AI News, Gartner Predicts the Future of AI Technologies artificial intelligence

Introducing Trustpilot's new machine learning tool: Review Insights

This way, you know you’ll be improving the areas that need your attention and really matter to your customers.

To successfully embrace machine learning in your business means to think how a human-AI relationship can be built - not just in one part of your business, but across all of it.

And as your customer experience improves, as that connection between you and your customer strengthens, it’s likely you’ll receive more and more positive reviews praising your customer experience.

Gartner Debunks Five Artificial Intelligence Misconceptions

IT and business leaders are often confused about what artificial intelligence (AI) can do for their organizations and are challenged by several AI misconceptions.

“With AI technology making its way into the organization, it is crucial that business and IT leaders fully understand how AI can create value for their business and where its limitations lie,” said Alexander Linden, research vice president at Gartner.

“AI technologies can only deliver value if they are part of the organization’s strategy and used in the right way.” Gartner has identified five common myths and misconceptions about AI.

The involvement may come from experienced human data scientists who are executing tasks such as framing the problem, preparing the data, determining appropriate datasets, removing potential bias in the training data (see myth No.

3) and – most importantly- continually updating the software to enable the integration of new knowledge and data into the next learning cycle.

“In addition to technological solutions, such as diverse datasets, it is also crucial to ensure diversity in the teams working with the AI, and have team members review each other’s work.

With the advancement of AI in the workplace, business and IT leaders should adjust job profiles and capacity planning as well as offer retraining options for existing staff.

Podcast: Artificial Intelligence and Accounts Receivable with YayPay

YayPay uses machines learning technology to predict risks for businesses — such as late payment of invoices — and suggests work-flow strategies such as how and when to follow-up with a customer about an overdue invoice.

At YayPay, Eugene is responsible for the company’s strategic technology vision and core product architecture in addition to other duties including product delivery and talent acquisition.

JW: So, for these companies, YayPay is using technology to predict when payments are likely to come in based on past payment behavior of customers, and I’m wondering if you can paint a picture at, you know, how accurate these predictions can be, and how far into the future are you able to predict things about a company’s finances.

So, our first version of the prediction algorithm was attempting to predict an exact date of the payment of an open invoice based on the historical data and some other behavioral characteristics, just as you said.

And, with this first version, we achieved an accuracy of around 80 percent, which means that our predicted invoice full payment date for an open invoice would be in the range of minus three — plus three days from the prediction with an 80 percent of the cases.

Well, the problem with this approach was that this kind of predictions has a very significant dispersion, long tail, if you will, which means that it’s difficult with this type of algorithm to make sizable errors for some of the open invoices, and customers don’t really like that, obviously.

They don’t really need to know a particular date when an invoice is going to be paid, but they rather need to know in which phase of the invoice life cycle it is going to be paid, whether it’s gonna be paid before due date or it’s gonna go overdue or it’s gonna go 60 plus etc.

Knowing that, based on this information we changed the algorithm and we currently estimate whether the payment is going to be paid by the due date with an accuracy that is a way over 90 per cent right now.

And, once the invoice goes past due, we estimate whether it will be paid in 30, 60, 90 or more than 90 days and the accuracy for those estimates vary by buckets, with the first 30 days being the most accurate, again at around 90 per cent followed by 80 per cent accuracy for the 90+ bucket.

JW: That’s interesting and I’m glad you brought that up how you thought or saw that you could predict with precision, you know, they gonna pay the invoice by Tuesday, but your client might not care whether it’s Tuesday or Wednesday.

For example, for given business we can look at how their sales are distributed across individual buyers and sectors, we can then track how the sectors are performing relative to each other and overtime.

At the mental level that you just mentioned, we expect the changes in payment behavior, will not only allow us to predict payments when the invoice is not going to be paid but also would be an early indicator of softening economy in key sectors.

In the meantime, we are developing expert systems to provide early detection of issues that could disrupt to order the cash cycle as a whole.

A couple examples here, we are, you know, maybe notifying salespeople when a buyer is approaching his credit limit, which is, you know, simply relative — a simple thing.

We can flag at potential cyber fraud by detecting a certain drop in payments through a particular payment channel or, let’s say, another example: we can raise alerts on anomalies behavior such as an account that is highly likely to pay is going past due.

So, I know a lot of people who just pay a week after the first of the month of two weeks after the first of the month and that’s when they consider when the mortgage payment is due.

That’s why we start with the invoice but we also provide the system with the summary of fast payer behavior as well as the profiles of both the payer and the payee, including industry, the total revenue, and size, etc.

JW: In particular, with your business, I read a lot about smart contracts where conceivably a payment might be due under an invoice but the smart contract might have set it up so that the payer account just pays it under certain conditions being satisfied onto that smart contract.

And from your perspective, in your industry, where you are trying to automate the collections process, do you see the future where there is more automation between the payers and payees, where payment is set up to be automated from the outset?

It shows the significant rise of the interest in technology and then a certain pressure or disappointment when people are not meeting the expectations they had towards this technology and then a gradual levelling up at the medium-high level with the actual applications of technologies in different industries.

I mean, it actually seems like a no brainer that you can just take this and use that smart contract technology to control and automatically execute business transactions between different agents.

I know I can throw it around, it’s a buzz word, it’s used in a lot of headlines to collect clicks and attention whether it’s correctly used or not.

And actually it’s probably everywhere now, you can look at fraud detection, credit decision making, risk management, trading, all kinds of conversational services, insurance underwriting, etc.

It seems to me that many of the Artificial Intelligence start-ups are taking the same path as we are: they are applying machine learning techniques to already well-known inefficient processes and making them work better rather than trying a sort of reinvent an entire thing.

Basically, any area that requires decision making and has large amounts of structure data accessible, which is very important in this case, can and will become handled by AI at some point of the near future.

EV: Actually, you know, some of the researches, even if we get back to a human brain… Some of the researchers say, that there is one system, that is making decisions and there is a completely another system in the brain that is explaining those decisions.

That by itself is a very substantial mission to deliver on and it will take us some time to accomplish that, change mindsets of the finance teams and revolutionize market in a way.

AI: an Unlikely Ally of Your AP Staff

The technology accomplishes this by understanding the tasks that must be performed for a business application, analyzing data sets and patterns, training itself to recognize documents and perform the necessary tasks based on their characteristics, mining data to provide contextual insights, and monitoring processes.

Accounts payable departments will increasingly rely on AI to do nonroutine activities such as combing invoices for pricing errors and other discrepancies and reviewing approved invoice files for suspicious activity.

Offloading tasks to AI will free human employees to spend more time on tasks that AI cannot perform: those that require ingenuity and human interaction.

With AI as a helper to its human employees, accounts payable departments can improve productivity, better manage their cash, spending and operations, and mitigate potential compliance and fraud risks.

The Role Of AI In Customer Experience

In this post, I will lay out why artificial intelligence is a game changer in CX, take a look under the hood at how AI is applied to CX, and explore use cases for how leading edge companies are already reaping benefits from AI applications in customer experience.

Salespeople, call center agents and employees in other customer-facing roles cannot be expected to understand a customer’s entire history and derive their own insights from it in real time.

Customer journey analytics platforms provide this service for a fraction of the cost of the dedicated data services providers of yore—even delivering a level of data integration free of charge.

The truth is that, in addition to elegant SaaS data streams, most enterprises must rely on myriad on-site, home-grown and legacy touchpoint data sources—product interfaces, payment platforms, point-of-sale systems, customer care, etc..

Customer journey analytics platforms are now filling this gap with a host of APIs options and development kits to deliver comprehensive, real-time touchpoint integration with minimal investment.

By context, I mean more than simply designating a certain interaction as an “inbound call” and another as “order fulfillment.” AI must know the significance of these events in shaping a customer behavior.

That requires an awareness of both the journey that these touchpoints helped to shape and the KPIs which were subsequently impacted by that customer behavior—whether related to revenue, profitability, customer lifetime value, customer satisfaction or other factors driving high-level business performance.

With proper business context, an AI can find touchpoints and tactics which actually shape the customer behaviors behind the business’s primary measures of performance.

Now that we understand what it takes to successfully apply artificial intelligence in customer experience, let’s delve into some of those applications to see how AI is unleashing disruption across various aspects of customer experience by unifying data, providing insights in real-time, and incorporating critical business context.

As I previously mentioned, salespeople, call center agents and employees in other customer service roles cannot be expected to ingest and understand a customer’s entire history prior to each conversation.

Mining insights across billions of unique customer journeys using traditional analytics methods and tools is a laborious and slow process, which tends to confine it’s usage to a small set of pre-defined problems.

The power of AI-enabled customer journey analytics is that it can sift through a much, much larger and more complex data space and thereby uncover many more business opportunities—even opportunities you didn’t realize you should look for.

Artificial intelligence-enabled customer journey analytics can find answers to important CX queries like: Leading companies are constantly experimenting to determine the best way to employ AI to improve customer experience.

These companies have unified disparate customer data sources, analyzed end-to-end customer journeys and are using machine learning algorithms to predict future customer behavior.

Nordea also partnered with an AI-based text analytics solution provider to interpret hundreds of inbound customer communications per second and intelligently forward them to the right business unit.

Sephora has thoughtfully considered the entire customer journey—the Visual Artist tool ties in to Sephora’s entire inventory of products seamlessly, and driven by the AI engine, personalized recommendations and offers are made in real-time.

They use sophisticated analytics to glean insights from a customer’s purchasing history, and combine it with weather conditions and other relevant data to make product recommendations in real-time.

AI presents an opportunity to turn many-siloed, multi-channel enterprises into singular “personas” who remember, understand, and respond to their customers’ achievements and setbacks in a meaningful way.

Most companies find it difficult, if not impossible, to accomplish those tasks on their own, given the dearth of data scientists, the fact that disparate systems are not AI ready, and the need to rapidly build new systems, apps, and capabilities.

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