What are some examples of business value creation through AI/Machine Learning?

42.5k views71 Upvotes26 Comments

Head of Information and Data Analytics in Software, 5,001 - 10,000 employees
There is no doubt that AI and Machine Learning have had a tremendous impact on business. It fact, it is being applied to almost every industry there is. Here are some quick examples of how AI/ ML have created business value across several industries:Financial Services (My industry) – The Financial services industry relies heavily on AI/ ML algorithms to help detect fraud in real time, saving companies millions in losses. They also use these algorithms to give their customers (e.g. merchants/ SMBs) access to online business trends and peer benchmarking – adding real value to these businesses every day.Retail – This is an industry that has been on the forefront of this tech in order to improve customer experiences. They use AI/ML to create a seamless experience between online interactions and in-store purchases. Think about Walmart and their experiments with facial recognition technology to determine if their customers are happy or sad at their point of purchase.Farming – Yes AI/ ML has also reached farming. John Deere has actually invested in building robots that make decisions to treat plants with pesticide based on immediate visual data – taking weather and infestations into account! Manufacturing – Auto manufacturers have done a good job with collecting data from cars to predict when parts would fail or when they need servicing. This not only allows them to uphold their safety records but also allows them to create products to improve driver and passenger needs. 
9 2 Replies
Head of Corporate Development in Healthcare and Biotech, Self-employed

I agree with you Anil. It is amazing how this technology can be applied to every aspect of the value chain in your industry. From investment solutions, trade, risk management solutions and most of the transactional payments cycle. How do you see the predictive elements of signalling alerts through data matching analytics and surveillance capabilities in monitoring aberrant behaviour changing workflows in the future? Primarily with chargebacks while trading online?

Global Vice President of Sales in Software, 11 - 50 employees

Excellent insights and perspective, Anil.

CEO in Software, 1,001 - 5,000 employees
ML models can help with shortlisting resumes (better than simple keyword searches), predicting employee churn (which employees are most likely to leave in next 3 months), predicting customer churn, identifying customer segments, parsing supplier invoices, detecting fraud and more. You will need historic data to apply ML. 
CEO in Finance (non-banking), 11 - 50 employees
For any online advertisers, particularly ecommerce, using AI/ML to monitor and optimize digital advertising campaign effectiveness. Understanding how/where to best spend that money to maximize ROI. 

It's applying the same thinking as you noted Anil within financial services fraud of finding irregularities in the data, but just applied in the context of ad spending.
C-Suite in Energy and Utilities, 5,001 - 10,000 employees
It depends on the business industry. For example, AI helps to create better financial products and it can help to create better algorythms for credit approvals.
CEO in Services (non-Government), Self-employed
I would agree with Anil that at this stage there are plenty of areas where ML/AI helps to create the insight that fosters better business decisions (e.g.leading to efficiency and cost savings) true business value such as innovation, resiliency, increased revenue or growth have yet to be demonstrated across industries.

I'm certainly not a naysayer but the inconvenient truth is ML/AI are still nascent and emerging technologies with an Achilles heel, the inherent biases in data used to build their models.
VP of IT in Media, 10,001+ employees
Understanding your customers’ collective and individual preferences in order to present better products and effectively price discriminate.
Director in Manufacturing, 1,001 - 5,000 employees
The suggestions for purchasing a second product that many purchase along with your first selection are usually helpful too

Also near equivalent alternatives based on search requests can occasionally provide good alternatives
CEO in Real Estate, 2 - 10 employees
With targeted marketing, fraud detection, supply chain optimization, predictive maintenance, customer service, risk management, and product creation, AI/machine learning may add value to businesses.
CFO in Finance (non-banking), Self-employed
ML can help improve the accuracy of your business forecasting. Both Microsoft and Facebook use it and Microsoft found the algorithm it built was more accurate in many instances that the human built forecast. They look at it as a collaborative process with ML and the finance experts to improve overall accuracy.
Chief Technology Officer in Software, 11 - 50 employees
On the predictive analytics side where we use ML. For example on the Risk Analytics side or even targeted campaigns, AI/ML brings business value. There are many other areas of course... 

Content you might like

We lack AI governance policies24%

We’re banned from using these tools35%

Our staff and/or leadership are resistant37%

We have concerns about the results being generated46%

We have third-party security and risk concerns21%

We don’t trust current vendors6%




Yes, training sessions to help them use AI15%

Yes, guidance/governance sessions to set restrictions62%

Yes, town hall/feedback sessions for them to share sentiments14%

No, but we have some scheduled4%

No, and we don’t plan to4%