RCM Revenue Cycle Management Medical AI LLM Case Studies

Summary

David Hood, CEO of 42 Robots AI, discusses the transformative impact of AI, particularly in revenue cycle management for healthcare. He outlines the benefits of implementing AI, warns of common pitfalls, and emphasizes the importance of understanding AI capabilities and avoiding a one-size-fits-all approach. Hood shares insights on leveraging large language models for data processing efficiency and stresses the need for a strategic AI implementation roadmap, including assembling the right team with a deep understanding of AI integration.

Takeaways

• 😀 David Hood, CEO of 42 Robots AI, discusses the benefits of implementing AI in revenue cycle management for healthcare organizations.
• 🔍 The video script emphasizes the importance of understanding the capabilities of Large Language Models (LLMs) and their potential in healthcare, especially in data processing and analysis.
• 🚀 Hood highlights a positive feedback loop created by AI implementation, where initial AI use leads to increased understanding and value, helping organizations outcompete their rivals.
• ⏳ He mentions that modern AI tools, particularly LLMs, have significantly advanced, with GPT-4 being a turning point in 2023, and that multimodal capabilities are even more recent.
• 💡 The script points out that while LLMs are broadly applicable, they should be used as tools by traditional software, not as the central solution, to avoid common pitfalls.
• 🛑 Hood warns against relying solely on solutions like Microsoft Co-Pilot, as they may not leverage the full potential of LLMs and could leave significant value on the table.
• 🔧 The transcript details case studies where AI has been used to automate complex and unstructured data processing in healthcare, significantly reducing time and costs.
• 📉 The video stresses that the goal of AI implementation is not 100% automation but rather to improve efficiency and productivity, with an 80-90% automation rate being a realistic and valuable target.
• 👥 It is important to assemble a team with a deep understanding of AI, including how it works, how to leverage it effectively, and how to integrate it into the organization's systems.
• 🛠 The transcript differentiates between AI engineers who understand the practical application of LLMs and machine learning engineers who focus on the technical aspects of AI models.
• 🔄 Hood suggests starting with small, achievable AI projects to gain traction and then iterating and building on those successes to develop a comprehensive AI implementation roadmap.

Q & A

What is the main focus of the video by David Hood?

The video focuses on AI usage in revenue cycle management within the healthcare and medical space, highlighting the benefits and potential pitfalls of implementing AI.

Who is David Hood and what is his role in the video?

David Hood is the CEO of 42 Robots AI, and he discusses the company's expertise in helping organizations implement AI, particularly in revenue cycle management.

What does David Hood suggest is the starting point for implementing AI in a healthcare organization?

David Hood suggests understanding the capabilities of large language models (LLMs) and their benefits in revenue cycle management as a starting point for AI implementation.

What are the potential benefits of using AI in revenue cycle management according to the video?

The video suggests that AI can significantly improve efficiency, speed, and productivity in processing and analyzing large amounts of data in healthcare organizations.

What does David Hood warn against in terms of AI implementation?

He warns against relying solely on solutions like Microsoft Co-Pilot, which he says only utilize a fraction of the capabilities of large language models and may not fully leverage AI's potential.

What is the importance of not seeking 100% automation with AI, as mentioned in the video?

The video emphasizes that pursuing 100% automation can be unrealistic and less efficient; instead, achieving 80-90% automation can often be more valuable and manageable.

What is the significance of the 'positive feedback loop' mentioned by David Hood?

The positive feedback loop refers to the cycle of improvement that occurs as an organization begins to use AI, gains understanding, and builds momentum, ultimately outperforming competitors.

What are the key skills required for an effective AI implementation team according to the video?

The video outlines three key skills: understanding how AI works, knowing how to leverage AI effectively, and having the ability to integrate AI efficiently into the organization's systems.

Why is having a Chief AI Officer important for an organization implementing AI, as suggested in the video?

A Chief AI Officer is crucial for understanding the organization's systems, ensuring AI is integrated effectively, and for guiding the AI implementation strategy to avoid common pitfalls.

What is the role of an AI engineer in the context of the video's discussion on AI implementation?

An AI engineer is responsible for calling the LLM APIs, solving real-world problems, and ensuring that AI solutions are not LLM-centric but rather use LLMs as tools within a broader software framework.

What advice does David Hood give for building an AI implementation roadmap for a revenue cycle management company?

He suggests starting with assembling a team with the right skills, understanding the basics of AI, leveraging AI effectively, and integrating AI into the business systems, while also considering hiring fractional Chief AI officer services for guidance.

Outlines

00:00

🤖 Introduction to AI in Revenue Cycle Management

David Hood, CEO of 42 Robots AI, introduces the video's focus on AI usage in revenue cycle management within the healthcare and medical sectors. He emphasizes the significant benefits of implementing AI, particularly in data processing and analysis, which can greatly enhance efficiency and productivity. David also mentions the existence of several supplementary videos on topics like AI solution architecture and new capabilities of large language models (LLMs). He cautions about the potential pitfalls in AI implementation and suggests that there's a positive feedback loop in adopting AI, where initial successes lead to further integration and competitive advantage. The video aims to provide viewers with a basic understanding of AI's potential and a starting point for implementation.

05:00

📈 Leveraging AI for Data Processing and Analysis

The second paragraph delves into the transformative impact of AI, specifically large language models, on data processing and analysis within the medical and healthcare sectors. It highlights the underleveraged data and the costly manual labor often required for data processing. AI, and particularly modern LLMs, can drastically improve these processes by increasing speed, efficiency, and productivity. The speaker shares an example of a client case study where AI was used to process complex and unstructured data, reducing the time from months to a week for initial drafts. The video stresses that the solution should not be LLM-centric but rather use LLMs as tools within a broader software architecture. It also touches on the importance of having AI engineers who understand how to build and leverage LLMs effectively.

10:01

📑 Case Studies in AI Implementation for Revenue Cycle Management

Paragraph three presents further case studies illustrating the application of AI in revenue cycle management. It discusses the challenges of processing handwritten, unstructured, and variant healthcare faxes and how AI can be used to automate a significant portion of this manual work. The speaker clarifies that the goal is not 100% automation but rather achieving a substantial improvement in efficiency. They explain that attempting to automate everything can lead to increased complexity and diminishing returns, suggesting that focusing on 80-90% automation is more practical and valuable. The paragraph also emphasizes that the solution should be built around software that uses LLMs as tools, rather than the other way around, to avoid common pitfalls in AI implementation.

15:01

🛠 Building an AI Implementation Roadmap for Revenue Cycle Management

The final paragraph outlines the steps for building an AI implementation roadmap within a revenue cycle management company. It identifies the need for a team with three key skill sets: understanding how AI works, knowing how to leverage AI effectively, and having a systems understanding of how AI integrates into business processes. The speaker distinguishes between AI engineers who build AI tools and those who know how to use them effectively. They suggest starting with a fractional Chief AI officer to guide the hiring process and ensure the right AI talent is brought on board. The paragraph concludes by encouraging viewers to contact 42 Robots AI for assistance in developing a tailored AI implementation strategy, emphasizing the importance of acting quickly to gain a competitive edge.

Keywords

💡AI

AI, or Artificial Intelligence, refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the context of the video, AI is central to improving revenue cycle management in healthcare organizations. The script discusses how AI, especially through large language models (LLMs), can be leveraged to process vast amounts of data more efficiently and cost-effectively than traditional methods.

💡Revenue Cycle Management

Revenue Cycle Management (RCM) is the process of managing the administrative and clinical functions of a healthcare practice. In the video, RCM is highlighted as an area where AI can significantly improve efficiency and reduce costs. The script mentions how AI can be implemented in RCM to automate and streamline processes, such as processing complex and unstructured data in the medical field.

💡Large Language Models (LLMs)

Large Language Models, or LLMs, are AI systems that can understand and generate human-like text based on the input they receive. The video emphasizes the broad applicability of modern LLMs in business processes, particularly in healthcare, where they can be used to analyze and process large volumes of data, leading to improved decision-making and operational efficiency.

💡AI Implementation Roadmap

An AI Implementation Roadmap is a strategic plan that outlines how an organization will adopt and integrate AI technologies into its operations. The script suggests that having a clear roadmap is crucial for organizations looking to implement AI in their revenue cycle management. It mentions the importance of understanding what's possible with AI and having a basic starting point for implementation.

💡Chief AI Officer

A Chief AI Officer is a high-level executive responsible for overseeing an organization's AI strategy and initiatives. The video script highlights the importance of this role in understanding and integrating AI into business systems effectively. It suggests that a Chief AI Officer should have a deep understanding of AI, experience in leveraging AI technologies, and a broad systems thinking approach to AI integration.

💡Automation

Automation refers to the use of technology to perform tasks without human intervention. In the video, automation is discussed as a key benefit of implementing AI in healthcare revenue cycle management. The script provides examples of how AI can automate the processing of complex data, reducing the need for manual labor and increasing efficiency.

💡Data Processing

Data Processing is the manipulation of data by a computer to produce a desired output. The video emphasizes the importance of data processing in the context of AI, particularly in healthcare, where large amounts of unstructured data need to be processed. The script mentions how AI can significantly speed up and improve the productivity of data processing tasks.

💡AI Engineering

AI Engineering involves the design, development, and maintenance of AI systems. The video script discusses the distinction between AI engineers who understand how to build AI models and those who know how to effectively leverage AI technologies. It stresses the importance of having AI engineers who can build solutions that are not solely model-centric but use AI as a tool within broader software systems.

💡Microsoft Co-pilot

Microsoft Co-pilot is an AI-powered assistant designed to enhance productivity by integrating with various Microsoft applications. The video script warns against relying solely on solutions like Microsoft Co-pilot for AI implementation, as it suggests that doing so may not fully leverage the capabilities of large language models and could leave significant AI potential untapped.

💡Custom AI Solutions

Custom AI Solutions refer to AI systems that are specifically tailored to meet the unique needs and requirements of an organization. The video script discusses the importance of building custom AI solutions rather than using generic AI architectures. It emphasizes the need for AI solutions that are not model-centric but are built with a deep understanding of the organization's systems and processes.

💡Fractional Chief AI Officer

A Fractional Chief AI Officer is a part-time executive who provides strategic guidance on AI initiatives without being a full-time employee. The video script suggests that hiring a fractional Chief AI Officer can be a cost-effective way for organizations to quickly gain the expertise needed to develop and implement an AI strategy, especially when looking to rapidly adopt AI technologies.

Highlights

David Hood, CEO of 42 Robots AI, discusses AI implementation in revenue cycle management for healthcare organizations.

Emphasizes the significant benefits of AI in medical and healthcare organizations, particularly in revenue cycle management.

The importance of understanding the capabilities of Large Language Models (LLMs) and their potential in business.

Warns of potential pitfalls and 'landmines' in AI implementation and how to avoid them for value addition.

Describes a positive feedback loop created by AI, where initial use builds organizational 'muscle' and understanding.

Advises on the competitive advantage gained by early and aggressive AI implementation in business processes.

The transformative impact of modern AI capabilities, especially those emerging within the last year.

Caution against relying solely on Microsoft Co-Pilot or similar tools, as they may not leverage the full potential of LLMs.

Details the enhanced data processing capabilities of AI, particularly in unstructured or variant data scenarios.

Case study: Accelerating healthcare outcome and process metrics processing from months to weeks using AI.

The inefficiency of manual data processing and how AI can offer a 100x to 10,000x improvement in productivity.

Differentiating between AI Engineers who understand LLMs and those who effectively leverage them in practical applications.

The misconception of aiming for 100% automation with LLMs and the practicality of achieving 80-90% efficiency.

Highlighting the importance of building an AI implementation roadmap for a revenue cycle management company.

The necessity of assembling a team with the right skills to understand, leverage, and integrate AI within an organization.

The role of a Chief AI Officer in understanding the organization's systems and the impact of AI integration.

Encourages contacting 42 Robots AI for assistance in building an AI implementation roadmap and gaining a competitive edge.

Transcripts

hi this video is about AI usage case

studies with revenue cycle management

companies in the me in the healthcare

medical space my name is David hood and

I'm CEO of 42 robots Ai and we help

organizations Implement AI especially

revenue cycle management that's where we

have a lot of experience in the medical

space so there's links to several videos

that I'll put below that are all

relevant to and and supplement this

content here we got the chief AI officer

video bad AI solution architecture and

custom AI solution and llm new

capabilities recommend you check those

out uh before after you watch this video

so first of all there are real

significant benefits today uh to

implementing AI at any medical or

Healthcare organization especially

revenue cycle management um and I'll

explain to you that in a second based

upon the capabilities of llms some of

which don't really get talked about that

that much uh but there's definitely some

landmines to navigating this that you

want to keep in mind and I'll sprinkle

those throughout here uh and that should

help you avoid some of the biggest

pitfalls and move forward with things

that add the most value I'd say that

there's a positive feedback loop here

because you know you can't expect to be

like all right 3 months from now we're

going to use AI in all the places it's

going to be perfect in everything and

and I don't think anybody necessarily

reasonably expects that uh but as you

start to use AI with an organization as

an organization you start build some of

that muscle you'll get some

understanding you'll get some political

biy in and it starts to create a

positive feedback loop to add more and

more value to the organization which

helps you beat out your competitors uh a

lot of the latest capabilities some of

which are weren't even available until

within the last year um are uh not being

used by most companies uh at the at the

same time there's a lot of companies

that understand that this needs to be

implemented and if you're competitors do

some of the stuff we're going to talk

about here uh in this video before you

do it then they will leave you in the

dust and the other the opposite is true

if you get to this sooner and faster and

you implement some of these AI Solutions

very aggressively then uh you'll start

to to uh get ahead of them and it'll be

hard for them to even see what you're

doing so part of the objectives of this

video is to help you gain understanding

of what's possible and and at least a

basic starting point for how how to get

going on uh implementing AI at your

revenue cycle C uh management company

um put you in a better position to do

that and also to even give you just like

the really basic seed for building a

road map so let's talk about kind of the

new capabilities of that that come out

that have come out recently um because

of the modern large language model so

first of all I'd say the clock really

really doesn't start

for the modern tools until gp4 came out

in I think believe it it's April of

2023 um so you know some people might

say I've been an AI engineer for 10

years well that doesn't really mean a

whole lot because uh prior to GPT 4 or

if you really want gpt3 um there wasn't

a lot uh just the tools were different

it's it's like saying um uh I used to I

I've driven race cars therefore I'm an

expert at flying planes it's it's about

that much of a difference um well

there's certainly some overlap in skills

it's it's very different additionally

multimodal is even more recent um in

terms terms of uh the large language

model is actually being able to look at

images and um understand them and this

is useful in some use cases the the app

the capabilities um are super broad for

large language models um it the modern

llms are so broadly applicable that just

about every business and and about every

business process and just about every

business in the world can gain value

from large language models caution

though Microsoft co-pilot or chat GPT

Enterprise or whatever if that's your

solution I I've heard a lot of people

say oh our solution is we got Microsoft

co-pilot we're doing Ai No you are

leaving 99 plus% of the capabilities on

the table if you're doing that it is not

a solution for uh for actually

leveraging LMS I mean it is a solution

it gets some of the value but it does it

doesn't get the majority of the value um

but one of the key things that comes out

of this is that uh data processing has

been super leveled up uh good AI

Engineers who have experience processing

large amounts of unstructured or widely

Andor widely variant data can do a whole

lot with data to speed up and um make it

less expensive and and make people more

productive in the in the spaces where

there's lots of data that needs to be

processed which is huge in revenue cycle

management and Medical in

general so let's go a little bit deeper

into that so really what you have is you

have two major areas uh that are kind of

two sides of the same coin where there's

tons of data that is either

underleveraged

um which means that basically you have

this valuable data that you could do

stuff with that you're not or it's very

expensively leveraged oftentimes this

means you have very expensive manual

labor processing the data um and so in

both of these cases AI specifically

large language models the modern

Premiere you know Frontier models can

dramatically improve these processes and

some cases I I think especially when it

comes to like data processing and and

data analysis and data processing means

a bunch of different things you can get

a 100x 10,000x the value the the

productivity the efficiency the speed

that you could get from a human uh let's

say even a very experienced data person

this is not a knock on humans this is

just something that that a large

language models are really really good

at especially when you have an AI

engineer that knows how to build these

correctly again if you're using like

Microsoft co-pilot there's a little bit

of a value that you can get from this

but you're missing most of it which

brings me to real AI engineering I think

there's a lot of hand waving going on

with regards to AI engineering there's a

lot of people saying that they're AI

Engineers when they have no real world

experience or um or they're they don't

really have practical experience maybe

they think they're doing AI engineering

but they're building with like a bad AI

architecture again check out that video

or they're they've done a lot of hand

waving and they just put AI engineering

on there we can help kind of filter

through that nonsense and I I'll talk

about that uh in in a minute so let's

get into some of the the case studies a

little bit of details as to what we were

doing so for one client we did

Healthcare outcome and process metrics

so this is a revenue cycle management

SAS company the data was very large

comple complex and unstructured and even

hard for humans so some of the data that

they gave us that was supposed to be

good we figured out oh actually the

humans did some of it wrong too so it

was not easy to process um it would come

out this data would come out on a yearly

basis and their clients customers needed

it needed them to process it to put it

into their SASS and it usually took them

months and sometimes there's like a few

Key Resources a few key people that if

they were out it could take an

additional few months mean these are

very skilled humans these are highly

paid full-time salary people with lots

of experience in this specific stuff so

they're very expensive um and basically

with our solution we're able to make

that this can be months faster instead

of it taking 2 3 4 months it can take

about a week for them to get like a

first version draft of these to where

it's basically about 80% automated uh

maybe as much as 90% but 80% is kind of

the minimum bar and um this dramatically

improves things for them because they

get the the drafts it's highly accurate

for most of the different types of uh

inputs that they have there are a few

that it that some some of the more

complex ones uh more longtail ones that

it doesn't do well but that's fine

you're not really looking to fully

automate and I'll talk about that in a

second with the second case study why

this is actually good you wanting to

fully automate 100% automate things with

with LMS is not really a reasonable goal

in most

scenarios and I want to point out here

this is super critical the solution is

not llm Centric and that might sound

weird it's like well but you're using

llms they're they're key to the solution

yes um but the the solution itself does

not put the llm at the center the model

is not the solution the solution is not

the model and this is part of again if

you watch the bad architecture or the

how to build custom AI Solutions you

you'll understand this on a deeper level

but long story short the llms need to be

used as tools by classic code uh just

basically software versus the other way

around where uh most people are building

things LM Centric they have like a model

and then the model basically does

everything it it picks things it uses

tools they train everything everything

tries to get shoved in that model

basically and this is going to give you

a lot of trouble and you're going to

have you're going to really struggle to

solve more complex problems um

especially on a very customized basis

and and to automate to a very high

degree custom if you're liking this

video please like if you have any

questions or thoughts or your own

experiences please feel free to leave a

comment below as well um this is this is

a case study where the the inputs are

handwritten unstructured and variant

healthc care faxes um again another

revenue cycle management SAS company you

know sometimes the the writing was

unclear or even if it's not handwritten

sometimes like the the typing is like a

skew or um the structure of the document

is really different to where sometimes

they put this over here sometimes they

put it over here and it really there was

not a whole lot of consisten these are

coming from a wide variety of different

sources um and then not only is the

formatting and the structure

inconsistent but the the the way that

the input is put in there can be

different so for example a simple

version of this would be the date

there's a bunch of different ways to

write a date and if you're trying to

write like a script for that there's a

bunch of different variants that's a

much more simple version of it but a

more complex one might be like a

diagnosis there's a bunch of different

ways to write a diagnosis there's a lot

more variance in the outputs in and in

terms of how that data is labeled um but

also what the what the the actual field

or or category or or content of that

field looks like um I I want to point

out again the objective here is not 100%

automation so this is stuff that is

getting 100% manually processed so

imagine if we could put it through a

system in it only 50% of them are

automated that's still a huge

Improvement on this because it means

that for 50% of the documents you don't

need the manual inputs also just a

bigger Point here and again I I think I

covered this pretty detail in the

customing solutions video where you

shouldn't be looking for 100% automation

Solutions in most scenarios the reason

for this is that

going back to llms are so broadly

applicable that uh on top of the fact

that once you get to like 80 90% there

becomes a point where every percentage

Point becomes exponentially harder so

going from 90% to 95% is probably harder

or more challenging than going from like

uh 0 to 50% or 0 to 80% or something

like that and then going from 95% to 97%

etc etc etc and um um part of this has

to do with especially if you're doing

llm Centric this is almost impossible in

a lot of scenarios but even with a

really good structure to the solution um

as you build try to build more and more

of the edge cases and more and more of

the challenging things it can actually

degrade some of the more basic

fundamental things that you do and so

you have to build much many more

exceptions the complexity of the

solution grows and it and as the

complexity grows the the the overall it

just becomes exponentially harder with

software and this is not true for every

case but in general this makes sense so

another way to put this is in terms of

from an efficiency standpoint it's

better in the same amount of time you

could probably get 10 80 to 90%

Solutions than you could get one like

99% solution okay in terms of automation

so which one has more value and almost

always the first one now there might be

some scenarios where the 99% to Plus

Solution does add more value it's like a

key process and automating it just is

just huge for the company maybe that's

the case where it is worth it to try to

go for that really high level thing but

I think that's the exception and not the

rule at least in with the clients that

we've talk

to so I'll also briefly put in here a

just a general idea on how to build your

AI implementation roadmap as a revenue

cycle management company and then if you

really want us to help you with this

because there's obviously more nuance

and I can give in this quick video

please contact us you can give us a call

or click the link below and we'll help

you do this U and give you a lot more a

lot better of of ani implementation road

map so first you need to assemble the

team and there's three main skills that

you need how AI Works how AI can be

leveraged and how to integrate AI

efficiently into your organization okay

so let's cover each one of these

individually so how AI works most people

think that this is um an a machine

learning engineer and that is true but

you don't have to be a machine learning

engineer to get the fundamentals of how

AI Works down um a good AI engineer has

this and so this is isn't necessarily

understanding all the ins and outs of

how a large language model works at a

very computer science fundamental level

it's more about like understanding um

the the the basics and and the key the

some a handful of the key ideas so for

example understanding how large language

models turn the semantic meaning of

words into math that's a key

understanding and so somebody who has

some math experience with like um linear

algebra and linear programming and stuff

like that can be effective in

understanding this so some amount of

math understanding but at our company

some of our AI Engineers have machine

learning official machine learning

experience and some of them don't and

some of the ones that some of our best

ones don't have that experience but they

understand that it's next token

prediction they understand that there's

Randomness in there and some of the key

ideas there and then a lot of people

think conflate one and two they think oh

it's the same thing how does the LM work

a machine learning engineer understands

how to leverage llms effectively nope

um and let's

actually let's call these llms how llms

work and then how llms can be

effectively leveraged this is you know

is a little bit of a rough um metaphor

but it works generally there are two

different skill sets between the person

who drives the car and the person who

builds the car and that you can maybe

have somebody who understands both

really well um but it's two different

skills so think of the llms as a tool

and the person who knows how to use

these tools versus the person who knows

how to build these tools to to be really

good two different skill sets

unfortunately these get conflated a lot

and so pretty much all the time

resources and money in in the AI

industry are going towards item one here

and they're thinking that's all that you

really need and I'm telling you it's not

for sure definitely 100% uh that item

two here could actually end up being

more important especially if llms become

commoditized um and it's also definitely

where there's a big amount of

underserved population here

so uh this is where an AI engineer

really should Shine

versus a um I'm sorry yeah an AI

engineer versus a machine learning

engineering these are two different

skill sets um they need to have

experience actually calling the llm apis

actually solving real world problems

probably with python um there's some

other languages but usually it's with

python to solve problems in a way that

is not LM Centric okay and then this

third item this one's a little bit

harder and more challenging and some

people might have part of this this is

more of a systems understanding both

broadly how are these llms going to

affect your business systems as a whole

but also an understanding of your

specific Business Systems and this is

where uh this third item is where a

chief AI officer comes into play a chief

AI officer really should have one and

two pretty well maybe they're a previous

AI engineer they've done AI engineering

work and then some amount of especially

like systems thinking is really helpful

or you know if if they don't understand

your your organization they come in and

that's part of their job is to really

understand the organization how if I'm

going to be doing stuff over here how

does it affect this other part of the

business and vice

versa um so this is a really important

position Chief a officer if you don't

believe me watch the video on Chief a

officer I'll put a link to that below um

it is super important for all sorts of

reasons and you could you also want to

if you need to get this up and running

really quickly consider Hiring Our

fractional Chief AI officer services

this will really help you get AI

Engineers hired because you don't want

to just go out and start hiring ai ai

Engineers because I bet if you don't

have a CH Chief AI officer who if you

have nobody in there who actually has AI

engineering experience how will you know

how to tell the difference between the

posers and the real PE the real deal you

won't and these people are very

expensive because there's not very many

AI Engineers there's not very many

people who have these one two three

skills we do at our organization

generally have these in in a variety of

different different capacities so once

you have a chief AI officer or a

fractional one you hire an AI engineer

or you Outsource which we can also

provide and then you go you try and you

try and you try and because the first

few times maybe it won't work or you try

for something with super low hang fruit

to just get a win even if it's um it's a

quick win to get some Buy in um and then

you iterate from there um and you start

building the organizational muscle okay

give us a call if that if you're still

with me give give us a call click the

link below and I I I we can we can

definitely help you navigate through

this and get get this going as soon as

possible so you can start getting value

from llms at your organization before

your competitors do thank you very much

have a great day bye