledge so it can be governed, shared, and utilized by AI agents.

Foundation models are quickly becoming a standard capability across industries. The opportunity to build a sustainable AI advantage, however, remains open.

The organizations that act now to redesign their business systems for the age of AI agents will be the ones that establish durable, defensible competitive advantage.

To learn how your organization can build its enterprise AI capabilities, contact the experts at Artefact.