Dreamforce Live: Humans with Agents Drive Customer Success

 Auto-transcribed on Wednesday, September 18, 2024.media and text playback available on this page: https://aliceapp.ai/recordings/uOg2WEZfQbiP4re08UtAwo6Dj37VyKak

Key 	Value 	
Words: 	2,407 	
Duration: 	00:12:48 	
Recorded on: 	Unknown date 	
Uploaded on: 	2024-09-18 22:01:33 UTC 	
Last update: 	 	
At: 	Unknown location 	
Using: 	Uploaded to aliceapp.ai 	

Speakers:

 Speaker A - 22.85%

 Speaker B - 36.73%

 Speaker C - 5.4%

 Speaker D - 13.79%

 Speaker E - 21.23%

Notes:

-  Agents can generate high fidelity pipeline and provide personalized coaching. That is humans and agents together, driving customer success. We're going to hear firsthand from a customer putting AI to work.- Carnegie Learning is now taking that external AI use and trying to pull it into internal AI use. What's your advice when it comes to implementing something like Salesforce? What advice would you give them really quickly? Be thoughtful.- What's next for Carnegie Learning's use of AI? Oh my gosh, there's so much coming. Agent Force is going to be hopefully huge for us. All of the different implementations of AI are just going to make our customers feel so much more personal.- Sales agents built on agent force are going to help sales teams accelerate growth. They're going to be generally available in October. What can our audience do today to prepare for this and start to implement them?- Loreena Yee Larina: The biggest tech and AI trends that global executives and business leaders should be thinking about. She highlights agentic AI, context windows, and multimodal.- Lorena Yee: How is McKinsey seizing the moment to deliver on the AI promise for its clients? One of the things we're doing is we're partnering with Salesforce because we need to make the great pivot of strategy execution to results.

[00:00:07]  Speaker A 
Welcome to Dreamforce today. Here's the headline. Agents can generate high fidelity pipeline and provide personalized coaching, allowing reps to focus on higher value deals and better prepare for them. That is humans and agents together, driving customer success. I'm Kerry Chow. We're going to hear firsthand from a customer putting AI to work. Uh, so excited to be joined by Mariska Scalersio, VP of sales operations at Carnegie Learning. Marissa, first of all, thanks so much for joining us. Uh, Carnegie Learning is an educational technology company that uses prompt builder and assisted agents to boost sales efficiency. So Marissa, what were some of the challenges that you were facing at Carnegie learning that led you to adopt those Salesforce tools I just mentioned?

[00:00:54]  Speaker B 
Thank you so much for having me. Um, yes, we are so excited to be using it because we have grown significantly over the past several years and just needed to make sure that our sales process and our sales reps were as efficient as possible. So, uh, we have reps that probably, like most sales teams, spend a lot of time doing research and things that aren't selling. So we wanted to make sure, we're trying to give them those efficiencies back to make sure that they can do what they do best, which is be in front of a customer and sell.

[00:01:24]  Speaker A 
I love that. And we're just talking about how, uh, there were some stats. About 30%, uh, of the time salespeople are actually selling and the rest of the time they're doing all those tasks that you just mentioned that love. What differences have you seen then, either anecdotally or from a data perspective, um, after implementing Salesforce?

[00:01:41]  Speaker B 
Well, I'm really excited to have some ROI stats by the end of the year, but we have this amazing anecdote. One of our sellers is using, um, the agent force and has asked it to summarize an account while she was on the phone with a customer and she's a newer customer and she said it would take her 20 minutes to do that. Normally it took her 30 seconds, was able to answer it while she was on the phone and she said, uh, and I quote, I think I thanked Einstein aloud. So it was really exciting to actually see a user use it in real life and actually work.

[00:02:13]  Speaker A 
I love, can we make that a hashtag? Yes, I would love to make that a hashtag. I think, I think Einstein out loud. Uh, what does a unified platform of CRM, AI and data mean for Carnegie learning's sales team?

[00:02:26]  Speaker B 
It means they can work out of one place. I mean, it is, there's so many different products out there. There's so many different ways that we've used, um, or we've had sales reps on in the past that we now have tried to only have them in Salesforce. They have enough to do while they're driving from place to place selling that they don't need to have a million platforms and know where to go. So having everything in one place and then being able to use all of the genai pieces because we have it all in one place, it's really gonna change their lives and make them so that, that 30% hopefully gets boosted to 40, 50, 60% of their time is selling.

[00:03:01]  Speaker A 
That would be amazing. Uh, can you even walk us through like a specific use case or example that's been especially impactful, I would say.

[00:03:07]  Speaker B 
The sales emails, I'm really excited by how typically emails for our reps take about ten minutes. We did a survey at the beginning of this year. So ten minutes takes them to write a really personalized good email that they're hoping to get a response from. And sometimes they do, sometimes they don't. As if any sales reps out there. No. Uh, so being able to write that email in about 30 seconds and then revise it for 30 seconds and just have it sent saves so much time, if you think about that throughout an entire day, throughout a week, throughout the year, I mean, the amount of time that they're going to save and then also it's going to be written with personalized data from Salesforce. So all of the activity history that we already have, I mean, it's all.

[00:03:47]  Speaker A 
Going to be right there, still super personalized. And as you mentioned, all adds up. All that time adds up so quickly. Uh, can you walk us through, I love this, your thought process before adopting some of these tools because were you skeptical?

[00:03:59]  Speaker B 
Oh my gosh, I was, I was. Um, so we actually have a department at Carnegie Learning called Cl next and I'm very thankful for them because they're the ones who, we have been in AI since we started. So we built our, um, software that we now still have today, 26 years ago built on AI. So this, um, Cl next is now taking that external AI use and trying to pull it into internal AI use to make sure that we are reflecting inside and outside the company that we are the best innovators and that we are really preparing our teams internally to make sure that they have the tools that they need and they're as efficient as possible. So luckily they pushed me to do this. So, uh, I had that push at the beginning of the year and it has been eye opening and changing, just learning about all of the different tools out there. And I'm very thankful that, uh, Salesforce had these tools ready to go and implement instead of me having to go really go find them and find what really is going to work.

[00:04:59]  Speaker A 
I love that the confidence is now there. I really appreciate that. A lot of companies would love to see efficiency and productivity. Um, what's your advice when it comes to implementing something like Salesforce? What advice would you give them really quickly?

[00:05:13]  Speaker B 
Be thoughtful. So one thing I wanted to do was roll everything out quickly and then realized very quickly that you shouldn't do that. You really do need to take your time. You have to really understand what use case and business case you are solving and what that ROI should look like before you roll it out and then test it. So we have a sales team, it's a pretty large sales team. We want to make sure that it's tested. We have a couple pilot people so that they can start using it. Get us quotes like the fabulous one that we had, and also then tell their peers how exciting it is so that we really have a good adoption of it. I think that's the most important part in one sentence.

[00:05:49]  Speaker A 
What's next for Carnegie Learning's use of AI? Uh.

[00:05:51]  Speaker B 
Oh my gosh, there's so much coming. Agent Force is going to be hopefully huge for us. I'm very excited about it. And I mean, all of the different, um, implementations of AI that we're going to be doing across the entire company are just going to make our customers feel so much more personal. They're going to feel great about themselves and we're going to feel great as well.

[00:06:11]  Speaker A 
I love it. Thank you so much. Mariska Sclercio, VP of sales operations of Carnegie Learning. Thanks so much for joining us.

[00:06:22]  Speaker C 
Hey, I'm Diane Mizotta here with Carolyn Bathauer. Now let's recap where we are on the agent force learning journey. Carolyn, sales agents built on agent force are going to help sales teams accelerate growth and they're going to be generally available in October. What can our audience do today to prepare for this and start to implement them?

[00:06:40]  Speaker D 
Yeah, so one of the things we've been talking about with customers is ask your question, your sales teams, a simple question if what do you spend the most time doing that you wish you didn't have to? And that's how you start building your list of where you could potentially deploy agents. Uh, and it all starts with data. So when exploring implementing SDR agents, understand how many touch points does it take to qualify a lead, to get a meeting to pipe a deal. And then how can an agent take those first few steps for you? What would your rep spend more time on if an agent took those actions instead? And then just make a list of what you do and you don't want an agent to do. And that becomes your list for guardrails when you're ready to really build or just customize.

[00:07:23]  Speaker C 
I love a good list. All right, well, tell me, how is this going to work?

[00:07:26]  Speaker D 
Yeah. So agents are grounded in your data. So as you build, you determine the topic. It's the job to be done, and then you give it the source of data it should use and then what actions that it should or should not take. From there, the agent engages in the medium, the language, the tone that you select, you really control and build in natural language. There is even a dashboard that helps you monitor and audit the CR agent, what it's doing and how effective it is. So you can actually make tweaks where you need to.

[00:07:56]  Speaker C 
This is now autonomous AI, as opposed to generative Aih. Do I have that right?

[00:08:03]  Speaker D 
Yes, absolutely. So think of autonomous as AI technology, and you're asking them complete this job for me, taking it to that final step, that's exactly how, uh, you think of autonomy.

[00:08:15]  Speaker C 
All right, so let's put agent force in context. It's not just one product or one offering. This is offered across the whole platform, right?

[00:08:22]  Speaker D 
Yes, because it's built on the platform. It's across all apps. And then we have those no code, low code and pro code options and out of box agents that are available. So you really. Yeah, you take advantage of existing elements you've already built as well. So you just keep going.

[00:08:37]  Speaker C 
All right, well, thank you so much, Carolyn. Well, we'll be right back after this.

[00:08:45]  Speaker A 
Now, I'd like to thank our innovator sponsor, McKinsey, for their support of Dreamforce. And joining me now is McKinsey senior partner, Loreena Yee Larina. So, uh, excited to have you.

[00:08:55]  Speaker E 
So excited to be here.

[00:08:57]  Speaker A 
You know, everybody's talking about AI, right?

[00:08:59]  Speaker E 
Yes.

[00:09:00]  Speaker A 
The biggest tech and AI trends that global executives and business leaders. What are those that they should be thinking about? What are the biggest trends?

[00:09:07]  Speaker E 
Well, it's a candy store. So incredibly hard to ask because everything looks like an amazing dessert. But if I were to pick three that I'm pretty excited about, agentic AI, context windows, and multimodal. And the reason I mention that is agentic AI. You know, over the last year and a half, we've been so excited with all of this information and how it's been able to be structured, be put together. I mean, it's really incredible. But agentic, uh, AI is the next step of being able to add an action, and that just takes us so much more forward. So that's pretty exciting. But I'm going to geek out on two others, right, because there's more than agentforts. Although agentforts super exciting context windows is essentially your short term memory. It's the short term brain of the AI, and you can now put the equivalent of 20 novels into the short term context. So that just means that without training and working with the base model, you can put so much more information to have that contextual dialogue. And then the last thing is multimodal, because we talk a lot about text, but the unstructured data in enterprises that I'm excited about are images, pictures, sound. I mean, what if you were able to have all five senses turned on and used in how we're able to prompt and generate things? So there's a lot to be excited about.

[00:10:34]  Speaker A 
Yeah, I love it, and I kind of geek out on that as well. But to your point about the unstructured data and all the information from pictures and images and videos that are all valuable when it comes to those customer experiences, what are some of the biggest impact areas you're seeing with the application of AI and generative AI?

[00:10:52]  Speaker E 
Well, we know that in the long run, there's over $4.4 trillion of productivity value. That's a huge number. 41% of that. Of that huge number is in sales, marketing and customer care. So these areas, by no surprise, are where we see so much of the innovation activities. So how do you reimagine one of the oldest professions in the world, a, uh, sales rep? You know, how do you reimagine marketing? The tasks, the campaigns? How do you even rethink, there's something that we're really excited about in terms of synthetic customers and how you create a digital twin of customers to test more ideas and service. I mean, how do you have a different experience when calling for help on a product that you have? The things we see in pilot and the early signals we see in enterprises, that's because they're part of bigger trends.

[00:11:48]  Speaker A 
I love that. Uh, what energizes you about the future of AI and generative AI?

[00:11:53]  Speaker E 
Well, I'm already pretty excited, but I think the most amazing thing is that this is about powering humans. This is about that super backpack that you and I get. And imagine, like, imagine how great you would be. You're already fabulous. But you have three AI agents helping you as your pit crew, helping you synthesize, think, reply. I mean, imagine all the things that you can do, all the places you can go with that type of power.

[00:12:23]  Speaker A 
Uh, it's incredible. In one sentence, really quickly, can you tell me, how is McKinsey and company seizing the moment to deliver on the AI promise for its clients?

[00:12:31]  Speaker E 
Well, it takes a village. So one of the things we're doing is we're partnering with Salesforce because we need to make the great pivot of strategy execution to results.

[00:12:39]  Speaker A 
I love it. Thank you so much. Lorena Yee, senior partner at McKinsey. Thanks.

[00:12:43]  Speaker E 
Thank you so much.