AI best practice is changing rapidly. New frontier models are released every few months, existing models are updated regularly, pricing structures change and organisations are continually evaluating new providers. Yet operators are still building their AI strategies around the assumption that the model they choose today will be available (and affordable) in 3, 6 or 12 months time.
Integrating AI requires significant investment, not just in terms of training and developing the AI itself, but also to support the deployment of new features or platforms to internal teams.
If an operator’s investment is tied to a single AI model, the business becomes dependent on that model and is forced to react to decisions that can sit outside their control.
An AI-agnostic approach reduces this dependency by making the model interchangeable, helping operators protect their AI investment over the long term. Rather than building workflows around one specific model, operators can create an architecture where the underlying AI can be changed without losing the data, governance, workflows and skills that make the solution valuable.
Five benefits of an AI-agnostic approach:
- Greater commercial control: Operators are not tied in to any one provider. If their current AI provider becomes too expensive, the business can move to a competitor rather than being forced to swallow the costs.
- Access best-fit technology: The underlying AI can be changed to use the newest technologies. Connecting multiple models allows the user to choose the one which is most suitable for the task.
- Improve operational resilience: If a model breaks or is pulled by the provider, another can be connected to minimise the disruption on day-to-day operations.
- Provide greater flexibility: New capabilities can be added without redesigning existing workflows or skills. Technical users can also create their own skills and upload them directly, without having to wait for a developer.
- Support future growth: Skills and knowledge documents can be edited, enabling operators to develop their approach over time as priorities and technologies change.
What does AI-agnostic look like?
If the underlying AI is the only source of intelligence, teams are left exposed if it changes or is no longer available. However, if the operational knowledge sits in the skill layer, the AI becomes interchangeable.
One model may be replaced by another, but the organisation’s way of working remains intact.
We work with operators to develop the framework around the AI. This includes the trusted data, integrations, governance, workflows and skills that define how it should behave. These skills can capture the specific processes, language, thresholds, escalation routes and reporting requirements, so that the AI becomes the engine that powers this intelligence layer.
The result is an AI solution that is resilient by design: powered by models, but not dependent on any single one.
To book a demo of our AI-native network intelligence platform and discover what AI-agnostic architecture looks like in practice, contact marketing@metricell.com.
