Developing an AI platform for small and medium businesses isn’t a walk in the park. Many companies have built robust data and machine learning systems, but the leap to comprehensive AI platforms introduces a whole new set of challenges. If you’ve ever grappled with the costs and complexities of emerging tech, you’re in good company.
The first challenge is establishing a reliable AI infrastructure. Think of it as tapping into a modern form of electricity, where large tech firms rule the landscape with their centralised and highly efficient AI inference setups. Competing with giants like DeepSeek, which can operate hundreds of times more efficiently than traditional models, is a steep hill to climb. Not only is the investment heavy, but it also demands specialised expertise that smaller organisations may struggle to access.
Next, consider the ever-shifting world of AI applications. Unlike the more mature fields of data and machine learning, AI development is like aiming at a constantly moving target. As the technology evolves rapidly, the building blocks for today’s applications might become obsolete almost overnight, leaving many scrambling to keep pace.
There’s also the risk of clinging too tightly to past successes in data and ML platforms. What worked before might not suit the dynamic needs of today’s AI, and relying on outdated standardisation can sometimes hold you back rather than propel you forward.
Yet, there are clear strategies that can lead to success. For instance, developing AI model gateways for centralised auditing, creating SDKs to simplify AI agent development, or standardising practices like retrieval-augmented generation (RAG) all offer promising pathways forward—as long as you’re ready to scale and adapt with the technology.
Ultimately, the potential of AI platforms to transform your organisation is immense. By embracing a flexible, forward-thinking approach and keeping an eye on the latest trends, you can turn these challenges into opportunities for creating rich, semantic data products and cutting-edge AI-centric DevOps practices.
