In a landscape where Artificial Intelligence is reshaping industries, enterprises often find themselves struggling with the costs associated with developing and deploying AI solutions. It is commonly believed that in order to achieve high performance, significant resources must be invested into computational power and data infrastructure. However, smarter alternatives exist which can help enterprises cut AI costs without compromising on performance.

Firstly, focusing on ‘computing smarter, not harder’ is essential. The traditional approach may advocate for scaling up hardware to meet processing demands, but this can be an expensive and unsustainable solution. Instead, enterprises can adopt more efficient algorithms and techniques that optimize computational resources without affecting the accuracy or speed of the AI models. Model compression, for example, can significantly reduce the complexity of AI models without losing their performance edge.

Secondly, leveraging cloud-based AI platforms can provide a scalable solution for businesses. Cloud services reduce the need for hefty upfront investments in infrastructure, enabling enterprises to pay only for the resources they consume. This model not only slashes costs but also offers the flexibility to scale operations as required.

Thirdly, enterprises must invest in robust AI training frameworks. The efficient training of AI models is crucial to minimizing costs. Adopting frameworks that incorporate techniques like transfer learning can accelerate model training and reduce the associated computational expenses.

Furthermore, adopting an agile approach in AI development allows companies to be more adaptive to changes and optimize their processes continually. This flexibility can prevent resource wastage and ensure that projects remain on budget and ahead of the curve.

Finally, fostering a culture of innovation and collaboration can also help reduce costs. By encouraging cross-departmental collaboration and sharing of insights, enterprises can collectively devise smarter, cost-effective AI strategies.

Overall, the key takeaway for enterprises is to redirect their focus away from merely increasing computational capacity. Instead, they should aim to revolutionize how resources are utilized through smarter computing strategies, thereby achieving cost efficiency and maintaining, if not boosting, AI performance.