AI in Logistics Industry

Artificial Intelligence (AI) has become a major topic in almost every business sector. Leaders talk about all kinds of positive impacts that robotics, machine learning, and other AI technologies can make possible. Optimizing these advanced technologies can save time and improve quality as well.

Parcel and logistics is one such industry that has started leveraging AI to influence supply chain and other associated processes.

AI is reshaping almost every procedure of parcel and logistics. Interested to know which areas has it impacted? Read on to know more

1. Automation in productivity

Productivity in logistics doesn’t rely solely on human expertise now. Advanced algorithms and robotics are bringing automation and reducing human errors. Automatic processes provide better quality products in terms of packaging, management and distribution preparation. Plus, they also reduce overall logistics cost for companies.

2. Enhanced delivery models

Automakers and Artificial Intelligence experts are partnering to incorporate best technologies. Logistics and parcel industry can get the best outcome with this partnership. Future presents a chance to incorporate autonomous vehicles, self-driving drones and parcel carriers for delivery. All of these technologies can provide exceptional accuracy and cost-effectiveness to the industry.

3. Efficient route optimization

Most logistics and parcel companies struggle with route optimization. Bad route selection increases fuel consumption and also affects customer satisfaction. Ultimately, it all impacts the cost of delivering parcels.

An AI integrated vehicle can resolve all these problems. Modern self-learning technologies can offer a platform for parcel companies that optimize routes on its own. Technology can find a coordinated and faster route for a delivery. This can reduce about 30 percent of travel distances and also decrease the need for vehicles for about 10 percent.

Machine learning uses data in real-time to provide a dynamic route for delivery teams. It anticipates potential traffic problems, weather conditions, distances and many other factors to decide the most cost-effective routes for delivery.

4. Improved customer experience with chatbots

In recent studies, more than 62% of consumers accepted that they feel comfortable about having a virtual assistant answer their questions. Chatbots can automate and also enhance customer interaction. Both websites and call centers of logistics companies can improve customer experience with immediate and accurate assistance.

5. Delivering intelligent interfaces

Self-learning via Artificial Intelligence is allowing machines to understand vast data related to logistics. Analyzing scenarios in terms of historical data, machines can resolve complex issues related to the supply chain. Machine learning enables them to create intelligent interfaces for automated decision-making.

6. Understanding consumer behavior

Two-way communication is a necessity in parcel and logistics industry. Companies need to know when and why customers need a product. This allows them to understand an overall demand for a product in a particular location. However, manual analysis of consumer behavior seems almost impossible.

On the other hand, technologies are becoming smarter and better in terms of consumer behavior analysis. This is absolutely perfect for the industry, as they can now know why consumers want a product. Anticipating consumer behavior with AI proves much more accurate and efficient. Algorithms take much less time to conduct a predictive analysis and anticipate consumers’ demands.

A rapid growth in AI is ready to empower this industry. Are you ready?!

NLP tools for Artificial Intelligence

There is consistent research going on to improve Artificial Intelligence (AI) so that it can understand the human speech naturally. In computer science, it is called as Natural Language Processing (NLP). In recent years, NLP has gained momentum because of the use of neural networks. With the help of these networks, there has been increased precision in predictions of tasks such as analyzing emotions.

With its advent in the world of computer science, a non-linear model for artificial computation has been created that replicates the neural framework of the brain. In addition, this structure is capable of performing NLP tasks such as visualization, decision-making, prediction, classification, etc.

Artificial Neural Networks that benefit NLP

An artificial neural network combines the use of its adjoined layers, which are input, output and hidden (it may have many layers), to send and receive data from input to the output layer through the hidden layer. While there are many types of artificial neural networks (ANN), the 5 prominent ones are explained in brief below:

1. Multilayer perceptron (MLP)

An MLP has more than one hidden layers. It implements the use of a non-linear model for activating the logistic or hyperbolic tangent function to classify data, which is linearly inseparable otherwise. All nodes in the layer are connected to the nodes following them so that the network is completely linked. Machine translation and speech recognition NLP applications fall under this type of ANN.

2. Convolutional Neural Network (CNN)

A CNN neural network offers one or many convolutional (looped or coiled) hidden layers. It combines several MLPs to transmit information from input to the output. Moreover, convolutional neural networks can offer exceptional results without the need for semantic or syntactic structures such as words or sentences based on human language. Moreover, it has a wider scope of image-based operations.

3. Recursive neural network (RNN)

A recursive neural network is a repetitive way of application of weight inputs (synapses) over a framework to create an output based on scalar predictions or predictions based on varying input structures. It uses this transmission operation by crossing over a particular framework in topological order. Simply speaking, the nodes in this layer are connected using a weight matrix (traversing across the complete network) and a non-linear function such as the hyperbolic function ‘tanh.’

4. Recurrent Neural Network (RNN):

Recurrent neural networks provide an output based on a directed cycle. It means that the output is based on the current synapses as well as the previous neuron’s synapses. This means that the recorded output from the previous information will also affect the current information. This arbitrary concept makes it ideal for speech and text analysis.

5. Long short-term memory (LSTM):

It is a form of RNN that models a long-range form of temporal layers accurately. It neglects the use of activation functions so it does not modify stored data values. This neural network is utilized with multiple units in the form of “blocks,” which regulate information based on logistic function.

With an increase in AI technology, the use of artificial neural networks with NLPs will open up new possibilities for computer science. Thus, it will eventually give birth to a new age where computers will be able to understand humans better.

How AI will lead to the era of Robotics

The concept of robots has been around for so long that it precedes the roots of most modern technology. Automated machines that are capable of doing more menial tasks could be dated back to as early as 4th century BC with steam-powered automatons doing menial tasks.

Back then scholars and philosophers like Homer saw robots as a means of human salvation as it presented the possibility of human equality, slavery being a huge issue at the time. However, a couple of millennia later the take on robotics had a slightly darker undertone. Works of post-industrial novelists such as James Orwell and movies such as ‘The Terminator’ depicted robotics as the end of mankind.

Even early 20th-century technology experts believed that robotics could lead to massive layoffs and could destroy working-class communities and they were not wrong. The first sign of mainstream robotics came in the early 70s when hydraulic arms started taking over production lines at a car and heavy machines factories. Cities such as Detroit and Munich suffered massive layoffs. Yet the use of automated machinery continues with several verticals such as AI built around them.

The State of Robotics Today

Robots have come a long way from their early hydraulic single motion ancestors and are surprisingly doing a lot a lot of things that most humans could only dream of achieving in their entire lifetimes. From Toyota’s Kirobo having a conversation in space to Sophia’s Saudi Arabia Citizenship, robots are going places and bringing certain science fiction theories that were once dismissed as hogwash, to life. With the exception of breaking Asimov’s three laws of course. Nonetheless, robotics is going through huge advances today especially with technologies like AI and Machine Learning catching up quite fast. Certain industries have become so accustomed to robotics that industry veterans now wonder how they survived without them. So, let us take a closer look at the some of those things.

1. The Space Bots:

When the seven Mercury Astronauts were under the threat of being replaced by a monkey, the last thing they would’ve been thinking would be that after 50 years people have to go through the same thing with robots. Well even if they did think that, they would not have been wrong. Because today most of the transplanetary missions are being carried out by rovers and the robots aboard the ISS are beginning to function more and more like their human counterparts. For astronomers and researchers, it is truly hard to imagine sending another human to the moon let alone to Mars.

So, in that respect robots have allowed us to go farther than we would have ever imagined possible and they aren’t just there planting a flag, instead they are drilling on its surface, running tests and sending in chunks of data that would have taken a human a lifetime to collect.

2. Drones:

There is no better field to measure the impact of robotics than the defense sector. Robots have been a huge part of many important military operations in the past two decades. Particularly the Drones guided by AI are capable of flying, targeting and even firing from long range as a staple of the U.S. air force.

However it is not just the terminator style drones that are making the headlines, but recent years have also seen a hike in the number of shopping drones and transport drones. The use of flight-capable drones guided by AI could be argued as one of the largest prospects for the retail industry.

3. Transportation:

Automated transport is a field that has been picking up pace off late. Self-driven cars, locomotives and aircrafts are thought by many experts to be the future of transportation. Every day, AI advancements in transport is showing promising results that could in the near future be translated to mainstream modes of transport.

4. Machine Learning:

One of the most recognizable features in robotics today is the technology known as machine learning. Machines and programmes that are capable of analyzing various patterns of the tasks that they are assigned to and create their own set of algorithms to function around more effectively. The introduction of machine learning to robotics has been one of the largest leaps in the industry. Humanoid robots are making a mark in several areas of the industry with many acting in movies, working as astronauts, therapists, nurses, yes that’s right nurses! Soon the caring feminine touch will be replaced by the cold metal claws of a robot named after the tiny metal gerbils from ‘Thundercats’.

Is the ‘Storm’ really Coming?

Well back in 1984, that line could be dismissed as James Cameron just being crazy but today we are really not in a position to tell. As a matter of fact, something big is bound to happen by the year 2029. Not on Skynet proportions but more on the lines of an AI-based global network that will function all by itself. Coming to think of it that sounds exactly like Skynet but on a more nerdy tone.

Levity aside in the decade to come organizations like SpaceX, Tesla and Google are going to put most of their resources into developing AI technology to such an extent that total automation would be possible. Elon Musk’s dream of setting up a space colony on Mars revolves largely around the prospect of AI technology that would help us from the designing phase of the space crafts to the setting up of habitats on the red planet.

Backed by AI, the possibilities of advancements in robotics are endless. Even the hardware capabilities are going through an overhaul with robots now being equipped to mimic human-like body physics. At this point the possibilities seem endless, but, only time can tell how far we can go with this.   

How AI will shape the future of Internet of Things (IoT)

The Internet of Things (IoT) has been the topic of discussion for the past few years. It seems as though everyday the IT universe is finding new applications for IoT and its mainstream plausibility is becoming more. While considered a brand new vertical with endless possibilities IoT is just an extension of Artificial Intelligence. The very idea of IoT spawned from the prospects that AI has shown in the past. The idea of devices being connected with each other and communicating is something that is truly a remarkable point in human civilization. When we take a closer look, it sheds light on the extent to which AI has grown and the development it has brought about in other verticals.

While the application of AI in other verticals such as robotics, automobile, marketing etc. create reason for argument due to the various threats they pose, IoT is a vertical that at the moment poses no such threat, unless they start transforming into little killer robots and tearing your house apart.

Device Development and AI

Today we have more machines around us than we have human beings. If we introspect it might seem that we are spending more time with our smartphones than we are with other human beings. While a frightening realization, it is the future that we have been building since we first sprouted on this earth. At the brink of achieving that reality, we are now at the stage where we are exploring choices and trying to make the right steps towards them. The devices that we are coming up with reflect these steps and that is where the concept of AI raises some interesting questions for IoT.

While AI is primarily used as the cornerstone of devices, in IoT it plays several roles. There is much there that could influence how the IoT would react with our world. Further along the way it would boil down to the popular paradox of the chicken and the egg. Which technology would shape our future- AI or IoT?

It is easy to argue that advances in both these technologies would be of equal consequence. However, that is not the case. The very correlation between these two verticals is just as defined as how contrastingly they could influence each other. Machine learning is a key aspect of the progress that IoT is making. An IoT network that would consist of devices with sensors, video surveillance tools etc. will be capable of monitoring the functioning of the other devices. For software related issues certain devices will be equipped with troubleshooting tools both for themselves as well as other devices. Data is the instigating factor that could influence all these technologies and it is data that will continue to govern them in the future. The expectations would again fall upon AI to make the best out of the data.

IoT in Data Analytics

The idea of developing actionable insights is something that in recent years has provided a huge update for the use of AI and IoT services. As these technologies function using data, the uses become well defined and the margin of error depends only on the validity of the data. This creates avenue for wearable ‘smart’ devices to actually function in a sentient manner. Devices such as the heart rate monitor watches, various goggles allow provide vital data that could be relayed to your doctor, your banker, even your barber, who could avail the analyzed output that they could use to customise the service they provide.

Deep Learning

Deep learning is a breakthrough in IoT. This technology facilitates devices to go beyond the prosaic machine learning algorithm. Deep learning draws from a plethora of sources to arrive at a solution on any given subject. This comprehensive approach to producing solutions could become a key driving force for IoT and how the various devices around us function under it.

Conclusion

The many exabytes of data that is being produced now allow for further proliferation on the IoT front. Going ahead, it is AI’s data analytics capabilities that could facilitate this growth. Both machine learning and deep learning both function on the data that is procured through AI data analytics.

With the AI data analytics process being non-stop, big data and other verticals are proving to be vital resources for IoT. Many industry experts believe, actionable insights will be the key to the future. The possibilities with actionable insights are endless and investments in AI have been made to speed up and increase the productivity.   

Machine Learning enhances User Experience for Music

When creativity meets technology, you get incredible outcomes. And that is what the music streaming industry is investing in these days to improve user experience amidst brutal competition. They push new boundaries with technology and diversify the music genre so that everyone can appreciate it.

The era of personalized music with machine learning

In the latest news, the music discovery process is getting personalized results with revolutionary machine learning. Nowadays, almost every big name of the industry is leveraging AI to create better and more personalized music lists.

So, you should not get surprised if the suggested music from Spotify, Pandora and Apple Music seems exactly what you want to hear. All these music-streaming providers implement complex algorithms to pick subtle cues and create personalized music list for you.

• Pandora combines the same technology with data analytics to make suggested playlists for listeners. The algorithms used by Pandora evaluate the songs or artists selected by a user. With that, it creates a playlist that has similar attributes, matching the personal preferences of that user.

• Spotify is probably the most enthusiastic player when it comes to using algorithm technology in music streaming. The company uses a collaborative filtering approach. The algorithms collect music streaming data from multiple users and compares it together. This comparison is conducted with Echo Nest, which is considered best in this technology for music search. Apart from collaborative filtering, Spotify also includes NLP and audio models in its method of providing personalized music.

How Machine learning is evolving music streaming personalization

As mentioned earlier, music-streaming companies are using a variety of AI technologies to make song discovery advanced and personalized.

Here are three major technologies revolutionizing the music-streaming industry.

1. NLP or Natural Language Processing

NLP enables algorithms to understand human language. APIs are used for sentiment analysis, which harnesses the meaning behind spoken and written words. The model of NLP allows music streaming providers to collect data from a variety of resources all over the internet. Algorithms collect data from articles, news, blogs and other resources available on the internet. Using the written text regarding a music, the machines understand the characteristics and provide them with the right playlists.

2. Collaborative filtering

Collaborative filtering is a comparative study of the users’ music listening behavior. The technology helps in understanding the popularity and characteristics of songs. Algorithms collect data from a wide range of users. These datasets include information regarding stream counts, saved tracks, page visits and many others.

By incorporating all kinds of streaming data together, algorithms create a personalized list of tracks for the listeners.

3. AI audio models

Companies like Spotify understand that NLP and collaborative filtering cannot offer justice for new songs. That is why they use another form of AI-Integrated audio model. This technology works just like the face recognition technology. However, the algorithms inspect the audio models instead of pixels. With raw audio evaluation, companies provide new songs to the users in their playlist.

Thus, it would not be wrong to say that machine learning has found a strong place in the large ecosystem of music discovery. With proven phenomenal outcomes, the justification of marrying AI with music does make total business sense!

How AI is changing the Advertising and Media industry

Over the past decade or so advertising has changed drastically. From the humble copywriter/editor complement, advertising today has turned into a multidimensional effort with professionals from multiple verticals pitching in to achieve the end result. This, of course, is no surprise considering the deep impact that IT has had on almost anything and everything. If a copy editor were to tell you 15 years ago that his computer will be taking care of your advertisement and its standing, you would have thrashed him with his keyboard and taken him to a mental asylum. Yet here we are at the pinnacle of IT (as far as we know) and computers are planning ad placement, bidding for keywords and updating you the status of their efforts. So the question we need to ask ourselves is how far can this be leveraged.

The use of AI and Machine learning for such processes is nothing new. As a matter of fact, the current usage of AI in advertising is still relatively primitive. But the inroads we are making through the use of this technology is substantial. However, for AI and machine learning to make any sort of assessment the most important thing is data and it needs lots of it.

What is Data Driven Advertising? 

Anything you do on the internet required the use of data and while you do it generates data as well. From a business perspective, one of the biggest reasons why organizations use the internet is for advertising. Advertising is a multi-billion dollar industry with many dimensions within. Among them internet today is the most prominent and offers the most comprehensive results. So what kind of data is it that floats around the internet to help out with advertising. Well, the answer is pretty much everything, from search histories to personal information, social media updates to data pertaining to behavioral attributes, the internet is a repository for all these. Big data as we all have come to know it is what drives this process. While in the past data-driven advertising was largely based on manual analytics, the vertical today relies on automated technology.

The AI and Machine Learning Influence on Advertising     

While still in their inception stage, both machine learning and AI are being used more than we might have anticipated.

• Search History: Most of the data that is available today on the internet comes from search history. For advertisers, tools like Google AdWords offer keyword suggestions that tend to draw in more viewers based on their activity. This largely automated service provides an edge over competitors to place your ads with the right keywords. Be it product, service or information, anything you search for on the internet gets registered irrespective of the search engine. This information is then transferred to the highest bidder like in the case of Google as part of the google analytics tool. So advertisers who are registered with the tool gain access to your location, the products you were searching for, your brand preference if you have purchased anything and so on.

• Voice Recognition: Online shopping’s next frontier-voice recognition devices like Amazon’s Alexa and Google Home are currently taking the entire e-commerce sector by storm. The ability of these devices to relay your requests as well as make suggestions based on your activity is truly something that will be influencing e-commerce in the years to come.

• Social Media Bots: While being the cause of much controversy recently, the use of bots in social media has made the process of gathering information lightning fast. Social media is the source for a plethora of sensitive information and since inception has been exploited by advertisers and marketing companies to plug their products.

• AI Content Creation: The use of AI for content creation particularly for social media and BuzzFeed, have truly revolutionized advertising and marketing. Several types of content on multiple platforms are being written by AI today. Still, in a primitive stage, this is a technology that will surely pick up in the years to come and who knows maybe be even replace human writers. It is predicted that by 2043 we might have the first number one best-seller authored by AI.

Conclusion

While the current state of AI does leave much to be desired for advertisers, we are not that far from perfecting it for that purpose. There are plenty of prospects to be had on the advertisements themselves as AI and machine learning develops. Machine learning, in particular, could be leveraged to design ads without any human involvement. Currently, technologies such as deep learning are being used in the imaging process in games and movies, which point to good prospects on that front.

There are many more technologies that are still in the prototype stage being tested under various scenarios to eventually integrate into the mainstream processes within advertising. So let us wait and watch as this bit of technology evolves and its story unfolds.  

Myths about Cognitive Technology Debunked

Cognitive technology is one of the widely discussed concepts in the world of business. These discussions help businesses understand the importance and opportunities of the technology. However, there are several myths associated with cognitive technology that limit the knowledge of enterprises.

According to a survey on cognitive technology, businesses and enterprises feel confident about the future of AI and cognitive technologies. However, it would be important to clear the myths in order to successfully adapt cognitive technologies.

In this article, we are going to clear the air around the common misconceptions around congnitive technology.

1. Cognitive technology is all about automated functions

There is a myth among enterprises that cognitive technologies are only used to bring automation in the workforce. The technology is used to reduce the required human labor. However, this is not the whole truth.

AI and cognitive technologies are much more than automation solutions. The applications of these technologies can be used in multiple processes including insights. For instance, cognitive technologies can be used to create better customer service for the end users. The technology helps in understanding the customer data through insights and provide relevant and satisfactory services to the customers. So, the application is more about the intelligence, rather than just automation.

2. The financial outcomes are very basic with cognitive technologies

There are business owners who feel that AI technologies require a lot of investment and result in very basic financial outcomes. Also, they argue that the time lag between the investment and benefits is too long for general organizations.

However, the above-mentioned survey suggests that about 83% of companies that invested in cognitive technologies have obtained impressive or moderate benefits in terms of finances. So, the improved functions of the business get much better economic results with the application of cognitive technologies.

3. Cognitive technologies increase unemployment

One of the most argued topics in the application of cognitive technologies is the automation that brings unemployment. However, this is all wrong. In fact, cognitive technologies present great opportunities for the human employees to work side-by-side with the artificial intelligence.

The technology definitely enhances the productivity of employees, but it doesn’t reduce their importance in any manner. Plus, the arrival of cognitive technologies has created multiple new jobs for professionals, which is also a positive outcome in terms of the future of employment.

4. Cognitive technology is just a trend that will fade away

Many people suggest that AI and cognitive technologies are just a trend that is getting hyped by the media. But what they don’t know is that AI presents clear signs of acceptance and growth on a global scale. In fact, the AI market is expecting $59.8 billion revenue worldwide by the end of 2025. And that says a lot about cognitive technology’s future.

5. The application of cognitive technologies requires a complete transformation

This is another myth that stops companies and organizations from implementing AI in their business. A few company leaders think that cognitive technology application changes the functionality of the business drastically. However, it is not about the transformation, but the integration of cognitive technologies in business.

Final words:

The studies are presenting clear signals towards the success of the cognitive technology. It is the time that you understand it too. Debunking these myths will help businesses embrace this technology wholeheartedly and gain from it.

How Machine Learning can help with Business Development

Accelerated business growth has always been about innovation in functionalities such as customer experience, employee management, and others. And with advanced machine learning technologies, businesses are now able to make useful changes to bring better results for their companies.

The biggest advantage of machine learning in the corporate sector is the ability to make automated decisions without taking risks in any manner. This is why the corporate sector is expecting $59.8 billion revenue with machine learning and AI by 2025. More and more companies are integrating machine learning into their businesses to innovate various aspects.

Here are the top 4 ways that businesses can leverage machine learning.

1. Bringing personalization to customer service

Businesses keep on looking for effective ways to improve the quality of customer service and reduce the investment requirements. And machine learning offers those exact solutions to obtain those goals. With ML technologies, businesses get the ability to combine their years of data related to customer services and merge it with natural language processing technology.

The NLP algorithms make customer interactions more personal by leveraging the data to provide satisfactory services. Each and every customer gets the most accurate answers to their questions, which makes them happy. Plus, the same technology reduces the need for too much investment, resulting in lower customer servicing costs.

2. Making recruitment process convenient and successful

For a long time, hiring and recruitment processes faced multiple struggles. The difficulty in shortlisting the right candidates, removing the human biases, asking the right questions and keeping it cost-effective have presented troubles for recruiters.

But now, with machine learning, it is possible to bring automation in the hiring process. Corporates are now able to shortlist candidates among thousands of applications without skipping a valuable candidate. The machine learning tools are able to analyze credentials and match them with relevant job profiles.

Also, the same technology can detect biases and remove those factors while conducting the assessment. It all makes machine learning a cost-effective and successful way of hiring people.

3. Improving finance management and handling methods

Machine learning also offers the capacity to manage financial processes of a company. In fact, the processes such as payment, invoice analysis, and others can become automatic with machine learning.

A huge number of invoices can be analyzed in no time. The companies can reduce their efforts and time on managing their finances and save a lot of cash too. Plus, the security of machine learning technologies provides protection to the processes at the same time.

4. Marketing and Management

Marketing and management can also get innovative results with machine learning. The AI tools are already being used in gathering customer data, supply chain management, and other processes.

Companies are leveraging machine learning tools to find data related from social media about products, logos, and other factors. All this data is used to create a better brand exposure and to get successful outcomes.

All in all, AI and machine learning offers innovation to almost every part of a business. So, it would be wise to integrate right tools and algorithms to improve ROI and make your business a success.

Artificial Intelligence in Social Media Marketing

It is increasingly being proven that AI is helping marketers find leads and engage like never before through social media. Targeting social media users over the online space is a highly specialized and complex field. Artificial Intelligence (AI) can be a potential catalyst that can drive the fortunes of digital businesses across the globe.

Why the need to introduce AI into marketing?

73% of B2B users need to be nurtured for quite some time before they are converted to active leads for the business. In a traditional ecosystem, marketers simply cannot keep pace with the lead management scenario that evolves daily for each and every enquiry. AI can help devise and deliver personalized content to nurture leads to a better degree.

How does this happen?

Marketers can leverage the basic principle of AI to understand human psychology and its implications in the real world scenario. When marketing automation uses AI, it helps understand and monitor various aspects of customer behaviour like –

1 – How they spend their time online

2 – What posts or products get the most visibility from them on social media

3 – What do they use the social media for

They combine this with past marketing campaign data and then build appropriate marketing messages that have good potential for success. Here are some ways in which this can be done.

1. Better insights on CRM

Bits and pieces of useful information may lay hidden in plain view in various mediums like emails, phone calls, or social media posts/comments. With Artificial Intelligence, you get the right hints about what steps can be taken to subtly induce the prospect to move up the sales funnel towards successful conversion.

With the presence of sufficient amount of data, you can also configure a sentiment analysis activity to grasp the purchase goals/motivators behind the words used by the prospects on social media.

2. Align social media content with buyer persona

With a structured buyer persona, a marketers’ targeting efforts yield better fruits. AI can help smartly segregate customers into personas for a better level of personalized marketing. AI can also learn about which type of content (blog posts, whitepapers, videos, case studies, or other marketing collaterals) will help which type of personas at a particular level in the sales funnel. This way, customers will find only the content which is relevant and timely to their particular need.

3. Social sentiment analysis

With continuous social media analysis a viable brand analysis picture can be formed. However, keeping track of all the posts and ads and analyzing these on different platforms can be complex.

It eats up a lot of time for the marketer which could have been otherwise used to push fresh and unique content for the readers. This is where AI comes in. Take NY Times’ case – it has been using chatbots in innovative ways to create a deep one-on-one experience for its readers.

Thus, with astounding levels of competition to capture eyeballs on the social media, these advantages prove that AI adopters will have an upper hand in this regard.

Chatbots in Recruitment

Did you know that 74% of the candidates for a job recruitment drop out after starting the job application process? The lengthy process and reams of paperwork are some factors for this stat. How good would it be if technology could present a solution that takes out the tediousness from the entire recruitment process?

Well, technology already has a solution ready that fits in perfectly in this context – chatbots

What is a chatbot?

A chatbot is an AI programme that converses with humans in a meaningful and contextual way. Their ability to be accessible to customers round the clock adds multiple business advantages. They not only elevate user experience but also reduces costs of maintaining full-time customer support personnel.

Because of the immense business value that a chatbot offers, it finds applications in multiple industries. The recruitment industry is the latest one to have enjoyed the benefits of having a chatbot. Mya and Job Pal are two examples of chatbots that are revolutionizing the recruitment industry to a great deal.

How can a great chatbot make recruitment effective

1. Save time and money

Chatbots have evolved to be smart and useful. Right from sifting resumes to answering initial queries from a job applicant, it can do it all without the need for an actual executive to sit in front of a computer to carry out these tasks. It can also determine if a particular job opening is aligning well with a particular candidate during a conversation.

2. Application process made more effective

A chatbot is a better option to engage in the initial phase of candidate application. It can save time and get the needed information by taking the course of a natural conversation. This way the high chances of midway drop-outs through the application process can be brought down significantly.

Even if the candidate leaves midway, the chatbot can nudge him/her later on in subtle ways to try and get the entire application process carried out. From scheduling appointments for interviews to sharing information on new job openings and letting the candidate know about the application status, a chatbot can help make the job application process less cumbersome.

3. Pre-screening process made transparent

The traditional interaction between a recruiter and a candidate is filled with uneasy periods of silence post the interview. A chatbot can help fill this gap by a pre-screening process and making the entire activity an interactive and transparent one.

By instantly providing information on approval or rejection, the candidate can take the next appropriate step. This transparency of application process helps candidates get a quick update on the status of their job applications and reduces a lot of back an forth procedures for the HR management and candidate.

4. Automate routine tasks

Mechanical tasks such as sifting through resumes, scheduling interviews, and internal coordination are routine yet necessary tasks within the recruitment activity. With a chatbot, all these routine tasks can be easily automated and HR professionals can focus on more complex activities such as employee branding, improving the outreach and other management related aspects.

To wrap up, while many recruiters feel that chatbots are likely to make jobs obsolete, the fact remains that they actually make the recruiters’ job more powerful and effective rather than making them redundant.