Artificial intelligence (AI) has changed workflows and made for more efficient business outcomes across industries, but some roles and departments are only beginning to see how AI can support them.
Conversational AI is a growing field in which artificial intelligence and machine learning (ML) tools provide contextual — and sometimes real-time — assessments of what’s happening in a conversation, and how either party can respond.
Gryphon.ai is one pioneering company in the conversational AI space, working to make a tool that helps salespeople and marketers to know their buyers better, while also creating a space for live sales training and sentiment analysis.
Greg Armor and Michelle Tilton from Gryphon.ai recently spoke with CIO Insight about how Gryphon.ai is supporting sales teams internally and externally, as well as some of their predictions about the potential for conversational AI in the future.
CIO Insight: What do you do in your current role at Gryphon.ai?
Armor: My name is Greg Armor, and I’m the Executive Vice President at Gryphon.ai. I oversee a majority of the daily functionality of our company from a revenue standpoint. So anything that has any type of revenue touch comes through me. What I mean by that is marketing, customer success, implementation, help desk, sales, enterprise and commercial sales, business development, sales enablement, sales operations. And then I also oversee Product.
Tilton: My name is Michelle Tilton, and I’m the VP of Marketing at Gryphon.ai. I primarily focus on demand gen, operations, brand, content, and thought leadership for the company.
CIO Insight: What does Gryphon.ai do as a company? What products or services do you primarily offer?
Armor: What we build here at Gryphon.ai is a little bit different than most technology companies. We’re the alpha utilizer of all the technology that we build. So when we’re building technology, we like to say we build technology for sales teams, by sales leaders. It’s because it comes to us, we play with it, we tweak it, we go back to Product, then Product makes it go on.
Gryphon is in the sales enablement/sales acceleration space. We focus very heavily on guided selling, leveraging AI and ML in real time to offer up advice, to offer up objection handling, to offer up competitive analysis to somebody in the moment, whereas other technology that’s out there is after-the-fact, or a game film kind of technology.
“We focus very heavily on guided selling, leveraging AI and ML in real time to offer up advice, objection handling, and competitive analysis.”
Ours does that, too, but more importantly, leverages the power of AI and ML to do that in real time so a user, a salesperson, a customer service rep, a collection agent, or any type of agent that’s handling a help desk conversation has the necessary information for them to be more efficient and effective in the moment.
An Insider Perspective on AI
CIO Insight: How did you first develop an interest in artificial intelligence? How has the AI industry changed since you first started engaging with it?
Armor: I chose to take the career path here to Gryphon.ai because AI and ML are leveraged and utilized by every company. Tech companies use it to build better technology. Other companies use it to produce better results. AI and ML are infused into the DNA of very large organizations.
But not in sales, right? There wasn’t a need to have AI introduced into the sales environment. Technology, pharma, manufacturing: these are all major utilizers of AI, robotics, all of that. And so when I think about the areas of growth that I wanted to be involved and engaged in, AI is that evolution.
I started in mobility, I moved into cybersecurity, and then artificial intelligence is obviously the hottest space that you could possibly be in — because it allows companies to make better decisions with more information in real time.
And now, I take my passion for what I’ve been doing as my career. My entire career has been in managing and building sales organizations, and being able to leverage AI and ML to have them perform, produce, and overachieve is just an added benefit to the growth of technology in the sales space.
Tilton: I actually came from the data and analytics space, which is why discussions about unstructured data are super interesting. [My previous company] actually ran AI and ML models for the data and the companies that we work with in the data industry.
So for instance, the company that I came from owned very large data sets that they sold to many of the largest credit bureaus and financial institutions in the country. And we also sold large datasets to companies that were developing AI models.
What was fascinating to me was that good training data for a machine to learn from was so critical. I was fascinated by that: how the foundational data that you put into your machine to learn, to train, creates more accurate outputs, and produces more accurate intelligence. So I think that’s where my passions come from.
Gryphon was super appealing to me when I came over here about a year ago, because it combined really cool tech that I was already using in other roles with this understanding that the AI could produce so much more.
Trends in Conversational AI
CIO Insight: Why do you think conversational AI can make such a difference in marketing and sales?
Armor: I think that conversation in AI is the needle in the haystack. So you can gather as much information as you want about the conversation, but if you can’t provide any value of what took place in that conversation to the person who’s analyzing it, then you’re missing the boat, or you’re missing the gap. The power of artificial intelligence around the conversation is the information that it gives back to the person who’s receiving, listening, or utilizing that type of conversation.
Our power at Gryphon is that needle in the haystack: it’s giving better information so that leaders and sellers can communicate better and provide more value to their clients. It provides more value to the actual customer that is buying their technology. It allows you to pick up on things that are missed, whether you’re not paying attention, or you’re taking too many notes, or the conversation’s moving too fast.
“It allows you to pick up on things that are missed, whether you’re not paying attention, or you’re taking too many notes, or the conversation’s moving too fast.”
The artificial intelligence that exists helps you not only understand the conversation and analyze the conversation, but provide more value back to the customer, to the buyer, and also to your own company.
CIO Insight: What’s happening now in the world of conversational AI?
Tilton: I would say it’s really interesting in terms of conversational AI, because it is changing so quickly. DeepMind Gopher is part of that, and they’re part of Google. Without going into too many details, we partner very heavily with partners like Google who are leading in the space. So it’s shifting so quickly and developing so fast, that it’s so much fun to see things like sentiment starting to come up on calls with analysts.
How do you pick up, with AI tools like DeepMind Gopher, things like sentiment? How do you know how someone’s feeling on a call and how to respond? These are some of the coolest new things that we’re starting to see.
Customer success is becoming increasingly interested in things like sentiment analysis; it’s going to go as far as body language soon, with video. And I think that’s where AI is really getting cool, and it’s some of the stuff that we’ll start to see on our own roadmap in the future.
Armor: I think Michelle’s right: the evolution of conversation intelligence moves to biometrics. And since you can’t be in front of a room or see everybody that’s on a Zoom call, it’s good to have something else analyzing the room, the conversation, the body language, because it provides more value back to the person who’s doing the communicating.
The best thing about conversations is they go two ways. If you can get more value out of the conversation, it’s more valuable for you, and it’s more valuable to the people that you’re communicating to. Conversation doesn’t get lost in the AI space; it actually enhances it.
Tilton: And I think that’s the most important part of this. We talk to a lot of people who are assuming we’re in AI to replace the human connection. And it’s very much the opposite: it’s to enhance the human connection.
“We talk to a lot of people who are assuming we’re in AI to replace the human connection. And it’s very much the opposite: it’s to enhance the human connection.”
Because there are tools, AI-powered tools, that can remove manual work from a sales rep’s role, things like note taking, it’s so much easier to hit a button to record, because then you don’t have to be worried about notes. Now you can focus on the conversation. And that’s what our goal is: to provide that tool that enables people to be really having meaningful conversations, so that they can close deals or progress customer success issues a lot quicker.
Armor: Salespeople just want to sell, right? They don’t want to be bogged down with entering stuff into Salesforce. They don’t want to be bogged down with writing notes and filling that in and tying things to an opportunity. And they’ll forget things, right? They’ll forget some pieces of that conversation. With the AI technology that we have, we wanted to cut down on the mundane aspects of roles that require a lot of paper trail.
Data is extremely important when it comes to our industry. Metrics are extremely important. And AI can take some of that mundane work away from people and allow that to be automated, put into the right place, and then leveraged and utilized by other different areas of the business, without having to have five or six different conversations about the same thing that you just did. It’s all there, it’s all inputted, it’s all done for you. It’s all automated, and you can go on with having better conversations.
Conversational AI Use Cases
CIO Insight: What are some additional use cases for conversational AI?
Tilton: I think just one other area I want to touch on is we’re seeing a lot of corporations as a whole, not just on the sales side, leveraging conversational AI in order to track competition in pricing.
So think about how many agents you are having conversations with, let’s just say insurance: how many people on the phone say, ‘well, I just got an offer from your competitor at this rate.’ We can tag all of those things and then inform future pricing and discount strategies in order for corporate structures, product departments, things like that, to make better offers so that they can be more successful.
So even on that side of the house, for product pricing, marketing even, we’re seeing huge growth in use cases outside of just the sales and customer success arena.
Storage, Compliance, and Security Concerns
CIO Insight: How does Gryphon approach storage for the types of data that it collects, particularly for future or executive use?
Tilton: All of the data is stored at the tenant (client) level and is encrypted at rest and in transit. Gryphon performs extensive security audits, including audits for the largest financial firms in the country, to ensure the highest degree of data protection.
Armor: More importantly, what I do think is we have the ability to not even store data at all. So we can use conversation AI in the moment and not have the need to record that conversation at all. And that’s a big differentiator in the market because there are a lot of different laws that are coming into play.
Tilton: Part of ethical AI is ensuring that sensitive information about a consumer, as you would protect it in any other data environment, is being protected within the AI tools also.
Conversational AI Implementation: Advice for Leadership
What advice would you give to a company’s leadership that’s working to incorporate conversational AI for the first time?
Tilton: So one of the biggest things that come out of conversations we have with leadership is who owns the conversations that you’re having with the customers? Is it your top salesperson? Or is it the company itself?
Intellectual property of conversations with clients, customers, and individuals: that’s company intellectual property. And if a salesperson leaves, you don’t get that intellectual property back.
“Intellectual property of conversations with clients, customers, and individuals: that’s company intellectual property. And if a salesperson leaves, you don’t get that intellectual property back.”
You can capture emails, right? You can tag emails, you can forward someone’s email if they leave the company, you have a history of that. But you don’t have a history of conversations. And those are really valuable, especially if you have a top salesperson leave the company. All of that intellectual property goes away, and then you’re starting to train other individuals from scratch, or from a middle level, when you had this great intellectual property.
So I would say: own your own intellectual property at the company and understand the conversations that are happening. Even in the C-Suite, knowing the conversations that are occurring at the street level is so important to inform business decisions, and you’re really left in the dark unless you’re speaking with customers yourself or capturing voice-related conversations with conversational AI.
Armor: I preach that the biggest IP every company has is the knowledge of their employees. I’ve been running sales organizations for quite some time, and great salespeople hate this technology; it makes them feel as if they’re obsolete because now they can take everything that they know, and everybody else has it now.
From an executive side, I would say more and more companies are being driven by data-driven and metrics-driven conversations and analytics as to how they run their business. You can now analyze conversations and it provides you with better data and better metrics.
So as an executive leadership team, you’re making informed decisions, not just based on hopes and thoughts, but on actually data-processed information that allows you to become more accurate in how you move your business in one direction or another. When you have real data that conversation intelligence drives, you’re able to have a better picture and make a more informed decision. And that’s how we use it.
Future Changes and Challenges for Conversational AI
CIO Insight: How do you expect to see this field evolve in the next year, or five years?
Armor: Biometrics, absolutely. Being able to leverage facial recognition and body language. Take AI and say, ‘hey, that person smiled’ or ‘that person didn’t like what you were talking about.’ Anything that can provide more value to help people communicate better.
In my opinion, this is about communication and communication skills, and the digital transformation that is happening out there doesn’t allow for the human interaction that has always taken place throughout the history of the world. And so if we can leverage technology that provides value on what’s taking place in a conversation during a digital conversation, then I think that’s probably the biggest area of investment for us as we enter into 2023 and beyond.
“The digital transformation that is happening out there doesn’t allow for the human interaction that has always taken place throughout the history of the world.”
And then making sure that the compliance aspect and the security aspect continue to evolve because we’re just getting started with the different regulations and different mandates that are going to continue to happen. I think Michelle brought up a really strong point around integrity and ethics. Providing value to people without crossing that line is extremely important because AI makes people nervous.
CIO Insight: What AI challenges do you see companies facing now and what challenges do you predict for the future?
Tilton: I think for conversational AI as a whole, there’s a lot of companies out there that say ‘I have AI, I have ML, we have AI,’ but not a lot of them actually do. A lot of them have what we would call replies or responses to keywords.
I think being able to continuously develop AI, so it understands context, is super important. For instance, in our world, if I say something like ‘tell me about your competition,’ that’s an easy keyword search. But if I start asking things like ‘tell me about some of the other options out there,” you have to be able to equate what’s being said with that person’s true query and understand the context of the conversation.
I think that’s the main challenge that we’re seeing with people who are trying to replicate conversational AI solutions on their own. They don’t realize it’s actually about natural language processing. Why the right tools are so powerful is because they take the sentiment and actual context of the sentence, the paragraph, or whatever the conversation is, and translate that into something meaningful that can predict a response or help you send a response to better answer the question.
“Why the right tools are so powerful is because they take the sentiment and actual context of the sentence … and translate that into something meaningful.”
I think understanding the use cases is really important, too, for how AI can help. AI is not meant to be a crutch; it’s not meant to regurgitate things, and have you read a script; it’s meant to be an assistance to you, above and beyond what you’re capable of doing. I think it’ll be interesting to see how AI continues to evolve as companies like Google continue to advance their AI, too.