The AI Governance Challenge | PulumiUP 2024

Summary

In this session, Paa Kalahan, a technical director at Charles River Laboratories and a Google Developer Expert, discusses the importance of AI governance in scaling AI across organizations. He emphasizes the need for ethical and responsible AI use, highlighting the role of governance frameworks in ensuring transparency, compliance with legal standards, and risk management. Kalahan also explores the current global landscape of AI regulations, including the EU AI Act, and outlines the stages of AI adoption within organizations, from exploration to strategic integration.

Takeaways

• 🤖 AI governance is crucial to guide the ethical, transparent, and responsible use of AI in organizations.
• 💼 AI governance ensures AI systems align with organizational values, legal standards, and societal needs.
• 🚀 AI adoption has exploded in recent years, but businesses face challenges in implementing AI effectively.
• 🌐 The global regulatory landscape is evolving rapidly, making AI governance more important than ever.
• 📜 Key AI regulations include the OECD AI principles, G20 AI principles, UNESCO's AI ethics framework, and the EU AI Act.
• 🔍 AI governance involves managing risks such as bias, unethical use, and regulatory non-compliance.
• ⚠️ Without AI governance, organizations risk reputational damage, legal issues, and inefficiency in AI projects.
• 💡 AI governance should be a multidisciplinary effort across all business units, integrating with existing governance structures.
• 📊 Effective AI governance focuses on data quality, reducing complexity, and balancing innovation with control.
• 🏢 The AI adoption journey progresses from exploration to strategic integration, with governance needed at each stage.

Q & A

What is AI governance, and why is it important?

AI governance refers to frameworks, policies, and practices that guide the ethical, transparent, and responsible use of AI. It ensures that AI systems are developed and deployed in alignment with organizational values, legal standards, and societal benefit. It's important because it helps manage the risks associated with AI as its adoption rapidly grows.

What challenges do businesses face when adopting AI?

Businesses face challenges such as lack of interest or knowledge among employees, coordinating AI efforts across multiple regions, managing regulatory compliance, and handling the rapid evolution of AI technologies. Without a coordinated effort, organizations struggle to adopt AI effectively.

What are the key pillars of AI governance?

The key pillars of AI governance are AI lifecycle governance, regulatory compliance, and risk management. These components ensure that AI is used responsibly, complies with evolving regulations, and mitigates associated risks.

What could happen if AI governance is not implemented properly?

Without proper AI governance, organizations may face damage to their reputation, legal and regulatory issues, wasted resources, inefficiencies in product development, and inability to use data effectively. It can also lead to biased or unreliable AI outcomes.

How does AI governance intersect with data governance?

AI governance overlaps significantly with data governance, as both are crucial for ensuring the ethical, secure, and legal handling of data. Data governance ensures that AI models are built on high-quality, unbiased data, which is essential for achieving reliable AI outcomes.

What are the different levels of AI risk under the EU AI Act?

The EU AI Act classifies AI applications into four levels of risk: (1) Unacceptable risk, which is prohibited unless authorized for national security purposes, (2) High risk, which requires conformity assessments, (3) Limited risk, which includes transparency obligations, and (4) Minimal risk, which faces no legal obligations but may adopt voluntary codes of conduct.

What was the significance of the 2019 OECD AI principles?

The 2019 OECD AI principles were significant because they marked the first internationally adopted AI governance guidelines, focusing on ethical, transparent, and responsible AI use. This laid the groundwork for further AI governance frameworks globally.

What are some ethical concerns that AI governance addresses?

AI governance addresses ethical concerns such as bias in AI models, lack of transparency in AI decision-making, manipulation of human behavior, surveillance, and potential discrimination in critical areas like employment, education, and access to public services.

How does AI governance help in risk management?

AI governance helps organizations manage the risks associated with AI by implementing processes and tools that oversee the development and deployment of AI systems, ensuring that these systems align with ethical standards, legal regulations, and organizational goals.

Why is AI governance a multidisciplinary effort?

AI governance is a multidisciplinary effort because it involves various aspects of business, including legal, technological, data management, information security, and change management. Effective AI governance requires collaboration across these different areas to ensure comprehensive oversight.

Outlines

00:00

📢 Introduction to AI Governance and the Need for Organizational Adoption

In this opening section, Paa Kalahan, a technical director at Charles River Laboratories, introduces the importance of AI governance. He outlines his roles, including being a Google Developer Expert, and sets the stage for discussing the governance of AI. Kalahan emphasizes that AI adoption in businesses requires coordinated efforts and governance frameworks to manage risks and scale effectively across multiple regions. He highlights how AI tools like ChatGPT are easily accessible, but AI adoption for businesses poses unique challenges that governance can address.

05:03

🌍 Global AI Regulations: Timeline and Key Milestones

This paragraph provides a historical timeline of significant global milestones in AI governance, starting from the adoption of the OECD AI principles in May 2019 to the EU AI Act, the world’s first comprehensive AI regulation. Kalahan highlights key events such as the G20 AI principles, UNESCO’s AI ethics framework, and the G7 generative AI risks forum. These initiatives have been crucial in setting international guidelines for AI use, with a focus on ethical, legal, and regulatory frameworks.

10:05

🚦 Categorizing AI Risks: From Unacceptable to Minimal

Kalahan explains how the EU AI Act classifies AI applications into four levels of risk: unacceptable, high, limited, and minimal. He details examples of unacceptable risk, such as AI systems used for social scoring or mass surveillance, which are prohibited unless authorized by law. High-risk AI applications must undergo conformity assessments to ensure compliance. AI chatbots and other limited-risk applications require transparency, while minimal-risk AI systems have no specific regulatory obligations but may benefit from voluntary conduct codes.

15:08

🏢 Organizational Strategies for Effective AI Governance

This paragraph delves into how organizations can implement AI governance by addressing questions about accountability, oversight, and system complexity. Kalahan emphasizes the importance of maintaining a balance between governance and innovation. He warns that excessive governance can hinder progress by introducing unnecessary administrative burdens, slowing down innovation. The key challenges include managing fast-evolving AI systems, ensuring accountability, and reducing complexity without stifling creativity or operational efficiency.

20:10

⚠️ Risks of Poor AI Governance: Legal, Financial, and Reputational

Kalahan outlines the negative consequences of lacking proper AI governance. These include potential reputational damage, legal risks due to non-compliance with emerging regulations, and inefficiencies that lead to wasted resources. He explains that without governance, organizations may struggle with inconsistent product development, misuse of data, and an inability to align AI projects with business objectives. Effective governance helps mitigate these risks by ensuring alignment with ethical practices and legal standards.

25:12

🚀 The AI Adoption Journey: Stages of Integration

This section breaks down the AI adoption journey into six stages: exploration, experimentation, adoption, expansion, systemic application, and strategic integration. Kalahan explains that organizations move from initial curiosity and pilot projects to broad AI integration across business units. At the final stage, AI becomes central to long-term strategy, driving both operational efficiency and strategic decision-making. This progression requires careful planning, regulatory alignment, and continuous innovation to ensure that AI remains scalable and effective.

Keywords

💡AI Governance

AI governance refers to the frameworks, policies, and practices that ensure the ethical, transparent, and responsible use of AI technologies. In the video, AI governance is discussed as a crucial factor for companies to scale AI solutions while minimizing risks and adhering to legal standards. It emphasizes the need for governance to manage AI deployments across industries and regions to align with both organizational values and societal well-being.

💡Ethical AI

Ethical AI involves ensuring that AI systems are used in ways that are aligned with societal values and ethical standards. The speaker highlights that AI must be developed responsibly to avoid issues like bias or harmful decision-making. Ethical considerations are vital for maintaining public trust, especially in industries like healthcare and finance, where AI's impact on people's lives is direct and significant.

💡Risk Management

Risk management in AI refers to identifying, evaluating, and minimizing potential risks associated with AI systems. The video stresses that as AI becomes more advanced and widely adopted, the risks increase, necessitating strong governance frameworks to manage these risks. The speaker discusses the importance of addressing risks such as data bias, privacy violations, and unintended consequences from AI technologies.

💡AI Regulations

AI regulations are legal standards and laws governing the development and deployment of AI systems. The speaker provides a timeline of global efforts to regulate AI, including milestones like the OECD AI principles and the EU AI Act. These regulations ensure AI is developed safely and in compliance with evolving legal standards, which are crucial as AI technology continues to grow at a rapid pace.

💡EU AI Act

The EU AI Act is the world's first comprehensive regulatory framework for AI, aimed at classifying AI applications by risk levels, from minimal to unacceptable risk. The speaker explains that the Act sets standards for AI systems to protect people’s rights and safety, prohibiting high-risk applications like mass surveillance and social scoring unless explicitly authorized. It represents a significant step in global AI governance.

💡Transparency

Transparency in AI involves making AI systems understandable and ensuring that users are aware when they are interacting with AI. The video emphasizes the importance of transparency in mitigating risks and ensuring ethical AI use. For example, in cases of limited-risk AI applications like chatbots, users should be informed that they are engaging with an AI system, which builds trust and accountability.

💡AI Adoption

AI adoption refers to the process by which businesses and organizations integrate AI technologies into their operations. The speaker discusses various stages of AI adoption, from exploration to strategic integration, and emphasizes that a coordinated effort is necessary for successful implementation. AI adoption can offer competitive advantages, but it also requires governance to ensure that it aligns with an organization’s strategy and values.

💡Generative AI

Generative AI refers to AI models capable of creating new content, such as text, images, or code. In the video, generative AI is noted as a rapidly growing area with unique risks, which has prompted governments to address its potential ethical and legal challenges. The speaker mentions tools like GPT and Gemini as examples of how generative AI is being used by individuals and businesses, highlighting the need for governance to manage its impact.

💡Compliance

Compliance in AI governance ensures that AI systems adhere to legal, regulatory, and ethical standards. The speaker emphasizes that with the introduction of various AI laws, companies must ensure that their AI deployments comply with these regulations to avoid fines and legal repercussions. AI governance helps organizations stay compliant by implementing necessary frameworks to meet regulatory requirements.

💡Multidisciplinary Effort

AI governance requires a multidisciplinary effort, involving different departments like legal, data, IT, and security teams within an organization. The speaker underscores that AI impacts various aspects of a business, not just technology, and that its governance should involve collaboration across all units to effectively manage risks and ensure successful adoption. This shared responsibility is critical for addressing the complex challenges of AI.

Highlights

AI governance is essential for ethical, transparent, and responsible use of AI across organizations.

AI adoption has exploded in recent years, especially in business, with significant challenges for organizations not fully engaged with AI.

AI governance involves frameworks, policies, and practices that ensure AI aligns with organizational values and legal standards.

AI is impacting various industries including drug discovery, fraud detection, and cybersecurity, making governance crucial.

AI's rapid growth introduces risks and necessitates governance to manage evolving global laws and regulatory standards.

The OECD AI principles in May 2019 marked a significant global effort in regulating AI before the rise of large language models.

UNESCO's 2021 AI ethics recommendation was adopted by nearly 200 member states, highlighting the global commitment to AI regulation.

The EU AI Act is the first comprehensive global AI regulation, entering into force in 2023, setting standards for risk-based AI applications.

The EU AI Act classifies AI into levels of risk from unacceptable to minimal, with certain high-risk AI uses requiring conformity assessments.

Excessive governance can stifle innovation by adding unnecessary administrative work and slowing down AI adoption.

AI governance must be multidisciplinary, involving legal, data, technical, and ethical units across the organization.

Organizations must balance the need for innovation with ethical and responsible AI use, as rapid AI evolution poses significant challenges.

Without AI governance, companies risk legal issues, wasted resources, and inefficiencies in product development.

AI governance overlaps with data governance and corporate governance, ensuring AI is responsibly integrated within the organization.

The AI adoption journey involves exploration, experimentation, adoption, expansion, systemic application, and strategic integration.

AI becomes a strategic tool when integrated into an organization's core functions and long-term goals, influencing decision-making at all levels.

Transcripts

hello everyone welcome to my session I'm

very happy to be here with you today in

this amazing conference and to talk

about the AI governance challenge uh my

name is paa kalahan I'm a technical

director at Charles River Laboratories I

am also a Google developer expert in Ai

and machine learning and I am an

appointed member of the Google developer

Advisory Board as well so yes I'm very

excited to be here today uh and to cover

this very interesting topic and let's

just

begin so uh again today I'll be

discussing why the governance of AI is

so

important why it is needed right now and

how it can help organizations to

effectively scale AI deployments across

multiple use cases and most importantly

across multiple regions and of course

um from the last couple of years uh AI

adoption has just exploded in in the

business world uh for Curious people

it's very easy to start you know playing

with AI using these tools and maybe you

know start um finding value on on the

interactions and the applications of of

uh tools like like um you know CH GPT or

Gemini and so on and uh for business

however is is a different challenge uh

maybe not everyone within a company

within an

organization uh they are interested in

learning about AI or using AI as a tool

to help them in their work and for a

business to generally uh be successful

in the AI adoption proc process a

coordinated effort is needed and this is

where H the initiative of AI governance

is is very very important so what is AI

governance so AI governance mostly

refers to Frameworks and policies and

practices that can guide the ethical

transparent and responsible use of AI

and it also ensures that AI systems are

developed and are deployed in ways that

are aligned with the organization values

uh also legal standards and of course uh

it's a aligned to um to be helpful and

useful for society and the time is now

this is a critical moment uh AI is no

longer something that is going to happen

in the future is everywhere is driving

innovation in drug Discovery from broad

detection uh cyber security in the

medical field and um a lot of use cases

that we can imagine right now they might

have an AI component into it but it's

very important for us to understand that

as AI grows and the implementation and

Adoption of AI grows so do the risks and

we are dealing with an incredibly fast

moving technology

uh

alongside rapidly evolving Global laws

and regulations and we're going to talk

a little bit about that uh so how can

organizations can start to adopt good AI

governance practice we're going to talk

about that uh today uh why should

organizations prioritize AI governance

right now and why um why should we care

right

um and I believe that we all should care

about AI governance because we are

navigating Uncharted Territory and and

with a rapid with a rapid pace of AI

development

and moving regulatory targets all around

the world that is a real real need to

ensure that we are using these powerful

technology safely responsibly and that

we uh maintain

compliance so let's have a look at

before we continue it's good to stop for

a minute and have a look at the current

landscape of you know laws and

regulations that are around the world

and how how has been like the timeline

for this so in May 2019 and we have to

be mindful that this was before the

explosion of llms and CH GPT Gemini

llama all of these models uh the

oecd uh um AI principles were adopted uh

for the first time then in June 2019 the

G20 AI principles were adopted as well

um then the

gpai which is the global partnership on

AI was launched to Foster of course

International cooperation then in

November 2021 I believe this this was a

very important um moment for the whole

uh Regulatory and and and and the the

whole worldwide effort on uh regulate

and take care of how II is being used so

the

UNESCO um recommended they they work on

this AI ethics framework and is a it was

a really important one and if you are

interested in this area I really would

recommend you to go and have a look at

this AI ethics recommendation and it was

quite significant because it was adopted

by almost 200 un uh member states from

the United Nations uh then there was a

u20 leaders declaration around the AI

regulation and pro

Innovation uh October 2023 the G7

countries um they launch a forum on the

generative AI risks so this was

important because the generative AI boom

already started and uh organizations and

governments around the world started to

um realize that it was going to be a

huge huge area of AI that um can come

with huge risks as well so um they need

to start working on on on analyzing the

risk the risks and creating of course

the the regulations around it uh on

November 2023 also there was the bled Le

declaration here in the United Kingdom

it was signed by 28 countries and the EU

as well and they during that during that

uh time they also did the they run the

first ai ai I safety

Summit then this year in March there was

a a AI resolution that was adopted by

the United Nation general assembly and

then we had uh o ecd the oecd principles

were updated from the one that was uh

created on

2019 then the council of Europe um Al

adopted an AI treaty there was a very

interesting um AI Summit in South Korea

that it was a followup on the summit

that was um run in in the in the UK in

the blery Declaration and finally what

many people believe is the the most

robust and important uh AI regulation

set of regul

regulations uh finally entered into

Force which is the EU AI act and is the

world's first comprehensive AI

regulation then they spend um a good a

good amount of years working on it they

had to adapt it to include generative AI

as well so this is the current um you

know uh the the the current landscape

that we have around uh regulations World

while and again I will highly suggest

you to go and and have a look at

it so just to have a brief overview of

specifically the euii ACT what they're

doing is that they are um analyzing the

AI applications and they are classifying

it into levels so there are it comes

from unacceptable risks all the way it

down to minimal risk so for example

certain AI applications are going to be

prohibited by law and those are the

unacceptable risk to the safety

livelihoods and the rights of people so

these use cases are prohibited unless

authoriz authorized by law for national

security purposes and that might include

uh the ones that are forbidden social is

scoring AIC s manipulation of human

behavior that might cause harm and mass

surveillance then the second level is

related to high risk uses and it will be

subject to uh a Conformity assessment

before they can be

deployed and this assessment looks at

the quality of the data and it says to

minimize the risk and discrimin

discriminatory

outcomes um so so for example access to

employment education public services and

so on then the third level is H related

to limited risk uh uses and it will only

have transparency obligation so for

example in the case of a AI based

chatboard users should be aware that

they are interacting with a AI

application and finally the last level

um includes the minimal risk excuses and

those are not going to be subject to any

obligation uh however the adoption of

voluntary codes of conduct is

recommended uh this could enhance the

trust for adoption of AI and and give

like a competitive advantage to to

service

providers right so let's start talking

about what can organizations do uh

around AI governance so again um AI

governance let's remember is a set of

rules practices processes and tools that

are employed to to

ensure uh that the organizational use of

AI Technologies aligns with the

organization's strategies the objectives

um their values and that they also

fulfill legal requirements and something

that is very important and we cannot

forget about it is that it meets the

principles of ethical and responsible

use of AI followed by the

organization

so we can start by acknowledging that

fast moving AI Innovation is putting

pressure on companies so there is a comp

competitive Advantage companies are

adopting AI uh very very in a very rapid

way to stay competitive and avoid losing

market share to AI adopting uh Rivals

then we have the Regulatory Compliance

uh governments are beginning to discuss

an enact regulations around AI adding a

layer of complexity to its adoption and

and and its use then there is the

pressure to innovate again uh companies

must quickly integrate AI or they risk

failing behind in the in in this rapidly

evolving technological landscape then we

have the ethical and governance

challenges

integrating AI into

business uh we need to be careful we

need to have a careful consideration of

ethical implications and of course how

are we going to uh how the governance of

this is going to happen and then the

evolution of uh AI

capabilities AI Technologies is going to

continue advancing very fast and

companies they need to keep up with the

latest development to um be able to take

advantage of its You full potential and

then of course a very very important

piece of this is the risk management so

companies need to have a very robust

framework to to be sure that they can

take care of the risks that are

associated with these kind of

Technologies and we're going to talk

about a little bit about this later but

uh one thing that is important to start

is that the effort of AI governance is a

multidisciplinary effort within a

business it has to be shared across all

of the business units because AI the the

impact of this is not is not only

technological a technical problem it's

also a legal problem it's also a data

problem and and so so is change

management is information security of

course so it covers a lot of different

areas so we need to be we we need to

have in mind the AI life governance the

Regulatory Compliance and the risk

management those are the three pillars

that we have to uh think about when when

we start to uh Implement an AI

governance

um strategy within your

company so when we are starting to

implement AI governance we need to ask

these questions so as the system grows

and it gets more

complex uh the the the challenge of

maintaining and updating these uh

intensifies

so um how how is this going to affect uh

these processes then who will be

responsible for having uh or overseeing

the entire AI application uh or the this

AI system who who's going to have the

overview then if something goes wrong

who is accountable accountable for

responding to to issues when when they

arise who takes actions when something

goes wrong then uh we need methods for

recognizing and and rectifying the use

of maybe incorrect data or

algorithms how will the use of wrong

data or algorithms is are going to be

detected uh then we need to think about

the approaches to enhance the

system and reduce its complexity how

will the system uh be improved and

complexity reduced and this is a very

tricky thing in AI again because AI is a

field that is evolving very very fast

and it's very hard to keep up and it's

very hard maybe you have an

implementation using

llms and maybe you need to use now a

better a better model came about maybe a

new version of the model that you

already used and changing that is is is

very complex and and we have we're

dealing with a new way of uh creating

software a new way of uh integrating

these tools into our Solutions so that

is something that we really need to

think about as

well and then the danger of excessive

control right if you implement too much

governance uh it can be actually contr

counterproductive because if we increase

the administrative work and we put more

red tapes around it more bureaucracy it

will further complicate the system so uh

adding too much governance again is

counter effective and and will only add

to the that burden and it will create

more complexity and it might slow down

innovation

so let's think about what happens when

AI governance is not in place so we can

have obviously um you know as as

exciting as AI is without proper

governance it can lead to serious issues

that can impact not just the technology

or or the solutions that we're using but

also the entire organization so first of

all damage uh uh to the organization's

reputation uh a lack of AI governance

can can damage our reputation when AI is

misused or makes harmful decisions it

can be due to bias lack of transparency

unethical practices copyright issues it

can really impact the public trust so

especially in industries that work in

the areas of Health Care Finance law

enforcements uh this is very important

because in these areas trust is

everything so then we have the legal and

Regulatory issues without governance we

expose our organization to legal and

Regulatory risks uh again as we saw

earlier governance are governments are

introducing very rapidly laws and

regulations around the use of AI and if

the if our AI system don't comply with

this evolving standards we can face

fines sanctions or even lawsuits so

these consequences are becoming more

common as AI regulations they get like

more important globally so we really

need to think about is our legal team

ready to handle this so an AI governance

strategy an AI governance initiative can

help to

organize and and and and yeah like it

can help to yeah to organize all of

these different aspects across the whole

uh organization then wasted resources if

we don't have a clear governance

framework uh the AI projects they can

become inefficient they can become

expensive we can be doing repetitive

task duplicated effort so

um maybe teams they might be spending

time building models or system that they

don't align with the company objectives

they don't align with the legal

requirements

so the risk is to be investing heavily

in projects that don't deliver

value then that brings also to

inefficiencies in developing

products uh so so governance or the

lacking governance gaps or the lacking

of of governance can also lead to this

in in in the product development without

a proper oversight AI models might be

built with the wrong data they might

fail to do the uh the work correctly uh

or or maybe just yeah not working as as

intended so this can mean this can bring

delays on bringing the products to the

market uh and sometimes just canceling

the whole projects um another

issue is the inability to use the data

for training AI models in an efficient

way so without governance um this you

might not be able to use your data

effectively um AI thrives on quality

data but if you don't have a strong

governance to ensure that the data is

handled ethically securely and legally

then you might be on a position that

you're unable to leverage the full power

of your data sets and um what's even

worse of improperly managed data can

result in bias or unreliable AI outcomes

but for that we have data governance

right so you will see how data

governance and AI governance they

overlap and and they need each

other so that's what I'm talking about

here uh as you can see in this uh graph

to have an effective AI governance we

should uh build on other areas of

corporate governance that is already

there so we avoid duplicating like the

duplication of processes so like I said

before AI governance overlaps with data

governance and that are part of the it

governance that the company probably

already has and that lies on also onto

the whole corporate governance so

um if we want to to ensure that AI

systems are developed and deployed

responsibly AI governance must be like a

core component of the

organizations like the overall

governance structure is not enough and

this is very important to manage AI in

isolation AI govern governance

needs to work in harmony with other

governance Frameworks in order for it to

be truly effective so um we need to

implement an AI governance plan uh then

supervise the AI life cycle monitor data

provenance ensure the quality of data

reduce the risk associated with the use

of AI so you can see there that we have

uh we can have the

Data Business unit uh involved in this

we can have information security involv

in this so is a it's a

effort that has to be done by many

different business units across the

organization so let's have a look at the

AI adop adoption journey and as you can

see the the journey can be broken down

into several stages and each represents

like a different level of maturity in

how II is integrated into an

organization so we can first uh it all

starts with the first stage which is

exploration so this is very fast and if

you can see this whole process it starts

really really fast and then it it will

slow down as we uh progress into the

journey so first it starts with

exploration right so in this stage

organizations are just starting to look

into AI possibilities they explore how

AI could be applied to their operations

they identify use cases they might

gather initial data and this is more

driven by curiosity and

experimentation and it's is a fast uh

movement because teams are exploring

just uh opportunities and then we go to

the second uh stage which is

experimentation so after identifying

potential use cases organizations start

running proof of concept projects or

pilots and they test AI models maybe on

a small scale on isolated environments

and they want to see how well the

technology performs if it it's deliver

if it if the solutions might deliver

meaningful results so this stage is all

about learning iterating and and gaining

like Trust on the

technology then the next step if the

experimentation phase shows positive

results we move into the adoption stage

so at this point the organization they

start to adopt AI Solutions more broadly

and AI begins to be embedded in in

certain uh maybe business units or

workflows but it's still very limited in

in

scope um so this phase of adoption is

key for building AI into the like the

foundation of the business and it really

helps to lay the groundwork for a a a

broader um

deployment so after adoption

organizations and enter the expansion

stage so here the use of AIS scales

across more Department uh

projects or or again a business units

and an AI starts driving value in in a

lot of different areas so it's just not

isolated experiments anymore so now

organizations can focus on refining thei

models improving the data pipelines and

integrating AI into a larger business uh

strategies then at this stage AI has

move into systemic application so AI

becomes a critical component of business

operations and is used is standardized

across the whole organization so what

this really means is that AI is no

longer seen as like an experimental tool

but as a regular tool tool that supports

like everyday processes and decision

making and then on the uh finally we we

reach the Strategic integration so in

this phase AI becomes an integral part

of the organization's long-term strategy

is not just use

um in operations but also helps to drive

a strategic decision making at the

highest levels so AI is embedded into

the company's core function and its

potential is fully

realized however this stage is is often

slower at because it requires alignment

with the company's long-term goals the

culture and the regulatory

Frameworks so as we can see the AI

adoption Journey starts fast during

exploration but becomes

like more methodical as AI becomes

Central to the

organization and by understanding where

you are on this journey you can plan the

necessary steps to move forward with

confidence and and and so you can ensure

that AI is both scalable and and is

aligned with your business

strategy and yes again uh thank you so

much for for having me and you can scan

that QR code over there if you want to

get in touch with me and of course I

will be around to answer uh all of your

questions and if you have further

questions about AI governance or AI uh

any AI related topic I will be more than

happy to help you so thank you everyone

for joining me and keep on enjoying this

fantastic conference thank

you a