توصیه هایی از تیم مایکروسافت متحد ما

در صفحات آینده، بینش ها و آموخته های تیم مایکروسافت  را به اشتراک 
می گذاریم - گروهی از کارشناسان که وقف واقعی ساختن هوش مصنوعی برای
مشتریان خود هستند.

(4steps to accelerate your AI journey   (

4مرحله برای سرعت بخشیدن به سفر هوش مصنوعی 

Recommendations from our Microsoft Unified Team

توصیه هایی از تیم مایکروسافت متحد ما

step1
learn about AI, explore and define
what it means for your business

 

در صفحات آینده، بینش ها و آموخته های تیم مایکروسافت  را به اشتراک می گذاریم – گروهی از کارشناسان که وقف واقعی ساختن هوش مصنوعی برای مشتریان خود هستند.

 Recommendations from our Microsoft Unified Team

In the upcoming pages, we will share insights and learnings from the Microsoft Unified team – a group of experts who are dedicated to making AI a reality for our customers.

We will discuss their recommended four steps to accelerate your AI journey:

Learn about AI, explore and define what it means for your business

Set priorities in your AI journey, review use cases, and plan the AI strategy across the organization.

Formalize the execution of the AI strategy.

Tap into the experts to get it done

Let’s get into it!

First, you need to understand what AI means. In the nearly 70 years since the American computer scientist John McCarthy coined the term “artificial intelligence,” we’ve seen wave after wave of technological innovation, from the rise of personal computing to the internet, mobile devices, the cloud, and now, generative AI. Each of these shifts has created new opportunities and new questions for leaders.

How can these technologies help my organization thrive? What new possibilities could they offer? How should I organize for optimal impact? And, perhaps most importantly, how can I use these technologies in a way that promotes trust among customers, citizens, patients, partners, shareholders, and the public?

ابتدا باید معنی هوش مصنوعی را بفهمید. در تقریباً 70 سال از زمانی که دانشمند رایانه آمریکایی جان مک کارتی اصطلاح «هوش مصنوعی» را ابداع کرد، موجی پس از موج نوآوری‌های تکنولوژیکی، از ظهور محاسبات شخصی گرفته تا اینترنت، دستگاه‌های تلفن همراه، ابر و اکنون، دیده‌ایم. هوش مصنوعی مولد هر یک از این تغییرات فرصت ها و سوالات جدیدی را برای رهبران ایجاد کرده است.

The theory and development of computer systems that can perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

A subset of AI and computer science where algorithmic models are trained to learn from existing data to make decisions or predictions.

A machine learning technique that uses layers of neural networks to process data and make decisions.

A type of AI technology that uses algorithmic models to create new written, visual, and auditory content when given prompts or existing data.

While the term artificial intelligence has been around since the 1950s, the pace of AI innovation has accelerated in the past several years, thanks to plentiful data, greater access to computing power, and the size and sophistication of algorithmic models.

AI is fundamentally different from technologies that have come before it, both in its approach and its capabilities. It is based on probabilities, rather than rules, and “learns” from data without requiring explicit instructions. AI makes it possible for computers to perform certain tasks that previously only humans could do, such as visual perception, speech recognition, decision-making, and language translation.

Generative AI takes that one step further. It can be used for, among other things, content creation, summarization, and question answering, as well as the creation of images based on text prompts, all using everyday language.

AI is disrupting every industry and every business. For the last decade, AI has enabled companies of all sizes to achieve better business results. There’s already mainstream business use of AI thanks to these three trends:

Access to massive amounts of data.

 Access to massive computing power through the cloud.

 Access to AI algorithms.

AI is now experiencing major breakthroughs. A new generation of LLM enables new use cases that weren’t possible a few years ago, such as those based on high-quality generative AI. With these technologies, organizations will experience a second wave of AI-powered transformation. However, businesses need an easy way to access the latest AI if they want to take full advantage of it.

Microsoft is working to democratize AI usage. For this, it has designed a wide range of solutions and services to bring AI to everyone, regardless of their level of AI expertise. There are four approaches, ranging from the level of AI and coding expertise required.

Microsoft has embedded AI in everyday applications, so business users can benefit from it, even if they don’t know anything about coding or data science. In this approach, AI is delivered as a Software as a Service (SaaS) and becomes transparent, that is, it’s fully integrated within the provided service without users having to worry about it.

For example, Microsoft 365 Copilot incorporates the latest generative AI in the shape of a virtual assistant that performs tasks for you in Microsoft 365 apps.

It covers several low-code products

that help you build different pieces of applications. These products have a layer of AI, but it’s transparent as well and you can benefit from it without handling it directly.

These are the solutions for users who want to create their own AI solutions – Azure AI models are designed to accelerate AI innovation, simplify model operations, and build on trust.

Azure Machine Learning is a platform that enables you to build, deploy, and manage machine learning solutions at scale.

Keep in mind that Microsoft has designed all these products and services following strict responsible AI principles and any AI implementation should be equally respectful.

As we get to the end of this first step, you must understand your business and organization’s current state when embarking on the journey of AI. Just as a captain assesses the weather, you should imagine this tool as a compass to navigate through waters of data and innovation.

You also need to know that your AI transformation won’t happen in one day – but if you prioritize the goals and set a strategy, it’s easier to get to the future of innovation. Are you ready for the next step,

captain?

There are a few steps to take to establish priorities for your AI journey:

 Driving a strategic and tactical alignment to understand your strategy and how operational capabilities are needed to align to best deliver against your strategy effectively.

Defining governance and how to use responsible AI. AI governance is a framework or a set of processes that outlines and guides the use of AI in an organization or a society. The goal of AI governance is to ensure that AI is ethical, transparent, responsible, fair, and compliant with legal and regulatory standards. AI governance also involves managing the risks, quality, and accountability of AI systems and their outcomes. AI governance is used to establish clear policies and standards for AI development, deployment, and usage.

Step 2

 Delivering a tactical focus, illustrating which key capabilities need to be enabled for the service in scope.

 Establishing a pragmatic roadmap and a clear transition plan on how to move forward.

 Setting up an AI Council of senior executives who will guide your organization’s AI adoption.

There are many examples of industries in which AI transformation can happen. For businesses of all types and sizes, AI is reshaping nearly every aspect of how we work—it has the potential to boost productivity, optimize processes, and ultimately drive robust, sustainable growth across a variety of scenarios and industries, including finance, manufacturing, retail, and healthcare.

Learn how AI can benefit your industry:

 AI in Financial Services – Unlock business value and deepen customer relationships.

 AI in Healthcare – Provide patientcentered care at optimized costs.

 AI in Manufacturing – Modernize manufacturing operations.

 AI in Retail – Deliver personalized retail services and experiences.

Plan how your AI strategy will land in your organization by following these four milestones for execution:

Data Management

Adoption & DevOps

Change Management

Measure success

Step 2

As you can see in this chapter, embarking on the journey of AI requires more than understanding the seas you’ll go through. Like a top crew ensures a ship’s success, you’ll need to discover the talent and skills of your employees within your organization.

Think of them as the wind that pulls you forward – they’ll not only guide you in this but also empower your crew to navigate the challenges that lie ahead.

Identifying those talents and nurturing them is key to

unlocking their full potential – each member of your team brings a unique set of skills. So, empower your crew, as they’re the ones who will guide your ship to the future of intelligent innovation.

Are you ready for the next step through this AI expedition?

Data is the fuel that powers AI technology, so planning for any successful AI implementation requires that you identify the right data sources and ensure that the data is complete, of high quality, in the right format, and representative of your target customers and business objectives.

“Organizations continue to see their data as their competitive advantage,” says Wangui McKelvey. “Unlocking insights from their data, across their organization, in a single, integrated platform, empowers businesses to take advantage of AI. If you don’t understand

the insights you can deliver from your data with AI, then you’re going to be left behind.”

Make sure to have in place data governance/compliance rules, security factors, data protection, data ownership and accountability, retention and purging policies, and the labeling for data and metadata.

Data quality and transparency: Enhancing AI performance and reducing reputational risks by improving data quality, ensuring data transparency, reducing bias in AI models, and having safeguards for data sources. Also, use persona for better AI model development.

Assess Infrastructure needs and goals: Moving successfully from proof-ofconcept (POC) to production with AI depends on a mix of technology and business factors that, ideally, must work together. “Understanding how your business strategy maps to your product strategy, and then how your product strategy maps to your infrastructure, is key,” says Omar Khan, General Manager of Microsoft Azure Product Marketing. “AI- optimized infrastructure will help accelerate both building AI solutions and integrating AI into applications.” From a technology perspective, the most critical requirement is access to infrastructure that is built for AI—with the ability to run large AI workloads and perform securely and reliably at scale.

Streamline your development environment by incorporating DevOps / Machine Learning Operations / LLM Operations processes and tools in your organization. Foster the collaboration and integration of your Data Science, User Experience, and Development teams and accelerate your time to market.

Drive skilling within the organization: As AI transforms the way work is done, many businesses are struggling to implement AI solutions due to an AI skills gap. Many businesses struggling with AI lack a structured approach to AI learning, partitioning AI into small teams, and waiting to engage to accelerate AI transformation, organizations need to take a skills-first approach.

 Take a structured approach to AI skill building: Understanding AI, Preparing for AI, Using AI, and Building AI solutions.

 Upskill across every business function—from leadership to IT to dev to line of business users.

Follow security guidelines: Just as AI enables new opportunities, it also introduces new imperatives to manage risk, whether related specifically to AI usage, app and data protection, compliance with organizational and legal policies, or threat detection. Microsoft’s holistic approach to generative AI security considers the technology, its users, and society at large across four areas of protection: data privacy and ownership, transparency and accountability, user guidance and policy, and security by design.

Integrating change management practices is a must for ensuring the successful adoption and utilization of AI technology in your business. The “Awareness, Desire, Knowledge, Ability, and Reinforcement” (ADKAR®) model, leveraged by Microsoft and our customers, provides an approach to managing change effectively. Here’s how you can apply it in the context of AI adoption:

Measuring the impact of AI projects should encompass a range of stakeholders and objectives and include both quantitative and qualitative methods. Some suggested metrics are:

AI activities should be monitored for compliance and ethical considerations.

Regular reporting to the board provides visibility into the effectiveness of AI governance.

Successful AI development is a blend of diverse teams, continuous learning, and a healthy tolerance for ambiguity. But the most important step is the first one.

As Eric Boyd, Corporate Vice

President of Azure AI Platform at Microsoft, said: “You’ve got to get in the game. “Try something. Iterate and learn, try different things, and see what works for your application. Empower everyone in your organization to discover how AI can transform your business.”

This effort represents a learning curve, and Microsoft has a team of experts you can rely on during this journey – we’ll talk about that in the upcoming section. The good news? You’re almost ready to embark on this adventure.

Microsoft stands at the forefront of the AI revolution, being pioneers that redefine industries and empower organizations to achieve more. With a team of experts dedicated to AI research, development, and implementation, Microsoft offers unparalleled expertise in harnessing the transformative power of AI.

Our commitment to advancing AI technology is evident in our comprehensive suite of AI solutions, spanning from Azure AI services to the AI capabilities of Dynamics 365, enabling organizations to drive innovation, enhance customer experiences, and unlock new growth opportunities.

Our team of experts will work with you to imagine and realize the art of the possible. By combining your unique environment and business goals with deep knowledge of Microsoft technologies, to methodically develop and implement an effective plan for your continued success.

With Unified you can access expert engineering and data science support for your AI applications. Our team combines the technical expertise of Azure AI and machine learning engineers with data science capabilities to help you develop and deploy AI solutions. Your integrated team —an AI and machine learning Cloud Solution Architect partnered with Customer

Step 4

Success Management Services — will help you stay on top of ongoing AI application development, Azure OpenAI use cases and maintenance. These experts will be your partners as they assess your specific environment,

4 steps to accelerate your AI journey                                                                16

science capabilities, and provide highquality proactive support.

augment your team’s IT and data

Learn more about the value delivered by Unified to help you reach your desired state:

Unified is uniquely positioned to help you build, deploy, and operationalize AI products and services. Harness the power of infinite-scale infrastructure and intelligence with datasets through deep learning frameworks to drive growth, innovation, speed to

market, and cost efficiency. These offerings combine the deep technical expertise of Azure

AI and machine learning engineers with data science capabilities.

Step 4

By partnering with Microsoft and tapping into our AI capabilities through Unified, organizations can empower their initiatives, drive operational efficiency, and stay ahead in this rapidly evolving digital landscape.

           4 steps to accelerate your AI journey                                                        17

Together, we can unleash the full potential of AI to transform businesses, drive innovation, and shape the future of work. Get ready to sail with us.

©2024 Microsoft Corporation. All rights reserved. This document is provided “as-is.” Information and views expressed in this document, including URL and other Internet website references, may change without notice. You bear the risk of using it. This document does not provide you with any legal rights to any intellectual property in any Microsoft product. You may copy and use this document for your internal, reference purposes.

 

 

(4steps to accelerate your AI journey   (

4مرحله برای سرعت بخشیدن به سفر هوش مصنوعی 

Recommendations from our Microsoft Unified Team

توصیه هایی از تیم مایکروسافت متحد ما

step1
learn about AI, explore and define
what it means for your business

در صفحات آینده، بینش ها و آموخته های تیم مایکروسافت  را به اشتراک می گذاریم – گروهی از کارشناسان که وقف واقعی ساختن هوش مصنوعی برای مشتریان خود هستند.

 

 

 

 

 

 

 

 

 

 

 

 

Recommendations from our Microsoft Unified Team

In the upcoming pages, we will share insights and learnings from the Microsoft Unified team – a group of experts who are dedicated to making AI a reality for our customers.

We will discuss their recommended four steps to accelerate your AI journey:

Learn about AI, explore and define what it means for your business

Set priorities in your AI journey, review use cases, and plan the AI strategy across the organization.

Formalize the execution of the AI strategy.

Tap into the experts to get it done

Let’s get into it!

First, you need to understand what AI means. In the nearly 70 years since the American computer scientist John McCarthy coined the term “artificial intelligence,” we’ve seen wave after wave of technological innovation, from the rise of personal computing to the internet, mobile devices, the cloud, and now, generative AI. Each of these shifts has created new opportunities and new questions for leaders.

How can these technologies help my organization thrive? What new possibilities could they offer? How should I organize for optimal impact? And, perhaps most importantly, how can I use these technologies in a way that promotes trust among customers, citizens, patients, partners, shareholders, and the public?

 

ابتدا باید معنی هوش مصنوعی را بفهمید. در تقریباً 70 سال از زمانی که دانشمند رایانه آمریکایی جان مک کارتی اصطلاح «هوش مصنوعی» را ابداع کرد، موجی پس از موج نوآوری‌های تکنولوژیکی، از ظهور محاسبات شخصی گرفته تا اینترنت، دستگاه‌های تلفن همراه، ابر و اکنون، دیده‌ایم. هوش مصنوعی مولد هر یک از این تغییرات فرصت ها و سوالات جدیدی را برای رهبران ایجاد کرده است.

 

The theory and development of computer systems that can perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

A subset of AI and computer science where algorithmic models are trained to learn from existing data to make decisions or predictions.

A machine learning technique that uses layers of neural networks to process data and make decisions.

A type of AI technology that uses algorithmic models to create new written, visual, and auditory content when given prompts or existing data.

While the term artificial intelligence has been around since the 1950s, the pace of AI innovation has accelerated in the past several years, thanks to plentiful data, greater access to computing power, and the size and sophistication of algorithmic models.

AI is fundamentally different from technologies that have come before it, both in its approach and its capabilities. It is based on probabilities, rather than rules, and “learns” from data without requiring explicit instructions. AI makes it possible for computers to perform certain tasks that previously only humans could do, such as visual perception, speech recognition, decision-making, and language translation.

Generative AI takes that one step further. It can be used for, among other things, content creation, summarization, and question answering, as well as the creation of images based on text prompts, all using everyday language.

AI is disrupting every industry and every business. For the last decade, AI has enabled companies of all sizes to achieve better business results. There’s already mainstream business use of AI thanks to these three trends:

Access to massive amounts of data.

 Access to massive computing power through the cloud.

 Access to AI algorithms.

AI is now experiencing major breakthroughs. A new generation of LLM enables new use cases that weren’t possible a few years ago, such as those based on high-quality generative AI. With these technologies, organizations will experience a second wave of AI-powered transformation. However, businesses need an easy way to access the latest AI if they want to take full advantage of it.

Microsoft is working to democratize AI usage. For this, it has designed a wide range of solutions and services to bring AI to everyone, regardless of their level of AI expertise. There are four approaches, ranging from the level of AI and coding expertise required.

Microsoft has embedded AI in everyday applications, so business users can benefit from it, even if they don’t know anything about coding or data science. In this approach, AI is delivered as a Software as a Service (SaaS) and becomes transparent, that is, it’s fully integrated within the provided service without users having to worry about it.

For example, Microsoft 365 Copilot incorporates the latest generative AI in the shape of a virtual assistant that performs tasks for you in Microsoft 365 apps.

It covers several low-code products

that help you build different pieces of applications. These products have a layer of AI, but it’s transparent as well and you can benefit from it without handling it directly.

These are the solutions for users who want to create their own AI solutions – Azure AI models are designed to accelerate AI innovation, simplify model operations, and build on trust.

Azure Machine Learning is a platform that enables you to build, deploy, and manage machine learning solutions at scale.

Keep in mind that Microsoft has designed all these products and services following strict responsible AI principles and any AI implementation should be equally respectful.

As we get to the end of this first step, you must understand your business and organization’s current state when embarking on the journey of AI. Just as a captain assesses the weather, you should imagine this tool as a compass to navigate through waters of data and innovation.

You also need to know that your AI transformation won’t happen in one day – but if you prioritize the goals and set a strategy, it’s easier to get to the future of innovation. Are you ready for the next step,

captain?

There are a few steps to take to establish priorities for your AI journey:

 Driving a strategic and tactical alignment to understand your strategy and how operational capabilities are needed to align to best deliver against your strategy effectively.

Defining governance and how to use responsible AI. AI governance is a framework or a set of processes that outlines and guides the use of AI in an organization or a society. The goal of AI governance is to ensure that AI is ethical, transparent, responsible, fair, and compliant with legal and regulatory standards. AI governance also involves managing the risks, quality, and accountability of AI systems and their outcomes. AI governance is used to establish clear policies and standards for AI development, deployment, and usage.

Step 2

 Delivering a tactical focus, illustrating which key capabilities need to be enabled for the service in scope.

 Establishing a pragmatic roadmap and a clear transition plan on how to move forward.

 Setting up an AI Council of senior executives who will guide your organization’s AI adoption.

There are many examples of industries in which AI transformation can happen. For businesses of all types and sizes, AI is reshaping nearly every aspect of how we work—it has the potential to boost productivity, optimize processes, and ultimately drive robust, sustainable growth across a variety of scenarios and industries, including finance, manufacturing, retail, and healthcare.

Learn how AI can benefit your industry:

 AI in Financial Services – Unlock business value and deepen customer relationships.

 AI in Healthcare – Provide patientcentered care at optimized costs.

 AI in Manufacturing – Modernize manufacturing operations.

 AI in Retail – Deliver personalized retail services and experiences.

Plan how your AI strategy will land in your organization by following these four milestones for execution:

Data Management

Adoption & DevOps

Change Management

Measure success

Step 2

As you can see in this chapter, embarking on the journey of AI requires more than understanding the seas you’ll go through. Like a top crew ensures a ship’s success, you’ll need to discover the talent and skills of your employees within your organization.

Think of them as the wind that pulls you forward – they’ll not only guide you in this but also empower your crew to navigate the challenges that lie ahead.

Identifying those talents and nurturing them is key to

unlocking their full potential – each member of your team brings a unique set of skills. So, empower your crew, as they’re the ones who will guide your ship to the future of intelligent innovation.

Are you ready for the next step through this AI expedition?

Data is the fuel that powers AI technology, so planning for any successful AI implementation requires that you identify the right data sources and ensure that the data is complete, of high quality, in the right format, and representative of your target customers and business objectives.

“Organizations continue to see their data as their competitive advantage,” says Wangui McKelvey. “Unlocking insights from their data, across their organization, in a single, integrated platform, empowers businesses to take advantage of AI. If you don’t understand

the insights you can deliver from your data with AI, then you’re going to be left behind.”

Make sure to have in place data governance/compliance rules, security factors, data protection, data ownership and accountability, retention and purging policies, and the labeling for data and metadata.

Data quality and transparency: Enhancing AI performance and reducing reputational risks by improving data quality, ensuring data transparency, reducing bias in AI models, and having safeguards for data sources. Also, use persona for better AI model development.

Assess Infrastructure needs and goals: Moving successfully from proof-ofconcept (POC) to production with AI depends on a mix of technology and business factors that, ideally, must work together. “Understanding how your business strategy maps to your product strategy, and then how your product strategy maps to your infrastructure, is key,” says Omar Khan, General Manager of Microsoft Azure Product Marketing. “AI- optimized infrastructure will help accelerate both building AI solutions and integrating AI into applications.” From a technology perspective, the most critical requirement is access to infrastructure that is built for AI—with the ability to run large AI workloads and perform securely and reliably at scale.

Streamline your development environment by incorporating DevOps / Machine Learning Operations / LLM Operations processes and tools in your organization. Foster the collaboration and integration of your Data Science, User Experience, and Development teams and accelerate your time to market.

Drive skilling within the organization: As AI transforms the way work is done, many businesses are struggling to implement AI solutions due to an AI skills gap. Many businesses struggling with AI lack a structured approach to AI learning, partitioning AI into small teams, and waiting to engage to accelerate AI transformation, organizations need to take a skills-first approach.

 Take a structured approach to AI skill building: Understanding AI, Preparing for AI, Using AI, and Building AI solutions.

 Upskill across every business function—from leadership to IT to dev to line of business users.

Follow security guidelines: Just as AI enables new opportunities, it also introduces new imperatives to manage risk, whether related specifically to AI usage, app and data protection, compliance with organizational and legal policies, or threat detection. Microsoft’s holistic approach to generative AI security considers the technology, its users, and society at large across four areas of protection: data privacy and ownership, transparency and accountability, user guidance and policy, and security by design.

Integrating change management practices is a must for ensuring the successful adoption and utilization of AI technology in your business. The “Awareness, Desire, Knowledge, Ability, and Reinforcement” (ADKAR®) model, leveraged by Microsoft and our customers, provides an approach to managing change effectively. Here’s how you can apply it in the context of AI adoption:

Measuring the impact of AI projects should encompass a range of stakeholders and objectives and include both quantitative and qualitative methods. Some suggested metrics are:

AI activities should be monitored for compliance and ethical considerations.

Regular reporting to the board provides visibility into the effectiveness of AI governance.

Successful AI development is a blend of diverse teams, continuous learning, and a healthy tolerance for ambiguity. But the most important step is the first one.

As Eric Boyd, Corporate Vice

President of Azure AI Platform at Microsoft, said: “You’ve got to get in the game. “Try something. Iterate and learn, try different things, and see what works for your application. Empower everyone in your organization to discover how AI can transform your business.”

This effort represents a learning curve, and Microsoft has a team of experts you can rely on during this journey – we’ll talk about that in the upcoming section. The good news? You’re almost ready to embark on this adventure.

Microsoft stands at the forefront of the AI revolution, being pioneers that redefine industries and empower organizations to achieve more. With a team of experts dedicated to AI research, development, and implementation, Microsoft offers unparalleled expertise in harnessing the transformative power of AI.

Our commitment to advancing AI technology is evident in our comprehensive suite of AI solutions, spanning from Azure AI services to the AI capabilities of Dynamics 365, enabling organizations to drive innovation, enhance customer experiences, and unlock new growth opportunities.

Our team of experts will work with you to imagine and realize the art of the possible. By combining your unique environment and business goals with deep knowledge of Microsoft technologies, to methodically develop and implement an effective plan for your continued success.

With Unified you can access expert engineering and data science support for your AI applications. Our team combines the technical expertise of Azure AI and machine learning engineers with data science capabilities to help you develop and deploy AI solutions. Your integrated team —an AI and machine learning Cloud Solution Architect partnered with Customer

Step 4

Success Management Services — will help you stay on top of ongoing AI application development, Azure OpenAI use cases and maintenance. These experts will be your partners as they assess your specific environment,

4 steps to accelerate your AI journey                                                                16

science capabilities, and provide highquality proactive support.

augment your team’s IT and data

Learn more about the value delivered by Unified to help you reach your desired state:

Unified is uniquely positioned to help you build, deploy, and operationalize AI products and services. Harness the power of infinite-scale infrastructure and intelligence with datasets through deep learning frameworks to drive growth, innovation, speed to

market, and cost efficiency. These offerings combine the deep technical expertise of Azure

AI and machine learning engineers with data science capabilities.

Step 4

By partnering with Microsoft and tapping into our AI capabilities through Unified, organizations can empower their initiatives, drive operational efficiency, and stay ahead in this rapidly evolving digital landscape.

           4 steps to accelerate your AI journey                                                        17

Together, we can unleash the full potential of AI to transform businesses, drive innovation, and shape the future of work. Get ready to sail with us.

©2024 Microsoft Corporation. All rights reserved. This document is provided “as-is.” Information and views expressed in this document, including URL and other Internet website references, may change without notice. You bear the risk of using it. This document does not provide you with any legal rights to any intellectual property in any Microsoft product. You may copy and use this document for your internal, reference purposes.