In this Q&A, Kunad discusses how the demand for in-application help and training capabilities has changed, especially over the past two years with the challenges of a global pandemic and millions of 麻豆原创 users working from home. She also explains new functionalities and how customers can benefit even more from this great solution.

Q: In your role as solution owner of 麻豆原创 Enable Now, what have been your observations over the past two years, when businesses were struggling with the impact of the pandemic?

A: I noticed several interesting trends during the pandemic. Some businesses had challenges from not being ready to respond in a fast and agile way to the new situation 鈥� for example their entire workforce working from home suddenly or not being ready to boost their digital transformation, enhance their rollouts, or implement new business models within a very short time frame. On the other hand, I observed that some of our customers accelerated their investment in user enablement and training.

The reasons seem obvious. They wanted to make a good use of the time they suddenly had due to the circumstances, time which they would normally have invested in their own customer projects. Instead, some customers decided to kick off long-term projects on user enablement earlier than originally planned. In other cases, we noticed a change in how user enablement is valued. The unpredictable change due to the pandemic and entire workforces suddenly working from home revealed gaps and needs for upskilling. The help system of colleagues at the next desk suddenly fell away. User enablement and upskilling became priority tasks for many. To help customers respond to this in a fast and agile manner, 麻豆原创 Enable Now offers a range of possibilities to help overcome those gaps and needs.

Sounds like some businesses managed to turn their challenges into something positive by installing or enhancing the use of 麻豆原创 Enable Now. How would you summarize the key features of this in-application help in one sentence?

麻豆原创 Enable Now helps customers easily create, maintain, and deliver in-application help, learning materials, and documentation for existing applications as well as for the rollout of new features or solutions.

What is the unique selling proposition for 麻豆原创 customers when working with 麻豆原创 Enable Now?

There are many good reasons that come to my mind, let me mention a few. 麻豆原创 Enable Now is already integrated in many 麻豆原创 solutions. For example, RISE with 麻豆原创 customers automatically have access to 麻豆原创-generated content available via 麻豆原创 Companion* in-application help. There are many more with this great help and customers can also enhance it.

The second aspect is that we have a huge volume of ready-to-use content available, which saves our customers time as they will not have to create the content themselves but can basically hit the ground running from day one. 麻豆原创 Companion works like a digital assistant for users, aiming to simplify and ease the consumption of our solution. It is available for browser-based solutions, for in-application help for non-browser-based solutions, and even for non-麻豆原创 solutions in general. 麻豆原创 Enable Now offers many ways to create enablement content 鈥� from interactive quizzes to e-learnings with text-to-speech over customized documents. Finally, I would like to mention that our customers can use 麻豆原创 Enable Now across their whole portfolio with just one license per user. This breadth of possibilities has something for every use case.

This all sounds easy to apply, easy to consume, and easy to use. What do you hear from customers about recent enhancements?

What I hear most of the time is 鈥淧lease give us more of this.鈥� Customers love the direction we鈥檙e heading in, and our vision of an intelligent adoption platform resonates. Be it fine-tuning our existing feature set with enhancements like the push help and what鈥檚 new banner configuration or coming up with completely new features like machine translation integration, we seem to have hit a sweet spot, especially with in-app help delivered by 麻豆原创 Companion.

With a more flexible approach for new releases, more proactive customer communication, and our lively user enablement community, we continuously listen to our customers to best respond to their needs. This is not just about the features; it鈥檚 also about being a companion for our customers on their way to better adoption and productivity.

You indicated how important user enablement is to fully unfold the benefits of a solution and to help increase the overall performance of users. What are one or two things that you would like customers to be aware of?

First, I would like to point out that customers can use 麻豆原创 Enable Now for a huge number of 麻豆原创 solutions. It鈥檚 even part of the built-in application help, like with , where they can benefit from the pre-created ready-to-use content.

They can also use 麻豆原创 Enable Now for any other kind of enablement and training content they want to create, depending on the individual business needs of an organization or department.

Let鈥檚 say a company runs different 麻豆原创 applications, for example 麻豆原创 SuccessFactors solutions, plus their own custom-built applications. 麻豆原创 Enable Now could be used for all of them.

Often customer departments tend to act in silos when it comes to implementing training and enablement software. HR might consider using 麻豆原创 Enable Now for 麻豆原创 Success Factors Onboarding, for example, while the procurement department might not be aware that they could also use 麻豆原创 Enable Now 鈥� and with the benefit of not having to add additional licenses.

To fully benefit from 麻豆原创 Enable Now installations across departments within one organization, we strive to help our customers to close the gaps between departments, teams, and license usage, and to help them make best use of 麻豆原创 Enable Now.

Sophia Stolze is integrated communications manager for 麻豆原创 Enterprise Adoption at 麻豆原创 SE.

*麻豆原创 Companion will replace 鈥淲eb assistant鈥� to provide a harmonized and more descriptive name for in-application help channels provided by 麻豆原创 Enable Now.

In other words, great AI must be paired with a great user experience. So how do non-technical factors, such as design and acceptance, impact the efficacy of AI? How does the increasing use of cloud solutions change how AI is deployed? To understand the role of AI and machine learning today, it鈥檚 important to understand the context in which it is being employed 鈥� and the importance of context itself.

We sat down with Jens Trotzky, head of Artificial Intelligence Technology for Customer Solution Support and Innovation at 麻豆原创, to discuss the acceptance and application scenarios of AI-driven support. How can AI and machine learning-based features, solutions, and processes change the game and why is an AI mindset key for developing new solutions?

Q: Do you find that consumers are more attracted to AI than they were in previous years?

A: I would say there is strong interest and an uptick in customers approaching us to ask about our automation, AI, and machine learning capabilities. But I also think it’s a bit of a split topic when it comes to getting the actual end users to adopt the services that AI and machine learning provide. We still have to bridge the gap between the high-level executive view 鈥� which says 鈥淲e want AI鈥� 鈥� and the more day-to-day operations view, which asks how to make it actually work. There is a lot of education needed from our side for our customers, partners, and other stakeholders.

Additionally, I also see the expectation from customers that AI is somehow everywhere. Smart recommendations and smart helpers are expected. However, it can be a challenge to implement these functions so that they also achieve the targets we want. Of course, we want AI to add to a positive user experience, but we also want to improve processes for our users 鈥� to enhance them and speed them up. The challenge then becomes how to best integrate AI features in a way that both creates an attractive user experience and achieves these hard goals or KPIs.

Could you give an example of that?

Let’s take the support assistant. Support assistant is a guided workflow, in which a user creates a support request and is guided through the whole process. The user receives AI-based suggestions on the site and goes through an interactive dialogue. That sounds good and customers like it, but we also need to look at the numbers. What does this do to the number of tickets being created? We need to see the outcome of that process 鈥� it looks good on paper, but does it translate into tangible business results? For us, it turned out that a lot more process and AI optimization was needed. It can sound very nice to have AI in your processes, and customers want that, but at the end of the day it must also translate into tangible process optimizations for the customer.

What differs in how your team thinks about AI today?

鈥淐ontext鈥� is the key word, which increasingly moves us toward a world where every individual user can receive personalized support. For example, now we can determine what product the user is using. With the breakthrough of , we can capture the application context that caused the user problems and take that information into account. AI and machine learning allow us to tap into this vast knowledge of support history that we have about the customer. It helps us to connect the dots, like having some super support engineer on the 麻豆原创 side that can magically sift through billions of pages of support documents that are out there and then pick up on the right cues at the right time to bring everything together. And with the help of the contextual window of Built-In Support, we can make all these connections in real time. And I mean literally real time. AI is a game changer for support because it allows us to know our customers, provide real-time insight, and help to mitigate a customer challenge before it becomes one.

Are you seeing a different type of user interacting with your AI these days?

Yes, for our on-premise software solutions the audience tends to be more technical 鈥� more subject matter experts themselves. With our software-as-a-service (SaaS) offerings in the cloud, we deal more with business users, rather than a technical person. We are, accordingly, trying to serve the right audience the right content.

This is exactly what we have in mind when we talk about Built-In Support because we realize that we are increasingly dealing with business users. The question is, what can we offer to business users? This is where integration comes in 鈥� realizing when and why a user is running into a problem and what happens next. Will they create a ticket? Do they want immediate support? Maybe it’s a simple how-to question. Then we need to offer whatever is appropriate: perhaps an answer using an AI-enhanced search algorithm, but maybe also offer a community solution. If, instead, we have a key user in front of that application, then we should offer the option of using things that might require key user credentials, such as a real-time chat session with an expert or the ability to create a ticket. This all happens because of context and personalization. In the end, we need to focus on the user out there and ask ourselves, what is their role? What is the task? What is the goal? I think the exciting part of support is shaping the interface between the user and the context.

There鈥檚 also the aspect of getting a unified user experience. In the end, the customer expectation is to have a single user experience anywhere in the 麻豆原创 product portfolio. Here, Built-In Support can play a major role because by having the context, including history, the user experience can be truly unified. As a user, whenever I open Built-In Support, I can have the same experience, which can be customized or optimized for a specific product. The effort required to interact with support can be reduced and access to information can be faster than ever. I think that’s what most customers want: to help themselves.

What has been one of your biggest lessons from the last year?

I would highlight the role of integration. We have started to focus a lot more on the integration of AI and machine learning. We’ve learned the hard way how important it is to get the integration right. Often, it’s the integration that ultimately drives adoption.

Ultimately, the success of artificial intelligence is summed up in the equation user adoption multiplied by AI model accuracy equals realized business value. If you don’t have user adoption, then the value is limited. And often the biggest lever we have to drive user adoption is tightly tied to our ability to blend AI seamlessly into the user experience.

Sophia Stolze is part of Integrated Communications within Customer Solution Support and Innovation at 麻豆原创.

There are plenty of outcomes that AI could be used for, but how do we use it to maximize the impact and how do we connect it to businesses outcomes? And perhaps most importantly, how do we develop an AI mindset that will help us innovate the uses of AI in different products and for different businesses?

It is clear that we have only begun to realize the potential of this technology, but it can be hard to understand where it should be applied next.

We sat down with Jens Trotzky, head of Artificial Intelligence Technology for Customer Solution Support & Innovation at 麻豆原创, to discuss how 麻豆原创鈥檚 automated support strategy is changing, what an AI mindset looks like, and what to look for next.

Q: How do you see 麻豆原创鈥檚 AI strategy evolving this year?

A: We started off with something I like to call semi-automated machine learning, in which we try to recommend things to customers or internal stakeholders, but a human decides what content makes it to our customers or the customer themselves decide whether they want to accept what the AI suggests.

Now, we are heavily investing in predictive support, which is increasingly called preventative support. In this model, rather than having an event take place and then having the AI provide possible reactions, we try to anticipate the event itself and, when appropriate, prevent it. This is part of an effort to help our customers benefit from one another. Previously, if one customer had an issue, other customers wouldn鈥檛 benefit from their experience, at least not anytime soon but now through AI-enabled predictive support, that鈥檚 changing.

And one other part of the strategy worth noting is an increased focus on personalization. We are moving away from a one-size-fits-all strategy towards a more targeted approach. That鈥檚 possible because we’re curating more and more data from our services and support business. That allows us to then provide a more accurate, clear context around each of our customers, and then support them accordingly with machine learning.

How have you seen the 鈥淎I mindset鈥� changing within 麻豆原创 and among customers?

Previously, we would have other teams within 麻豆原创 coming to us saying, 鈥淚 have a business problem that I want to solve. How about we just use some AI?鈥� That indicated we needed to develop that AI mindset within 麻豆原创, because AI is fundamentally a data-driven topic, and so must be approached from that perspective. It’s the data that tells the story; it’s up to the company to unearth the potential that lies hidden in that data.

This can be even more of a challenge outside of 麻豆原创, because businesses are focused on their business problem first and foremost. Of course, we want to be inspired by the business challenges that we face, but the solutions ultimately grow from within the data. I think there is a growing understanding that you must pair a business requirement with data science. And then be prepared to make changes to your business process to collect data that you currently don’t gather to make it useful in AI processes.

One great example of the better understanding of the uses of AI among our external customers is a change that was made to using an Incident Solution Matching service. This service attempts to solve a problem by suggesting a solution before resorting to creating a support ticket, which customers really like. Last year, 麻豆原创 Ariba support started combining a classical search technique with AI. In brief, after the customer asked a question, the AI would guide the customer to provide more information. AI was deployed to refine the search by asking additional questions. By using AI in this way, the technology was brought into the customer’s support experience at the right point to have the maximum impact on getting the customer the needed support quickly and easily. We want to ask not just whether we can use AI for this, but how and when we introduce AI to have the most impact.

What do you see happening with AI in support over the next year or two and what are you most excited about?

One of the things that we are working on is called 鈥減rocess intelligence.鈥� Instead of thinking of everything as being a single transaction that we want to accelerate with AI or machine learning, we instead look at the overall customer journey. By mapping out that journey, we want to make use of these technologies to increasingly predict the path the customer would take and use AI and machine learning as an agent in the process, helping the customer journey along.

That is why today, 麻豆原创 is using innovative functions enabled by artificial intelligence (AI) and real-time support with cutting-edge Built-In Support, built directly into the application, to provide customers with a rapid and intuitive experience that is paving the way for increasingly predictive and preventative support.

鈥淭oday, user experience matters more than ever in the support context. Users expect a seamless end-to-end customer experience, and we are committed to providing it to them by offering connected and holistic support,鈥� said Andreas Heckmann, executive vice president and head of Customer Solution Support and Innovation at 麻豆原创. 鈥淭his means meeting the customer inside the application with contextually aware features.鈥�

This innovative customer experience leverages AI and machine learning to deliver intuitive experiences that users have come to expect from consumer applications, but with the full might of 麻豆原创鈥檚 engineering powering the interaction behind the scenes.

Here, Heckmann goes deeper on how these innovations came to be and how they can show us the future of support.

Q: There has recently been an evolution in Built-In Support as part of 麻豆原创鈥檚 Next-Generation Support approach. Could you tell us about what鈥檚 changed?

A: Built-In Support, which means support that is available within the application, has been substantially enhanced recently, including a complete re-platforming, making it a lot more feature-rich by adding AI functions and real-time chats. That means customers don鈥檛 have to leave the application to experience these cutting-edge innovations; it’s all right there for them, and it鈥檚 contextualized because we know where they are in the application. So if you ask to chat with an expert inside the application, it won鈥檛 take you to some unrelated person. The AI system will know what type of question you probably have.

How does Built-In Support impact user experience, and why is user experience (UX) or user interface (UI) so important today?

With Built-In Support, I can now interact with support the same way I interact with my smartphone as a consumer. Normally when I’m using my smartphone, I have an expectation that everything is there on the spot, easy, intuitive, fast. That鈥檚 what Built-In Support feels like to the user.

In the past, we in support mostly interacted with IT departments, which were filled with very experienced IT professionals. For them, clumsy UI wasn鈥檛 much of a problem. Nowadays, we are interacting with business users from any line of business more and more. And those users increasingly belong to an age group that consists of 鈥渄igital natives,鈥� hence having an entirely different expectation on user interface and experience. To meet all our users on eye level and provide them with the same consumer-grade experience, we invented a solution like Built-In Support, making it easy and seamless to engage with product support, no matter if you are an IT expert or an expert in your specific line of business. Our sophisticated technology is designed to back this all up.

With AI and machine learning helping to simplify, improve, and accelerate processes and workflows — just to mention a few advantages — what is unique in how 麻豆原创 employs AI?

Today, already every single one of the interactions we have with customers is monitored, processed, and analyzed by our AI system because we embedded it in our incident management process. That means that customers are typing away and describing their problem and we are analyzing the likely cause in real time. They don’t even have to press a button or activate something. They just go through the normal process like they always did. That鈥檚 pretty unique because 麻豆原创 is not a one product company, not a 10-product company, but a hundreds-of-products company. In that context, being able to give answers very, very precisely and to be right most of the time, that’s quite unique in the industry, and we were only able to get there by developing several of our own algorithms to work seamlessly with our proprietary systems and data.

Did your team encounter setbacks as you established this AI program?

To be honest, more things didn’t work out than did work out. When we first began using AI, we thought it would be relatively easy, just applying some algorithms. It didn’t work at all. We learned the hard way that because of the many products we have and because many different technical terms employ the same words but sometimes with different meanings, traditional approaches to AI didn’t work for us.

Take, for example, the word 鈥渢ransport.鈥� What does transport mean? We have a logistics system, and within that, if a customer discusses a transport, she is probably referring to transporting items on a truck, on a ship, or on a plane. But it could also mean the transport of a piece of software from one system to another, because we have software transport systems as well. It could refer to moving data or it could refer to moving people.

That was the nut we had to crack. And we had to learn that AI is always made for the data you are using. At the very beginning, I didn’t get AI. I looked at it more like a search engine, a better search engine with a smarter filter, but that鈥檚 completely wrong. For any type of data and every use case you must build your algorithm and then you must train your algorithm. You must find the patterns within. Each situation is somewhat unique. That’s the biggest lesson we learned.

How have AI and other emerging technologies paved the way for predictive support?

AI is a bit like a key ingredient that we need to prepare the meal, which is predictive support. All the things we鈥檝e been discussing 鈥� real-time support, AI, and so on 鈥� all of it is the innovation that brought us to the current status quo. So why do we already regard this as the old world? Because the customer is still experiencing the problem, and we are just getting much better, faster, and more creative in how we respond.

What we are trying to do now and for the time to come is combine real-time support and AI and other innovations to find the problem before the customer even knows they have it. Say a customer reports a problem, and then another customer reports that same problem, a short time after, and maybe a third customer reports the problem. Today, we will automatically detect that and conclude, “Hey, something’s happening. We have three customers reporting this type of problem. What can we learn?” And we look automatically at these customers and discover they are all using a specific functionality in a specific configuration. And in that constellation, they are running into that problem. Then we can automatically determine which other customers have that module, use it in that very specific constellation and solve the problem for them before it even occurs, and provide a predictive and preventative service to help those customers before they even experience the problem.

The self-service enablement course takes a little more than one hour to complete and can be accessed by any 麻豆原创 customer with an S-user.

Since its 2020 launch, the program has already attracted many customers and partners and has increased its learning content along new launches or extended functionalities and features. Customers and partners may even integrate this program as part of their own enablement trainings.

How does Product Support Accreditation change the customer support experience, what are the key benefits, and what are the steps to get started? I sat down with Gagandeep Kaur, program lead for the Product Support Accreditation program at 麻豆原创, and she shared her experiences on how to best get started, along with insight into new offerings from the program in 2021, and how 麻豆原创 customers and partners perceive the program.

Q: The Product Support Accreditation program was first launched in early 2020. Can you briefly describe what the program is and who benefits from it?

A: In Product Support we have a variety of great tools to help customers solve their technical issues more efficiently. Live support channels like Expert Chat and Schedule an Expert, the peer-to-peer platform Ask an Expert Peer, or guided help through 鈥渟upport assistant鈥� are well received by users. However, not every user is fully aware of those tools or may not know how to get started. With our Product Support Accreditation program, we wanted to create an easy-to-use, easy-to-consume enablement program that any 麻豆原创 S-user can access and enroll in at no additional cost.

Every S-user can benefit from this program at very little effort and at their convenience.

How does it work for an S-user who would like to take advantage of the program?

Very easily! Your first step would be to enroll in the program. You can, for example, access the program directly from 麻豆原创 Support Portal with your S-user and enroll immediately.

The second step would be to make yourself familiar with the learning modules available. You can click through the individual modules and decide in which order you want to start. Let鈥檚 say you are interested in using Expert Chat. You could start with the related module and, even if you have not yet completed the learning program, you already know how to make the best use of Expert Chat.

The third step would be to complete all the modules so that you are entitled to get your certificate and badge, which documents the successful completion of all learning modules. If you don鈥檛 want to complete them all on the same day, you can stop and continue with a module at your convenience.

The fourth step would be to receive your certificate and badge, which are free to share with your colleagues and peers and even through social media.

Finally, the fifth step would already be the renewal of the program. You can register to receive a reminder e-mail at the beginning each calendar year to consider renewing your accreditation. The renewal is not just repeating the known modules, you will discover new learnings around new tools or improved functionalities.

That closes the loop and you are up to date around all the helpful tools and features from Next-Generation Support.

This sounds like an easy-to-access and easy-to-consume enablement for users who want to make the best out of their engagement with 麻豆原创. Do updates only occur annually or will there be updates throughout the year as well?

I personally value the opportunity to frequently explore the program for new learnings since my last visit. My recommendation to customers is to make use of the annual renewal program so that they get an update at least once a year on improvements or enhancements. Beyond this, they can decide individually if they want to check for any updates over the year. If there is a big change during the year then we might provide a mid-year update and inform program members about the new content available.

For updates during the year and an annual renewal of the accreditation with a renewal of the certificate and badge, how would these work in regards to the modules users take? Is there a track history, or would I have to repeat all modules, even without changes?

When new content is added to the program you will only be going through the new or updated content in the program. Progress is tracked within your profile. You can of course choose to refresh your memory by watching all the videos, but you won鈥檛 need to. You would need to do the final quiz at the end to get the new badge.

How about the future of this program? Can you share what is planned next?

The goal of the program is to keep it simple and easy to use. This will remain our focus in the future too. However, we also want to keep growing the learning content along the launches and improved features.

We will keep growing awareness around the program and to engage even more customers and partners, thus keep growing the community around Product Support Accreditation.

麻豆原创 users can now access the Incident, Schedule an Expert, and Expert Chat support channels via the 鈥淩eport an Incident鈥� single entry point, easily choosing their preferred support channel without having to navigate through different support channel tiles.

John Bowley, product owner of the 麻豆原创 ONE Support Launchpad Incident Management in the Support Experience Process and Innovation Office of 麻豆原创 UK, describes the unified support experience and many other key features users can benefit from, as well as what’s coming next.

Q: The last 12 months have seen many improvements and enhancements around 麻豆原创鈥檚 incident creation and incident management process. With the latest releases in September 2020, your team has been focusing on the 鈥渦nified support experience.鈥� What is behind this latest improvement? How does this affect the customer support experience?

A: For our incident creation and incident management, the unified support experience means that our customers start their interaction with 麻豆原创 support in one place and can easily transition to different support channels depending on their support needs. You simply describe your issue and the available support channels will be highlighted for you automatically.

The most obvious benefit is that customers no longer have to choose their support channel upfront, only to discover the support channel is not available. The customer can simply decide among support channels , making it a more seamless and efficient way. If one communication channel doesn鈥檛 suit them, customers can choose an alternative.

How does this work for the incident management process?

With our efforts to improve the user experience and create a unified support experience, customers now benefit from an enhanced incident management. One example of this is the redesigned incident list via the new 鈥淢anage Incidents鈥� tile in the 麻豆原创 ONE Support Launchpad. It allows users to easily search, view, edit, or confirm their incidents. It鈥檚 also possible to customize the filtering and search options to save your聽own personalized views. The user-centric redesign of incident edit layout provides an intuitive workflow for customers to interact with 麻豆原创 more efficiently.

When it comes to simplification of the process and accessibility, what benefits can customers experience?

Let me explain this with our simplified incident categorization functionality. Simplified incident categorization helps you to find the correct incident category and product expert to get an issue resolved faster. The new, product-based incident categorization will automatically be taking the system and product you鈥檝e selected into consideration. This process is enabled by artificial intelligence (AI)聽technology to recommend the most relevant product function category for your issue.

Can you share other examples of new features and functionalities recently launched as part of the simplification and optimization?

There are quite a few key features. For one, customers can now update their contact details in real time during the incident creation to help ensure 麻豆原创 support can reach them effectively. They have also the possibility to automatically analyze logs files as they update their incident, which is a feature that resulted from our surveys with customers and 麻豆原创 Mentors.

Another nice feature is the automated聽alerting of any 麻豆原创 Cloud service outage automatically on system selection. You have the option to request a subscription to updates as the status of the outage changes. This allows for easily tracking the outage until resolution.

The recommended help solutions are now triggered on product selection, just like in different consumer applications and apps in your private life.

Last but not least I need to mention the support assistant, which offers a guided logging experience formulated on decision trees to narrow down an issue and collect all the relevant information for troubleshooting. We will even recommend the right component for your issue.

Has 麻豆原创 achieved its goals for the optimized incident management and creation? Or will there be more to come 鈥� and what would that be?

We have achieved many goals with the key features being released approximately every six weeks over the last 12 to 18 months. The intent was not to overwhelm our customers with a big-bang effect, but to steadily validate each feature tailored to customer needs. Shaping the support experience has always been a customer-centric initiative.

That said, there are some additional enhancements in the pipeline around increasing the transparency of the status of an incident throughout its life cycle. We are currently prototyping a visual indicator to highlight at which point in the resolution an issue resides, with an estimated time to resolution.

Our key goals are almost achieved, but there is always room for improvement, especially with leveraging the opportunity to embed AI and newer technologies as we strive to achieve the perfect support experience for our customers.

His goal is to flip the engagement model with customers to provide end-to-end solution support and a superior customer experience.

Q: Customer centricity is influencing how organizations operate. What is your organization doing to provide a superior customer experience?

A: The customer has always been at the center of what we do here at 麻豆原创, whether it involves customizing a product or service to meet their needs or delivering support in a way that accommodates their evolving business models.

And while providing superior customer experience has always been our focus, we are constantly looking at ways in which we can enhance this 鈥� both in the way we鈥檙e internally organized and in how we鈥檙e integrating emerging technologies such as AI and machine learning into our Next-Generation Support services and real-time support channels.

In the past, our organization was focused on delivering product support for the various products and services within our portfolio.

Now, as we evolve, we strive to deliver solution support to provide our customers with end-to-end coverage of their solutions and an end-to-end experience across their business.

While our approach has always been holistic, we are now looking at our solutions the same ways in which our customers are looking at their own business processes and solutions. With this lens, we are able to deliver all the tools they need to be successful, and also those that will help drive their transformation. In some cases, customers might not even be aware of what they need yet.

By bringing our support and engineering teams together under one umbrella, we are working together behind the scenes to help and support our customers鈥� mission-critical business processes. These are the processes that are truly critical on several counts. They need to be leading-edge to differentiate their business model and they need to be constantly available to protect their business. By integrating innovation services, we can also provide all our customers with specific innovations tailored to their business and industry. This approach is allowing us to truly meet our customers where they are and where they want to be in the future.

You have previously spoken about flipping the engagement model to drive customer experience. What does that mean? What are some of the developments in this area and what do you envision for the future?

In the support and services industry, engagement is typically initiated by the customer. But nowadays, customers expect to hear from us first and rely on a constant stream of communication to help address any issues they may have 鈥� and even those they don鈥檛 anticipate having. This involves flipping the engagement model and reaching out to our customers proactively before they even face a problem.

We鈥檝e already seen great success in this by leveraging AI and machine learning technologies across functionalities and solutions within Product Support. While many of our processes are already automated, we鈥檝e added a lot of intelligence to accelerate these and make them more accurate. This intelligence kicks in from the moment our customers first interact with us, whereby we鈥檝e created algorithms that automatically triage our support tickets.

By examining the technical context, analyzing the sentiment, and evaluating the potential business impact of an incident, we are currently exploring the ability to identify the priority of incidents with great accuracy and pass them along to an expert in that particular area. Automating such manual processes will, more and more, free up time of our engineers who can then focus on the important work of alleviating our customers鈥� issues.

And while automation has played a huge role in customer support, it鈥檚 no longer just about automation but now about avoidance and prediction. We want to move toward a world that is self-healing. Our vision here is to shield our customers from facing any issues and to minimize the frequency of which they have to reach out to us. To do this, we need to take a lot more data into account. This will be in compliance with all respective laws and data privacy standards and anonymizing data where necessary or requested. With these data analytics, we鈥檒l be able to anticipate questions, predict when our customers might face issues, and proactively engage them.

How have you integrated machine learning and AI-based technologies to enhance customers鈥� experience?

While many of our support solutions already leverage AI and machine learning technologies, over the past year we鈥檝e focused on integrating new, automated, and interactive features that provide our customers with the tailored solutions they need. We want to simplify and enhance the customer support experience so they can receive the answers they need faster.

With , customers will be able to refine the results by reviewing the key elements suggested, driving them to relevant answers to their technical questions faster. The models continue to improve by collecting this interaction data and integrating machine learning capabilities. We will also make this service available to our 麻豆原创 Ariba customers, which will continue to allow the platform to improve as it collects more data on various incidents.

Our real-time support channels 鈥� and 鈥� are also based on a foundation of AI and machine learning capabilities, which help equip our engineers with the relevant information they need to provide our customers with targeted recommendations. We鈥檝e recently introduced Schedule a Manager, which allows customers with high-priority incidents that meet certain business requirements to speak directly to a relevant Product Support manager.

As we look toward our customers鈥� evolving needs and the future of their businesses, we want to make sure we鈥檙e one step ahead and can engage them in a way that is both meaningful and inspiring. I鈥檓 excited for the opportunities these enhancements will bring to both our customers and our employees.

Sophia Stolze is the integrated communications manager for Customer Solution Support & Innovation at 麻豆原创.

It has not even been a full decade since technologies such as artificial intelligence (AI) and machine learning were introduced into the B2B support world, so we know we have only scratched the surface in terms of how these technologies can help improve business processes.

We sat down with Jens Trotzky, head of Artificial Intelligence Technology for Customer Success Services at 麻豆原创, to discuss what he and his team achieved over the last year in the development of automated support 鈥� and what they are looking forward to in 2020.

Q: What successes have you seen with AI and machine learning at 麻豆原创 over the course of 2019?

A: You could say that 2019 was the first chapter of our ever-evolving journey toward an automated service and support function 鈥� one that is fueled by the Intelligent Enterprise, for the Intelligent Enterprise, and one that integrates AI and machine learning applications in support.

AI did its first real-world heavy lifting for 麻豆原创 customers in 2019, allowing our team to observe various use cases within automated support and how they鈥檝e impacted our customers in the early stages. Over the course of last year, we conducted a field test, put the intelligent machine out there, and gathered a lot of feedback from our customers, internal experts, and support engineers, which allowed us to continue to improve and build upon our intelligent and automated services.

Most prominently, we have seen the greatest success through the official go-live of via . The service automatically connects users with the appropriate resources through natural language processing as they type, driving them to relevant answers to their technical questions faster. Through the collection of this interaction data, and fueled by AI and machine learning, the models are automatically trained and continue to improve. Customers who have used this service are extremely satisfied with the results of receiving the correct solution to their incidents so early in the process.

What has surprised you about the application of AI and machine learning in 麻豆原创鈥檚 support?

I am actually very happy about the stability of the cloud architecture we developed to deploy our AI and machine learning capabilities. Our AI applications are running on cloud-native applications that are able to scale exponentially, as needed, so we don鈥檛 need to invest any additional resources in order to cater to a growing customer base.

As we begin to see the implementation of our services among our customer base, we are learning that data is incredibly important in AI- and machine learning-driven applications. Not only have we learned that data quantity is important, but that data quality truly determines the business value we can provide our customers. While we鈥檝e been able to gather vast amounts of data over the past year, we are working on getting access to additional streams of data to better streamline the interaction between the user interface and machine learning.

Where will you be focusing your efforts in 2020?

While 2019 marked the year we introduced semi-automated AI solutions to the services and support world, 2020 will revolve around testing the grounds for full automation, where AI will take on a completely independent business process step.

We will focus our efforts on perfecting our current solutions and rolling out more automated, personalized, and interactive features that provide our customers with the tailored solutions they need. Not only will we be introducing new services, branching out into the areas of predictive support, but we will also be bundling together previous standalone machine learning services to form an intelligent network for our customers in services and support.

Along with the deployment of these services to our customers, we will continue to observe, analyze, and improve the quality of our results even further — a great testament to the fact that research and in-depth data analysis does fuel the future of data-driven innovation that will help businesses run better.

Sophia Stolze is integrated communications manager for Customer Success Services at 麻豆原创.