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AI is a hot topic and everyone is trying to figure out how to start using it. But what’s around the corner and how do you get ahead of it? Join this session to hear directly from VCs who have a bird’s eye view of AI trends and how various companies in their portfolio are thinking of using it. This panel includes VCs from Andreessen Horowitz (a16z), Menlo Ventures, Success Venture Partners, and The New Normal Fund.
Nik Mijic - Founder & CEO at Matik [MODERATOR]
Nikola (Nik) Mijic is the CEO & co-founder of Matik. Prior to Matik, Nik worked in various roles and companies that focused on helping customer success teams retain and grow customers. At LinkedIn, he built internal tools that helped them leverage LinkedIn data in their presentations and be more efficient while doing so. Nik was the first non-engineering hire at Bluenose Analytics, where they built a platform that used predictive analytics to engage at-risk customers and identify drivers of churn.
Allison Pickens - Founder & General Partner at The New Normal Fund
Grace Ge - Principal at Menlo Ventures
Grace is passionate about supporting entrepreneurs at their most vulnerable stage to enable the growth of emerging technologies, specifically around AI/ML, the modern developer and GTM stack, and next-generation cloud and SaaS. These areas of interest are a natural extension of Grace’s work prior to venture. Before VC, Grace was a builder and buyer of tooling for a swath of departments at different tech companies (Google – ML and ops, NetApp – product and sales, eBay – marketing and analytics, Facebook – internal productivity). Many of the companies she was bringing in to solve company pain points were early-stage startups and she quickly realized she would rather be helping founding teams than tech behemoths. Today, she invests in companies that make up the modern AI infra stack (Pinecone, Eppo, Orb), or those leveraging these new AI capabilities to power better automation and workflows (Matik, Vivun, Lindy).
John Gleeson - Founder & Managing Partner at Success Venture Partners
John Gleeson is a respected Customer Success executive, seed investor, and industry thought leader known for his impressive success in building and scaling companies. Before creating SuccessVP, he served as VP of Customer Success at Motive Inc., where he led the company's post-sales efforts as it grew from $1 million to over $300 million in revenue, supporting the creation of almost $3 billion in enterprise value. Recognized as one of the top influential Customer Success leaders from 2017 to 2022, he has made significant contributions to the industry, including appearances on notable SaaS podcasts and being featured in the book 'The Customer Success Economy' (Wiley, 2020).
Zeya Yang - Partner at Andreessen Horowitz
Zeya is a partner at Andreessen Horowitz, where he focuses on early-stage enterprise and SaaS investing. His investment interests include the product management stack, the future of collaboration, and the next generation of go-to-market solutions.
Prior to joining a16z, Zeya led product for a number of teams at Plaid, augmenting the core Link product and adaptive authentication features. Before Plaid, Zeya was the founding PM of the Subscription Growth team at Dropbox, where he drove self-serve monetization across all product surfaces. He started his career as a consultant at Bain & Company.
Can you recommend a resource that lists some of these products (that Allison talked about) that we can explore? I’d love to explore more products that can support CX.
(1) Making CSMs more productive
• Summarizing meetings and action items (lots of companies - e.g. NotetakerAI)
• Brainstorming ways to save an account - e.g. Pi.ai
• Synthesizing research about a particular company's priorities (based on public filings and published job descriptions) and generating talking points about how the product can help that client in those priorities (Wizia) • Auto-generating decks for meetings (Matik)
(2) Automating the role of CSM/Support
• Support chatbots for helping with ""how-to"" questions
• Automatically emailing long-tail of customers with upsell campaign, using the above (Wizia)
(3) Improving the product
• Chatbot in-product that can be useful for understanding what customers want - useful for product roadmap
• Creating/maintaining documentation using AI (Atlas - joinatlas.ai)
• Automatically configuring software based on prompts - a stealth company working on that; Swantide for SFDC
What would be your red flags as far as an organization not using AI responsibly for CS/CX specifically?
Lack of Transparency and Accountability: Customers deserve clarity on whether they're interacting with AI or humans. Similarly, within organizations, there should be clear responsibility for the AI's decisions and behaviors.
Data Privacy Concerns: Proper permissions for data collection, processing, and secure storage are fundamental. A breach in any of these areas can cause severe trust issues.
Over-reliance on Automation and Poor Handling of Edge Cases: Automation should enhance, not replace, human intervention. An over-reliance can lead to issues, especially when AI misinterprets unique customer inquiries. There should always be a straightforward option to switch to human assistance.
Biases, Discrimination, and Inaccurate Responses: The AI system should be free of biases that can lead to discrimination. Consistently wrong or inaccurate information can also erode trust in the customer service operation.
Feedback, Auditing, and Training: Not having a feedback mechanism, neglecting regular audits of the AI's decisions, and forgoing ongoing training are all signs of irresponsibility. These elements ensure the AI's continuous improvement and relevance.
Regulatory and Ethical Adherence: Not following industry or regulatory standards, like GDPR, points to an organization's potential neglect of its ethical responsibilities.
Impersonal Interactions: A balanced CS/CX AI should offer personalized interactions and know when human empathy is crucial, ensuring that customers don't feel treated as just another number.
This transcript was created by AI. If you see any mistakes, please let us know at email@example.com.
Matik MC: Hey, everyone. Once again, welcome to Matik, AI and customer success summit. You are currently watching untangling AI, what to expect in the future? I'm super excited to introduce our speakers for this session. And as I introduce them, I'm going to have them answer our ice breaker question. What would you use AI to automate in your life outside of work? So to kick us off, excited to introduce Nik, match, cofounder and CEO of Matik, who will also be the moderator for this panel, Nik, what's your answer?
Nik Mijic: I don't know if, I don't know if we're quite there yet, but I would probably say laundry. I can't stand doing Andrew. So laundry would be top of the list, to automate if possible.
Matik MC: Awesome. Allison Pickens founder and general partner of the new normal fund.
Allison Pickens: Hi, everyone. I think for me, if I could have an AI, they could just like automatically detect when we're low on diver supplies, low on ice and like other types of foods that my household consumes repeatedly. And just, you know, auto refills them, that would be outstanding.
Matik MC: We also have gray ski principal at menlo. Ventures.
Grace Ge: Thanks, fax. I feel like mine is really basic. I just want someone where I can give a stream of consciousness of like personal tasks and versus putting it in a notion to do list, just being able to say it verbally and have all of that decision making, all those preferences, all of those actions be automatically taken care of.
Matik MC: I love that one. I think I would also use that. We also have John Gleason founder and managing partner of success venture partners.
John Gleeson: Everybody. So I love to serve and I also have a young family. So I don't have a lot of time to go serving and the surf work casts are fine. But I've got all of these little algorithms running in my head about which spot is best on the best day. So I would build AI to say, okay, you have 45 minutes your best bet to get the best way if at this day, is right here, go get it. And then I could do my activity as much as I want because I don't get to do it nearly as often as I like with all of the other commitments in life.
Matik MC: And last but not least, a partner at Anderson or which.
Zeya Yang: Mine is sort of like building on what Allison said. I was thinking I would have a fridge that would restock itself automatically. And then every day, it would also tell me what I need to eat or cook. And then I don't know, maybe we get to the point where it also cooks for me.
Matik MC: Love it. Thank you all for your responses before I pass off. Pass it off to Nik. Just a friendly reminder to everyone. Please add any questions you have to the Q and a which can be found in the Zoom controls bar. We'll spend the last 10 minutes of the session answering your questions. Awesome. So without further do Nik?
Nik Mijic: Awesome. Well, thank you all for being with us here today to kick things off. The first question that I'd love to ask the panel here is what are some of the things that you guys are seeing from an investment standpoint? When it comes to a, are the categories that you guys are seeing a lot of investment in?
Grace Ge: Yeah, Nik, I'm happy to tackle this one head on just because, you know, I think a and perspective firms, menlo and a 16 Z. We've had a really interesting front row seat to see what's happening in this new golden era of AI and it's kind of incredible. I think if you look at some of the data around, you know, investment activity, I think PitchBook recently put this out. But in just like the first six months of 2023 I think more than 15,000,000,000 has gone into figurativ AI companies globally. Obviously, like the majority of that is, you know, Microsoft's 10,000,000,000 investment. But even if you take that aside, the value of VC investing going into generativeai is up, you know, 60 percent or so compared to the same period in 2022. And when we look at areas that investors are really excited about, this is how we kind of break it down at menlo, there's core infrastructure. So this is the foundational model layer companies and vector databases. So, menlo is an investor in an tropic, obviously open AI is one co here's one. Then you have the vector DDS, pine cone, Chrome. We then you move up the stack and this is where things start to get really interesting. This is what we call the middle layer are Dev tooling. This is anything from agent app frameworks like, you know, lancing fix, auto GB T, there's model selection, routing, training deployment. So companies like replicate modular base 10 mosaic was just acquired by data bricks for a 1,000,000,000. And then you get to the app layer, right? And so we classify that as horizontal like Jasper runway or vertical like Harvey or re, voice. And I don't think there is one area necessarily that is getting more attention than others. I think the reality of it, is investors are excited up and down the stack and we continue to be.
Zeya Yang: And I'm happy to add on there. I think we think about it very similarly in terms of whenever there's like a big tech shift like this, it's helpful to think about the tech stack. And so, we have this piece we wrote like the emerging LM app stack. But, you know, there's very much the foundational model layer then you need a database layer, then there's like a frameworks and orchestration layer. You're gonna need observability and monitoring. And then ultimately the applications and echoing grace like, I think we've seen investment activity throughout the entire stack. One other way we can think about it is like, is the company like totally AI or LM native or is it a start up that maybe was doing something else or like, was still trying to figure out product market fit and then now had the opportunity to adopt LMS or AI very early on? And sometimes that helps frame like, you know, cause team and DNA and how they think about the advancement is so important. So that's another angle that we'll look at when we look at companies.
Nik Mijic: Makes sense. Yeah. And I think, I mean obviously, when you go on LinkedIn, right? My LinkedIn feed is just filled with, you know, companies posting about AI in general. So where do you guys see in terms of the just, the hype cycle or just adoption? You know, are we kind of at the, are we crossing the chasm at this point? Like where do you guys see that in terms of the adoption of AI cross portfolio?
Grace Ge: So I, to tackle, I guess the first question adoption across our portfolio companies has been incredible. I think, you know, we talk about this internally a lot unlike other paradime shifts in tech, you know, mobile cloud, et cetera. This feels like one of the first ones where both, you know, startups and incumbents are embracing it fully head on. And just like the speed of adoption has been insane. And so, you know, we have this internal graph where we talk about complexity of the AI stack and where companies are on the maturity curve, right? You can be, you know, at the bottom where you're just doing prompt engineering, maybe you're going up the stack, you've adopted a vector DB. And then at the highest end, you're creating your own custom LM, and I think for us, we believe as companies, you know, gear up the, their maturity and sophistication and understanding of AI, they'll go further up, the AI complexity curve. And then I think to your second question, where are we in the hype cycle? I think we're still, very much, you know, at the peak of expectations, I know like Gartner has that curve there's the trigger of innovation. Then there's the peak. And then you go down the trough of disillusionment. I don't think we're there yet, right? I mean, I think despite, you know, the metrics from ChatGPT saying, hey, for the first time in June, you know, we saw decrease in unique visitors, you know, decrease in the amount of time you look at and videos blow out quarter and their earnings this week. And just like they added 50,000,000,000 evaluation because they blew out, their forecast. And I think we're still, very much in the early innings of excitement here.
Nik Mijic: Makes sense. So, I guess kind of, you know, transitioning a little bit more, tying it back to customer success. Obviously a big topic of the summit is how I can impact. Yes, John, both you and I have a background in CS. So how do you guys envision a team using AI?
John Gleeson: Yeah. I was thinking through this because, you know, not too long ago, I was operating, in a high growth company that was only last January and in, from last January to now just, I almost feel like I've aged or become a dynastore because these topics were just starting to emerge. You know, how do we equip our teams? How do we think about, our teams maybe using this? Or was a lot of maybe fear early on? Like what is this gonna mean for CS? Where we completely, you know, automate the whole thing to actually like moving? And, you know, we're not in CS. We generally don't deal with the whole AI stack. We're generally dealing with, you know, the points. So in the app layer of it and so, you know, just like when thinking about dealing with teams, it's a little bit. It's a little bit like, any like change management process in a lot of ways. I think like initially what I started to hear and, you know, talk to people about there. It's like, okay, we're going to do an outright band on the app layer. We're not gonna let our team to use ChatGPT. In reality, what ends up, you know, happening is people skirt that, you know, there really are productivity gains. And so people working from home or using, their personal computers, to get those gains. So then, you know, when it comes to like equipping your teams, it becomes like change management, like anything else where, you know, selecting, you know, what are the right needs of the team, what are the right solutions for it? And then the layers of education and training and equipping them with the safety and the privacy concerns, for using the different solutions that you're gonna give them. So… I would say like, yeah, there's a real duty, for managers and leaders to kinda figure out that, for their team. But I would say like we're still definitely in the early innings of all of the solutions that will come to market, and support CS teams. I think we're just like at the tip of the iceberg when it comes to the app layer for sure.
Allison Pickens: I can build on that. Definitely we're at the tip of the iceberg, I see so much activity people trying to figure out how to make CSM more productive. Also like automate the role of CSM and improve the product as well in a way that makes CSM jobs much easier. I'll just at a few examples in those three categories in making CSM more productive. They're ton of products out there right now that are summarizing meeting notes and action items from those meetings like note taker AI is one that I'm seeing used a lot. I actually just this morning was brainstorming with ti AI which is like a conversational AI where, you know, gave it a situation with a client that was gonna churn and like talk to it about like how could we actually save this client? Curious to know if other people have used it that way? I'm seeing products for synthesizing research about companies that, you know, are made public like through public filings or job descriptions and then generating talking points for CSM. There's auto generating decks for meetings. Obviously Matik is, you know, paving the way in that category. And then, you know, under that second area of automating the role of CSM or support, I see a lot of companies using like support chat box which I think could be used for like answering probably basic how to questions as well as, you know, automatically emailing the long tail of customers with upsell campaigns with us. One, a company that's like initially targeting sales people for this use case. But I think, you know, also relevant for CSM and then under like category of improving the products that CSM don't have to do as much work. Those chat bots and product can be really useful for gathering feedback from customers, and kind of engaging their interest about, you know, like what would they like to see on the road map as well as, you know, their products like at lists that are trying to make it easier to create and maintain, a documentation for the product which right now, like teams like stripe staff up like huge, you know, groups or companies like stripe as, you know, staff up huge teams of people to go out and do that. And then finally there's a self company I've been working with that's helping people auto, generate configurations for software to like customize the product to your use case that could open up like, you know, a whole, so many possibilities for automating the onboarding process completely. So, I think there are a lot of interesting things on the horizon.
Nik Mijic: Yeah, I agree. And I think you touched a lot upon the data aspect and I think we see this a lot in the market. I mean, CSM are touching a variety of data sources, right? Whether it's CRM data, usage, data that's living inside of a product or even inside of a BI tool, and they're having to go and pull all that stuff together to make those meaningful insight. So, I think like I mentioned on the first session, I think the data enablement piece is going to be a huge lever, you know, to be able to up level your CSM to be able to talk to data in more confidence especially being able to tie back to value. So, I love that. I guess what, you know, from your guys perspective, what are the organizations, right? That you feel like our boys in terms of having success in an AI, let future, right? So, I know you guys all mentioned we're early on, but it seems like…
Zeya Yang: Isn't here to say, I.
Nik Mijic: Only gonna get bigger and bigger. So what organizations do you feel like are set up for?
Zeya Yang: Yeah, I can jump on this one. I think there's been a lot of talk the last few months about how well the incumbents seem to be positioned especially in the early innings for the reasons that they have the distribution, right? But I think again because we're in the early innings, I think the expectation is that the full potential of AI capabilities is going to require a bigger like more fundamental shift and how products are getting built. And I think naturally in that case, the you'd have to believe that the startups are going to be the ones who can, you know, innovate and come up with what that new paradigm looks like because at the end of the day, the incumbents are somewhat constrained by their existing products and their core business. And maybe today, if all you're really trying to do is use like an API and then, you know, embed some auto response capability into your product like that's very easy to do. But if you're actually trying to change like a user experience or the way a CSM does the job, then, you know, I think somebody who can move faster and isn't confined to an existing product is probably going to have the advantage there.
Grace Ge: Yeah, I would agree, with a point, I think the only thing that I would add is especially as an investor, you know, the question is what does the future look like? You know, if you're investing at the app layer, you know, what is the future of apps look like that are powered by generate a I, and I think my response is, I don't think we've seen the best apps that have been created. Yet. I think there's so much innovation in the future and I think there's so much ahead of us that will really shock us in terms of what the future will look like and what's possible. And so at, as an investor, I think where incumbents have advantage especially in distribution and product is if you're just creating a nother, you know, company that looks like an existing incumbent, but just with generativeai powered by, you know, open I and tropic, I think the incumbents have the advantage. If you're creating a completely different application that, you know, we haven't even been able to dream of then I think, you know, obviously innovators dilemma. So startups will prevail…
Matik MC: I…
Allison Pickens: I really like what said, and I would add that AI changes the power dynamic within companies and changes what the needs are. As a result. You know, one example I have met recently in a company called ignition which is helping to power, you know, marketing campaigns, you know, end to end using AI. And there.
Matik MC: Your…
Allison Pickens: Point of view is that product marketers which used to be, I think like a well respected team but like broadly considered a small sub function within broader marketing that those teams are now going to be the most important team within a marketing organization because they own the content which now is like basically, the sort of focal point for using AI. And so, you know, ignition is now selling to and empowering product marketers as they're wedge. There's not really an existing incumbent that's owning product marketers at the moment. So to grace this point, I think new categories will be created simply because their new needs, and resulting from their being like new or different roles within companies.
John Gleeson: Yeah, I completely agree with that too. Like, you know, I reflect on even what we did at motive. We brought the trucking industry online for the first time but largely the way in which we did that the start was like digital transformation and we mimic things that kind of existed in real life and put them, you know, on a computer for the first time. But when you think about AI, you know, was that actually the best way to do things? And, you know, if we didn't have to do certain piece is where, yeah, where would the balance and power shift? What would be the best way to do that process? Would we even do the process in the same way? And so I think like you said, Allison, there's such a like reshuffling of like where maybe like power in organizations or where the value clusters and there will be like a shift in the teams that either like rise or become more important within organizations. And still watching that check as I think interesting, as well as things shuffle around.
Nik Mijic: I know we got time probably for one last question before we open it up to Q and a. So the last question that I have for the group is just what are some of the red Herring when it comes to AI that you guys have come across?
Allison Pickens: I can start, you know, one thing that I found to be distracting for early stage startups is embracing AI, like trying to incorporate AI into your product without really thinking about whether it's helpful on your path to product market fit. Although I think it's really important for startups that were born in like 2020 2021 to really think through what their new product strategy is in this new area. AI have noticed some actually like losing sight of what the core problem is they're solving because they're you know, trying to show that like they are an AI company, one company, I saw, you know, sort of embrace it, not in the service of product market fit, did actually amass a really significant weight list for their product because, you know, the AI branded website and launch like attracted, you know, a large demographic of people, but they still have to figure out how to use that weight list in service of, you know, accelerating their core product?
Nik Mijic: Makes sense. Any other final thoughts on that before we switch over to Q and a awesome, very cool. Well, I'll pass it over to you to handle the Q and.
Matik MC: Yeah. We have a couple of questions for the team here. First, one, can you recommend a resource that list some of these products that Allison talked about that we can explore? I'd love to explore more products that can support CX?
Allison Pickens: I can put some in the chat window and others can list them too.
Matik MC: Go ahead.
John Gleeson: I like to go to the website. I think it's there's an AI for that. Have you ever seen that? And it's kinda interesting just, you know, you see in, you know, the ways in which people are building different things. It's certainly at the app layer mostly.
Matik MC: Next question, how to safeguard yourself as a CSM moving forward?
John Gleeson: I think like as to CSM like relationships and the connection with people matter like more than ever before, you know, as we like strip away a lot of the tasks to that CSM. Do your interaction with people. Actually probably stands out even more than it did, in the past if we always talk in CS about kind of the busy work and the reactive work. But a lot of that I believe can get or will get stripped away. So I would say like the human connection, is still really important particularly like if you're in enterprise or, you know, bigger customers really like lean in to kind of, the core relationship side of things because now you have an opportunity to actually maybe even do that more at scale because some of those tasks to prepare for those interactions, are becoming so much easier. So I think there's I think CSM or customer facing roles, people doing business with people, is still a thing for a minute.
Grace Ge: Yeah. I would agree with John and I would look at the paradime of like generation one chop, but I mean, the reality of it, is there still needs to be a human interface. So, I would be worried less about job displacement and be more interested about how do I use these new technologies to make me more efficient and super charge? So I can become CSM, plus, right? Because I think the reality as you think about your workflow is we wanna be strategic, we wanna be able to personalize every customer interaction. We wanna be able to service them very deeply, but you can't do that at scale. When you look at your book of business and AI gives you that capability because now you have the ability to kind of go through your customer feedback at scale and proactively understand how to cluster and categorize and have, you know, something to help you with task management and generation. And so it really up levels what you're able to do your best self, I would think versus, you know, taking over.
Nik Mijic: I just to chimein, I totally agree with what has been said. I do think it's gonna help automate a lot of those busy tasks that customer as managers are doing and it's gonna give them the opportunity to do what they do best, which would build those relationships. As John mentioned. So I don't think that's gonna go away with anything. There's gonna be a higher priority on those type of tasks now that CSM have more time to go and do that.
Matik MC: How should we equip companies and employees to be responsible and secure?
Grace Ge: You know, that touches on, you know, a different topic of AI that I think a lot of investment excitement is around which is, you know, security and an AI world. And so we're starting to see companies where to name a few. I think cradle is one. There's one or two others that basically allow companies to set, you know, policies around what is the company specific endorsed way to use AI? And, and there's kind of enforcement, at the organ team level, there's obviously companies like hidden layer and so many more that are helping organizations really protect and red team, blue team kind of, the use of this new technology. But, you know, I think it's a new and can area and I think there's a lot of innovation and dollars going into this. And so I still think it's you know, TBD and I think it's still wild west.
Zeya Yang: And I also think this is why when it comes to the app layer in a B to B context, it still feels like we're very much in the early innings, right? Like a lot of this generative AI talk has come out of things like mid journey, you know, people like generating a lot of art or like really cool consumer use cases, but yeah, customer relationships matter a lot and you don't want to like put any of those at risk through some new technology. And that's why there's a lot more that's coming down the pipeline. We feel when it comes to AI applications.
Matik MC: What would be your red flag as far as an organization not using AI responsibly for CS CX specifically?
Zeya Yang: Opposite of what we just said… like maybe like letting it do too much or like maybe like putting automation ahead of the customers needs, or like actually solving their problem. But, you know, at the end of the day, it's you're still building like a workflow product or something to solve somebody's problem. It's not, we're not like trying to take what's the thing like take a hammer and then just like find a nail, right? It's like at the end of the day, like creating customer value is the north star. You can't lose sight of that because there's like a shiny new tool.
Nik Mijic: And I think John mentioned this earlier, but there's a lot of change management. I think we'll also need to happen, right? And enabling your team to be able to go and leverage AI in the right way to go back to what I just mentioned on creating value for your customers. So, I think it's going to have to happen in a phased approach and it's not gonna be, hey, we got to adopt AI for the sake of adopting AI, but going back to really helping your customers achieve their objectives. I think that's, the thing that we really need to stay focused on.
Matik MC: Awesome. So, with that, we are, at the end of our session, there's a couple of questions we weren't able to get to, but we will pass these along to the speakers and share their answers in our resource hub, which we will e-mail all of you when that is live. Again. Thank you to all of our wonderful speakers for joining us today and for sharing a lot of great insights and knowledge and thank you everyone else for attending. Have a great rest of the summit.
John Gleeson: Thanks everyone. Bye.
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