AI: Choosing Is Renouncing?
Making AI Decisions Without Locking Yourself
By Laurent ASSOULY
Executive Summary
In 2025, artificial intelligence has become a structurally strategic choice for companies, but also a new source of fragility. Where IT decisions once unfolded over multi-year cycles, AI now evolves at a pace that makes rigid commitments increasingly dangerous. The core issue is no longer selecting a high-performing technology, but avoiding being trapped by a decision that becomes obsolete within months.
Analyses published throughout 2025 show a clear pattern. The most exposed organizations are not those that delayed adoption, but those that committed too early and too exclusively. Strategic value no longer lies in the initial choice, but in the ability to adapt without disruption.
1. A Market Where Hierarchies Collapse in Months
The year 2025 provided multiple concrete examples of rapid technological reversals. Models widely perceived as leaders early in the year were overtaken or seriously challenged by autumn, often not because of raw performance, but due to operational factors such as inference cost, latency, regional availability, or regulatory compatibility.
Several organizations standardized on a proprietary model at the beginning of 2025, only to discover months later that an open-weight competitor delivered comparable results at lower cost and with better control over internal deployments. The issue was not that the initial decision was poor. The problem was that reversing it required dismantling entire chains of tools, prompts, and business processes already running in production.
These situations illustrate a reality now widely documented. Technological leadership in AI is no longer durable, and any strategy built on that assumption is structurally fragile.
2. When Lock-In No Longer Comes from the Vendor
One of the clearest lessons of 2025 is that vendor lock-in no longer originates primarily from infrastructure or contractual constraints. It increasingly emerges from usage itself.
Multiple analyses describe organizations becoming dependent on complex prompt libraries, orchestration pipelines, monitoring tools, and business logic tightly coupled to a single model. From a purely technical standpoint, switching engines remained possible. In practice, it required revalidating hundreds of use cases, retraining teams, and redesigning critical workflows.
This pattern appeared across sectors, from industrial groups to financial institutions where AI had been embedded into sensitive operational chains. In these cases, lock-in was not imposed by the vendor, but created internally through accumulated organizational complexity.
3. A Market That No Longer Accepts Exclusivity
A strong signal in 2025 came from the suppliers themselves. Historically competing vendors entered distribution agreements allowing customers to access multiple models through a single cloud infrastructure.
These arrangements are not ideological gestures. They respond directly to customer pressure. Enterprises increasingly refuse to abandon a model solely for infrastructure reasons. When even hyperscalers acknowledge that exclusivity hinders adoption, the message is unambiguous. Single-choice strategies have become a liability, not an advantage.
Organizations that persist in voluntary lock-in often end up more constrained than their own providers.
4. Agentic AI and the Failure of “Set and Forget”
The rise of agentic AI systems in 2025 produced particularly concrete examples of the risks associated with rigid choices. Several companies attempted to deploy semi-autonomous agents under a “configure once, operate indefinitely” mindset.
Field feedback revealed a different reality. As soon as agents interacted with complex systems or sensitive data, continuous adjustments became unavoidable. Organizations relying on closed platforms struggled to fine-tune autonomy levels, supervision rules, or control mechanisms.
By contrast, those that preserved modular architectures were able to adapt incrementally, sometimes switching models or vendors without operational downtime. Once again, the problem was not the original choice, but its irreversibility.
5. Regulation as an Overlooked Source of Renunciation
Regulatory developments in 2025 added another layer of constraint. Several European organizations discovered that earlier technology decisions exposed them to new compliance obligations without viable mitigation paths.
In some cases, companies that had adopted turnkey AI solutions early realized that emerging requirements around traceability or auditability could not be met without major redesigns. Vendor dependency quietly turned into regulatory dependency, sharply limiting strategic options.
For organizations operating across multiple jurisdictions, the ability to change solutions or deployment modes became a compliance lever in itself.
6. What Resilient Organizations Actually Do
Across the 2025 literature, a consistent pattern emerges. The organizations extracting real value from AI are not those that avoid decisions, but those that choose without locking themselves in.
Some deliberately deploy multiple models in parallel for different use cases. Others test new solutions on limited scopes before scaling. Several have architected their systems so that data, business logic, and AI engines remain decoupled, making replacement possible without systemic disruption.
In some cases, companies have even declined financially attractive exclusive agreements in order to preserve future bargaining power. These choices reflect not indecision, but a clear-eyed understanding of market volatility.
Conclusion
The examples observed in 2025 converge on a simple conclusion. In contemporary AI, the greatest strategic risk is not making the wrong choice, but losing the ability to change.
Choosing remains unavoidable. Renouncing adaptability is not. As 2026 approaches, strategic maturity is no longer measured by loyalty to a single partner, but by an organization’s capacity to remain mobile in a market that shows no sign of stabilizing.