In today’s market, it’s not enough for businesses to take advantage of enterprise AI strategies at all costs; they also need to do it effectively. Businesses must transform AI at a cost center to an income center in order to achieve this.
Organizations that effectively use machine learning and data science tools are more likely to improve overall business operations and processes. However, many organizations still lack the fundamentals to follow to generate value from AI when used at scale, and frequently face AI that leads to higher spend and lower profits. . Organizations are trying to solve this problem, more and more IT and business managers want to know more about the cost of deploying AI technology in their organizations.
When it comes to implementing enterprise AI, the most common method is to start with a small set of use cases. According to a 2019 Accenture study, companies that embrace this multipurpose case methodology early on see about three times the return on their AI investments than companies that seek siled proof of concept. When organizations are successful 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 on the tenth or twentieth use case of AI.
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However, there comes a point when enterprise AI loses its economic value, when the marginal value of the next use case is less than the marginal costs. Scaling use cases becomes either impossible or unprofitable.
Additionally, it is a mistake to believe that an organization can quickly mainstream enterprise AI across the enterprise by simply taking more AI initiatives. Each implementation involves a planned and carefully considered strategy; there is no one-size-fits-all solution. So what are the costs and how can a business manage them effectively?
Cleaning and preparing data is usually the most difficult or time-consuming part of the data process within an organization. In fact, data scientists spend the majority of their time locating, cleaning, and preparing data. To that end, it’s a huge undertaking in terms of costs and employee time, especially when companies do it for every use case or AI project.
Data scientists must prioritize the efficiency and reuse of data preparation to avoid repeating this activity across the enterprise. This can be accomplished by setting up processes that only require searching, filtering, and preparing data once. It will reduce the workload and overall costs at the same time.
With many workflows running simultaneously during the operationalization phase, the first version of any machine learning model could take months to reach production. Indeed, systematic packaging, release and operationalization are difficult and time consuming if there is no method to accomplish them consistently. This comes at a significant cost, not only in terms of working hours, but also in terms of lost income for the period in which the ML model is not in use and can benefit the business.
Organizations need to invest in developing standardized processes to manage code packaging, release, and operationalization. They can evolve without having to recode models and pipelines up front by including reuse from design to production.
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Costs of hiring and retaining data scientists
Data scientists are efficiency-oriented by nature, which means they don’t like to repeat tasks until absolutely necessary. Therefore, if they spend too much time preparing and cleaning data or performing repetitive tasks instead of solving problems, they will become dissatisfied and the organization will have to spend money to retain its employees.
Here, cost reduction is about giving employees the right tools and resources to build on lessons learned from previous projects and reuse work.
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