The implications of AI on a Center of Excellence
Summary
TLDRThe video script delves into the rapid rise of AI, especially ChatGPT, and its potential to revolutionize various industries. It emphasizes the importance of prompt engineering, data quality, and architectural considerations for organizations to harness AI's full potential. The speaker uses analogies to illustrate the early stages of AI development and warns against underestimating its future capabilities. The script also highlights the need for enhanced skills, such as prompt engineering, data governance, and business analysis, to leverage AI effectively. It encourages establishing centers of excellence to foster innovation, collaboration, and best practices within organizations, enabling them to identify AI's sweet spots and maximize its benefits.
Takeaways
- 😀 GPT (Generative Pre-trained Transformer) models like ChatGPT have brought AI capabilities to the forefront, generating human-like text based on prompts.
- 🚗 We are still in the early stages of AI capabilities, like driving a Formula One car compared to a regular car.
- 🧾 Prompts need to be more explicit and detailed to get better results from GPT models. Prompt engineering is a new skill to develop.
- 🧩 GPT models excel at solving puzzles and reasoning based on provided information, rather than using general knowledge.
- 👨💻 AI highlights the importance of skills we should already have, like good business analysis, data quality, and documentation standards.
- 🕵️♀️ AI forces us to be more disciplined in areas like governance, change control, and metadata management due to added risks.
- 👩🏫 AI creates a need for new skills like prompt engineering, but also emphasizes improving existing skills like data governance and architecture.
- 🚀 AI can be a catalyst for getting buy-in from executives to fund projects and improve areas we've neglected in the past.
- 🏆 Centers of Excellence (CoEs) should act as innovation hubs, bringing together people passionate about AI and exploring its use cases.
- 🎯 Organizations should focus on finding the sweet spots where AI can provide significant ROI, easy adoption, and consistent results.
Q & A
What is the main topic discussed in the transcript?
-The main topic discussed is the impact of AI and specifically GPT (Generative Pre-trained Transformer) on businesses, and the skills and processes organizations need to adopt to leverage AI effectively.
Why did ChatGPT gain such rapid adoption with 100 million users in 2 months?
-ChatGPT gained rapid adoption because it was easy to use (just type in a prompt) and the results were staggeringly good, allowing users to generate text like song lyrics and content that seemed beyond current capabilities.
What analogy is used to describe the current stage of GPT development?
-The analogy of a car is used. ChatGPT is like a high-performance Porsche GT3 on a track, which gives a glimpse of the capabilities of a Formula 1 car, but doesn't represent the full potential of AI, which is still in its very early stages like a flip phone compared to an iPhone.
What are some key skills organizations need to develop to leverage GPT effectively?
-Organizations need to develop skills in prompt engineering (crafting effective prompts), data quality, business analysis, following industry standards for documentation, understanding system architecture, data governance, and change management.
Why is prompt engineering important for GPT?
-Prompt engineering is important because longer, more explicit prompts are needed to get better results from GPT. Prompts can be thought of as code that needs to be monitored, refined, and managed for dependencies, just like traditional code.
What are some use cases where GPT excels?
-GPT is good at solving puzzles and generating text when given all the puzzle pieces (context and data). It performs well in areas like generating code, interpreting legal documents, and writing lyrics because it has ingested large amounts of relevant data.
What is the role of a Center of Excellence (CoE) in the context of AI adoption?
-A CoE acts as an innovation hub, bringing together passionate individuals and teams to collaborate on AI initiatives, find use cases with strong ROI and ease of adoption, and ensure consistent and reliable results.
How does AI highlight the importance of existing best practices?
-AI punishes mediocrity and exposes weaknesses in areas like data quality, business analysis, documentation, and architecture. It forces organizations to adopt disciplines they may have neglected in the past due to the increased risks and potential impact of AI on business operations.
What are some key pillars or skills needed for a strong AI Center of Excellence?
-Key pillars or skills include vision, leadership, governance, change control, methodology, standards, metadata management, architecture, security, change management, project management, tooling, and innovation.
How can AI be used as a catalyst for organizational change?
-AI can be used as a catalyst for organizational change by highlighting the need for improvements in areas like documentation, architecture, and data governance. It provides an opportunity to get buy-in and funding from executives to address these issues, which were previously overlooked or underfunded.
Outlines
🚀 Explosive Growth and Potential of AI
The speaker talks about the rapid growth of AI, particularly GPT, which gained 100 million users in just two months. GPT's ability to generate realistic text outputs, such as lyrics for a country song, has brought AI to the forefront. The speaker uses the analogy of driving a supercar (GPT) versus a Formula 1 car (the full potential of AI) to illustrate that we've only scratched the surface of AI's capabilities. The current perception of AI is based on experiences with ChatGPT, which is like a flip phone compared to the iPhone of the future.
🛠️ Prompt Engineering and AI as a Tool
The speaker highlights the importance of prompt engineering, which involves crafting effective prompts to get the most out of AI models like GPT. Prompts need to be longer and more explicit, with detailed context to get better results. AI excels at solving puzzles when provided with the necessary pieces of information. The speaker emphasizes that AI should be used as a tool, not a replacement for human expertise. AI can generate convincing but potentially incorrect answers, known as "hallucinations." Proper delegation and validation of AI outputs are crucial. The speaker predicts the need for new skills like prompt engineering, rather than entirely new job roles.
🎯 Importance of AI Highlighting Existing Best Practices
The speaker discusses how AI is highlighting the importance of existing best practices that organizations may have overlooked or undervalued. AI's performance is heavily dependent on the quality of data, business analysis, documentation, and architecture. AI's ability to read and process this information forces organizations to improve their standards and follow industry best practices, such as using the Universal Process Notation for process mapping. The speaker suggests using AI as a catalyst to get executive buy-in and funding for improvements in these areas.
Mindmap
Keywords
💡GPT (Generative Pre-trained Transformer)
💡ChatGPT
💡Prompt Engineering
💡Car Analogy
💡Use Cases
💡AI Skills
💡Hallucination
💡Metadata
💡Center of Excellence (CoE)
💡Innovation
Highlights
GPT reached 100 million users in two months due to its ease of use and impressive results in generating content like song lyrics.
ChatGPT is compared to driving a Porsche GT3 to illustrate the advanced capabilities beyond traditional tools, but the complexity of AI like a Formula One car's controls highlights the early stage we are at with GPT.
The potential of GPT is often underestimated when only seen through the limited experience with chat interfaces.
Prompt engineering emerges as a crucial skill for effectively using GPT, requiring detailed and explicit instructions to generate valuable outputs.
GPT excels at solving puzzles given all the necessary pieces, showing its strength in reasoning and generating text based on specific inputs.
Applications embedding AI with well-defined prompts can significantly speed up tasks like creating user stories or test scripts, turning hours of manual work into minutes.
The importance of not abdicating responsibility to AI is emphasized, as users need to be able to validate GPT's outputs to avoid hallucinations and inaccuracies.
AI's current stage is akin to the flip phone era, suggesting that we are only at the beginning of its capabilities and impacts.
Effective use of AI requires better prompts, which is a new form of interaction that needs to be learned and optimized over time.
AI punishes mediocrity by quickly exposing poor data or analysis, emphasizing the need for high-quality inputs.
Documentation quality and the architecture of systems become increasingly important as AI relies on these elements to generate accurate outputs.
AI-driven changes in roles are about acquiring new skills rather than creating entirely new job titles, such as prompt engineers.
The analogy of AI as Google on steroids highlights its potential to vastly outperform existing tools when provided with well-crafted queries.
The concept of 'prompt drift' suggests that the same prompt can yield different results over time, necessitating ongoing monitoring and adjustment.
The Center of Excellence (COE) plays a crucial role in guiding AI adoption, emphasizing innovation, collaboration, and the strategic implementation of AI across teams.
Transcripts
ai's been around what 15 16 years I
think what's happened in the last year
now uh GPT has suddenly and open Ai and
chat GPT has suddenly brought everything
to the Forefront GPT it got to 100
million users in two months and why was
that well first of all I think a it was
easy you went and you type something but
B the results were staggering you could
type in and go give me the lyrics for a
country song about this and it would
actually go and generate that and GPT is
just very good at working out what the
next best word is um so I spend quite a
lot of time on this on the confidence
circuit at the moment and the first
thing is that people think of GPT the
potential of GPT Through The Eyes of
what they can do with chat GPT over the
last like six nine 12 months um
and the analogy I like to use is the car
analogy there so everyone out here can
probably Drive there's a there's a
hondre cord fantastic car um and that's
what it's like not using GPT at all um
some of us have been lucky enough to
drive like that Porsche GT3 on a on a
track and you're staggered by the levels
of grip the acceleration you just can't
believe actually how fast a car will go
around a corner and you think okay I've
now I now understand what the world of a
Formula One driver is like and that's
sort of where we all are we think we
chat GPT we've seen what the future
looks like and then you you walk off the
pits and you bump into Lewis Hamilton
the Formula One drive and he goes no no
no you've you just don't understand what
the Futures look like on the right hand
side there's a picture there of the
steering wheel of a Formula One car
somewhere on there is a button called
the launch control how you actually get
it off the uh from from the the pit from
the pits or from the uh from The
Starting Line none of us could even get
the car off the track without stalling
and I think that's really where we are
with GPT we're at the very very early
stages and I think the first danger is
that if people think about the potential
of GPT Through The Eyes or or through
their experience of what chat GPT can
can do the other analogy is we're still
at the flip FL flip phone stage we're
nowhere near where an iPhone now is so
we are really at the very very early
stages it's not quite the wild west but
actually it's very unstructured I think
people are still finding out what use
cases work what don't work and we need
to try and cut through some of that and
work work through to a go what what as
leaders should we be worried about and
how can we support our teams in terms of
what they're currently doing with
GPT so I think when when we think about
chat GPT at the moment we tend to think
it as Google on steroids we'll ask it a
question it comes back with the answers
and the better the question the better
we ask the question the more complete
the question we ask the better the
answers if you just said to your intern
who's bright and excited book me a
restaurant they come back and they book
your a restaurant the uh they booked you
into the Italian restaurant for next
Tuesday they go no no no no no not not I
don't and and I really wanted a
restaurant for next Thursday and we seem
to have that conversation back and forth
with GPT if you said to your intern I'm
entertaining A clients they like Indian
food and they like Chinese food uh we're
driving and I'd like a table in a
private room for four that's that's a
good prompts suddenly the internet can
come back and go right if I know that
you're driving so I need we need parking
space I know when I and I think we need
to get to a point where we're asking
better prompts so that's the first thing
uh prompts need to be longer be need to
be lot more explicit to get some of the
benefits out of it and that is a new
skill call it prompt engineering but
there's actually more to prompt
engineering than that and I'll explain
that in a moment but the other thing
that GPT is very good at it's very good
at solving puzzles if you give it all
the puzzle pieces now a slight challenge
is that the limitation on the amount of
information you can give it uh but you
could give it I know a contract and then
say Okay I want to terminate this
contract against these four bullet
points and it will write a reasonably
good termination letter if you give it
all so rather than trying to get it to
use its knowledge of the world and
knowledge of History instead think about
it actually is using it its ability to
reason and then come back with text and
it's really really good at doing that so
think more about how you give it the
puzzle pieces than ask it to solve the
puzzle and that's actually where I think
we're getting into bit more of prompt
engineering or certainly where you're
seeing applications embedding AI into
them where they've already thought about
I'm asking GPT to do something in the
context of my application it knows where
to get the puzzle pieces it's now simply
using GPT as the back end to solve those
puzzle pieces and I mean an easy example
is is our world elements. cloud if you
map out a business process using the uh
Salesforce UPN standard Universal
process mapping
notation AI rgpt application will write
really really good user stories user
stor with acceptance criteria and also
test scripts also because we know what's
in your org because we pulled all the
metadata AI can then look at the
acceptance criteria and then work out
which metadata you could reuse or which
metadata needs to be you so again
solving what would take eight hours
manually could be done in five minutes
um but before we start getting worried
about losing jobs or actually the Coe
lead is going great well we don't need
anyone in the team we need we need to
think about what's actually happening
here first of all we need to delegate we
can't
abdicate we can't ask GPT to do anything
we can't do ourselves because we then
can't validate the answers I think
everyone's heard of the term
hallucination AI is very good it
actually it's rather like the male 22y
old intern uh and I I deliberately use
the word male because they are never in
doubt but not necessarily right it gives
very convincing answers which may not
necessarily be correct so you need to
make sure you a delegate the work scope
it correctly but secondly you need to
validate whether the answers has come
back with are correct and I think we're
still in the early stages of
understanding the use cases where it
works really really well and there's
less hallucination we're going to see
new skills not new roles so we won't see
the role of a prompt engineer the same
ways we don't have in our organizations
the job of Google search engineer but I
do think everyone needs Google search
skills the same way as we all need will
need prompt engineering skills um but I
think what AI is doing is it's
highlighting that some of the things we
know we ought to be doing and don't do
very well it's highlighting the
importance of those so message here is
first of all I mean phobo at the top
there fear of being obsolete I think
that's a very valid fear for all of us
we need to stay current we need to
understand the implications of new
technologies coming in and what it means
for ourselves but also for our teams but
don't think about hiring new ski uh new
roles think about actually how the
skills we need to generate inside our
organization I think this is about
Career Development which is where we
started this and then think about how
you delegate you can't just go gbt told
gave me the answer we need to think
about how we delegate the the question
to make sure we get the right in a way
that we get decent answers
back so first of all let me just think
very briefly about the sorts of skills
we need to to to create the first is
prompt engineering which is something we
haven't had before so the idea of
writing a prompt and again back to my
analogy of the Porsche versus a Formula
1 car a prompt isn't just like a Google
search with a little bit more there's
actually quite a lot more involved in
this first of all we need to a prompt
can actually be quite detailed it could
be almost like an email
template inside our own organization
we're using templates for prompts where
we're inserting say three or four bullet
points in different places and that
prompt is then writing a website for us
our internal website for all our new
features and fun functionality so
there's a website aimed at our customer
success marketing and sales team so they
get early sight of functionality that
website is built by GPT using some
templated prompts which are product
management team of have optimized and
revised over time so that's the first
thing a prompt isn't just what you type
in you could actually have a templates
which you reuse so that's the first
thing I think the second thing which is
interesting is that when you test that
prompt the result you're going to get
could be different when it's done today
versus a week's time versus A month's
time because that prompt is hitting a
large language model which could be
optimized being trained could be being
refined so unlike where I know we write
a flow and it executes the same every
time with a prompt it could change over
time so we need to start monitoring it
for
drift we also need to make sure that
when our teams are using those the
result of the prompt that we understand
how much they have to modify it so we
understand how good that prompt was if
it generates say an email that we send
out to a customer and then the service
agent or um the um support agent has to
make loads of changes to it maybe the
prompt needs to be
refined is that prompt even being used
uh or is it being used with no changes
whatsoever which would be quite
concerning so again there was some
there's some monitoring of prompts that
we probably would never do in terms of
code or um say a flow so declarative
code and then the other thing we need to
worry about dependencies so if that
prompt is is using say metadata from our
Salesforce system or a third party
system like data Cloud if that metadata
gets changed the prompt will still work
so we need to understand when we're
actually making changes to metadata does
a prompt use it almost the same way as
does an email template use it except
there's a bit more at stake now if that
prompt is using a large language model
to make some changes or decisions based
on the data coming out of that metadata
so think of prompts as certainly code
but actually um almost a riskier set of
code than maybe um a flow or Apex so
that's a new skill the other the other
four items on there are things that we
should or ordinarily be doing but maybe
is it probably doesn't have the level of
importance as it as we probably should
allocate to it uh but AI punishes
mediocrity if you've got poor data it
will give you poor results really
quickly if you haven't done very good
business analysis and you so the way AI
is reading those business process Maps
it will it won't give you very good user
stories and what we've discovered over
the last couple of months of using El
elements GPT is we looked at the user
story go oh that's not very good that
was GPT and then we went back and looked
at the process map that wasn't a very
good we actually didn't set it up very
well how often are we actually giving
poor user stories to our development
teams because we haven't thought about
the business analysis so it's suddenly
becoming making business analysis way
more important because now ai is reading
that documentation we're creating it's
not just a user and quite often when I'm
presenting when I I talk about um AI
reading either process documentation or
even metadata descriptions is it going
to be
confused or disappointed no longer have
we got a a a an individual or person
there think understanding the Nuance
understanding the assumptions
understanding those weird acronyms that
are specific to your company when AI is
now reading your business analysis
documentation it's reading it literally
literally and the the results are based
on us following some well-known
standards so UPN for process mapping um
say uh the user story has a set format
erds entity relationship diagrams data
flow diagrams all have some standard
industry formats we need to be following
those and as an organization we created
something called um MDD metadata
description definitions so if you're
going to document your metadata what
let's work out the best way of
documenting so a I can read it because
what we've discovered is with good
descriptions it can come up with some
really good recommendations with no
descriptions it's okay but it's not
nearly as good as when it's got good
descriptions so the last the last bullet
point on their documentation that
suddenly become more important on the
quality of the documentation
architecture again always important but
suddenly the architecture of our systems
is is not just our internal Salesforce
system and maybe the the uh the other
internal systems that we it connects to
but if we're now touching a foundational
Model A large language model suddenly
that that architecture got way more
complicated Salesforce is putting in
place the Einstein trust layer so that
we've got some confidence that our data
isn't being sent to a large language
model but we're now relying on yet
another moving part inside the whole of
our ecos inside of inside our it stack
plus of course we're using a large
language model which is potentially
outside or we have our own large
language model which we bring inside so
again there are a lot more moving parts
and understanding the architecture of
what we're building and and if we make
changes to any of those and the
interdependencies becomes really
important these these these things we're
talking about we should be doing anyway
it's just AI is forcing us to do them
we're realizing we haven't done a
particularly good standard um yeah we've
got all these fields but actually have
we got decent data governance on the
critical fields that are maybe in our
dashboards that our executives are
looking at have we even done that
probably not AI is going to be looking
at those same same Fields so I I also
because I've been around Chang projects
for 20 years I'm always looking for that
Catalyst that reason for executives to
go yeah we should fund this yes we will
support you we will give you the
sponsorship and I feel AI is that that
point where we can go if we're going to
benefit from AI
we've got to get our act together in
certain areas and this is the
opportunity to do to get the buy from
senior Executives to do the things we
knew we wanted to do but we never got
the time to do oh documentation we
haven't got time for that now we have oh
don't worry about architecture we'll fix
that later you can't yeah um well don't
worry about there's that classic cartoon
which is you start coding and I'll go
I'll go and ask the users what they need
big issue is that people don't spend
enough time understanding what they
really should be building before they
build it and hopefully will shorten the
time it takes to help understand what
should be built and then we can use this
as the excuse to get projects funded
correctly so let's turn our turn our
thoughts to the center of excellence and
what this means um you've heard probably
enough a man being but let me just go
and I've was looked at these these these
13 pillars of a center of excellence so
before everyone goes well hang on we
haven't got 13 people we can't have 13
pillars again these are a set of skills
that you need inside your organization
around a COA and we let me just run
through them very quickly so Vision so
who's driving the Strategic Vision the
direction for Salesforce from both the
business and the IT perspective and
that's different from leadership
leadership is the steering committee the
key sponsors who again are then
validating and setting that direction
governance is about now the business
case the investment uh risk management
is about the overall control of the
Strategic Direction which is different
from change control which is a lot more
tactical that's management of changes to
all all aspects of the program whether
that's code changes or changes to
training material or changes to or org
charts uh methodology okay this is your
implementation methodology and that's
how I mean covers like people process
technology so think about it as the
business analysis piece devops adoption
monitoring so that how do you drive
changes you don't Implement once but how
do you drive changes around your that
cycle uh standards is I know we just
talked about documentation standards and
business analysis but it's metadata
naming it's coding standards testing
standards uh standards for training um
uh standards for change management so
again setting some standards that make
it easy so people aren't writing
documents and recreating Things From
First principles uh metadata management
okay uh every Salesforce now is run on
metadata if you don't have a good handle
on that metadata management is as
important as as managing the code um
architecture clearly we're now building
complex uh systems which are
interdependent uh and we need to make
sure we think about the technical
architecture but also how it relates to
the Integrated Systems and then security
which leads me to security um security
needs to be architected in it's it's way
harder to try and actually do it after
the fact and we need to think about
security and performance and
architecture hand in hand um change
management okay this is the people
change management as opposed to the code
change management which I think comes
under change control so this is about
how do you heart change the hearts and
Minds how do you think about
organizational structure how do you make
sure the training is there to to get to
put the skills in place not just
Salesforce skills but I know the domain
skills the business skills and and
obviously know think about AI skills
uh to a pmo so this is your project
program management office or project
management office which is managing the
overall program making sure that we're
delivering against the targets um earn
that earn value um tooling okay what are
the platforms for tools that we use not
everything that you need comes in the
box when you can when you when you buy
Salesforce you need business analysis
tools data quality uh devops um backup
store so there's a set of tooling that
you need to run Salesforce um and they
either need to be built or you need to
be buying them from third parties um and
the last one on the list is innovation
so how do you Foster
Innovation that and spot it that's in
certain areas of your organization and
org and how do you then build that up
operationalize it and then make reapply
it across other orgs if you've got a
multiple org implementation or just make
sure that you're in get getting the
collaboration to get that Innovation and
make it
operational that's a huge list I've gone
through and many of you going fun we're
we're a we're a relatively small or this
feels like Overkill so what we've done
is we've put together what we think are
the most important items if you're a
small and by small it doesn't
necessarily mean not very many users you
could be a small wealth management
operation a VC a private Equity Firm not
very many years users but actually the
complexity means you essentially fall
into the large bucket so it's sort of
small low complex versus large High
complex and we've looked at this and try
to go okay which things do you have to
have in
place if you're a um if if you're at the
lower level of complexity so some
leadership metadata management
architecture security and so on um
obviously when you get to the top end
and you're You' expect to have all of
those all of those aspects in place
which ones I think are impacted by AI
directly and I the reason I I want to do
this is some of those things where if
you're small a smaller maybe that's
suddenly because of AI they they're now
they're now fitting into the mandatory
box if we are starting to use AI to
maybe write emails inside a uh inside a
page layout for sales May suddenly that
governance has become way more important
than actually just the whole idea of
actually we're not doing anything very
complicated because we've only got five
10 users or 50 users or 100 users fine
we got some email templates but suddenly
with AI Change Control governance the
methodology about how we're going to
control those prompts um metadata
management all of those things have
suddenly become way more important
because a it's forcing us to put in
those disciplines in place but secondly
we've actually we we've added a whole
set of extra risks into our org because
we've got prompts hitting a a large
language model which may which is
outside our control if you let it get
out of control it can kill reput
reputation so quickly so I think one of
the things on the bottom there
Innovation is typically oh only when
we're a large organization can we think
about Innovation I think that Innovation
piece is really important I mean we're
not a huge organization St Cloud but we
had an all hands call last you yesterday
Thursday and the topic was Ai and what
we wanted to do was not tell our
employees how AI works or educate them
but instead it was an open forum and
said okay let's go around each of the
different business units and talk about
how you're using Ai and what's working
what's not working so we actually used
it as a collaborative forum and I think
the the role of a Coe in terms of a AI
first of all is acting as that
Innovation Hub how do you get different
teams to collaborate
because even if you say our organization
is not using AI you know that
individuals are you know that people are
playing in the margins they they're if
they're not thinking about it they're
about to think about it there are people
outside looking using testing playing
and the center of excellence is the
perfect way to get those people together
and find out who are the people who are
passionate and out there doing things
versus those people are sitting on the
sidelines going maybe it won't work for
me so I think in amongst all the hype we
need to try and find what the sweet
spots are because AI won't solve every
problem for every person and I think I
the way I'm seeing it is we need to find
the intersection between is there a
really strong return on investment can I
get a 50x a 20x Improvement so with us
we're seeing 100x Improvement building
those user stories but the second one is
can it actually be
implemented if you've got to have
gigabytes of data data that's all
perfect then that's probably not
achievable with our again back to our
story if you can write a process
map boxes and lines in the UPN standard
you can immediately get that benefit so
can we find a sweet spot where it can be
easily adopted and then the I think the
other piece is and we we tucked on it
slightly earlier which is are the
results consistent enough to be to be
usable there's no point going oh 60% of
the time it gets the right answer if you
have to check every single line of every
then probably not worth using and
another a sweet spot there is code it's
very good because it's hoovered up lots
of code it's quite good at writing code
it's actually quite good at looking at
legal documents because it's got of
legal documents that it's read and
therefore it's quite good at
interpreting them so there are certain
places you can look to where it's very
good I mean writing lyrics for songs
because there's no right answer for a
song again not relevant to us but for
there are certain places where there is
that sweet spot and that's where that's
where I think open a and chat gbt is
taken off but as as C leaders we need to
start thinking about our Innovation Hub
how do we start to find those sweet
spots
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