After a few years of fantasy and unrealistic expectations, artificial intelligence (AI) has made the leap to practical reality and is quickly becoming a competitive necessity. It is expectable for new disruptive technologies to take time before getting the maturity to produce predictable, repeatable, and cost-effective business value. Yet, amidst the current frenzy of AI advancement and adoption, many leaders and decision-makers still have fundamental questions about what AI can do for their businesses in practice beyond the theoretical application. This is a comprehensible skepticism as AI application has matured disparately across industries and, in many cases, despite the high initial investment, the return did not reach the original expectations mostly because AI adoption is still a labor-intensive process.
In short, AI plays a key role in very different areas from transforming customer engagement to speed execution reducing the time required to achieve operational full automation and reducing complexity enabling enterprises to be more proactive, predictive, and able to see patterns in increasingly complex sources.
For many decision-makers, the important next step is to stop dabbling with AI and start embracing and industrialising it so that AI solutions can be deployed on a large scale across the entire enterprise. This would likely require core building blocks such as enterprise-wide data governance and clear strategies for harnessing the power of AI and data. Simply throwing more money at the problem won’t be enough.
At Thingbook, the "industrialisation" of AI, is a central topic and it is at the core of our products. However, this term remains unfamiliar among many industry professionals. Let’s start by explaining what we mean by industrialised AI (InAI) and the results that we have obtained so far.
Industrialisation entails expanding a technology application to increase its use, efficiency, and businesses benefits. This means that an InAI is scalable, reusable, with predictable costs and safe for use by any company. The goal of having an industrialised AI is to accelerate the growth and adoption of Data Analytics-based solutions and products. In this sense, the industrialisation process follows the same route as electronics, automobiles, and other products, from handcrafted with high production costs and end prices to a scalable, cost-effective production line.
As an example of the benefits of industrialising AI in terms of cost and Time to Value, in our latest deployment, our goal was proactively discovering abnormal patterns and categorise them as the data flows through the largest data center of one of the most important Telecom carriers in LATAM. Traditionally, a project like that would take a significant amount of Data Scientists and Data Engineers however, we achieve a reduction of delivery time of 74.7% and bring Total Cost of Ownership down by 68.5%. In this article, we explain how and why.
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