AI Increase RoI, Making Firms Happy

Updated November 2026. This was first published on 16 September 2022. The idea is the same. Any number below is what the original piece said then. It is not a new count.

Key takeaways

Artificial Intelligence is silently capturing almost every industry, and now companies see AI as an experimental Tool to add value to their product or service differently.
But things aren’t as simple as it looks- to get a positive and predictable ROI from AI projects comes with many challenges.
Recently, Capgemini’s research says that around 85% of AI projects fall without the expected value.

Artificial Intelligence is silently capturing almost every industry, and now companies see AI as an experimental Tool to add value to their product or service differently.

But things aren’t as simple as it looks- to get a positive and predictable ROI from AI projects comes with many challenges. Recently, Capgemini’s research says that around 85% of AI projects fall without the expected value.

This article will cover all possible ways to make ROI tracking easier for your AI projects and how to present the battle-tested list of activities and theories. But, before we move in-depth, it’s crucial to know.

What is AI? What is the ROI of Artificial Intelligence Projects? #

As per the Oxford dictionary, AI is the ability of computer systems to perform tasks more efficiently and quickly, requiring human intelligence, such as speech recognition, visual perception, translation, and decision-making.

You can use these different abilities of AI for different activities in many ways to improve multiple areas in almost every company. This is why business leaders are so excited about AI initiatives and always have their eyes open for new AI technologies.

But, applying new AI solutions at the production level is not that simple. many challenges and prerequisites should be taken care of before making any AI installation. AI is said to be relatively new and full of experiments. One of the major challenges that come in the report is a clear vision of ROI.

Technically, ROI means Return on Investment, which measures an investment’s overall gain/loss in a given time interval. ROI plays a significant role in influencing financial decisions and analyzing investments by assessing the efficiency and profitability of expenditures.

In AI projects, the Return of Investments doesn’t refer to pure profit in cash only but can be seen from many different angles. According to a survey made by Deloitte says, the top five areas where data science projects deliver values are.

These areas where data science projects deliver values, but what are different ways a company benefits from the AI setup is still a question.

Lower operation costs, Higher productivity, Resilience in changing environments, Increased customer satisfaction and retention, Improved profitability, Improved employee engagement, New products and services.

According to a report, the upcoming years will be excellent for AI, increasing the average ROI to 30% overall. Still, the average ROI of AI investments is 0,2% in companies that just started their first AI project and 4,3% in the case of market leaders.

How To Turn AI into a Profitable Investment? #

Planning the ROI from AI is one of the challenging tasks in this competitive market as many factors, experiments, and changes determine ROI.

Define Business Values, Needs, and Set Up Goals #

Depending upon the business structure, its characteristics and needs differ. This is one of the major reasons why the setup of machine learning should be tailor-made.

Working with an company, it becomes crucial to figure out pain points before defining a business case for AI. The ultimate goals and the required KPIs with the setup must be clear to you.

Funding Plan #

AI needs funds for setup, monitoring, and optimization. Making a funding plan helps in predicting the costs that your project can take; this includes- the cost of tools, team, technology, etc. This is why having a proper funding plan makes ROI calculation easy and smooth.

Verify Team Skills #

AI applications are usually more complex that require additional skills, and making sure about these additional skills with your team becomes crucial. Sometimes, you may need to add new members to your team if additional training isn’t sufficient. According to McKinsey’s study, the upcoming years aren’t going to be the same as now, and almost 30% of employees need to learn new skills or should change their jobs for organizational or self-growth.

Plan Data Strategy #

ML algorithms work on the data received from AI products. The more data you get, the better your ML algorithms will perform, making optimization smooth and smooth. According to a survey by The Pistoia Alliance, the majority of people, around 52%, stated that the biggest fear in developing AI systems is a lack of data. Also, there’s no other way to measure the ROI apart from taking the data from your AI project.

How to measure the ROI of AI? #

Measuring ROI includes multiple challenges; the major ones are developing internal standar #

Questions and answers #

What is the main point? #

Artificial Intelligence is silently capturing almost every industry, and now companies see AI as an experimental Tool to add value to their product or service differently.

What else did the original piece say? #

But things aren’t as simple as it looks- to get a positive and predictable ROI from AI projects comes with many challenges.