Becoming an AI Factory — Part 1: Why?
“What if a company built each component of its product from scratch with every order, without any standardized or consistent parts, processes, and quality-assurance protocols? Chances are that any CEO would view such an approach as a major red flag preventing economies of scale and introducing unacceptable levels of risk — and would seek to address it immediately.”
McKinsey’s above-mentioned imaginative scenario is not as far-fetched as it may sound. Many enterprises already approach digital product development exactly like this, as if we were making omelettes: producing each product one at a time with our own fresh ingredients without ever reusing the same pack of eggs or sauté pan. This is a recipe for inefficiency and high operating costs, which inevitably leads to lower profitability and stunted growth — which is precisely what companies are doing when they develop every component of their AI system from the ground up for every new project.
AI projects often begin with great fanfare and excitement — just consider how often you’ve heard about AI initiatives in the news lately. But often these projects don’t live up to the hype, failing to deliver the results promised and hitting critical roadblocks along the way. More often than not, these failures result from a poor understanding of the development process and poor implementation practices.
A more scalable and sustainable solution is to focus on building a digital factory capable of churning out high-quality software at scale while minimizing waste and downtime. An AI factory consists of a large suite of interconnected tools, processes, and technologies that work together to automate the end-to-end development and delivery of software products. These tools include things like code repositories, issue trackers, testing frameworks, continuous integration servers, cloud APIs, and machine learning tools, among others. The goal of creating an AI factory is to enable an enterprise to rapidly deliver multiple iterations of a new product or solution without having to rely on hand-coded prototypes or separate development teams. Once the infrastructure is in place, developers can shift their focus from manual tasks to creative problem-solving and delivering business value instead.
In this blog series (5 parts), we’ll take a look at how businesses can implement a comprehensive AI strategy to transform themselves into an AI factory. Let’s start by discussing why it’s important for businesses to operate as an AI factory today and how this process will help them to stay competitive in the future.
The benefits of operating as an AI factory include:
- Increase efficiency by automating processes and improving workflows
- Achieve greater organizational agility and flexibility to scale rapidly
- Reduce operating costs and streamline operations
- Drive innovation to accelerate growth and deliver new products and services to market faster
Operating as an AI factory requires businesses to take a holistic approach that takes into consideration all of the different components involved in creating an AI production line. This includes everything from data gathering to training and deployment, as well as managing the process along the way to ensure it delivers the expected results. By implementing an end-to-end approach to their AI efforts, businesses will be able to better leverage the technology and drive meaningful business outcomes.
Now that we’ve discussed why it’s important to operate as an AI factory, let’s take a look at some of the important building blocks that need to go into this transformation. Here are five critical steps every business should consider when looking to shift into a true AI Factory:
First, you’ll need to ensure that you’re collecting the right types of data of good quality to power your AI efforts. While this step may seem obvious, many businesses actually end up taking the wrong approach when it comes to collecting their data. For example, they may focus on collecting the data they’re most familiar with instead of choosing data types that have the greatest potential to drive value from their efforts. Besides, you need to explore the data comprehensively to derive actionable insights from it. Feature engineering is also an important part of this process which entails extracting features from raw input and mapping those features to your target variable.
Second, you’ll need to build and train your AI model for your intended purposes. This involves using relevant data to train your model and testing it on real-world scenarios to determine whether it’s able to effectively identify patterns in data and produce accurate predictions. In many cases, you’ll want to have multiple models prepared to handle different types of data scenarios in order to handle variety in your training data better.
Once you’ve built and trained your models, it’s time to deploy them so they can be put to work for your business. This involves placing them into production, allowing them to interact with other systems or applications, and making sure that they work as intended by testing their performance in production environments.
The successful deployment also requires you to establish a flawless data pipeline to ensure your models are fed with high-quality data at all times. This is vital to ensuring that your models are able to perform at their optimal levels and produce the most accurate results possible. ETL (extract, transform, load), data integration and streaming systems are crucial tools for establishing a robust data pipeline that is capable of handling high-volume data efficiently.
Finally, it’s important to evaluate your AI efforts on a regular basis to determine if they’re still performing as expected or if they need to be improved in any way. This will help you ensure that you’re getting the most out of the investments you’ve made into AI and that it can deliver real business value for your organization.
Setting up a factory requires machines and production lines to produce large quantities of goods at a much lower cost than it would take to manufacture each individual item with a job shop operation. It takes a team of 20 people six months to assemble a Bugatti Chiron super sports car whereas it takes only 30 hours to complete a car on the Volkswagen assembly line. Unless your clients are willing to pay $2.6 million to get their hands on one of these rare cars, why would you even consider job shopping rather than setting up a factory?
The same logic applies to machine learning and artificial intelligence (AI). You need machine-like tools and well-designed processes to get the same benefits from AI that you would get from a large-scale manufacturing operation.
