May 6, 2025 • 23 mins
Join Andrew Wesbecher (Powered_By) and Michael Krigsman on CXOTalk episode 878 to discuss how AI agents can help small businesses succeed in 2025. Learn from industry experts and discover the benefits of using AI technology for your SMB to stay ahead of the competition!
Learn more from www.poweredby.agency
===================
🔔 Don’t forget to like, subscribe, and share for more thought-provoking conversations with top industry leaders.
🔷 Newsletter: www.cxotalk.com/subscribe
🔷 Read the summary and key points: https://www.cxotalk.com/episode/ai-agents-for-small-business-winning-in-2025
🔷 LinkedIn: www.linkedin.com/company/cxotalk
🔷 Twitter: twitter.com/cxotalk
00:00 Democratizing AI for Small to Medium-Sized Businesses
02:38 Technical Architecture and AI Agent Implementation
07:59 Use Cases and Ethical Considerations
11:14 The Evolution and Capabilities of AI Agents
13:27 Practical Use Cases and Optimization of AI Agents
18:43 ROI and Democratization of AI Agents for SMBs
#aiagents #smb #smallbusiness #cxotalk
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
What does it really mean to democratize AI?
Andrew Wesbecker, founder of Powered By, reveals how SMBs can
deploy 24/7 AI agents. Powered By Builds AI agent
solutions for small to medium sized businesses across several
agent modes, including voice, phone, text, e-mail and digital
(00:26):
avatars. AI agents automate tasks that
historically have been performedby humans can.
You give us some concrete examples.
Let's say, for example, a hotel was answering a bunch of phone
calls at their various differentproperties to book reservations
from their clients or to check on reservation status or
(00:49):
concierge service. That typically is done by a
human today that is picking up the phone and handling that
interaction with those customersor prospective customers today.
Based on the advances made in voice agent technology, where
the state-of-the-art is such that it's almost
indistinguishable between speaking to a human and speaking
(01:09):
to an AI agent. The hotel, for example, could
front all of their customer facing phone interactions with a
astonishingly human like AI agent that could, for example,
answer the phone, ask customer question or answer customer
questions, book reservations on specific dates for specific
rooms, upgrade activities, or offer concierge services.
(01:32):
Or if the organization of the hotel wanted to have an AI agent
perform in room dining activities, the of hotel guests
inside the room could dial A number.
Instead of speaking to a human, they would speak to an AI agent
that would take their order and process the order and have it
delivered. I think everybody wants to know
how well does this work. The sophistication of AI agents
(01:56):
across the modes that I mentioned, voice AI, e-mail AI,
text AI, is to the point where it's about 85 to 90% away from
being indistinguishable from howa human behaves specifically in
voice. In on our website, we have a
couple of examples where you cansample this.
(02:17):
The voice quality tricks your brain into thinking as to
whether or not and questioning yourself whether or not you're
actually speaking to a human or in fact, speaking to an AI
agent. I tried the demo on your
website. I was really impressed.
You sort of get engaged in this conversation with this person
that's not really a erson. Our intellectual property is is
(02:40):
using the foundational AI LLM models and then building all of
the sophistication around text to speech, speech to text, real
time translation, real time transcription to make the
interactions with a user as human like as possible.
So you begin with the LLM and then you're layering various
(03:04):
other technologies on top of it in order to make it useful as an
agent for your customers. Yeah, Architecturally, just a
quick summary, at the bottom of our architecture is the LLM and
we dynamically switch from whatever LLM that best suits our
need, whether it's low latency, high quality, best voice
quality, lowest cost. We can dynamically switch from
(03:27):
GPT 4 dot O Mini to Claude Sonnet 37 to Meta Llama 2, all
very dynamically on top of that.Then you have a proprietary
prompt layer that allows us to train the agent based on their
specific use case. We build a training module that
gives the AI agent in whatever mode that it's operating in,
(03:48):
whether it's voice, e-mail, text, or digital avatars, the
instructions that guides its behavior.
And then we give it a knowledge base, a series of articles,
spreadsheets and other documentsthat we use, a technology called
RAG that the agent in real time,in addition to the prompt, can
(04:08):
inspect this knowledge base to inform the types of responses
that it has to customer questions and the type of
behaviors it carries out with the lowest latency possible.
On top of the prompt layer, thenwe implement the natural
language processing layer, wherewe have a variety of open source
and in source or proprietary tools that we use for text to
(04:32):
speech, speech to text, real time translation currently
across 15 languages and real time transcription on top of
that. Then we have the action layer
that takes all of the underlyingprompt model, the knowledge
base, the text to speech, the speech to text, all of the
natural language processing capabilities.
And then we automate the behaviour of the various
(04:55):
different agent through the modes that we want to it to
operate in, whether it's, as mentioned, voice, phone, e-mail,
text or digital avatars. What are the real benefits and
the real value for small to mid sized companies?
It's a variety of things. We really focus today on
(05:16):
customer engagement are wedge into our customers is all around
reducing your total cost of engaging with customers through
the various different means thatyou engage with customers today
while also improving the customer experience.
And so our primary value proposition is today you employ
(05:40):
humans to do a lot of menial repetitive tasks that can
otherwise be automated through AI agents in voice, phone, text,
etcetera. That then allows you to
reallocate your human staff members to higher priority tasks
or higher value priorities that you currently don't have the
(06:02):
bandwidth for. You can then allocate those
humans to focus on those activities and have the lower
level or repetitive tasks managed by the AI agents.
All that can be done 24/7 in themost human like manner possible.
What is the process for implementing these kinds of
(06:26):
agents as you were just describing?
We approach each customer, delivering them a turnkey AI
agent solution. We do all of the design, the
building, the systems integration where the the
customer has very little effort in the actual implementation and
management of these various different AI agents.
(06:47):
And that's sort of our key differentiation is that there
are a lot of vendors in the AI agent marketplace, which today
primarily sell dev tools to their customers for them to
build their own AI agents. For small to medium sized
businesses that don't necessarily have the internal
development expertise or AI knowhow to use and onboard those
(07:13):
developer tools. We take that out of the picture
and we build all of our solutions custom and deliver it
as a turnkey solution based on the customer's requirements and
business needs. We do that for you and we take
your requirements, we take your work flows, we take which modes
you want to operate in and buildthat and deliver that to you
(07:35):
without you needing to hire additional staff members,
additional AIML engineers that that a small to medium sized
business would wouldn't otherwise have.
So there's a configuration process, you speak with the
customer, you spend time, you understand their business, and
then you configure your solutions to adapt to what will
be most beneficial for them. Exactly.
(07:59):
You mentioned hospitality earlier, hotel chain, as an
example of where these agents might be useful, can you give us
some other use cases? Let's take an auto dealership.
Hypothetically, they have 35 locations across the state of
Florida. They employ over 200 sales reps
responsible for selling the various different automobiles.
(08:22):
Let's say that dealership also has 2500 leads in their CRM of
all of the individuals that haveeither come into the dealership,
visited their website and have opted in to receive marketing
and sales information from that dealership.
We have a module called OutboundAI that instantaneously you
(08:46):
could contact all 2500 of those leads in your marketing database
through phone calls, through emails and through texts to
promote, for example, a spring sales event at the dealership.
Let's say that dealership has 15% off MSRP of their latest
Mercedes models. They can have a voice call where
(09:07):
in the most human like astonishingly human like manner.
Me as a customer who I've opted in to receive marketing
information from that dealershipwill pick up the phone and on
the other end of the phone we'll, we'll sound like a human
and say hi. This is Dave Frankel from
Mercedes of Tacoma, WA. I understand that you've been in
the dealership before and wantedto let you know that we're
(09:28):
offering a spring sales event where we're having 15% off of
our MSRP. I'd like to see if you'd like to
schedule a time to come into thedealership and look at the
latest models and that agent then will handle all of the
booking reservation and scheduling for that human to
come into the dealership to drive any vehicle or all of the
(09:50):
vehicles of the customers choosing that same workflow
operates also in e-mail and text.
Now the biggest caveat here of using this today is the AI agent
cannot call on individuals that are not opted in to receive
(10:12):
sales or marketing information from the business.
How do you ensure that you are frankly not spamming customers?
We have built the Powered by outbound AI module to adhere to
all of the privacy regulations associated with the 1991 Act,
(10:32):
the TCPA Act, which prohibits bots or AI agents from cold
calling on individuals that haven't opted in to receive
marketing and sales information from a get in business that is
not permitted use for our product here at Powered By.
Andrew, it sounds like the key piece here is the dynamic
(10:56):
interactive aspect because of course, personalization by
e-mail or less so by phone calls, but even to some extent
by phone calls has been around in CRM systems for a long time.
But you're going far beyond personalization alone.
Exactly. And the reason for that, it's
(11:16):
not only personalized, but the textbook definition of an AI
agent is that it interacts with its environment and learns how
to become better at its task with the more interactions that
it has with memory. With AI agent memory, we can
also establish a long tail of that AI agent knowing who that
(11:41):
individual they are speaking to or emailing with or texting
with. Just like a human would.
The AI agent can have context into the conversations that it
has had previously. And so with this memory and this
kind of engagement, if you have,for example, a natural sounding
voice, that conversation from the point of view of the
(12:04):
customer will seem very comfortable and very intuitive.
That's the intent. So it's really the packaging of
all of these elements together that creates the unique
experience that you've been describing.
Yeah. And one unique element is that
we can stack the agents, which is you can have a voice
conversation with a astonishingly human like voice
(12:27):
agent that can then carry over to the same person in e-mail,
you know, nick@whatevercompany.com, who
then emails you to schedule appointment to then ensure that
you've got the right documentation signed and then
carry that into a text for further correspondence and
confirmation of appointments. So it's not just I isolated over
(12:48):
the vote voice chat. We can stack the agents for a
single vertical conversation or single vertical engagement with
a given customer. So it goes way beyond the
traditional notion of personalization, which compared
to this is extremely simplistic.Exactly.
And that's because textbook definition is that the AI agent
(13:11):
learns over time, and the foundation of these LLMS is
machine learning and machine intelligence.
And the more interaction it has with a given customer, the more
it can improve and tailor its interactions with that given
customer. Can you give us another example
or use case of AI agents in practice for small and medium
(13:35):
companies? Certainly.
So let's say for example, I'm a health clinic and I have, I'm a
paediatrician and I have 15 locations across the state of
Texas. And today I book appointments
either online or over the phone.There are a variety of questions
that a customer is going to have, Either they're an existing
(13:58):
patient or a new patient. When they pick up the phone and
they call the pediatrician's office, they want to know what
availability do you have? What insurance do you take?
What's the mechanism for gettingapproved by insurance to, to
book an appointment or book a, a, a medical doctor's
appointment for my child? All of that today is very human
(14:20):
intensive. And if you multiply that out
across a pediatrician's office, a large pediatrician's office
that may have 10 or 15 offices, they may have 5 or 6 or 7
receptionists or reservations managers that are responsible
for fielding all of these calls today, right now, that can be
automated completely in a HIPAA compliant manner such that the
(14:43):
AI agent can ask the individual's name, their child's
name, ask what type of insurancethey have, ask for the insurance
ID, ask for when they want to schedule a booking appointment
for. All of that can be done.
And then stacked from a voice call to an e-mail that sends a
calendar invite to confirm the booking and then a text reminder
(15:06):
a day before to confirm or reconfirm that that appointment
is occurring. Well, let me give you another
example. Yesterday, I had an unfortunate
experience trying to get a refund from a major airline that
changed something on my reservation that I felt that I
was owed a refund. I try to do so on their website
through their chatbot. Their chat bot is all script
(15:27):
based so you can't ask any questions with a lot of context.
It only follows a script. Additionally, when you want to
move that to a phone call, I wasin a phone queue, an IVR phone
tree for about 20 minutes beforeI could speak to somebody to
obtain my refund. With an AI agent you could do
that instantaneously 24/7 with phone and voice quality that is
(15:53):
remarkably human like, that can act and process very
sophisticated requests on the behalf of.
The customer, we touched on thisearlier, but again, how well
does it work? That's the magic question from
the customer standpoint. The more information that we get
(16:14):
from the customer on their workflow, if it's a voice agent,
often times we ask for recorded calls that they've had with
customers in the past to diagnose where a good call,
what's the, what's the example in workflow of a good customer
interaction. We also asked for examples of
where you've had perhaps unfortunate interactions where
(16:38):
customers hung up or been angry for the service that they've
been for. Provided we can extract that
information, obviously sanitize it and anonymize it to train how
the AI voice agent can operate in a manner to optimize the good
and and de optimize the bad. Additionally, we also ask for
(17:00):
FAQs and is there a certain amount of questions commonly
that you're asked? What are the responses commonly
to those questions? We build that into our knowledge
base. We also ask for a bunch of
documents, whether it's a menu, a catalog of services or
products, an inventory that cannot you know, it can be a
dynamic inventory in a spreadsheet or some sort of
(17:22):
service that we can put an API Ihook into.
It could be a static menu or static inventory.
The more information that we have that is accessible today by
the human staff member that is currently handling those calls,
emails or texts, if we get that information, we form the AI
prompt and our knowledge base tomake the AI agent as capable or
(17:48):
even more capable than a human because it's knowledge base is
limitless. So you can adopt the style or
tone or cultural approach. We could even say that a
particular organization maintains towards its customers.
(18:09):
Let's say, for example, we're working with a stylish, hip,
millennial or Gen. Gen.
Z focused hotel chain. We want the voice interaction,
the e-mail interaction to be more representative of that
brand, of that style of being hip and cool.
However, if we're dealing with avery staid insurance company,
that's by the book, you know, nohumor.
(18:30):
We only do what we say we shoulddo in the instructions.
We can tailor the behavior of the agent based on the brand or
the guidance of the the company that is using the.
Agent. What about ROI measurement?
How can an organization measure the results of an AI agent?
(18:52):
The first area is productivity increases and the reason for
that is an AI agent operates 24/7 tirelessly.
No breaks, no coffee breaks, no lunch, no stepping out for a
walk. So you're able to do things with
full utilization of an AI agentstime 24 by 7.
(19:13):
So what typically is performed by humans in a nine to five or
an 8 to 4 type of environment, we can spread that to 24 hours.
And so you're able to perform customer engagement activities
in a given day almost twice the amount of customer engagement
activities that you would otherwise be able to do with a
(19:37):
team of of human staff members. So the first first point of ROI
is productivity increases in terms of the time those
activities can be performed in agiven day.
The second is revenue growth. This also unlocks a series of
new opportunities to sell, market and upsell your customers
(19:58):
at as evidenced in the auto dealership example where you can
launch phone calls, emails and texts automatically and all
simultaneously to all 2500 leadsin your auto dealership lead opt
in database to convince them to come in the to the dealership to
visit. For their new spring sales
(20:19):
event. And the third is cost savings,
which is you can reallocate yourhuman staff members to, you
know, other priorities or let's say you have a shortfall in in
certain areas of your business and you can't hire more people
to make up for that shortfall. You can reallocate those
individuals that are currently doing these menial, I should say
(20:40):
labor intensive tasks on the phone, on e-mail, on text.
You can reorient those individuals to those on server
or lower, serve lesser served areas and have the AI agent
performed the labour intensive menial tasks on phone, voice,
e-mail, SMS text, etcetera etcetera.
(21:01):
And in fact, what you're really saying is the measures are not
much different from the ROI analysis that you would place on
people doing their jobs. A. 100%, 100%.
Andrew, you mentioned earlier that you're trying to
democratize AI agents. Can you elaborate on that?
(21:23):
What did you mean? The magnitude of the importance
that SM BS have on the US economy is enormous.
We believe that they should havethe right to access the same
innovative AI agent technology that the largest Fortune 100,
Fortune 500 companies are currently implementing right
(21:43):
now, but are out of reach to a small auto dealership or real
estate agency or law firm or accounting firm because they
don't have the AI engineers, They don't have the know how in
terms of AI agent technology andmost importantly, they don't
have the budget. We build custom solutions
designed to fit SMBs staff resources, their understanding
(22:09):
and knowledge of AI agent technology and and most
importantly, we operate and deliver solutions affordably
tailored to the budget of small to medium sized businesses.
That's our mission. Can you offer practical advice
for business owners who want to become more involved using AI
(22:29):
agents in their organizations? You have to start small and we
really recommend that you look at where your primary use cases
would be across the modes that we operate in voice, phone
assistance, e-mail, text, digital avatars.
We want you to become a fan of AI agent technology and we want
(22:51):
to delight your customers and wewant your customers to come to
you and say, wow, that was a really unique experience with
that voice agent that I just spoke to.
Or they say I had a very enjoyable conversation with your
assistant on the phone, not evenknowing that it wasn't a human.
Andrew Wesbecker, founder of Powered by Thank you so much for
(23:12):
taking time to chat with us. Thank you, Michael.
Appreciate it. We'll see you next time.
What does it really mean to democratize AI?
Andrew Wesbecker, founder of Powered By, reveals how SMBs can
deploy 24/7 AI agents. Powered By Builds AI agent
solutions for small to medium sized businesses across several
agent modes, including voice, phone, text, e-mail and digital
(00:26):
avatars. AI agents automate tasks that
historically have been performedby humans can.
You give us some concrete examples.
Let's say, for example, a hotel was answering a bunch of phone
calls at their various differentproperties to book reservations
from their clients or to check on reservation status or
(00:49):
concierge service. That typically is done by a
human today that is picking up the phone and handling that
interaction with those customersor prospective customers today.
Based on the advances made in voice agent technology, where
the state-of-the-art is such that it's almost
indistinguishable between speaking to a human and speaking
(01:09):
to an AI agent. The hotel, for example, could
front all of their customer facing phone interactions with a
astonishingly human like AI agent that could, for example,
answer the phone, ask customer question or answer customer
questions, book reservations on specific dates for specific
rooms, upgrade activities, or offer concierge services.
(01:32):
Or if the organization of the hotel wanted to have an AI agent
perform in room dining activities, the of hotel guests
inside the room could dial A number.
Instead of speaking to a human, they would speak to an AI agent
that would take their order and process the order and have it
delivered. I think everybody wants to know
how well does this work. The sophistication of AI agents
(01:56):
across the modes that I mentioned, voice AI, e-mail AI,
text AI, is to the point where it's about 85 to 90% away from
being indistinguishable from howa human behaves specifically in
voice. In on our website, we have a
couple of examples where you cansample this.
(02:17):
The voice quality tricks your brain into thinking as to
whether or not and questioning yourself whether or not you're
actually speaking to a human or in fact, speaking to an AI
agent. I tried the demo on your
website. I was really impressed.
You sort of get engaged in this conversation with this person
that's not really a erson. Our intellectual property is is
(02:40):
using the foundational AI LLM models and then building all of
the sophistication around text to speech, speech to text, real
time translation, real time transcription to make the
interactions with a user as human like as possible.
So you begin with the LLM and then you're layering various
(03:04):
other technologies on top of it in order to make it useful as an
agent for your customers. Yeah, Architecturally, just a
quick summary, at the bottom of our architecture is the LLM and
we dynamically switch from whatever LLM that best suits our
need, whether it's low latency, high quality, best voice
quality, lowest cost. We can dynamically switch from
(03:27):
GPT 4 dot O Mini to Claude Sonnet 37 to Meta Llama 2, all
very dynamically on top of that.Then you have a proprietary
prompt layer that allows us to train the agent based on their
specific use case. We build a training module that
gives the AI agent in whatever mode that it's operating in,
(03:48):
whether it's voice, e-mail, text, or digital avatars, the
instructions that guides its behavior.
And then we give it a knowledge base, a series of articles,
spreadsheets and other documentsthat we use, a technology called
RAG that the agent in real time,in addition to the prompt, can
(04:08):
inspect this knowledge base to inform the types of responses
that it has to customer questions and the type of
behaviors it carries out with the lowest latency possible.
On top of the prompt layer, thenwe implement the natural
language processing layer, wherewe have a variety of open source
and in source or proprietary tools that we use for text to
(04:32):
speech, speech to text, real time translation currently
across 15 languages and real time transcription on top of
that. Then we have the action layer
that takes all of the underlyingprompt model, the knowledge
base, the text to speech, the speech to text, all of the
natural language processing capabilities.
And then we automate the behaviour of the various
(04:55):
different agent through the modes that we want to it to
operate in, whether it's, as mentioned, voice, phone, e-mail,
text or digital avatars. What are the real benefits and
the real value for small to mid sized companies?
It's a variety of things. We really focus today on
(05:16):
customer engagement are wedge into our customers is all around
reducing your total cost of engaging with customers through
the various different means thatyou engage with customers today
while also improving the customer experience.
And so our primary value proposition is today you employ
(05:40):
humans to do a lot of menial repetitive tasks that can
otherwise be automated through AI agents in voice, phone, text,
etcetera. That then allows you to
reallocate your human staff members to higher priority tasks
or higher value priorities that you currently don't have the
(06:02):
bandwidth for. You can then allocate those
humans to focus on those activities and have the lower
level or repetitive tasks managed by the AI agents.
All that can be done 24/7 in themost human like manner possible.
What is the process for implementing these kinds of
(06:26):
agents as you were just describing?
We approach each customer, delivering them a turnkey AI
agent solution. We do all of the design, the
building, the systems integration where the the
customer has very little effort in the actual implementation and
management of these various different AI agents.
(06:47):
And that's sort of our key differentiation is that there
are a lot of vendors in the AI agent marketplace, which today
primarily sell dev tools to their customers for them to
build their own AI agents. For small to medium sized
businesses that don't necessarily have the internal
development expertise or AI knowhow to use and onboard those
(07:13):
developer tools. We take that out of the picture
and we build all of our solutions custom and deliver it
as a turnkey solution based on the customer's requirements and
business needs. We do that for you and we take
your requirements, we take your work flows, we take which modes
you want to operate in and buildthat and deliver that to you
(07:35):
without you needing to hire additional staff members,
additional AIML engineers that that a small to medium sized
business would wouldn't otherwise have.
So there's a configuration process, you speak with the
customer, you spend time, you understand their business, and
then you configure your solutions to adapt to what will
be most beneficial for them. Exactly.
(07:59):
You mentioned hospitality earlier, hotel chain, as an
example of where these agents might be useful, can you give us
some other use cases? Let's take an auto dealership.
Hypothetically, they have 35 locations across the state of
Florida. They employ over 200 sales reps
responsible for selling the various different automobiles.
(08:22):
Let's say that dealership also has 2500 leads in their CRM of
all of the individuals that haveeither come into the dealership,
visited their website and have opted in to receive marketing
and sales information from that dealership.
We have a module called OutboundAI that instantaneously you
(08:46):
could contact all 2500 of those leads in your marketing database
through phone calls, through emails and through texts to
promote, for example, a spring sales event at the dealership.
Let's say that dealership has 15% off MSRP of their latest
Mercedes models. They can have a voice call where
(09:07):
in the most human like astonishingly human like manner.
Me as a customer who I've opted in to receive marketing
information from that dealershipwill pick up the phone and on
the other end of the phone we'll, we'll sound like a human
and say hi. This is Dave Frankel from
Mercedes of Tacoma, WA. I understand that you've been in
the dealership before and wantedto let you know that we're
(09:28):
offering a spring sales event where we're having 15% off of
our MSRP. I'd like to see if you'd like to
schedule a time to come into thedealership and look at the
latest models and that agent then will handle all of the
booking reservation and scheduling for that human to
come into the dealership to drive any vehicle or all of the
(09:50):
vehicles of the customers choosing that same workflow
operates also in e-mail and text.
Now the biggest caveat here of using this today is the AI agent
cannot call on individuals that are not opted in to receive
(10:12):
sales or marketing information from the business.
How do you ensure that you are frankly not spamming customers?
We have built the Powered by outbound AI module to adhere to
all of the privacy regulations associated with the 1991 Act,
(10:32):
the TCPA Act, which prohibits bots or AI agents from cold
calling on individuals that haven't opted in to receive
marketing and sales information from a get in business that is
not permitted use for our product here at Powered By.
Andrew, it sounds like the key piece here is the dynamic
(10:56):
interactive aspect because of course, personalization by
e-mail or less so by phone calls, but even to some extent
by phone calls has been around in CRM systems for a long time.
But you're going far beyond personalization alone.
Exactly. And the reason for that, it's
(11:16):
not only personalized, but the textbook definition of an AI
agent is that it interacts with its environment and learns how
to become better at its task with the more interactions that
it has with memory. With AI agent memory, we can
also establish a long tail of that AI agent knowing who that
(11:41):
individual they are speaking to or emailing with or texting
with. Just like a human would.
The AI agent can have context into the conversations that it
has had previously. And so with this memory and this
kind of engagement, if you have,for example, a natural sounding
voice, that conversation from the point of view of the
(12:04):
customer will seem very comfortable and very intuitive.
That's the intent. So it's really the packaging of
all of these elements together that creates the unique
experience that you've been describing.
Yeah. And one unique element is that
we can stack the agents, which is you can have a voice
conversation with a astonishingly human like voice
(12:27):
agent that can then carry over to the same person in e-mail,
you know, nick@whatevercompany.com, who
then emails you to schedule appointment to then ensure that
you've got the right documentation signed and then
carry that into a text for further correspondence and
confirmation of appointments. So it's not just I isolated over
(12:48):
the vote voice chat. We can stack the agents for a
single vertical conversation or single vertical engagement with
a given customer. So it goes way beyond the
traditional notion of personalization, which compared
to this is extremely simplistic.Exactly.
And that's because textbook definition is that the AI agent
(13:11):
learns over time, and the foundation of these LLMS is
machine learning and machine intelligence.
And the more interaction it has with a given customer, the more
it can improve and tailor its interactions with that given
customer. Can you give us another example
or use case of AI agents in practice for small and medium
(13:35):
companies? Certainly.
So let's say for example, I'm a health clinic and I have, I'm a
paediatrician and I have 15 locations across the state of
Texas. And today I book appointments
either online or over the phone.There are a variety of questions
that a customer is going to have, Either they're an existing
(13:58):
patient or a new patient. When they pick up the phone and
they call the pediatrician's office, they want to know what
availability do you have? What insurance do you take?
What's the mechanism for gettingapproved by insurance to, to
book an appointment or book a, a, a medical doctor's
appointment for my child? All of that today is very human
(14:20):
intensive. And if you multiply that out
across a pediatrician's office, a large pediatrician's office
that may have 10 or 15 offices, they may have 5 or 6 or 7
receptionists or reservations managers that are responsible
for fielding all of these calls today, right now, that can be
automated completely in a HIPAA compliant manner such that the
(14:43):
AI agent can ask the individual's name, their child's
name, ask what type of insurancethey have, ask for the insurance
ID, ask for when they want to schedule a booking appointment
for. All of that can be done.
And then stacked from a voice call to an e-mail that sends a
calendar invite to confirm the booking and then a text reminder
(15:06):
a day before to confirm or reconfirm that that appointment
is occurring. Well, let me give you another
example. Yesterday, I had an unfortunate
experience trying to get a refund from a major airline that
changed something on my reservation that I felt that I
was owed a refund. I try to do so on their website
through their chatbot. Their chat bot is all script
(15:27):
based so you can't ask any questions with a lot of context.
It only follows a script. Additionally, when you want to
move that to a phone call, I wasin a phone queue, an IVR phone
tree for about 20 minutes beforeI could speak to somebody to
obtain my refund. With an AI agent you could do
that instantaneously 24/7 with phone and voice quality that is
(15:53):
remarkably human like, that can act and process very
sophisticated requests on the behalf of.
The customer, we touched on thisearlier, but again, how well
does it work? That's the magic question from
the customer standpoint. The more information that we get
(16:14):
from the customer on their workflow, if it's a voice agent,
often times we ask for recorded calls that they've had with
customers in the past to diagnose where a good call,
what's the, what's the example in workflow of a good customer
interaction. We also asked for examples of
where you've had perhaps unfortunate interactions where
(16:38):
customers hung up or been angry for the service that they've
been for. Provided we can extract that
information, obviously sanitize it and anonymize it to train how
the AI voice agent can operate in a manner to optimize the good
and and de optimize the bad. Additionally, we also ask for
(17:00):
FAQs and is there a certain amount of questions commonly
that you're asked? What are the responses commonly
to those questions? We build that into our knowledge
base. We also ask for a bunch of
documents, whether it's a menu, a catalog of services or
products, an inventory that cannot you know, it can be a
dynamic inventory in a spreadsheet or some sort of
(17:22):
service that we can put an API Ihook into.
It could be a static menu or static inventory.
The more information that we have that is accessible today by
the human staff member that is currently handling those calls,
emails or texts, if we get that information, we form the AI
prompt and our knowledge base tomake the AI agent as capable or
(17:48):
even more capable than a human because it's knowledge base is
limitless. So you can adopt the style or
tone or cultural approach. We could even say that a
particular organization maintains towards its customers.
(18:09):
Let's say, for example, we're working with a stylish, hip,
millennial or Gen. Gen.
Z focused hotel chain. We want the voice interaction,
the e-mail interaction to be more representative of that
brand, of that style of being hip and cool.
However, if we're dealing with avery staid insurance company,
that's by the book, you know, nohumor.
(18:30):
We only do what we say we shoulddo in the instructions.
We can tailor the behavior of the agent based on the brand or
the guidance of the the company that is using the.
Agent. What about ROI measurement?
How can an organization measure the results of an AI agent?
(18:52):
The first area is productivity increases and the reason for
that is an AI agent operates 24/7 tirelessly.
No breaks, no coffee breaks, no lunch, no stepping out for a
walk. So you're able to do things with
full utilization of an AI agentstime 24 by 7.
(19:13):
So what typically is performed by humans in a nine to five or
an 8 to 4 type of environment, we can spread that to 24 hours.
And so you're able to perform customer engagement activities
in a given day almost twice the amount of customer engagement
activities that you would otherwise be able to do with a
(19:37):
team of of human staff members. So the first first point of ROI
is productivity increases in terms of the time those
activities can be performed in agiven day.
The second is revenue growth. This also unlocks a series of
new opportunities to sell, market and upsell your customers
(19:58):
at as evidenced in the auto dealership example where you can
launch phone calls, emails and texts automatically and all
simultaneously to all 2500 leadsin your auto dealership lead opt
in database to convince them to come in the to the dealership to
visit. For their new spring sales
(20:19):
event. And the third is cost savings,
which is you can reallocate yourhuman staff members to, you
know, other priorities or let's say you have a shortfall in in
certain areas of your business and you can't hire more people
to make up for that shortfall. You can reallocate those
individuals that are currently doing these menial, I should say
(20:40):
labor intensive tasks on the phone, on e-mail, on text.
You can reorient those individuals to those on server
or lower, serve lesser served areas and have the AI agent
performed the labour intensive menial tasks on phone, voice,
e-mail, SMS text, etcetera etcetera.
(21:01):
And in fact, what you're really saying is the measures are not
much different from the ROI analysis that you would place on
people doing their jobs. A. 100%, 100%.
Andrew, you mentioned earlier that you're trying to
democratize AI agents. Can you elaborate on that?
(21:23):
What did you mean? The magnitude of the importance
that SM BS have on the US economy is enormous.
We believe that they should havethe right to access the same
innovative AI agent technology that the largest Fortune 100,
Fortune 500 companies are currently implementing right
(21:43):
now, but are out of reach to a small auto dealership or real
estate agency or law firm or accounting firm because they
don't have the AI engineers, They don't have the know how in
terms of AI agent technology andmost importantly, they don't
have the budget. We build custom solutions
designed to fit SMBs staff resources, their understanding
(22:09):
and knowledge of AI agent technology and and most
importantly, we operate and deliver solutions affordably
tailored to the budget of small to medium sized businesses.
That's our mission. Can you offer practical advice
for business owners who want to become more involved using AI
(22:29):
agents in their organizations? You have to start small and we
really recommend that you look at where your primary use cases
would be across the modes that we operate in voice, phone
assistance, e-mail, text, digital avatars.
We want you to become a fan of AI agent technology and we want
(22:51):
to delight your customers and wewant your customers to come to
you and say, wow, that was a really unique experience with
that voice agent that I just spoke to.
Or they say I had a very enjoyable conversation with your
assistant on the phone, not evenknowing that it wasn't a human.
Andrew Wesbecker, founder of Powered by Thank you so much for
(23:12):
taking time to chat with us. Thank you, Michael.
Appreciate it. We'll see you next time.
Popular Podcasts
United States of Kennedy
United States of Kennedy is a podcast about our cultural fascination with the Kennedy dynasty. Every week, hosts Lyra Smith and George Civeris go into one aspect of the Kennedy story.
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com
Bookmarked by Reese's Book Club
Welcome to Bookmarked by Reese’s Book Club — the podcast where great stories, bold women, and irresistible conversations collide! Hosted by award-winning journalist Danielle Robay, each week new episodes balance thoughtful literary insight with the fervor of buzzy book trends, pop culture and more. Bookmarked brings together celebrities, tastemakers, influencers and authors from Reese's Book Club and beyond to share stories that transcend the page. Pull up a chair. You’re not just listening — you’re part of the conversation.