Why enterprise AI is a profit center more than a cost center

One of the many reasons for businesses to be sceptical about investing in AI is the cost. You need good data, data scientist unicorns, a sufficiently large data team, and the right AI tools, to kickstart your AI project, all of which is translates to cost to the company.

Artificial intelligence (AI) is more than just one of the buzz words in the technological world. I have stopped keeping count of how often I make this statement when I talk about AI. It is set to disrupt the way we go about our business and lives. It’s time we recognize that AI has moved from hype to reality. According to Tractica, the revenues coming from software using artificial intelligence are going to increase from 9.5 billion U.S dollar 2018 to 118.6 billion U.S dollars by 2025.

One of the many reasons for businesses to be sceptical about investing in AI is the cost. You need good data, data scientist unicorns, a sufficiently large data team, and the right AI tools, to kickstart your AI project, all of which is translates to cost to the company. There are two parts to the solution to this problem. One, you don’t necessarily need a data scientist unicorn to launch your AI project. Two, it isn’t just enough to leverage AI at any cost, you need to do it efficiently and sustainably. In order to achieve this, you need to consider the economics of AI. You need to consider the costs and not just the profits when you study the economics of AI.

What happens when you find your first success with AI? Typically, you tend to repeat the process, you add more use cases to the list. As you continue doing this, you will see a positive impact on your revenue. However, after the first 20 or so AI projects, you will notice that the marginal value of the next AI project is lower than the marginal costs. Eventually, you will reach a point where the value derived from AI begins to decline.

You can draw three conclusions:

• The marginal cost of additional/new AI projects remains the same.

• The marginal value you derive from the additional/new AI projects decreases. The first AI project will deliver more value than the last one.

• The marginal profit of newer AI projects will quickly become negative.

In order to avoid the negative marginal value or profit, you might stop after your first 5 or 10 AI projects. But you need to consider the cost of maintaining the projects you have already built. And when you add the maintenance cost with the marginal costs, you are still left with negative value or profits. In order to address this problem and convert enterprise AI from a cost center to a profit center is by minimizing marginal costs for new AI projects and maintenance costs for existing AI projects.

Cost of AI to the company

Before we launch into how you can reduce costs, let’s take a quick look at what those costs are – the kind that is less tangible, add up over time and hamper your company’s ability to scale the AI efforts and profits.

Data Cleaning and Preparation

By now, you know that data is crucial to your AI project. However, you cannot dump large quantities of data into the AI system and hope to see accurate results. The kind of data you feed your AI system determines the outcome. You need to clean and prepare the data before you use it for your AI project. Turns out, most of the data scientist’s time is spent on dealing with this task. (We believe that you do not need a data scientist in the first place). This is a monotonous, time-consuming, and difficult part of the process. It’s a huge cost in terms of your team’s time.

Reducing costs for data cleaning and preparation isn’t about reducing the time your team spends on this task. You need to put systems in place that can handle this process efficiently. Brainalyzed Insight’s data preparation module automates the process. It quickly cleans and prepares the data that your AI models can be trained on.

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Operationalizing and Pushing to Production

The process of operationalization had multiple workflows within it. AI projects don’t just comprise of code but also include data. In order to reliably move code and data from one environment to another, these two elements need to be tightly packaged together. Packaging, release, and operationalization is a complex process. If it is not done consistently, it can be time-consuming. Ask any IT professional and they will tell you that it typically takes anywhere between three to six months to release an AI project to production. That’s a lot of costs not just in terms of time spent but also in terms of revenue that is lost during the time the AI model is not in production. Multiply that with the number of AI projects and the cost is incapacitating.

Model Maintenance

AI models are unlike software code where you move it to production and forget about it until the day something clogs the system. Since data is constantly changing, models tend to drift over time. This translates to continuous AI project maintenance. Depending on the kind of problem you are trying to solve, the AI model may tend to become less effective over a period of time or become counterproductive to the business’ efforts. This problem only gets magnified, as you begin to take on or launch new AI projects.

An additional sub-category under model maintenance is the cost of maintaining the infrastructure. Maintaining the AI model requires maintaining the infrastructure on which the model runs. Considering the rate at which the average company’s infrastructure stack is evolving, this can quickly escalate the costs.

Reuse and capitalize

The points I just discussed and other factors that contribute to costs add up over time. They hinder your company’s growth and scalability when it comes to AI. One of the things you can do to reduce costs is reuse and capitalization.

Reuse is a pretty simple concept that allows you to redo parts of an AI project from scratch. Processes such as cleaning data, data preparation, creating AI models, etc. can be reused. This drastically reduces the time spent on completing these processes and also saves costs.

Capitalization refers to sharing the cost incurred from an initial AI project – costs that are common to all the AI projects such as the cost of finding, cleaning, and preparing data. Brainalyzed Insight’s AI project management module allows you to clone data pre-processing pipelines in no time. This way you can implement both the solution of reuse and capitalization in a single step, thereby doubling the benefits of saving cost and time.

Brainalyzed Insight as a solution

Reuse and capitalization may seem easy in principle. However, they require company-wide centralized processes that are built-in from the start. AutoML and AI platforms such as Brainalyzed are tools that enable enterprise AI. These tools allow you to scale, provide transparency and reproducibility throughout the process and across teams through collaboration. When you go shopping for such an AI platform, here’s a snapshot of the buyer’s guide to AI software.

• Robust documentation that allows users/your team to explain what has been done in a particular AI project.

• A built-in module that enables you to quickly clean and prepare data for AI model training.

• The tool should allow you to copy or clone parts of a project and use them for new projects.

• The ability to create complex AI models with zero coding experience.

• The ability of the AI tool to allow advanced users to create AI models and share them with users who don’t necessarily need to understand the underlying complexities of the model.

• A dashboard to monitor the performance and predictions of each AI model.

• The possibility to automate and test extensively before deployment.

Brainalyzed Insight provides all these things with an additional layer of easy to use interface. When you truly make enterprise AI inclusive and democratize the data efforts, you take reuse and capitalization to a whole new level. You change enterprise AI from a cost center to a profit center.