Inspire 2022: Microsoft Unveils Digital Contact Center Platform

‘Contact centers specifically are at the heart of CX transformation … a big initiative for many of our customers,’ Charles Lamanna, Microsoft corporate vice president of business applications and platform, told CRN in an interview.

Microsoft executives say partners will play a critical role in the newly introduced Microsoft Digital Contact Center Platform offering, which combines technology from Teams, Azure and other portfolio products with newly acquired voice-to-text company Nuance.

Redmond, Wash.-based Microsoft introduced the offering during its Inspire partner event, which occurs from Tuesday to Wednesday online.

Unlike other tech conferences, which have returned in person or done a mix of live and online events, Microsoft held Inspire entirely online as the world continues to grapple with COVID-19.

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Phil Walker, CEO of Manhattan Beach, Calif.-based Microsoft partner Network Solutions Provider – a member of CRN’s 2022 Managed Service Provider (MSP) 500 – told CRN in an interview that a cloud-based contact center could appeal to his customers looking to improve customer experience.

“Moving away from old school voice products will reduce the amount of spam and get a more laser-focused approach to telecommunications,” Walker said. “Microsoft may be making a huge splash with this product.”

Partners are “absolutely essential” to bridging the contact center platform to market, Charles Lamanna, Microsoft corporate vice president of business applications and platform, told CRN in an interview.

“Contact centers specifically are at the heart of CX transformation, or customer experience transformation, which is a big initiative for many of our customers,” Lamanna said. ‘And doing that is something that people look for a partner to help them with.”

Some of the earliest partners to work with Microsoft on the contact center platform, he said, include PwC and Accenture – No. 1 on CRN’s 2022 Solution Provider 500.

The platform –which pulls in technology from Teams, Nuance, Azure, Power Platform and Dynamics 365 – integrates with other contact center and customer relationship management software, including Genesys, HCL, Nice and TTEC.

Microsoft closed on its $19 billion acquisition of Nuance in March. Microsoft will also roll out training and certification for the new offering, Lamanna said.

The platform brings in artificial intelligence (AI) to power self-service and live customer engagements, collaborative experiences with live and virtual agents, business process automation, fraud prevention and advanced telephony, according to Microsoft.

Technology from Nuance improves the conversational AI, security and automation capabilities with the goal of resolving customers’ issues faster and with more personalized service. Contact centers can also offer targeted incentives to improve customer loyalty and upsell opportunities for more revenue, according to Microsoft.

The platform uses AI and Microsoft’s Context IQ feature to anticipate customer requests and predict intent, according to Microsoft. Customers are protected with integrated biometric identification, authentication and fraud prevention. Users also receive customer journey analytics to improve experiences.

Lamanna, who oversees Dynamics and Power Platform, said that partners have long been a key part of bringing business apps to customers.

“Dynamics has a very rich history of the partner channel being a major way that we bring products to market, and that‘s because so much of your business applications requires consulting around business process transformation and customization,” he said. “So it’s different from a completely off-the-shelf SaaS product, and that there‘s a lot of implementation and consultation that happens after you purchase it.”

The contact center platform should please smaller and midsize businesses as well, he said. Companies of all sizes need help talking to customers with more than just customer support over the phone.

“There are oodles of stats out there about how much of a struggle it is for so many companies to do a great job here,” he said. “And it‘s really existential for so many B2C (business-to-consumer) companies. If you don’t have great customer care, great customer support, you‘re not going to have a great brand and people aren’t going to have a great relationship with you.”

As for future updates to the contact center platform, Lamanna said to expect more integration among Dynamics, Teams and other business applications and the contact center as well as advancement in artificial intelligence for more customer experiences.

“That‘s going to have all kinds of great ramifications – cost reduction, customer and consumer experience improvements,” he said. “And most importantly, just making the agents feel like they spend their time on creative and engaging activities as opposed to lots of data entry and data copying. So that’s a big area of investment for us as well is AI and what it means for the contact center and customer engagement.”

Partners well-versed in Teams and Teams Phone might want to consider adding the contact center platform to their practice as the next step in using Teams to interact with customers, Pete Daderko, director of Microsoft Teams product marketing, told CRN in an interview.

With a quarter of a billion users, Teams has brought new customer service features to Microsoft users, Daderko said. He’s seen attach rates – a measure of the number of add-on services and products sold – of up to 40 percent with Teams Phones.

“You see this team customer service coming to bear where you‘re turning any Teams channel into a team-based call queue,” Daderko said. “So you can collaborate, share information before, during and after the call.”

Even outside of the contact center platform, partners still have a lot of opportunity for growth with Teams, Daderko said. Globally, less than 10 percent of meeting rooms are video enabled

“That’s a massive opportunity,” he said. “As bullish as we are on cloud calling, it’s still a much smaller segment than than the on-prem world as well. And so there is plenty of opportunity.”

Microsoft CEO Satya Nadella had hinted at an upcoming contact center service back in April during the company’s quarterly earnings call.

When updating analysts on Microsoft’s Nuance acquisition, Nadella said that “Nuance is a platform layer for these AI-driven applications that are getting deployed, whether it’s in health care or even in the enterprise contact center.”

“So we’re very excited about Nuance now being part of the Microsoft family,” he continued. “You will see us pretty aggressively go innovate there and grow the impact of these solutions in both, sort of the large percentages of our GDP, like health care, where I think things like tackling issues like physician burdens with new innovative solutions is much needed.”

In February, Zoom Video Communications unveiled its first homegrown cloud contact center offering coming off its abandoned acquisition of cloud contact center software provider Five9. The company included its contact center offering in its new Zoom Up Partner Program.

In May, Avaya announced the expansion of its contact center-as-a-service solutions and re-entering the networking arena.

Why training LLMs with endpoint data will strengthen cybersecurity

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Capturing weak signals across endpoints and predicting potential intrusion attempt patterns is a perfect challenge for Large Language Models (LLMs) to take on. The goal is to mine attack data to find new threat patterns and correlations while fine-tuning LLMs and models.

Leading endpoint detection and response (EDR) and extended detection and response (XDR) vendors are taking on the challenge. Nikesh Arora, Palo Alto Networks chairman and CEO, said, “We collect the most amount of endpoint data in the industry from our XDR. We collect almost 200 megabytes per endpoint, which is, in many cases, 10 to 20 times more than most of the industry participants. Why do you do that? Because we take that raw data and cross-correlate or enhance most of our firewalls, we apply attack surface management with applied automation using XDR.”  

CrowdStrike co-founder and CEO George Kurtz told the keynote audience at the company’s annual Fal.Con event last year, “One of the areas that we’ve really pioneered is that we can take weak signals from across different endpoints. And we can link these together to find novel detections. We’re now extending that to our third-party partners so that we can look at other weak signals across not only endpoints but across domains and come up with a novel detection.” 

XDR has proven successful in delivering less noise and better signals. Leading XDR platform providers include Broadcom, Cisco, CrowdStrike, Fortinet, Microsoft, Palo Alto Networks, SentinelOne, Sophos, TEHTRIS, Trend Micro and VMWare.

The AI Impact Tour

Getting to an AI Governance Blueprint – Request an invite for the Jan 10 event.

Enhancing LLMs with telemetry and human-annotated data defines the future of endpoint security. In Gartner’s latest Hype Cycle for Endpoint Security, the authors write, “Endpoint security innovations focus on faster, automated detection and prevention, and remediation of threats, powering integrated, extended detection and response (XDR) to correlate data points and telemetry from endpoint, network, web, email and identity solutions.”

Spending on EDR and XDR is growing faster than the broader information security and risk management market. That’s creating higher levels of competitive intensity across EDR and XDR vendors. Gartner predicts the endpoint protection platform market will grow from $14.45 billion today to $26.95 billion in 2027, achieving a compound annual growth rate (CAGR) of 16.8%. The worldwide information security and risk management market is predicted to grow from $164 billion in 2022 to $287 billion in 2027, achieving an 11% CAGR.  

VentureBeat recently sat down (virtually) with Elia Zaitsev, CTO of CrowdStrike to understand why training LLMs with endpoint data will strengthen cybersecurity. His insights also reflect how quickly LLMs are becoming the new DNA of endpoint security.

Elia Zaitsev: “So when the company was started, one of the reasons why it was created as a cloud-native company is that we wanted to use AI and ML technologies to solve tough customer problems. Because if you think about the legacy technologies, everything was happening at the edge, right? You were making all the decisions and all the data lived at the edge, but there was this idea we had that if you wanted to use AI technology, you needed to have, especially for those older ML type solutions, which are still by the way, very effective. You need that quantity of information and you can only get that with a cloud technology where you can bring in all the information. 

We could train these heavy-duty classifiers into the cloud and then we can deploy them at the edge. So train in the cloud, deploy to the edge, and make smart decisions. The funny thing though, is that’s occurring now that generative AI is coming into the fore and they’re different technologies. Those are less about deciding what’s good and what’s bad and more about empowering human beings like taking a workflow and accelerating it.”

Zaitsev: “It’s not about replacing human beings, it’s about augmenting humans. It’s that AI-assisted human, which I think is such a key concept, and I think too many people in technology, and I’ll say this as a CTO, I’m supposed to be all about the technology the focus sometimes goes too far on wanting to replace the humans. I think that’s very misguided, especially in cyber. But when you think about the way the underlying technology works, gen AI, it’s actually not necessarily about quantity. Quality becomes much more important. You need a lot of data to create these models to begin with, but then when it comes time to actually teach it to do something specific, and this is key when you want to go from that general model that can speak English or whatever language, and you want to do what’s called fine-tuning when you want to teach it, how to do something like summarize an incident for a security analyst or operate a platform, these are the kinds of things that our generative product Charlotte AI is doing.”

Zaitsev: “Most of these automation technologies, whether it’s LLMs or something like that, they don’t tend to replace humans really. They tend to automate the rote basic tasks and allow the expert humans to take their valuable time and focus on something harder. Usually, people start asking, what about the adversaries using AI? And to me it’s a pretty simple conversation. In a typical arms race, the adversaries are going to use AI and other technologies to automate some baseline level of threats. Great. You use AI to counteract that. So you balance that out and then what do you have left? You’ve still got a really savvy, smart human attacker rising above the noise, and that’s why you’re still going to need a really smart, savvy defender.”

Zaitsev: “When we build LLMs, it’s actually easier to train many small LLMs on these specific use cases. So take that Overwatch dataset, that Falcon Complete data, that [threat] intel dataset. It’s actually easier and less prone to hallucination to take a small purpose-built large language model or maybe call it a small language model if you will. 

You can actually tune them and get higher accuracy and less hallucinations if you’re working on a smaller purpose-built one than trying to take these big monolithic ones and make them like a jack of all trades. So what we use is a concept called a mixture of experts. You actually in many cases get better efficacy with these LLM technologies when you’ve got specialization, right? A couple of really purpose-built LLMs working together versus trying to get one super smart one that actually doesn’t do anything particularly well. It does a lot of things poorly versus any one thing particularly well.

We also apply validation. We’ll let the LLMs do some things, but then we’ll also check the output. We’ll use it to operate the platform. We’re ultimately basing the responses on our telemetry on our platform API so that there’s some trust in the underlying data. It’s not just coming out of the ether, out of the LLMs brain, so to speak, right? It’s rooted in a foundation of truth. 

Zaitsev: When you start to do those types of use cases, you don’t need millions and billions and trillions of examples. What you need is actually in many cases, a couple of thousand, maybe tens of thousands of examples, but needed to be very high quality and ideally what we call human-annotated data sets. You basically want an expert to say to the AI systems, this is how I would do it, learn from my example. So I won’t take credit and say we knew that the generative AI boom was going to happen 11, 12 years ago, but because we were always passionate believers in this idea of AI assisting humans not replacing humans, we set up all these expert human teams from day one.

So as it turns out, because we’ve in many ways uniquely been investing in our human capacity and building up this high-quality human annotated platform data, we now all of a sudden have this goldmine, right, this treasure trove of exactly the right kind of information you need to create these generative AI large language models, specifically fine-tuned to cybersecurity use cases on our platform. So a little bit of good luck there.

Zaitsev:  Our approach, I’ll use the old adage when all you have is a hammer, everything looks like a nail, right? And this is not true just for AI technology. It is the way we approach data storage layers. We’ve always been a fan of this concept of using all the technologies because when you don’t constrain yourself to use one thing, you don’t have to. So Charlotte is a multi-modal system. It uses multiple LLMs, but it also uses non-LLM technology. LLMs are good at instruction following. They’re going to take a natural language interfaces and convert them into structured tasks.

Zaitsev: The output that the user sees from Charlotte is almost always based off of some platform data. For example, vulnerability information from our Spotlight product. We may take that data and then tell Charlotte to summarize it for a layperson. Again, things that LLMs are good at, and we may train it off of our internal data. That’s not customer-specific, by the way. It’s general information about vulnerabilities, and that’s how we deal with the privacy aspects. The customer-specific data is not training into Charlotte, it’s the general knowledge of vulnerabilities. The customer-specific data is powered by the platform. So that’s how we keep that separation of church and state, so to speak. The private data is on the Falcon platform. The LLMs get trained on and hold general cybersecurity knowledge, and in any case, make sure you’re never exposing that naked LLM to the end user so that we can apply the validation. 

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