6 Examples of Successful AI integration with Business

ai and business

Over the past few years, there has been a lot of hype about how AI is going to change business.

This hype has led to a boom in people looking to understand more about how AI can revolutionise their businesses and drive growth. Basically there has been a lot of buzzwords coupled with a lot of investment which has, in some cases, led to a lot of disappointment.

Before we go any further into this I want to say something I think is very important.

Not every problem known to mankind can be solved with AI.

Actually make that 2 things I want to say, AI is not what the media, film and many people think it is!

One of the big problems in business today is that many of the people who have invested in AI didn’t understand the technology or its limitations.

This means that some investors are now disappointed with the results of a promised silver bullet technology.

To help you avoid the pitfalls of implementing AI technology incorrectly, here we are going to discuss some applications of AI with proven use cases.

In addition to this, we will also look at some of the reasons why they were successful.

6 Examples of Successful AI Integration into Business

When you begin to think about building anything, it is important to know why you are doing it. In the following 6 case studies, we will look at different examples of successful machine learning-based applications and systems to try to understand what made them work.

You can then apply the same framework to your business to develop a targeted AI strategy and growth.

#1 Ranking and Relevance Algorithms

Perhaps the most famous example of a successful machine learning-based ranking algorithm is the Google Search Algorithm.

Developing this algorithm and serving it to Customers in a user-friendly format has helped Google’s parent company, Alphabet, become one of the most valuable companies in the world.

How it ranking algorithms work:

A ranking algorithm’s role is to find the most relevant features of the things it is trying to rank. Once these have been identified the items being ranked are assigned a relevance ranking score and surfaced to the customer.

Some things that could be important when ranking websites on Google for example are:

  • The speed of the website
  • The authority of the website
  • The relevance of the content to the query
  • Any indicators of how other customers have enjoyed the website, i.e. time spent on the website

What is the problem the ranking algorithm is trying to solve:

The problem the ranking algorithm is trying to solve is which is the most relevant content to serve the Customer.

The ranking algorithms job is to answer the customer’s question as quickly as possible.

The better the ranking algorithm, the more likely the customer will use the service again.

How does the ranking algorithm meet the customer need:

The ranking algorithm serves two customers, the user and the business who owns the algorithm.

The way that it meets the needs of both of these customers is by offering the most relevant content that will encourage the user to continue to use the service.

#2 Voice-enabled technology

The rise of voice technology has been significant in the last 10 years. Now three of the big tech companies offer voice assistants that can be integrated into multiple products. 

Once enabled, other businesses have the opportunity to work with this technology to improve their own customer experience. 

The artificial intelligence that powers this technology is called natural language processing

How it Natural Language Processing works:

When it comes to voice assistants, there are multiple technologies involved in converting human speech into actionable data for a machine.

However, if we focus specifically on natural language processing section, the algorithm learns which parts of speech are most relevant to assign meaning.

Once the algorithm understands this, it can also learn what it means when the customer says different questions to the assistant. 

Finally, the algorithm is able to make a prediction on how to answer the customer request.

What is the problem the Natural Language Processing is trying to solve:

Natural language processing is solving many problems. 

For the machine, it is solving the problem of how to interpret speech.

For the customer, it is adding convenience to getting their problems solved.  

Finally, when it comes to the business using voice technology, natural language processing offers a new way to engage with customers. This is in addition to, speeding up the delivery of certain requests. 

To this latter point, chatbots that use natural language processing are sometimes used to answer the most common customer service questions in a timely and efficient manner.

How does the Natural Language Processing meet the customer need:

Natural Language Processing meets the customer need by adding convenience. When applied correctly, technology can delight customers by improving the way that businesses and customers interact.  

#3 Predictive Sales Modeling

One area where artificial intelligence technology can really support business process improvement is by improving sales forecasting.

By developing models that are able to predict future demand to a high level of accuracy, businesses are able to run much more efficiently.

How it works:

Predictive modelling takes historical sales data as well as information about factors that influences sales, to forecast future demand.

This is a process that can be difficult for humans to do accurately. However, when using an algorithm, you are able to analyse data at a much larger scale to make better predictions.

As with the other use cases we have discussed, the algorithm can identify the most relevant features of a data set. Once these features have been identified, the algorithm can make predictions.

What is the problem the predictive sales modelling algorithm is trying to solve:

If we take the example of a retailer, there are two challenges predictive modelling can help with.

These challenges are overstocks and out of stocks. Both stock issues are costly for businesses. If you buy in too much stock it costs you money and can cause cash flow issues. 

If you don’t buy in enough stock, you are risking losing sales and customers who are disappointed with your service.

How does the predictive sales modelling algorithm meet the customer need:

Using artificial intelligence to solve these issues helps businesses optimise their supply chains. Having accurate sales forecasts is one less thing for managers to worry about.

When you know the expected demand, you can order to meet this, maintain customer experience and prevent cash flow issues associated with over-stocks.

#4 Outlier Detection

Fraudulent activity in insurance and banking is a major cost to businesses in this industry.

Another area when artificial intelligence technology can support is with outlier detection.

How it outlier detection works:

When analysing data on customer activity and claims, certain patterns will arise. Once you understand these patterns, you can detect outliers.

These outliers can be flagged to the customer, in this case, businesses, for further investigation.

What is the problem the outlier detection algorithm is trying to solve:

One application of outlier detection is trying to help businesses avoid the problems associated with fraudulent activity.

These problems include:

  • Legal issues with illegal banking activity
  • Money laundering
  • Paying out on false claims

Essentially outlier detection can be used to save businesses money.

How does the outlier detection algorithm meet the customer need:

A benefit of using an artificial intelligence algorithm to detect outliers is that it can run this analysis at scale. Instead of relying on the time of experienced analysts, you can automate the process of outlier detection to flag any issues for further investigation.

This has the benefit of both automation, saving time, and improves the likelihood of finding outliers. 

Implementing an AI outlier detection algorithm has the potential to save customers significant amounts.

#5 Marketing Optimisation

Targeted marketing that is better optimised for the Customer.

Optimisation and personalisation of advertising gets a bit of a bad reputation in the media. 

It is true that certain organisations use artificial intelligence to optimise advertising in a way that many see as unethical.

But it doesn’t have to be that way if you apply the technology responsibly. 

How it marketing optimisation algorithms work:

Algorithms developed to optimise targeting of marketing campaigns group customers into segments.

Once the customer has been segmented, the algorithm is able to predict which campaigns are the most relevant.

What is the problem the marketing optimisation algorithm is trying to solve:

From a business perspective, marketing optimisation algorithms help reduce the cost of customer acquisition and sales conversion. 

From a customer perspective, these algorithms prevent the customer from seeing campaigns online that are not relevant to their tastes.

How does the marketing optimisation algorithm meet the customer need:

The algorithm meets the needs of businesses by helping to automate how they present themselves to different customers.

For the customer, these algorithms help them to find products that meet their needs more easily.

#6 Computer Vision

Computer vision is an area of artificial intelligence that excites many people within the business world. 

One of the challenges, however, is how to make it work for your industry.

I want to exclude self-driving car type applications from this case study as I do not think this is a relevant use case for the majority of businesses.

Despite this, there are ways that you can use computer vision to help enhance your offering.

First of all, let’s reframe the idea of computer vision to image classification.

How image classification works:

Computer vision and image classification technology teach a machine to be able to extract information from images.

The way that this is done is by setting up an algorithm that is able to convert an image into a set of numbers. Once the system has these numbers, it can analyse the pattern to identify what the image contained.

What is the problem image classification is trying to solve:

Image classification solves the problem of how to turn images into useable data in business. This is similar to what natural language processing does technology for speech data.

Once the machine can understand images, this data can be applied to:

  • Scanning and facial matching technology for identification
  • Identifying features in an image that are useful for customers, i.e. what an image contains for image-based search
  • Photo tagging – identifying different people in your images. This is something Facebook pioneered for the mainstream.

How does the image classification meet the customer need:

Image classification is another example of how AI can be used by businesses to improve convenience for customers. 

Passport checking queues are faster, (in theory) when machines can be used at scale. The machines take less space than booths with people. Also, machines do not get tired or need to work in shifts.

Image searching and automated tagging also help customers get things done more quickly.

How to identify the right use cases for Artificial Intelligence in your business

When you think of using AI to add convenience to your customers, you can’t go far wrong.

When scoping out AI opportunities in your business you should:

  • Understand the problem you are trying to solve
  • Think about who it will impact
  • Complete a root cause analysis of why your system would be used by customers
  • Review the benefits of an AI solution vs other simpler solutions 
  • Understand in detail the data do you have available and how high quality is your data is
  • Think about the simplest way to implement 

Artificial intelligence has the potential to solve many problems within your business and delight your customers.

Artificial intelligence can be successfully integrated with businesses

Despite this, before implementing AI you should understand why it is the right solution for customers and how you will maintain the algorithms developed.

Think about the use cases highlighted above. Each had a clear customer problem where the opportunity related to being able to improve convenience or operate at scale.

You want to innovate for a reason to ensure you build something impactful. 

When you work with purpose, to support customers, artificial intelligence can expedite your growth significantly.

Think your business is ready to start driving growth with Lean AI? Let’s start the discussion.