AI/ML Business Use Cases You Cannot Afford To Ignore

AI/ML Business Use Cases You Cannot Afford To Ignore

Unsure or anxious about your team's AI/ML strategy?

Like many business leaders, I too was a bit confused and anxious.

Every day I see new developments in AI/ML flowing into my feeds.

And such deluge of activity can be overwhelming for business leaders.

How a conversation got me thinking

In such a context, I was lucky to meet Krishna Kumar of GreenPepper.

We had an excellent conversation - about how AI will impact business in the days to come.

Krishna happens to be an experienced consultant, entrepreneur, & executive whom I had the fortune of learning from.

One question which Krishna kept asking to focus the conversation was:

Shrijith, what business use cases do you think are coming up? What will the talented people be working on in the future?

I thought that was a great focusing question for the discussion.

Two options facing any business leader

I wanted to make a list of AI/ML areas to watch out for.

What follows is a list of use cases which have received significant attention & investments from top talent across the world.

In any of these areas, the business leader must choose to either:

  • Adapt external AI/ML solution
  • Build your own AI/ML solution

Either of the above could work.

But ignoring these critical areas is not an option, if you wish to survive the upcoming storm.

AI/ML Business Use cases You Cannot Ignore

So here is a non-exhaustive list of business transformations possible through AI/ML.

In my experience, good Enterprise Search Engines can mean the difference between a slow or fast moving team.

A documentation oriented culture becomes powerful over time.

As the fund of experiences and knowledge accumulate, accelerated innovation and progress open up.

At the same time, we are living in the age of information explosion and attention deficit.

We deal with a dozen or so apps on a day to day basis to get work done.

Every tool stores valuable context & information.

Compared to web search, Enterprise Search is in some ways harder to get right.

It is more difficult to separate the trivial from the important due to limited traffic within an organization.

So - how can AI/ML boost Enterprise Search?

  • Use industry-specific training & configuration to find more relevant content
  • Higher level of "common sense" filtering
  • Easy to understand & apply information in answer form
  • Contextually relevant responses, through linkages with various tools & information portals in the environment
  • More accurate responses
  • ML can improve all the above, & deliver up to 5-10x team productivity

A powerful method for many traditional sectors to go truly digital

For a long time, many traditional areas such as healthcare, accounting and legal have had restrained technological impact.

The reason has been the almost intangible nature of the physical & psychological world:

  • The workings of the human body cannot be modeled with simple heuristics or rules. And medical documents contain many highly technical assessments.
  • The knowledge of arcane accounting rules plus that of commonsensical contextual knowledge was not easy for machines to represent.
  • Large number of legal ideas, especially the spirit of legal pronouncements could not be grasped by technology.

But with the advent of powerful machine learning models & computing power, the above difficulties have become tractable problems. Document Understanding as a field has benefited enormously from ML methods.

  • Supercharged OCR is here: With some of the latest cloud offerings, we see the machines performing as well or even better for many types of character recognition tasks. You can get machines to "read" pretty much anything that is human readable.
  • Never before seen accuracy levels possible now
  • Layout Detection is a separate piece of tech, which is critical for understanding many types of documents. Multiple columns, text blocks, images & tables can be detected reliably now.
  • Line detection was a big piece which was not happening accurately earlier. Now this too has been more or less conquered.
  • Documents from all sorts of sources --- contemporary or historical --- are fair game now for the machines.
  • Moreover, fine-tuned LLMs can create precise interpretations out of these scanned documents. Therefore, the machine can represent not only the literal information but also create comparatively sophisticated interpretations & arguments.
  • All the above points lead to a definite conclusion: Healthcare, Accounting, and Legal about to get transformed in a big way

Serve more customers, to higher satisfaction, with fewer support agents

One of the biggest impact areas from AI/ML is in Customer Support Automation.

I've seen many people express a negative views of this development.

But already, we are seeing evidence that AI/ML tools are in fact of increasing customer satisfaction.

Customers have to wait less, they get support immediately, without any variations in quality.

Each customer's history can be used to debug and present solutions faster.

Moreover, each of the human agent can work with 10x effectiveness, with AI support.

The AI can help the agents with the following tasks and more:

  • Real-time response assistance for agents
  • Post-call analysis
  • Sentiment analysis
  • Proactive customer engagement

Increase Sales & Customer Satisfaction with Sharper Personalized Customer Recommendations

One of the strongest drivers for the growth for the e-Commerce giant Amazon has been their precise personalized customer recommendations.

The principle of personalized customer recommendation is applicable across various industry verticals.

It is extremely effective, and can increase sales, while also increasing customer satisfaction, since the solutions are tailored to the customer's circumstances.

Personalized Customer Recommendations provide many advantages & can be deployed in wide variety of situations:

  • Personalization is possible in Search results
  • Media recommendations
  • Product recommendations
  • Communication style personalization
  • The quality of personalised recommendation has a big impact
  • Up to 5x spike in customer responses

Increase the Value of Various Media Assets via Media Enhancement & Optimization

Modern knowledge-work in organizations revolve mainly around meetings.

Much of the tribal knowledge of the organization had to remain in such an unstructured state due to necessity.

But with AI/ML video/audio/text processing capabilities, finally it has become possible to give structure to unstructured tribal knowledge.

And the best part? Once such a system is setup, the effort required to get structured knowledge out of unstructured interactions is quite minimal.

The system can do most of the things on its own.

  • Automatically transcribe & caption meeting videos
  • Automatically tag datasets, say of images.
  • With improved metadata, search quality goes up as well
  • Automatically translate between languages for global accessibility
  • Improve translation, content moderation & content discovery

Engage the Whole Workforce Optimally through Demand Forecasts

Organizations have to continuously meet the demands of the environment.

Earlier it took complex simulations, calculations & months of expert work to make accurate predictions.

But these days with off-the-shelf models one can accurately forecast in many work situations.

With more accurate forecasting, the entirety of the organization can be engaged optimally.

In short, forecasting helps with:

  • Better allocation of people to tasks
  • Improve sales targeting
  • Enhance supply chain efficiency
  • Create better budgets & capital allocation methodologies

What do you think?

Did I miss any of your favorite AI/ML business use case in the list above?

What do you think of my assessment of the impact of AI/ML on the future of business?

Do leave a comment below sharing your thoughts.