As per a survey by Alegion, 8 out of 10 businesses who have invested in AI have their projects stalled. In most cases, failure strikes at the initial stages only. After having invested money, efforts, and time to get a Proof of concept (POC) done and proving how it can enhance their business, they still can’t move beyond the POC stage leading to frustrated teams.
What leads to such a disappointment end? It is in fact, hard to integrate AI into the overall software architecture of a company. It may require incorporating new tech into bigger IT infrastructure and systems. But one needs to understand that by connecting an enterprise-class AI to your existing systems will not do you any good.
The two missing components here are the Right Environment and Right Team
Select the Right Environment
Most of the companies focus only on the AI models and forget to test how users interact with them. Deployment of the right production environment not only helps to test your model’s functionality in the right way but also allows you to check whether the model fits in with your technical architecture and systems.
Consider the following three criteria for selecting the environment:
Flexible
Business objectives keep changing regularly. For AI models to deliver the desired results, data has to be refreshed regularly to avoid stale data, up-to-date reporting mechanisms, and data imports at regular intervals. Hence the production environment should be quick and smooth enough to accommodate data synchronization and system reconfiguration without compromising efficiency.
Scalable
Business expansion often involves scaling up existing systems and their capabilities. This means your AI models also have to be upgraded. But the catch here is to embed the upgraded AI models into new systems without affecting the functionality.
Dependable
Data bottlenecks like wrong and malformed data and processing huge amounts of data seem to bring about a crash in the AI systems. A dependable environment with good storage and processing architecture will help to tackle these issues
Choosing the Right Team
The right team means choosing to develop an AI system with an in-house team or outsourcing the project. Each option has its benefit.
In-house development gives you control over the entire development process and setup saving you from contractual and management hassles associated with outsourcing. But the downside is, it requires your business to deal with upfront costs to develop expertise in-house and buy the required infrastructure. It won’t just end there, installations of hardware and servers will shoot up the budgets.
An alternative is to outsource the project to an AI vendor. A good vendor will work closely with your team, will have the right expertise to create and run an environment that is apt for your IT infrastructure and support both self-built or third-party AI models. This is what our company RightClick.AI is known for. RightClick.AI has the right AI team who can leverage the right environment to produce the right business outcomes.