In today’s market, it’s not enough for businesses to leverage Enterprise AI strategies at any cost; they also need to do it efficiently. Companies must transform AI from a cost center to a revenue center in order to achieve this.
Organizations that use machine learning and data science tools effectively are more likely to improve overall company operations and processes. Many organizations, however, still lack the fundamental principles that must be followed to generate value from AI when employed at scale, and are frequently confronted with AI that results in rising expenses and declining profits. Organizations are attempting to address this, more and more IT and business leaders want to learn about the cost of deploying AI technology in their organizations.
When it comes to implementing Enterprise AI, the most frequent method is to start with a small set of use cases. According to a 2019 Accenture study, organizations that adopt this multi-use case methodology from the start see roughly three times the return on their AI investments than companies that pursue siloed proof of concepts. When organizations achieve success with their first set of use cases, they naturally repeat the process, adding more cases. This will normally have a positive effect on the balance sheet by the tenth or twentieth AI use case.
However, there comes a point at which Enterprise AI loses its economic worth, when the marginal value of the next use case is less than the marginal costs. Scaling use cases becomes either impossible or unprofitable.
Furthermore, it is a mistake to believe that an organization can quickly generalize Enterprise AI throughout the business by just taking on more AI initiatives. Each implementation involves a planned, well-considered strategy; there is no such thing as a one-size-fits-all solution. So, what are the costs, and how can a company handle them effectively?
Data cleaning and preparation is usually the most challenging or time-consuming component of the data process within a business. In fact, data scientists spend the majority of their time locating, cleaning, and preparing data. To that end, this is a massive undertaking in terms of both cost and employee time, especially when companies are doing it for each and every use case or AI project.
Data scientists should prioritize data prep efficiency and reuse to prevent repeating this activity across the enterprise. This can be accomplished by putting in place processes that only require data to be found, filtered, and prepared once. This will cut workload and overall costs at the same time.
With numerous workflows running concurrently during the operationalization phase, the first version of any machine learning model could take months to reach production. This is because systematic packaging, release, and operationalization are difficult and time-consuming if there is no method to accomplish them consistently. This results in a significant cost, not just in terms of employee hours but also in terms of lost income for the period the ML model is not in use and able to benefit the company.
Organizations must invest in developing standardized processes for managing packaging of code, release, and operationalization. They can scale without having to recode models and pipelines from start by including reuse from design through production.
Costs of Hiring and Retaining Data Scientists
Data scientists are efficiency-driven by nature, which means they don’t like to repeat tasks until absolutely necessary. As a result, if they spend too much time preparing and cleaning data or doing repetitive work instead of problem-solving, they will grow dissatisfied, and the organization will have to spend money on employee retention.
Here, cutting costs is a matter of giving the right tools and resources for employees to leverage lessons learnt from previous projects and reuse work.