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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.&nbsp;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.&nbsp;

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.

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.

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.

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

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.

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...

Good question. I enjoy learning from other comments, thank you for sharing. AI/ML with the right algorithms along with advanced data analytics that suit the business operations can also help to provide better quality data and smarter supply chain optimization, (physical supply chain network optimization, inventory optimization, including transportation routes optimization, for example.)

Does anyone have specific use cases for the insurance industry? Thank you!

Think about a problem where it's difficult to create a formula for success or the formula is different for every customer.  


Take a low balance alert for a checking/savings account customer...why does everyone get a low balance alert when the account hits $200?  Shouldn't that alert take my spending habits into account? I spend money in large bursts and my partner spends money more predictably, could I use ML to find a personalized low balance alert model?


What about problems that really drive your business? Recidivism? Is there a non-trivial pattern that you can't see that points to why customers leave your service?  If an ML model found a 1000 variable equation that you could test...then you could inject new process like sending them a $10 coupon to see if anything changes.  


Conceptually this is really simple but in in reality, it's pretty difficult to articulate the value of these projects and get them prioritized over things that are easier to explain.  


I think hardest part of this journey is saying "We need to do some experiments and I don't know where we are going to end up, but I know that I have to devote energy to ."

Apart from other pointers in comments the critical value I feel is through simplifying decision making and knowledge base for internal employees and customers.

One example is the creation of new things, e.g. a prototype of a system, a new way of understanding a set of data, or of crafting a reply or a response to a certain legal argument or set of arguments.

I have worked with R&amp;D team in my previous company to generate new ideas for ingredients and recipes for snacks, lots of time saved in the R&amp;D process which translates in cost savings and time to market/revenue.

I interviewed close to 30 AI Founders on my podcast over the past 4-5 months and have been developing use cases for revenue growth over the past 7 months.  Here are the opportunities I have seen from that experience:


 1.  Turning decades of knowledge into minutes- when I tested the consumer version I was able to unearth 10 years of insights from hundreds of meetings in ChatGPT with 3 prompts.


2. Functional strategic workflows- I created a whole department within 20 minutes for Marketing, Sales and for Go to market.  They weren't 100% complete but if I or a peer was doing this manually it would take 6-8 hours.


3. Automated outcomes- without even using an agent I was able to acquire above average skills by typing in prompts in graphics (Midjourney), Copy writing (ChatGPT, Claude, etc.), summarize 300 page books (ChatGPT) and use a 1 prompt workflow that created a video, summary and social content in Chatgpt by using plugins.


There are many other use cases, but here are some great examples that come to mind.

There are quite a few examples. The most common that I can think of is if you've ever used a chat bot on a website, you've used AI/ML in some form.

We are also seeing a lot of organizations using AI/GenAi for conversational search. Enabling the many knowledge workers find information quicker and thus enhancing their productivity. Often, this is where I see most organizations start, particular those who are risk averse and want to try AI and GenAI internally first.

AI/ML can add value to businesses in a variety of ways, including:

Increased productivity and efficiency
New items and services
enhanced customer experience
Cost-cutting measures

Here are some examples:
Artificial intelligence and machine learning are utilized in financial services to detect fraud, manage risk, and provide personalized financial advice.

Retail: Artificial intelligence and machine learning are utilized to improve product recommendations, optimize inventory management, and personalize the customer experience.

AI/ML is used in manufacturing to increase product quality, automate operations, and optimize production processes.

AI/ML is used in healthcare to detect diseases, discover new medicines, and improve patient care.

The impact varies across sectors, but its significance is substantial. In the realm of investment, AI/ML's capacity to analyze vast datasets and take decisive actions proves invaluable to investment firms. Similarly, in the healthcare sector, the capability to identify health trends and enhance patient care plays a pivotal role in propelling healthcare forward.

The most common use case I see across industry is on customer service delivery value proposition. Multiple techniques are used here, the most popular AI technique is NLP or natural language processing. These GenAI techniques helps in creating quicker responses to customer cpmplaints thereby nullifying requirement for manual intervention, hence lowering costs

Reduced workload when some tasks are taken over by AI.

One might be an AI engine that detects knowledge that is assisting the user and adjusts to present that as the customer issues change. This optimization reduces time on contact and mttr. 

The other is search suggesting like or adjacent products when a user searches. The algorithm develops weighted profiles.

AI and Machine Learning can create significant business value across various industries. Here are some examples:

1. **Improved Customer Service:** Chatbots and virtual assistants can provide 24/7 customer support, resolving routine inquiries and issues efficiently.

2. **Personalized Marketing:** AI can analyze customer data to deliver tailored marketing messages and product recommendations, increasing conversion rates.

3. **Predictive Maintenance:** AI can predict when equipment or machinery is likely to fail, enabling proactive maintenance and reducing downtime.

4. **Supply Chain Optimization:** AI can optimize inventory management, demand forecasting, and logistics, leading to cost savings and better service.

5. **Financial Fraud Detection:** Machine learning algorithms can identify fraudulent transactions in real-time, saving businesses from financial losses.

6. **Healthcare Diagnostics:** AI can assist in medical image analysis, early disease detection, and personalized treatment recommendations.

7. **Content Recommendation:** Streaming services and websites use AI to recommend content to users, increasing engagement and retention.

8. **Quality Control:** AI-powered vision systems can identify defects in manufacturing, ensuring product quality and reducing waste.

9. **Human Resources:** AI can streamline recruitment, automate administrative tasks, and enhance talent management.

10. **Energy Efficiency:** AI can optimize energy consumption in buildings and industrial processes, reducing costs and environmental impact.

11. **Legal Research:** AI-driven tools can assist lawyers in legal research, contract analysis, and due diligence.

12. **Risk Assessment:** In insurance and finance, AI can assess risks more accurately, leading to better underwriting and investment decisions.

13. **Language Translation:** AI-driven translation services facilitate global communication and business expansion.

14. **Agriculture:** AI can optimize crop management, automate harvesting, and monitor soil conditions, improving yields.

15. **E-commerce Pricing:** AI can dynamically adjust pricing based on demand, competition, and other factors to maximize revenue.

These examples showcase the diverse ways in which AI and Machine Learning can add value by enhancing efficiency, reducing costs, improving decision-making, and delivering better products and services. The specific application of AI depends on the business's needs and goals.

Here are five examples of how AI/ML is creating value for business:

1. Improving customer service: AI-powered chatbots are now commonplace on many business websites, providing 24/7 customer support and answering questions quickly and accurately. AI is also being used to personalize the customer experience, such as by recommending products or services based on a customer's past purchase history and browsing behavior.


2. Automating tasks: AI can automate a wide range of tasks, from simple data entry to complex decision-making processes. This can free up employees to focus on more strategic and creative work, leading to significant cost savings for businesses.


3. Optimizing operations: AI can be used to optimize all aspects of a business's operations, from supply chain management to manufacturing to marketing. For example, AI can be used to predict demand for products and services, optimize inventory levels, and identify and address bottlenecks in production.


4. Developing new products and services: AI is being used to develop new products and services that would not be possible without it. For example, AI is used to develop self-driving cars, medical diagnostic tools, and personalized educational platforms.


5. Making better decisions: AI can help businesses make better decisions by analyzing large amounts of data and identifying patterns and insights that would be difficult or impossible for humans to spot on their own. For example, AI is being used to predict fraud, identify high-risk customers, and optimize marketing campaigns.

Here are some specific examples of how large companies are using AI to improve their business:

Netflix: Netflix uses AI to recommend movies and TV shows to its users based on their viewing history and preferences. This has helped Netflix to increase customer engagement and reduce churn.


Amazon: Amazon uses AI to personalize its product recommendations, optimize its supply chain, and detect fraud. This has helped Amazon to become one of the most successful companies in the world.


Walmart: Walmart uses AI to automate inventory management, pricing, and forecasting tasks. This has helped Walmart to reduce costs and improve efficiency.


Google: Google uses AI to improve its search results, develop new products and services, and personalize the user experience. This has helped Google to become one of the most valuable companies in the world.


BTW, Fred used AI to create the outline for this answer because it is efficient. AI is an accessible tool that can drive efficiency in text-based responses when the author prompts leveraging personal experience, and fact checks/edits the outcome based on expertise and human understanding.

AI-powered chatbots and virtual assistants enhance customer service by providing instant responses to inquiries, resolving issues, and on the other hand, Financial institutions use machine learning algorithms to assess credit risk by analyzing various data points, improving the accuracy of credit scoring models.

In the promotional industry, AI and machine learning can help businesses in several ways. Here are some examples:

1. Personalized Recommendations: AI can suggest promotional items based on customer preferences, increasing the chances of sales.

2. Targeted Marketing Campaigns: Machine learning can analyze customer data to create marketing campaigns that reach the right audience, leading to higher engagement and sales.

3. Predictive Analytics: AI can analyze past data to predict future trends and customer behavior, helping businesses plan better and reduce costs.

4. Efficient Supply Chain Management: Machine learning can optimize inventory and production based on demand patterns, improving efficiency and reducing stockouts.

5. Fraud Detection: AI algorithms can detect fraudulent activities, protecting businesses from financial losses.

6. Chatbots and Customer Support: AI-powered chatbots can provide instant customer support, answering questions and resolving issues quickly.

AI and machine learning offer great opportunities for the promotional industry to improve customer experiences, streamline operations, and grow their business.

I think AI/ML Is great for anything that is on the back end.


Don't tell me it should be used to write emails or anything client facing.

If we extend lessons learned from Machine Learning into Generative AI, then we can move beyond operational improvements (which are a good early early 'win') and focus on improving customer experience.  Generative AI can improve customer experience by helping the bottom 50% of your customer-facing teams by prompting them with the questions and suggestions of your top performers.

It's a great question and I see a lot of use cases already pointed out by colleagues.
I believe for the legal and contract team is really a game changer by comparing and extracting koi among the bidding and execution docs.

If 2023 was the year when AI truly captivated everyone's attention, 2024 is when AI will start chipping away at every aspect of business value creation in industries such as e-commerce and marketplaces in 2024, including:

- Productivity - AI will automate repetitive tasks and software development, freeing up employees to focus on higher value work. For example, AI could generate reports, process returns, or handle simple customer inquiries. This will boost productivity.

- Infrastructure - Behind the scenes, AI will help optimize infrastructure like cloud costs, server loads, and delivery routes. This "AI infrastructure" will reduce costs and delays.

- Applications - AI-enabled apps will transform experiences. Product recommendations, search, personalized promotions, and chatbots will all become smarter and more intuitive through AI.

- Content - AI tools will help generate product descriptions, blog posts, emails, ad copy and more. They'll allow faster iteration to find top performing content. Humans will still oversee for quality.

- Automated Communication - More communication between software systems will happen automatically through AI APIs rather than human setup. This includes order information, inventory updates, analytics reports and more being shared seamlessly.

There are numerous business cases for using AI and Machine Learning by industry and by segment. Every corporation is different, however in a Center of Excellence model, the gold standard is a use case that delivers both operational efficiency (reducing wasted value) and revenue (new value in a revenue stream). Data as a Service is the best example.

In healthcare, ability to use AI to analyse non-traditional data structures such as MRI scans enriches understanding of health risk in a way that enables clinicians to drive preventive care plans, early diagnosis, drug discovery and personalised treatment plans. AI further enables care providers to coordinate care to help navigate patients to the most cost effective, better health quality outcomes, better patient journey experience. These optimises the care system and enable greater access to quality care that both state and private sector can enable for society.

First and foremost is the importance of understanding the problem that is causing pain in your organization, such as reduced performance, slow growth or negative client experience/retention. Once the pain is identified, you can begin to look at methods to reduce the pain through multiple methods, including AI. As a result, some examples of business value creation through AI include improved cycle times for sales proposal creations which will increase revenue due to faster sales cycles; enhanced efficiency of talent management through the use of more effective analytics for data driven decision making; reduced costs of goods sold due to greater operational excellence.

Unlocking business value through AI and Machine Learning, especially in the unique landscape of government, requires a nuanced approach, a perspective honed through all the years I've worked in both health and human services and academia. Picture this: leveraging machine learning algorithms to enhance predictive analytics for resource allocation in public health initiatives. By strategically deploying AI, we can optimize service delivery, efficiently allocating limited resources where they're needed most, fostering positive outcomes. Additionally, imagine AI-driven models streamlining bureaucratic processes, mitigating regulatory hurdles, and enhancing decision-making. In higher education, AI, when implemented properly (as a TOOL and NOT as a CRUTCH), can transform data into actionable insights, from student success predictions to optimizing resource allocation for impactful research. These examples underscore the transformative power of AI in government realms, transcending constraints to create tangible and far-reaching business value.

Consistently I've seen AI used to help with employee efficiency, operational efficiency, customer experience, managing risk and compliance, and business growth/new business models. Different use case/scenario priorities in sectors. Measuring the value, particularly in a quantifiable way, is the tougher task.

1. Improved Customer Experience

AI-powered chatbots and virtual assistants can provide personalized, 24/7 customer service, leading to improved customer satisfaction and loyalty. For example, AI can be used to power recommendation systems on e-commerce websites, providing personalized product suggestions based on the customer's browsing and purchasing history.

2. Enhanced Decision Making

Machine Learning algorithms can analyze large amounts of data to identify patterns, trends, and insights, enabling businesses to make more informed decisions. For example, predictive analytics can be used to forecast sales, identify potential market trends, or detect fraudulent transactions.

3. Increased Operational Efficiency

AI can automate repetitive tasks, freeing up human employees to focus on more strategic work. Examples include automating data entry using Optical Character Recognition (OCR) technology, or using AI-powered scheduling tools to optimize resource allocation.

4. Product and Service Innovation

Businesses can leverage AI/ML to innovate and create new products or services. For instance, healthcare companies are using AI to develop more accurate diagnostic tools, and automotive companies are using AI to develop self-driving vehicles.

5. Risk Management

AI/ML can be used to identify and mitigate risks. For example, in the finance sector, machine learning algorithms can be used to predict the likelihood of a loan default or to detect fraudulent credit card transactions.

The use cases are endless.  While Chatbots are probably the most prominent, I have dealt with two different chatbots in my personal life in the past 24 hours.  Both were terrible experiences - so there is customer experience risk with those if not done well - they are not a panacea.  One of the more interesting recent developments we have done is with Market Intelligence - using AI to summarize large amounts of information, both structured and unstructured, and allow for prompts to allow analysts to ask questions of large documents like the Inflation Reduction Act - very powerful!

The use of AI and machine learning dates to the mid-20th century. One of the earliest examples is the creation of the Turing Test by Alan Turing in 1950, which was designed to determine if a machine could exhibit intelligent behavior indistinguishable from that of a human.

The biggest shift in business value enabled by AI and machine learning is the ability to deliver highly personalized customer experiences at scale, optimize business processes for greater efficiency, and uncover deep insights from vast amounts of data. 

Summarizing - democratization is the biggest shift that I am seeing.

The number of potential use cases here is practically endless.  You need to ask yourself 'what business problem am I trying to solve?'.  Once you've answered that, you can better understand if AI/ML is the right approach.

One of most interesting I directly worked on is the modeling of a finite state machine (FSM) for the definition of the working phase activities in the construction sites.

AI and machine learning are completely changing the game for businesses. It's amazing how they can use AI to understand customers better and give them exactly what they want. Plus, with AI handling routine stuff, companies can focus on bigger, more creative ideas. And let's not forget how AI is helping businesses make smarter decisions with all the data it can crunch. It's like having a super-smart assistant that helps you see opportunities you might miss.

GenAI reminds me of the Big Data era when everyone got excited about the evolution of the newer technology around 2010. It took over a decade to mature. However, I believe GenAI is a different beast altogether. The biggest challenge is to build proper usecases where the tech can help in value realization at scale while considering regulations around privacy, compliance etc. Content generation, code generation etc are some of the use cases, but has not evolved to a degree that it still requires human intervention to ensure the outcomes are trustworthy and reliable. As the LLM's get more sophisticated, the progression could be geometric.

AI and Machine Learning create business value by quickly cutting through data and the noise to uncover key insights, allowing people to focus on what they do best—building relationships and collaborating on new ideas. For example, AI in customer service can predict issues before they arise, enabling proactive solutions and stronger customer relationships. In procurement, AI helps identify market trends and optimize supplier choices. By automating routine tasks, AI frees up time for employees to engage in more meaningful, creative work, ultimately driving innovation and better human connections.

Hi Gaurav,
that's question we could write for hours about! Do you have any specific sector in your mind? Otherwise really we can pick up almost all the scenarios then apply the AI (starting from Analytics, up to Machine Learning, Deep Learning and ending up with the Generative AI), and write about.


Cheers,

McKinsey published a report on the economic impact of Generative AI earlier this year which is a solid read. The economic potential of generative AI: The next productivity frontier <a href="https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#introduction?ref=pulseqa">https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#introduction</a>

AI-powered chatbots and virtual assistants enhance customer service by providing instant responses to inquiries, resolving issues, and on the other hand, Financial institutions use machine learning algorithms to assess credit risk by analyzing various data points, improving the accuracy of credit scoring models.

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

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