There are two divided sides when it comes to Artificial Intelligence (AI): those excited about its possibilities and those scared by its potential. Love it or hate it, AI already exists in our everyday lives.

From asking Siri to perform a Google search to asking Alexa to play your favorite song, AI is being used daily. It has several application uses, both personal and business related. Facebook Messenger chatbots are just one example of how quickly AI is being utilized by companies to streamline customer service and mundane tasks. AI also has the potential to improve your B2B lead generation, starting with the four reasons below.

1. Provides more accurate prospect information

Potential leads are sourced from a variety of channels — LinkedIn, Google, etc. — when you introduce AI into the process it can validate your data against several sources, helping you gather the most accurate data possible.

When you are contacting accurate data from the first stage of the prospecting process, it drastically improves your overall success. You aren’t calling disconnected phone numbers, dead email addresses or contacting people who have changed positions or companies. Your follow-up success and close ratio improve when you are working with accurate data from the beginning.

2. Improves productivity.

AI can work 24 hours a day, seven days a week and 365 days a year. It never calls in sick to work, it doesn’t need a break in the middle of the day, doesn’t take a week off for vacation and it doesn’t ever leave the office.

AI can’t replace the value an employee delivers in terms of relationship-building and human-to-human communication skills, but it can be a huge help in generating more leads and interest around the clock while your sales team isn’t working, thus improving overall productivity. They can arrive at the office daily with ultra-qualified leads thanks to AI.

3. Makes automated messages feel more human.

Automated marketing is nothing new — from automated social media posting to email drip sequences, there are plenty of automation toolsavailable to help generate leads. AI takes it a step higher, allowing you to show more specific marketing messages relevant to a lead’s specific needs. These targeted messages increase your conversion rates because you are sending them automated marketing that is more likely to convert them based on data AI is able to identify.

A salesperson would need to take the time to create a personalized email and write it in a certain voice/tone depending on several factors. One of the biggest drawbacks when it came to things like automated email, was that it was too generic. Now, AI is able to give your automation more of a human-feel.

“AI creates superhuman salespeople. They cannot be replaced by a machine, but a machine can certainly make them smarter and stronger, suggests Olin Hyde, CEO and founder of LeadCrunch, a B2B lead generation company that relies heavily on AI and machine learning.

4. Reduces missed opportunities.

It takes six to eight touches to generate a viable sales lead and there are preferred times to contact leads as well as response times that all come into play, as well. B2B leads need to be followed up with quickly, and AI can streamline the process.

It’s an older study, but consider the data from this Harvard Business Review lead response study:

• The average first-response time of B2B companies to their leads was 42 hours
• Only 37 percent of companies responded to their leads within an hour
• 16 percent of companies responded within one to 24 hours
• 24 percent of companies took more than 24 hours
• 23 percent of the companies never responded at all

AI allows you to immediately reach out to prospects and follow up with leads in your pipeline that show interest, with precision, eliminating missed opportunities. Imagine if a prospect in your sales funnel took an action at 3 a.m. that signaled a high level of interest — you could wait six hours until your sales team arrives at the office, or you can implement AI to contact them right away, which greatly increases the likelihood of them converting.

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We live in a world of chatbots. Chatbots improve human interaction with systems by giving a response based on the user input. This means chatbots are simple automated programs that can process simple user inputs and provide a meaningful output.
But with time, chatbots evolved and now are impacting the industries around us. The last decade has been glorious for chatbots. In fact, the growth has already prompted the increased availability of learning material on the internet.

This being said, current chatbots work in a very simple way. They take input in the form of language which is then processed by the computer using natural language processing. Once they understand the meaning, they give the most accurate answer to the user. The accuracy of the chatbot interaction depends on the algorithm being used. Chatbots also use expert systems to make decisions and act accordingly.

The most recent chatbots are using deep learning to break the next barrier of communication between humans and machine. The aim is to create a chatbot that can completely imitate a human response and not let the user understand if he/she is talking to a chatbot.

Right now, chatbots are used in almost every sector. From social media to management to healthcare, chatbots just cannot be ignored anymore. For example, Siri, a personal assistant helps iOS users to get answers. It uses a natural language user interface to respond to questions from users.

Limitations and Opportunities

There are limitations to what has been currently achieved with chatbots. The limitations of data processing and retrieval are hindering chatbots to reach their full potential. It is not that we lack the computational processing power to do so. However, there is a limitation on “How” we do it. One of the biggest examples is the retail customer market. Retail customers are primarily interested in interacting with humans because of nature of their needs. They don’t want bots to process their needs and respond accordingly.

So what is the solution? Artificial Intelligence (AI).

Artificial intelligence has been evolving at a rapid pace. The growth has been so substantial that the best AI minds are concerned with how journalists are interpreting information without understanding it. Despite the misinterpretation by journalists, it is evident from the current progress that AI has the potential to completely make chatbots more mainstream and indistinguishable from a real human interaction.

If you are curious on how modern chatbots work, you can try out these chatbots built by Cuor99, Cookkkie, and DasCode. All of them work at a different level and try to solve a problem which would require real human interaction.

The future of AI and Chatbots

The future of AI bots looks promising and exciting at the same time. The limitation in regards to accessing big data can be eradicated by using AI techniques.

The ultimate aim for the futuristic chatbot is to be able to interact with users as a human would.

Computationally, it is a hard problem. With AI evolving every day, the chances of success are already high. The Facebook AI chatbot is already showing promises as it was able to come up with negotiation skills by creating new sentences.

E-Commerce will also benefit hugely with a revolution in AI chatbots. The key here is the data  collection and utilization. Once done correctly, the data can be used to strengthen the performance of highly-efficient algorithm, which in turn, will separate the bad chatbots from the good ones.

Emotional Processing

Automation is upon us, and chatbots are leading the way. With a fully-functional chatbot, e-commerce, or even a healthcare provider can process hundreds of interactions every single minute. This will not only save them money but also enable them to understand their audience better.

Nevertheless, for this to happen, companies should restructure their approach and start investing in the Machine Learning (ML) infrastructure so that they can gather new data on a regular basis, feeding the algorithm and improving its performance over time.

In the end, the only thing that matters is how chatbots fulfill the emotional needs of the people for whom they are made for.

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