Research shows that between 80 – 90 % of AI projects fail to reach production and of those that do, some 40% are not profitable. Although there are many reasons that an AI project can fail, one of them is often lack of strategy when it comes to data and cloud infrastructure. Many medium sized businesses leap into training models while neglecting the fundamentals: a robust cloud infrastructure and data pipeline that enables data to be aggregated and transformed consistently and seamlessly to be used as input into AI and Machine Learning models.
When you first start thinking about implementing an AI solution there are many decisions to be made by your technical teams as well as management. Depending on whether you operate in AWS, GCP, Azure, other cloud provider or on prem there are various technologies and off-the-shelf solutions you can use to ensure the ML system operates smoothly and efficiently. Getting it right up-front can result in significant cost-savings further down the line.
Another important thing to remember is that AI needs to be introduced into your organisation gradually. You need to teach your current teams to co-exist with AI. In order to achieve maximum operational efficiency it is important to encourage your employees to collaborate with the new tools and technologies, showing them the benefits of their work being enhanced, not replaced. Without addressing these core requirements, building Artificial Intelligence solutions might be possible, but it will likely lead to poor results and certainly won’t be scalable to an enterprise-grade product.
Success with AI depends on taking a holistic appreciation for planning, building and integrating solutions into your existing tech stack and organizational structure so that it is an organic extension rather than something bolted on. This requires more thought, time, effort and engineering resources than simply building a prototype of a product. If you are planning to undertake AI/ML at scale, a key consideration is that the processes required to source data, train and deploy a model must be automated. Otherwise, you will find yourself simply replacing one manual process with another.
That is why best practice is to exercise due diligence by introducing AI and ML into your organisation gradually. Define the business priority, scope the requirements, test, validate, create value and reinvest in innovation. Reaching full operational efficiency with AI systems is not easy, luckily the Brainpool network has the right resources to help. If you want to optimise your AI infrastructure get in touch with us: [email protected]