Highlights:

  • According to a Harvard Business Review study, businesses that use AI for sales can boost leads by more than 50%, shorten call durations by 60–70%, and reduce costs by 40–60%.
  • Inventory management is one process where machine learning, a branch of artificial intelligence, has been shown to increase productivity and efficiency.

AI (Artificial Intelligence) is revolutionary, as demonstrated by projects like Chat-GPT – 2022 and Dall-E image generation. Can businesses, however, actually use the superpowers of AI in their day-to-day operations?

They most certainly can!

AI has advanced in more ways than one. Additionally, thanks to businesses implementing AI technology in the hands of business people, it has become more approachable for non-tech users.

AI and ML (Machine Learning) are transforming how we conduct business and making people more productive. The application of AI in companies is becoming indispensable in ways that have never been seen, from speculating customer behavior to lowering manual data entry.

You can now make decisions much more quickly and accurately than ever before, thanks to AI. In this article, we’ll discuss a few artificial intelligence business applications.

How Can ML and AI Benefit Your Business?

Data analysis, automation, and Natural Language Processing (NLP) are popular applications of AI. But what does this mean, and how does it streamline procedures and boost operational effectiveness?

Data analytics – Businesses gain previously unattainable insights by identifying new patterns and correlating data.

AI Automation – Due to automation, people are freed from monotonous, repetitive tasks. Teams are freeing up their time to concentrate on higher-value work because they no longer spend countless hours on repetitive tasks. AI automation is also more accurate and less likely to overlook crucial information. Processes have improved, and so has employee satisfaction.

Natural Language Processing (NLP) and Tone Detection – Natural language processing is on many people’s minds because it makes chatbots more helpful and increases accessibility for those with disabilities, such as hearing loss.

Let’s look at AI’s most common and practical applications in business.

AI in Sales

Experienced salespeople and sales organizations are reevaluating the ratio of humans to machines in sales. Automation AI is already impacting sales and will continue to do so. According to a Harvard Business Review study, businesses that use AI for sales can boost leads by more than 50%, reduce call times by 60–70%, and reduce costs by 40–60%. These numbers demonstrate the need for businesses looking to boost their bottom line to look into artificial intelligence.

A few instances of the application of artificial intelligence in sales are as follows:

Lead scoring – AI helps with lead prioritization. With these AI tools, salespeople can rank customers based on how likely they are to purchase. AI can employ the pipeline of opportunities or leads to rank them or by how likely they are to close successfully. This is done by putting together past information about a client, their social media posts, and the salesperson’s history of customer interactions.

Outbound email campaigns – Email campaigns have been around for a long time in sales and marketing, and for a good reason: they work. But sending hundreds or even thousands of emails and keeping track of the responses can be tiring. With AI solutions, you can track, sort, and file email responses in any way you want. They eliminate the need to check for replies and mark important emails manually.

Demand forecasting – Forecasts can be automated, despite being complicated. AI enables the automated and precise generation of sales projections on the basis of all client interactions and past sales outcomes.

Artificial Intelligence in Marketing

Finding the right balance between operational effectiveness and customer experience is essential, as anyone in marketing will attest. Using AI technological solutions is one of the best ways to maximize both.

A few of the most effective methods for doing this are listed below:

Search Engine Optimization (SEO) – The term “search volume” in search engine optimization tells us how many people look for specific terms and phrases when looking for products or services. New technology like Machine Learning (ML) algorithms is now helping understand better – both the content of searches and the intent behind using search terms. Another benefit is to analyze SEO strategies used by competitors to examine any weaknesses in your own or benefit from keywords they aren’t using. AI is also capable of creating marketing content for your website that is SEO-optimized.

Competitor analysis – Rather than spending hours scrolling through your rivals’ tweets, AI can be used to categorize them by topic or theme and alert you regarding emerging trends.

Market research – Don’t throw away quantitative data from surveys of customers or other sources. The insights are centralized for easy access, and AI tools analyze these at scale without requiring manual tagging from you or your teams.

Image recognition – Through computer vision, devices like computers and systems can extrapolate meaning from digital pictures, videos, and other visual inputs and then take appropriate action or make suggestions. Marketers can analyze the millions of images posted to social media sites daily to learn more about how and where goods or services are used. So, new metrics can be developed to assess market penetration and brand awareness.

Artificial Intelligence in Operations

The application of AI for business operations, or AIOps, is already an essential part of organizations undergoing a digital transformation. Here are a few applications of AIOps:

Inventory management: Inventory management is one process where machine learning, a branch of artificial intelligence, has been shown to increase productivity and efficiency. It is easy, just like uploading image data to an AI tool that can identify flaws in the images or classify and label them. These apps can even be linked to your current tool stack or online store to assign labels automatically.

Artificial Intelligence in Customer Support

There is no denying that a business’s success depends on its customers. The main goal is to solve their problems, but sometimes it is impossible to manage varied tasks simultaneously. To maintain positive online reviews in the interim, give customers who contact your business prompt and helpful responses; all at once is challenging. It costs money and takes time to allocate resources to monitor customer messages. Additionally, the volume of customer emails can vary, so your customer support team might be overworked one afternoon and completely unoccupied the next.

Here’s how AI can solve these challenges:

Prioritize and initiate – AI can be used to shuffle through many chats with clients and leads. AI systems are capable of identifying the most crucial words that convey urgency. AI can recognize crisis-prone words or phrases in customer queries, such as “I’ll leave your business.” Regarding leads, AI can identify phrases like “This offer sounds interesting,” “I’d like to buy next week,” and other similar-sounding phrases that might result in a potential sale.

Automatically analyze customer messages – AI tools can determine the tone and intent of client messages with the help of advanced technologies. AI will evaluate the entire context of the message rather than just identifying a specific keyword, like “complaint,” as it does now. Consequently, even the longest text can be correctly classified and distributed to the appropriate team. You don’t need coding experience to implement this functionality, as there are tools designed for non-engineers that offer it.

Conclusion

AI and machine learning has revolutionized businesses and will remain so for a long time.

Implementing AI into business environments reduces time spent on repetitive tasks, increases employee productivity, and improves the overall customer experience across marketing, operations, sales, etc.

It is no surprise that businesses utilize it across all functions, so why lag in benefitting from such helpful technologies?