Estimating AI & Machine Learning ROI?



Machine learning is one of the most rapidly emerging trends across the world. Companies are pouring billions of dollars to adapt their systems according to the technologies that are coming into existence with the mainstreaming of Machine learning and Artificial intelligence.

According to McKinsey, analytics will potentially reach $9.5 trillion to $15.4 trillion in value annually, and the AI will be participating by 40% that makes between $3.5 trillion and $5.8 trillion.

Meanwhile, investing billions of dollars in Machine learning, companies also expect something like a Return on investment (ROI). According to Web Evaluator MarketMuse, in 2019, about 80% of the IT-related companies wanted to know the implementation cost of AI, and 69% were seeking the proper way to calculate authentic Return on Investment (ROI) for newly implemented AI solution.

Cost of Machine Learning

Before we find out the answer to the question of how to estimate ROI for a Machine learning project, let’s have a look at the costs that are incurred in implementing Machine learning technology;

  • Cost of Tool/Equipment/Software: The licensing costs of the tools will be added.

  • Transaction Cost: In an ML project, transaction cost may be referred to as the mean time spent on designing, testing, and deployment of a solution.

  • Development Cost: It takes into account the expenditures on compensation for employees, desired man-hours, IT, and maintenance costs.

  • Taxes: You have to add the taxes that you are paying in return for having a greater number of employees at your office. Of course, you may need more employees to get a timely completion of an ML project.

  • Cost of Educating/Training the Data Team: If you introduce any new tool, you will come across the need for training, and you have to consider the cost in terms of the training and education of your Data team.

  • Miscellaneous Cost: Now, the leaders of ML projects have to sum up all the costs required to run the AI/ML, such as energy and data center costs, cloud costs, integration costs, system and business process revisions costs, etc.


Estimating AI/ML ROI

Now, let us peep into the details of four essential points that may provide you with a leading edge in calculating the ROI for ML projects;

The Context for Considering ROI:

The term “ROI” is often coined to express short-term financial attainments. However, ML projects can also be mainstreamed to improve user experience, increase efficiency and productivity of the team, and reduce costs.

The point to understand is to add all those factors in the calculation of ROI that are not even the financial gains; rather, any type of positive business outcome had to be summed up in the calculation of ROI.

Key Performance Indicators (KPIs):

Usually, there is a metric that you are trying to solve, and so, defining KPIs is required in Machine learning. Let's say, on account of segmentation, you have to realize what the segments will be utilized for and what performance indicators will be defined.

Measuring ROI in Stages:

To draw an authentic realization of returns from ML-related projects, businesses need to test and optimize their initial assumptions, experiments with AI systems, and distinguish use-cases as fast as could reasonably be expected.

For instance, it must be comprehended by the business leaders not to run the pilot test projects at scale over the enterprise. What they should exactly do is to think about this in stages, and, in the first stage, they should figure out whether it would be beneficial to work with this or not. That is how they can conveniently identify which small AI projects are capable of helping them out in their business through their AI capabilities.

Making Financial Arrangements for ML Projects:

To measure a particular return on investment, authorities should also be very clear about what kind of budgets they might need to complete ML projects.

In this way, data scientist team leaders would be in a better position to understand what experiments they should conduct realistically, and they might skip.

Conclusion

ML projects are more tentative in nature than other software engineering projects. In this way, it is more difficult to estimate the scope of work, time spans, costs to accomplish the desired degree of accuracy, and the results before the solution goes live.

Despite having challenges in determining the ROI of implementing Data-Science and Machine Learning, positivity could still be judged here. Moreover, according to a survey, 82% of the respondents of Deloitte’s State of AI in the Enterprise said that they had achieved their financial returns from their investment in Machine learning.

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