Artificial Intelligence is redefining how we look at technology and use it, especially the mobile app industry. A lot of people have used this technology to improve the engagement level within the app, and increase the conversion rate for the business. We don’t have an Iron Man-like mobile app as of now but, we are constantly progressing towards making apps revolutionary, usable, and predictive. We are using predictive analytics’ engine, and other coding methods to enhance the mobile apps and make them more experiential.

There are many ways in which Artificial Intelligence has enhanced the mobile app industry. Here, we will take you through some of the ways in which the apps have been revolutionized and transformed with mobile apps.

1.     Personalization of Mobile Apps

Inspiring minds and influencing people can help enhance engagement. However, to make sure you are able to inspire the right minds, you ought to have insights that cater to their thoughts and help you understand them better. As a result, it is important for you to incorporate Artificial Intelligence into your apps.

This technology has transformed the way users use mobile apps, and engage with them. AI with deep-rooted learning techniques can easily understand what goes into a user’s mind, and mine the insights that help you offer better recommendations and a more personalized screen.

Netflix, which is a popular streaming platform, uses AI to help recommend programs to its viewers. This streaming platform tends to record the past views of the user along with their natural preferences. ML an integral part of AI, records these aspects, runs an algorithm to understand what the viewer likes most.

It then recommends programs based on their history and preferences. This personalization helps the user stay on the app for a longer time, and enables better engagement and conversion.

2.     Better Sales Forecasting

Want to know what will be in demand this season? For a long time, the one thing that most salespeople have been unable to define or properly manage is the demand. We are hardly aware of how the demand changes or evolves for the new year.

However, with Artificial Intelligence tied to your mobile app, you can easily understand the demand for a particular product. You will also be able to understand the demand for a new product you are going to launch. Let’s take a look at the how before we understand any further.

With sales, you are immediately turned to the number of the particular product that will be sold in a go. The forecasting for this particular number is quite difficult. Artificial Intelligence, in case of a known product, looks at past sales figures. It understands why the sale happened when it happened and how it converted into a sale.

Similarly, it will also look at demographic patterns for the sale and other aspects. Immediately, an Algorithm is built which helps forecast the sale for the product in the coming times. It will help understand the demand, which in turn will allow you to keep up with the inventory needs.

In the case of a new product, the trend of the product is observed for a period of a few days before deriving with finality the forecast for its demand.

3.     Retail Assortments

When you apply AI to retail, you will see new store layouts and a better understanding of the placement of the products. How? When you enter the store, there is a specific way in which you purchase. This is true for online stores as well. That’s why Artificial Intelligence can help you. it will help you understand the exact way in which users tend to shop online/offline, and will offer to design the layouts accordingly. for instance, if the shampoo is placed next to oil, there is a good chance that you might be able to convert both products, and gain more profits.

The idea is to understand the methods and develop an algorithm that will connect your customer’s purchase behavior with the way they complete the purchase. What gets them engaged with the store and how they route their shopping?

4.     Extends Customer Support

Artificial Intelligence can help small businesses and enhance your customer service. With Chatbots, you can easily connect with the users, with fewer resources and that too in real-time. bots will represent your customer service executives, and help people complete their needs.

For instance, if someone has a query, and your executives are running busy, then you can get the bot to engage the person for a while. In case these questions can be answered by the bot, you will not only be saving the resource’s time but also saving a lot of your money

In the case of small businesses, it is very time consuming and money intensive to hire resources who can understand your needs, and offer help to the customers immediately. However, you can avail of the bot service, which helps in connecting with the customers immediately and get you the much necessary support.

5.     Fraud Detection

For many companies, safety and security play an important role. If your customers don’t feel that you are safe enough, they may not even consider you for their transactions.

Security also needs to ensure that anytime a fraud is likely to occur, the system should detect, and immediately report. This is not possible without technology. That’s why you need to implement Artificial Intelligence, as it will detect fraud immediately, and help you connect with the customers and report the same.

Any fraud that happens in the transactions will be immediately reported, and you will be able to safeguard your business against fraudsters and make the place safe and secure. The algorithm will match with the customer’s transaction habits, and immediately report any haywire transaction.

6.     Real-time Decision Making

With the Internet of Things associated with Artificial Intelligence, you will be able to get real-time data from all the devices connected with your business. for instance, the businesses operating in the manufacturing industry would be able to understand the data that is needed from the shop floor and get it immediately through the connected smart sensors. Artificial Intelligence will be able to apply algorithms to this data and gather insights. all this happens in real-time, which helps businesses make real-time decisions essential for better business growth.

7.     Improved Search Experience

With fewer keywords and better keyword analysis, the online stores and businesses should be able to offer key search results that match the needs of the user. For instance, if the user types a wrong spelling, even then the search engine within the app should be able to detect the right spelling and offer the results accordingly.

For this purpose, you need AI which will study all the wrong keywords and ways in which the user searches for a particular product. When you are more than aware of the search methods, it becomes easy for you to create an algorithm that will enhance the search results.

The idea is to make sure that the user is not disengaged from the app, and the app provides them with the results they are looking forward to, despite the keywords they enter.

Summing Up

Artificial Intelligence will ramp up the mobile apps and will ensure better user engagement within the mobile apps. With businesses incorporating AI within mobile apps, it will surely be a good start for them, as it will enhance user experience and will result in more conversions.

The idea is to understand how exactly AI fits into your organization, and how you can use it to fulfill your needs. Once the requirement analysis is done and you have an algorithm ready, get started with incorporating AI into your mobile app, and see how your app grows.

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Meena, Google’s latest AI chatbot built on neural networks, is a technological leap forward toward human conversation, analysts said. While Meena has potential to improve web and call center customer service by taking on more complicated tasks than current chatbots, the rip-and-replace may be painful because the bot potentially will require more — and more nuanced — training.

Most of today’s website and contact-center chatbots are rules-based. They typically connect a company’s knowledge base content to inquiring customers or agents as a custom search tool tailored to deflect people asking the top 100 or so questions most frequently asked from humans to self-service tools. Some chatbots use AI to enhance the accuracy of their responses.

“Modern conversational agents (chatbots) tend to be highly specialized — they perform well as long as users don’t stray too far from their expected usage,” wrote Daniel Adiwardana, Google senior research engineer and his colleague Thang Luong, senior research scientist in a blog post last week introducing Meena. The tool strives to be an open-domain bot — one that can be led by a human into many topics of conversation, not just the few it can search out of a knowledge base.

“Besides being a fascinating research problem, such a conversational agent could lead to many interesting applications, such as further humanizing computer interactions, improving foreign language practice and making relatable interactive movie and videogame characters,” the Google researchers continued. “However, current open-domain chatbots have a critical flaw — they often don’t make sense.”

Meena may overcome some of that issue, with Google claiming the AI chatbot can converse with a human through “seven turns,” or responses, before it may stop making sense. It has a larger model capacity, meaning it has more knowledge and digital wits to draw upon.

Meena also was trained on hundreds of gigabytes of publicly available social media conversations. That is eight and a half times more than the current state-of-the-art neural-network chatbot, GPT-2, developed by OpenAI, a nonprofit funded by Microsoft and Khosla Ventures. As a result, Meena can crack jokes and mimic human conversational affect for a little longer than today’s chatbots.

“The challenge with existing chatbots is that they were designed to scale automated experiences with the semblance of human engagement, but I’d argue they were not trained based on real one-on-one conversations — they were developed upon existing platforms such as knowledge bases,” said technologist and analyst Brian Solis, founder of digital marketing agency FutureWorks. “Google’s Meena was developed to respond sensibly to any given conversational context. Build upon that, and you have a platform for delivering intelligence, accuracy and humanity over time.”

Who will use the Meena AI chatbot first?

While Meena shows much promise in helping customer service and marketing teams automate service and e-commerce operations, the bot’s adoption will be tied to how well Google commercializes it and eases enterprise deployment, said Gartner analyst Brian Manusama. If it’s too complicated or too costly to train, Meena won’t get the investment backing among prospective customers to become a standard.

Most iPhone customers were sold on the marketing behind the digital assistant Siri, only to become disillusioned at the frequency it returns nonsensical answers, web searches or those beginning with “I’m sorry.” Manusama said Meena could potentially be the technology Apple sold iPhone users as Siri.

“It looks like they made the Siri that we were thinking about,” Manusama said. “We should be excited that [Meena] is more human-like, in terms of making jokes and being specific. I’m also a little bit skeptical on that, because I haven’t seen anybody break that code and [make a chatbot] that’s really sensitive and understands what you’re talking about.”

Early adopters of Google’s AI chatbot in customer service and website deployments will likely be in the financial industry, Manusama said. Banks have the largest R&D budgets for piloting new CX tech, tend to be on the cutting edge of chatbot usage, and hold their programs to measurable metrics proving their business worth. Large B2C companies and retailers could also experiment with Meena, too.

Google customers already using its contact center AI platform may also be early Meena users.

“Google’s AI is industry-leading, and contact centers are a natural place to augment and automate existing processes,” Solis said. “Every enterprise vendor is going after this space. While some executives focus on cost, others will focus on experience. Google is hyper-focused on using conversational platforms to deliver human-like experiences.”

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With technology prevailing in nearly every single area of life, we often end up making conversations with artificially intelligent algorithms through our day. Think of when you asked Siri to identify a song for you, or Alexa to translate a particularly difficult Hindi word, or even the Google Assistant to read out a message you just received. While all of these features mostly work fine as standalone queries, these voice based conversations do not quite sound natural, or have a flow to them the way they would if you were to ask them to a fellow human being. It is exactly this that Google Meena, the company’s research division’s newest AI chatbot, wants to achieve.

In essence, the way Google Meena differs from other chatbots in the market is by the complexity of conversations that it is capable of. As a human being, our brains are contextually aware of topics, and can intelligently gauge aspects such as a conversation’s tone, to take a dialogue forward. For AI digital assistants, this is still a distant dream. For instance, ask Amazon’s Alexa for how many runs Virat Kohli scored in the previous cricket match, and it is likely to give you the correct response. However, if you follow this up with a second query of “did he play well?”, you’re likely to hit a dead end, with Alexa stating, “I don’t know what that is, but I could look it up for you.” Occasionally, Alexa might also end up playing a song called ‘Well’ from one of the linked streaming services, or in the worst case scenario, lead you to an Amazon listing of a book titled ‘Play Well’.

Such conversations are evidently not very natural. Giving a rather detailed, technical explanation of Meena in Google’s AI Research blog, Daniel Adiwardana and Thang Luong, senior research engineer and scientist (respectively), write, “Meena (is) a 2.6 billion parameter end-to-end trained neural conversational model. We show that Meena can conduct conversations that are more sensible and specific than existing state-of-the-art chatbots. Such improvements are reflected through a new human evaluation metric that we propose for open-domain chatbots, called Sensibleness and Specificity Average (SSA), which captures basic, but important attributes for human conversation. Remarkably, we demonstrate that perplexity, an automatic metric that is readily available to any neural conversational models, highly correlates with SSA.”

In simpler language, Meena has been built on a larger pool of data, computed through more complex networks and ways of structuring sentences. While the full technical explanation, figures and parameters can be found in Google’s blog here, what’s more interesting to note is the scope of usage of such AI algorithms. As a result of its ability to resolve complications, Meena is the first draft of an AI engine that can host a full conversation with you — even what we humans call “small talk”. With this, you can ask the assistant “What do you think of Richard Ashcroft?” Instead of giving a “It’s not my thoughts that matter, it’s yours”, an engine like Meena will probably be able to add context, and offer an opinion of its own.

This is also what adds aspects such as personality and depth to an AI engine — abstract concepts that have so far stayed away from the robots. Barring episodes of Black Mirror, AI development is progressing towards more advanced implementation of natural language processing, and more intricate neural networks that strive to emulate the human brain. Samsung’s Neon, the artificial humans, is one such early straw in a haystack that is sparse now, but bound to grow going forward. This, however, raises many questions about the use of such technologies, and raises the need to discuss the massive mental health and security implications that technology of such stature bring to the table. Multiple research papers have noted the impact of natural voice digital assistants can have on people, and while there have been plenty of positive anecdotes as well, careful safeguarding is required before such tools become mainstream.

Adiwardana and Luong have stated that Meena’s achievements were solely aimed at showcasing specificity and sensibleness, and the future research projects would be looking at aspects such as personality and factuality of information. For now, though, Meena looks to be somewhat adept for a first attempt at small talk, although that’s nowhere near your next door neighbour’s levels of intrusion.

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New Delhi: Changing customer expectations, falling customer satisfaction, and the promise of lower operating costs are among the major reasons why companies are increasingly adopting artificial intelligence (AI)-powered chatbots for use as virtual agents or assistants.

The banking, financial services and insurance (BFSI) sector is clearly taking the lead in India. Consider the case of HDFC Bank Ltd, which said this September that its Facebook Messenger chatbot OnChat had registered a 160% month-on-month growth in transactions. HDFC Bank also has an AI chatbot called ‘Eva’, built for it by Bengaluru-based Senseforth AI Research, which has “successfully addressed over 2.7 million customer queries in the six months since its inception”.

OnChat, which received over 2.4 million messages, “witnessed nearly 25% usage by non-HDFC Bank users as well, who used the bot to transact and use services”, Nitin Chugh, country head-digital banking, HDFC Bank, said. The chatbot was launched in collaboration with an AI start-up, Niki.ai, on Facebook Messenger platform in December 2016.

HDFC Bank is not alone. Forrester Data reveals 57% of firms globally are already using chatbots or plan to begin doing so in 2017.

Artificial intelligence banking platform Payjo, for instance, announced this September that it had launched an AI-powered chat assistant for the State Bank of India (SBI) to address customer enquiries. The chat assistant—called SBI Intelligent Assistant, or SIA—according to a press statement, can handle nearly 10,000 enquiries per second, or 864 million in a day.

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The bot is expected to handle key work processes—answer customer queries which revolve around home, education, car and personal loans, and recurring and term deposits.

Last April, DBS Bank Ltd launched a banking app in India with in-built AI. ICICI Bank Ltd, on its part, installed ‘Software Robotics’ in 2016. It imitates human actions and is used in over 200 business processes across its various operations. Yes Bank Ltd’s AI-powered bots are expected to reach about 0.25 million customers by fiscal 2018, the bank said this July.

Chatbots are being used in other sectors too. According to a 9 May study by Juniper Research Ltd, chatbots will redefine the customer service industry, with healthcare and banking sectors set to benefit the most. Juniper forecasts that the success rate of bot interactions in the healthcare sector (those completed without relocation to a human operator) will move from 12% currently, to over 75% in 2022. In the banking sector, Juniper expects this to reach over 90% in 2022.

This March, for instance, iPredictt Data Science Labs Pvt. Ltd—a data analytics company—said it had introduced the chatbot functionality based on AI to their recruitment intelligence platform, Careerletics. The AI chatbot automatically identifies the scope of missing data in the profiles of candidates who have uploaded their resumes on Careerletics and reaches out to those candidates to fill in the missing information via chat links.

AI chatbots are getting a voice of their own too. On 27 September, for instance, Chennai-headquartered software product company Zoho Corp. Pvt. Ltd launched Cliq, a chat software that “blends instant messaging with video, audio, and group conferencing”. Cliq integrates with Zoho and third-party applications.

In April, Tata Capital Ltd—the financial services arm of the Tata Group—unveiled its AI-based online chatbot to handle “over 70% of the routine customer queries” including product information, application tracking and sales and after-sales service. Currently, the chatbot services Tata Capital’s retail products including personal, home, car, business and two-wheeler loans.

Chatbots, according to a 24 May note by research firm Forrester, can also improve efficiency in completing simple tasks like pushing coupons and redeeming loyalty points, and also build emotional connections between customers and brands and increase engagement.

However, Forrester analysts point out that chatbots can also fail due to common mistakes like not clearly defining their purpose; setting goals that are too ambitious for existing tech capabilities; and launching them before they are ready.

According to Yashraj Erande, partner and director, The Boston Consulting Group (BCG), there are two broad categories of enterprise chatbots–one that talk to employees, and the other that talk to consumers.

The first chatbot is primarily aimed at internal communication (chat/SMS/email) among the management, employees, vendors and partners. So far, such chatbots typically cater to “non-mission critical” tasks. “They can help banks and financial institutions cut costs but they do not necessarily provide a better consumer experience”, believes Erande.

Chatbots that can add true value to business, according to Erande, are those that are “capable of almost real-time chat and emailing and not those that simply cater to standard mundane communication”. Besides, he adds that AI-enabled enterprise chatbots find it difficult to scale up especially when you compare them to Apple Inc.’s Siri, Amazon Inc.’s Alexa, Google Inc.’s Allo and Duo, or Samung Electronics Co.’s Bixby due to relatively limited learning data sets.

To be really effective, AI chatbots need to deal with things like real-time workforce management and delivering a personalised customer experience, according to Erande. He cited the examples of DBS and Santander innovating on using AI-enabled chatbots.

As for security, Erande believes that as long as the chatbots are used in a pre-defined workflow and not transacting with unregulated external application programming interfaces (APIs), they “won’t pose a (security or privacy) risk”.

To truly meet the need for enterprise-grade customer service, Forrester analysts explained in a 29 June note, chatbots (also called virtual agents and cognitive agents) must be able to understand what a customer speaks or types, discern their intent, respond in a conversational manner, and act on the customer’s behalf. All of this must take place in a secure environment, and the chatbot must seamlessly hand off the interaction to a live agent when required.

Else, chatbots will simply prove to be clever marketing gimmicks rather than adding value to businesses.

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