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AI embedded in everyday applications may not be enough to power the business applications an organization needs. In these cases, Power Platform is the next step towards more customizable AI solutions. It provides a simple, low-code way to introduce AI in your business applications without having to create or manage the AI yourself.

What is Microsoft Power Platform?

Microsoft Power Platform provides low-code and no-code services designed to simplify the process of building technical solutions. It provides building blocks that help teams work faster. Even if Power Platform isn’t centered on AI, its services are often powered by AI and help you create smart solutions.

The Power Platform portfolio includes five different products: Power BI, Power Apps, Power Automate, Copilot Studio, and Power Pages. It also offers three additional tools: AI Builder, Microsoft Dataverse, and Connectors. Let’s see what each of them can do for you.

What can you do with Microsoft Power Platform?

All of the products contained in Power Platform are used to speed up business app development. Beside the specific AI functionality they include, they can be connected to Copilot. Thanks to this feature, users can leverage the Copilot generative AI to automatically create the report, workflow, app, website, or chatbot just by describing what they need.

Product	Description
Power BI	Power BI is a business analytics service. It provides insights on a customizable dashboard. It helps organizations be more data-driven and take better decisions based on data. This data-driven approach aligns with one of the core principles of AI, which emphasizes using data to gain valuable insights and make better choices.
Power Apps	Power Apps is a low-code development environment that enables businesses to easily create custom apps without extensive coding knowledge. With the inclusion of AI Builder, developers can seamlessly integrate prebuilt or custom AI models, optimizing business processes and enhancing the intelligence of their applications.
Power Automate	Power Automate is a powerful tool that allows businesses to automate repetitive tasks and streamline workflows without the need for extensive programming. With the integration of AI Builder, users can effortlessly incorporate prebuilt or custom AI models, enabling intelligent decision-making and driving efficiency in business processes.
Copilot Studio	Copilot Studio is a tool for building chatbots. It supports many AI models, mostly those enabling natural language understanding (NLU), so the bot can understand what is being said. However, its AI can also detect parts of the bot that can be improved, and even automatically implement the improvements.
Power Pages	Power Pages is a low-code software-as-a-service (SaaS) platform for creating, hosting, and managing websites. Power Pages simplifies the website development process, making it accessible even to users with limited technical expertise.
Data connectors	Data connectors establish seamless connections between various components (apps, data, devices) and the cloud. These connectors ensure smooth integration and communication, creating a cohesive experience across the platform.
AI Builder	AI Builder empowers developers to incorporate AI capabilities into their applications and workflows without requiring data science expertise. With prebuilt and customizable AI models, AI Builder enhances Power Apps and Power Automate by enabling functionality like sentiment analysis, category classification, entity detection, key phrase identification, and language analysis.
Dataverse	Dataverse acts as the storage solution in the Power Platform, enabling seamless integration with all its products. It serves as a central repository for data, allowing for efficient organization and accessibility.

In summary, Power Platform is a suite of powerful tools designed to help businesses create apps, analyze data, automate tasks, build chatbots, and manage websites. With Power BI, you can get valuable insights from your data and make better decisions. Power Apps lets you easily build custom apps without coding, and AI Builder adds intelligent features like language analysis and sentiment analysis. Power Automate helps you automate repetitive tasks and save time, and Copilot Studio allows you to create chatbots that understand and respond to users. Plus, Data connectors ensure smooth integration between different components, and Dataverse provides a central place to store and access your data. By using these tools together, you can enhance productivity and make your business more efficient.

What is the business value of Microsoft Power Platform?

There are two main ways in which Power Platform creates business value for organizations:

• Reducing development costs: It provides the building blocks for teams to create custom solutions in much less time than required when starting by scratch. Teams can build custom apps in just a matter of days or weeks.
• Enabling more agile, scalable development: The low-code philosophy is central to Power Platform. It allows for faster, more agile solution development. It empowers citizen developers, that is, employees with less coding expertise, to provide working solutions to end users. Professional developers can iterate on this version for further improvement. This collaborative development approach implies solutions are available to end users at an earlier stage and are less costly. This structure is easy to escalate by adding custom functionality.

The diagram shows how this fusion development approach works.

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To truly realize the potential of AI, it’s essential to bring AI to every employee in ways that are relevant and meaningful to their work. Microsoft makes this possible by embedding AI in the applications people use everyday. No code or data science expertise is required because AI is delivered as just another feature of a SaaS product. The result is a wide range of intelligent applications for business users.

Copilot refers to AI embedded into applications. Microsoft Copilot provides a transformed experience across business functions and everyday routines.

Business functions

Some AI solutions are specialized in helping solve problems and gain insight in some specific horizontal functions and sectors. These intelligent business applications weave relevant AI capabilities into their existing workflows. For example, Microsoft Dynamics 365 helps workers from specific business lines and functions automate and improve certain tasks. Microsoft 365 does the same by addressing a more general audience.

These solutions are often delivered as SaaS AI solutions, which deliver fast and cost-effective results. With powerful intelligence in their existing workflows, business users can be more proactive and effective in their core competencies. Let’s take a look at some examples of these applications that can help anyone use AI to get more done. Here are some examples of powerful scenarios where AI is already having proven, beneficial effects:

With business applications that use AI as a core ingredient, users can bring together relationships, processes, and data across applications to gain increased visibility and control.

Everyday AI

There are also numerous AI capabilities that are already included in the applications everyone uses in their everyday routine, since they’re integrated into almost every job and function. Anyone can use them to address the realities of the modern workplace like virtual communication and the overwhelming amount of information.

For years, Microsoft has been putting AI to work in the Microsoft 365 apps that people use every day—like Microsoft Teams, Outlook, and Office. With these intelligent productivity experiences, employees can collaborate and conduct meetings more effectively, focus their time on value-added work, and uncover timely insights to improve their work.

Microsoft 365 Copilot adds another layer of AI. Business users can ask this virtual assistant to perform certain tasks just by using natural language. The assistant uses the latest generative AI technology to understand the request and do what is asked.

These solutions can improve your routine by boosting your remote work, your focus, your productivity, and your search power.

Everyday AI for remote work

Virtual meetings are becoming increasingly critical in most of our lives. While there’s no true replacement for in-person collaboration, there are new AI tools that can decrease pain points, increase human connection, and make virtual work more engaging.

For example, intelligent experiences in Microsoft Teams like background blur and custom backgrounds can help meeting participants minimize the chances of disturbances appearing on their screen. Live captions help improve accessibility for meeting participants who are hard of hearing or have hearing loss, non-native English speakers, or people with a sleeping baby nearby. Business users can even leverage real-time noise suppression to reduce distractions such as loud typing or a barking dog.

When you’re not speaking in person, some nuances are missing and misunderstandings can occur. Copilot can help business users find the right tone for their emails in Outlook to help address such issues.

Everyday AI for focus

Nowadays, workers’ routines are too often interrupted by distractions, calls, and multitasking. AI can also help cope with this problem and enable employees to focus their time and attention on what matters most.

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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 a 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 experiencing major breakthroughs. A new generation of Large Language Models (LLMs) enables new use cases that weren’t possible a few years ago, such as those based on high-quality generative AI. Based on these technologies, organizations will experience a second wave of AI-powered transformation. However, businesses need an easy way to access the latest AI capabilities to take full advantage of them.

Microsoft is working to democratize AI use. It has designed a wide range of solutions and services to bring AI to everyone, irrespective of their level of AI expertise. There are four approaches, varying based on the level of AI and coding expertise required.

Microsoft Approach	Description
Microsoft Copilot	Microsoft has embedded AI in everyday applications, so business users can benefit from it even if they don’t have coding or data science expertise. In this approach, AI is delivered as 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 Copilot for Microsoft 365 incorporates the latest generative AI in the shape of a virtual assistant that performs tasks for you in Microsoft 365 apps.
Microsoft Power Platform	A suite of 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.
Azure AI Services	These are the solutions for users who want to deliver an AI project but have little data science expertise. They offer pretrained AI models for you to reuse or customize.
Azure Machine Learning	All machine learning tasks can be handled from this service. It helps data science teams in setting, automating, and enabling machine learning best practices.

Microsoft has designed all these products and services following strict responsible AI principles. Any AI implementation should be equally respectful.

The rest of this module covers each of these options. Next, we’ll explore the simplest approach—AI as a copilot, seamlessly integrated into everyday applications.

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Modern AI is built on a foundation of data science and machine learning. The primary goal of AI is to use machines for capabilities that are usually associated with humans. Let’s explore data science concepts that support the foundation of AI.

What is data science?

Data science is an interdisciplinary field whose aim is to achieve AI. It primarily uses machine learning and statistics techniques. In most cases, data scientists are the experts in charge of solving AI problems.

What is machine learning?

Machine learning is a technique where a machine sifts through numerous amounts of data to find patterns. Machine learning uses algorithms that train a machine to learn patterns based on features of the data. The more training data, the more accurate the predictions.

Here are some examples:

• Email spam detection – Machine learning could look for patterns where email has words like “free” or “guarantee”, the email address domain is on a blocked list, or a link displayed in text doesn’t match the URL behind it.
• Credit card fraud detection – Machine learning could look for patterns like the spending in a zip code the owner doesn’t usually visit, buying an expensive item, or a sudden shopping spree.

What is deep learning?

Deep learning is a subset of machine learning. Deep learning imitates how the human brain processes information, as a connected artificial neural network. Unlike machine learning, deep learning can discover complex patterns and differentiating features about the data on its own. It normally works with unstructured data like images, text, and audio. It requires enormous amounts of data for better analysis and massive computing power for speed.

For instance, deep learning can be used to detect cancerous cells in medical images. Deep learning scans the image as input to a neural network. The nodes analyze each pixel to filter out features that look cancerous. Each layer of nodes pushes findings of potential cancerous cells to the next layer of nodes to repeat the process and eventually aggregate all of the findings to classify the image. For example, the image might be classified as a healthy image or an image with cancerous features.

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Human beings are incredible. We have so many unique capabilities that no machine can replicate creativity, empathy, inventiveness, and imagination. Business value comes from human expertise. At Microsoft, we believe the right AI tools can amplify these capabilities to help everyone achieve more. You should consider AI as a copilot, which helps you fulfill your potential.

Business users in every industry can take advantage of a wide array of AI solutions. For example, subject matter experts like researchers and engineers can use AI to apply their expertise more effectively and efficiently.

In this unit, we discuss what knowledge workers can do with available AI tools that don’t require coding or data science expertise, from software as a service (SaaS) to low-code products. The goal is to gain independence from data science teams so these subject matter experts can focus on what they do best. Let’s look at some examples of how anyone can work with and even create AI to achieve more.

Build AI without code

Employees don’t need to be data scientists to be able to use AI in their everyday work. Microsoft is working hard to deliver business users AI-powered products and services. The latest advances in AI technologies focus on prebuilt models, like GPT models, which everyone can use. You can use these models from a wide range of Microsoft products, such as Bing, Microsoft 365, or Microsoft Dynamics 365.

 Note

Nearly a third of white-collar workers (27 percent) have tried to incorporate prebuilt generative AI models such as GPT in their work routines.³

For more complex solutions, business users may want to create their own AI models or integrate a model into an app themselves. With no-code tools and platforms such as those provided by Microsoft Power Platform and Azure AI Services, now business users can add AI capabilities to their apps and automate their workflow regardless of their technical expertise.

The module Leverage AI tools and resources for your business gives you a more detailed overview of the AI products and services available for these cases.

AI for reasoning

Real transformation happens when everyone can use a wide range of AI models to reason over complex, unstructured information. The availability of a wide range of models means people can choose which AI models to use for different purposes and what information sources to analyze with them.

AI for reasoning is exactly that. It’s highly valuable for people with business-critical expertise like researchers, operations managers, field technicians, marketers, business developers, and more. With powerful AI applications, they can apply their knowledge more efficiently and effectively, speed up learning cycle iterations, and deliver real business impact at a rate never before possible. Let’s see some examples.

• Pharmaceutical industry: Some pharmaceutical companies are using AI to test molecules as a first step in their drug development process. This procedure enables them to accelerate drug discovery.
• Food industry: Food processing industries are applying AI to generate new recipes based on existing recipes, sales data, and customer preferences. These initiatives allow experts to launch new products faster than with traditional research.

Research-related use cases are numberless and apply in almost every industry. In many scenarios, you can use prebuilt AI models such as GPT, which can be embedded in many options, to extract insights from papers, documentation, or research results. However, the same AI can also be used to find new ideas and create content, such as ads or keywords for marketing campaigns.

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AI creates most value when business users participate

AI can empower all people to achieve more, not just developers and data scientists. In fact, McKinsey predicts that latest generative AI has the potential to automate as much as 58.5 percent of scenarios requiring to apply expertise. These scenarios were especially hard to automate without this technology.² Organizations can achieve most of this automation from business users and subject matter experts working directly with AI and generative AI.

Let’s take some real examples:

• Pharmaceutical companies: Workers in this sector deal with a vast amount of biomedical data. With a range of AI models, employees can quickly understand and derive key insights from this information to take on the next wave of challenges in medicine. They can iterate and test new hypotheses faster if they can use AI on their own, without relying on data science teams.
• Sales: The first attempt by Microsoft to use AI to score marketing leads failed. Salespeople realized the models were returning highly improbable results. This error came from a misalignment between data scientists and salespeople, and leads were being erroneously disqualified. We realized we needed a collaborative forum, one where we technologists and salespeople would share how AI models were being used, underscoring the need for highly accurate data. Sellers now share with the technologists what types of data are most useful to them in scoring the leads. Based on those learnings, the results coming from the AI models have improved dramatically.

Pharmaceutical companies and sales are just two examples. AI provides business users and subject matter experts with limitless opportunities to do things that weren’t possible before. With access to AI, they can uncover hidden insights, find critical information, improve collaboration, and even automate repetitive tasks.

AI for everyone

At Microsoft, we’re working to ensure that any user from accountant to researcher can achieve more with AI. It shouldn’t require a background in data science to benefit from AI experiences.

We believe AI should be used to enhance human capabilities. AI works best as a copilot, not a replacement. The right AI tools help employees better apply their expertise and complement it with AI-powered insights, making them more innovative and effective. To accomplish AI for everyone, we’re weaving intelligence into business applications that people use every day.

Finally, we’re committed to advancing the responsible development and use of AI. We believe it’s critical to take a human-centered approach to AI development and governance. This approach should value diverse perspectives and emphasize listening, learning, and responding as technology evolves. Together, we can ensure the next generation of AI is designed, built, and used responsibly.

But how does this approach work in complex jobs where expertise is key? Next, let’s look at some examples of how business users can work with AI already embedded in the applications they use every day.

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Enabling AI in your organization is a collective responsibility

Everyone has a role to play in AI transformation, not just IT. It’s important to empower people from all functions across your company to actively contribute ideas about AI applications. It’s key to foster collaboration between business and technical teams when planning design and implementation. After deployment, teams across the technical and operational sides of the business need to be involved in maintaining AI solutions over time:

• Measuring business performance and ROI from the AI solution.
• Monitoring model performance and accuracy.
• Acting on insights gained from an AI solution.
• Addressing issues that arise and deciding how to improve the solution over time.
• Collecting and evaluating feedback from AI users (whether they’re customers or employees).

It’s the ultimate responsibility of the senior executive leadership team to own the overall AI strategy and investment decisions, creating an AI-ready culture, change management, and responsible AI policies.

As for the other leaders across an organization, there’s no single model to follow, but different roles can play a part. Your organization needs to determine a model that’s suited to your strategy and objectives, the teams within your business, and your AI maturity.

Line of business leader

This person is a business executive responsible for operations of a particular function, line of business, or process within an organization.

• Source ideas from all employees: People from every department and level should feel free to contribute ideas, ask questions, and make suggestions related to AI. We’ve discovered that ideas for our most impactful application of AI have come from our employees within business functions, not from outside or above.
• Identify new business models: The real value of AI lies in business transformation: driving new business models, enabling innovative services, creating new revenue streams, and more.
• Create optional communities for exchanging ideas: They provide opportunities for IT and business roles to connect on an ongoing basis. You can implement this measure virtually through tools such as Yammer, or in-person at networking events or lunch-and-learn sessions.
• Train business experts to become Agile Product Owners: A Product Owner is a member of the Agile team responsible for defining the features of the application and streamlining execution. Including this role as part or all of a business expert’s responsibilities allow them to dedicate time and effort to AI initiatives.

Chief Digital Officer

The Chief Digital Officer (CDO) is a change agent who oversees the transformation of traditional operations using digital processes. Their goal is to generate new business opportunities, revenue streams, and customer services.

• Cultivate a culture of data sharing across the company: Most organizations generate, store, and use data in a siloed manner. While each department may have a good view of their own data, they may lack other information that could be relevant to their operations. Sharing data is key to efficiently using AI.
• Create your AI manifesto: This is the ‘north star’ that clearly outlines the organization’s vision for AI and digital transformation more broadly. Its goal isn’t only to solidify the company’s strategy, but to inspire everyone across the organization and help them understand what the transformation means for them. The CDO needs to work with other members of senior executive leadership team to create the document and message it to the company.
• Identify catalyst projects for quick wins: Kick-start AI transformation by identifying work that can immediately benefit from AI, that is, H1 initiatives. Then, showcase those projects to prove its value and gain momentum among other teams (H2 and H3).
• Roll out an education program on data management best practices: As more people outside of IT become involved in using or creating AI models, it’s important to make sure everyone understands data management best practices. Data needs to be cleaned, consolidated, formatted, and managed so that it’s easily consumable by AI and can avoid biases.

Human Resources leader

A Human Resources (HR) director makes fundamental contributions to an organization’s culture and people development. Their wide-ranging tasks include implementing cultural development, creating internal training programs, and hiring according to the needs of the business.

• Foster a “learning culture”: Consider how to encourage a culture championed by leadership that embraces challenges and acknowledges failure as a valuable part of continual learning and innovation.
• Design a “digital leadership” strategy: Make a plan to help line of business leaders and the senior executive leadership team build their own AI literacy and lead teams through AI adoption. Keep in mind that any AI strategy should comply with responsible AI principles.
• Create a hiring plan for new roles such as data scientists: While upskilling your employees is the long-term goal, in the short-term you may need to hire some new roles specifically for AI initiatives. New roles that may be required include data scientists, software engineers, and DevOps managers.
• Create a skills plan for roles impacted by AI: Creating an AI-ready culture requires a sustained commitment from leadership to educate and upskill employees on both the technical and business sides.
  • On the technical side, employees need core skills in building and operationalizing AI applications. It can be helpful to partner with other companies to get your teams up to speed, but AI solutions are never static. They require constant adjustments to exploit new data, new methods, and new opportunities by people who also have an intimate understanding of the business.
  • On the business side, it’s important to train people to adopt new processes when an AI-based system changes their day-to-day workflow. Training includes teaching them how to interpret and act on AI predictions and recommendations using sound human judgment. You should manage that change thoughtfully.

IT leader

While the Chief Digital Officer is charged with creating and implementing the overall digital strategy, an IT director oversees the day-to-day technology operations.

• Launch Agile working initiatives between business and IT: Implementing Agile processes between business and IT teams can help keep those teams aligned around a common goal. Implementation requires a cultural shift to facilitate collaboration and reduce turf wars. Tools such as Microsoft Teams and Skype are effective collaboration tools.
• Create a “dark data” remediation plan: Dark data is unstructured, untagged, and siloed data that organizations fail to analyze. It isn’t classified, protected, or governed. Across industries, companies stand to benefit greatly if they can bring dark data into the light. To do so, they need a plan to remove data siloes, extract structured information from unstructured content, and clean out unnecessary data.
• Set up agile cross-functional delivery teams and projects: Cross-functional delivery teams are crucial to running successful AI projects. People with intimate knowledge of and control over business goals and processes should be a central part of planning and maintaining AI solutions. Data scientists working in isolation might create models that lack the context, purpose, or value that would make them effective.
• Scale MLOps across the company: Managing the entire machine learning lifecycle at scale is complicated. Organizations need an approach that brings the agility of DevOps to the machine learning lifecycle. We call this approach MLOps: the practice of collaboration between data scientists, AI engineers, app developers, and other IT teams to manage the end-to-end machine learning lifecycle. Learn more about MLOps in the corresponding units of the module “Leverage AI tools and resources for your business.”

The function of business workers isn’t just to deliver insights to data scientists. AI must help them work better and faster. In the next unit, let’s see how this goal can be achieved with no-code tools that don’t require data science expertise or mediation.

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Map initiatives to a prioritization grid

Start with a matrix with four quadrants that organizes planned initiatives by strategic impact on one axis and business model impact on the other.

The matrix’s horizontal axis represents a spectrum of “tactical” to “strategic” initiatives. “Tactical” initiatives are confined to a single team or use case. “Strategic” initiatives represent larger investments that might affect the entire organization. The matrix’s vertical axis represents a spectrum of business models. Existing business model initiatives address competitive and disruptive threats, improve operations, or empower employees. New business model initiatives create new value propositions and revenue streams.

As you map initiatives, it’s helpful to involve the Chief Financial Officer (CFO) office and other stakeholders to ensure you’ve made the right assumptions around the opportunity valuation.

Let’s try filling in the prioritization grid using the earlier manufacturing example. You might place automation of quality control in the lower left quadrant. It’s an initiative that digitizes and optimizes an existing business model without requiring systemic changes.

Scenarios that fall below the middle line help the organization survive more than thrive. They might address competitive and disruptive threats, improve operations, or empower employees in the organization. Scenarios above the middle line help companies create new value propositions, revenue streams, or business models.

Once you are done classifying your initiatives on the grid, you can map the quadrants to horizons. The quadrant that an initiative fits determines which horizon it belongs to. The initiatives in quadrants one and four belong to Horizon 2. The initiatives in quadrant three belong to Horizon 1. The initiatives in quadrant two belong to Horizon 3.

Prioritize investments based on horizons

We recommend prioritizing initiatives in phases: start with foundational initiatives in the bottom left of the Prioritization framework quadrant and move towards transformational initiatives in the top right of the quadrant.

Having mapped the initiatives to their horizons, you tackle them in order: Horizon 1 initiatives first then Horizon 2 initiatives, and finally Horizon 3 initiatives.

We recommend this approach because it’s helpful to grow capabilities and get buy-in before you move to more complex projects. Begin by forming technical teams that can prepare data appropriately and familiarize themselves with AI models. Starting with foundational initiatives also helps establish trust across the business and manage expectations related to AI initiatives. The success and value you’re able to demonstrate in early initiatives pave the way for the more transformational projects.

Another reason to start at the bottom left of the prioritization framework quadrant is that the technology used to support H1 initiatives is typically more accessible than advanced use cases. There are countless out-of-the-box AI models you can apply to common use cases. These applications cost less and their effect on the business is easier to estimate. As you build maturity with these accessible models, you can experiment with more complex AI initiatives and hone your objectives.

Horizon 2 and Horizon 3 initiatives require more sophisticated data science capabilities, which may result in unintended or unexpected outcomes. These initiatives often require businesses to work with partners to create a custom model that can’t be bought off the shelf. These solutions require the most resources, time, and risk, but they offer the greatest reward. Achieving a lasting competitive advantage requires solutions that aren’t easily duplicated.

Define clear value drivers and KPIs for your AI investments

Once you’ve chosen AI initiatives, it’s important to identify value drivers and key performance indicators (KPIs) for each project. The framework provides a useful way to think about any investment including AI initiatives.

As you invest in initiatives, it’s important to develop market and financial models to help balance potential risk and return. Consider factors such as the total addressable market (TAM), net present value (NPV), and internal rate of return (IRR). Work with the CFO office and other key stakeholders to ensure the financial models make sense within the context of the business. These metrics can help secure their buy-in and ensure support throughout the process.

Moving forward, we advise putting systemic processes in place to manage and evaluate value throughout the project lifecycle. We recommend taking an agile approach that happens in stages—after you invest in an initiative, evaluate the initial results. Then you can determine whether to continue, adjust your approach, or take another path. Continue to evaluate value at major milestones throughout the project.

 Tip

Take a moment to come up with some potential example investments for each of the 3 horizons. 

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Adopting AI throughout an organization implies a serious investment. However, investing in AI projects requires a different perspective than most investments. If you use AI to improve or automate an existing process, then it’s possible to measure return on investment (ROI) in the straightforward, traditional way. But there are a few characteristics of AI initiatives that make it difficult to estimate their costs and benefits.

First, most AI models require upfront investment before it’s even possible to measure effectiveness. It’s hard to predict the accuracy of the model and its business impact until you’ve prepared data and completed model training and testing. Additionally, it’s hard to predict the amount of long-term maintenance a model needs. Individual models improve over time in ways that are difficult to calculate in advance.

With AI initiatives, you need to think like a venture capitalist. That means being willing to invest and take risks amid uncertainties. But you don’t have to guess. Instead, you can use a framework to help prioritize AI investments.

What is Microsoft’s horizon-based framework?

At Microsoft, we use a horizon-based framework to evaluate and prioritize AI investments. The horizon framework is a way to break development initiatives into phases called “horizons”. AI initiatives are three horizons, from improving core business functions to creating brand new revenue streams. The risk and uncertainty of specific applications depends on a company’s level of AI maturity, size, business objectives, and more.

Horizon 1: Running (operate and optimize the core business)

Not every AI application involves revolutionary changes. In fact, using AI to improve or automate existing processes is becoming essential to remaining competitive. Horizon 1 (H1) represents AI initiatives that optimize core business functions.

For example, perhaps you manufacture electronic components. While you might manually inspect quality for 100 parts per hour, an AI model with image recognition capabilities could inspect 1,000 parts per hour.

Horizon 2: Growing (improve market position)

Horizon 2 (H2) initiatives take advantage of emerging opportunities. These initiatives might create new services or new customer experiences.

For example, a manufacturer of electronics might use IoT to collect operational data and AI to suggest optimal times for maintenance. These initiatives facilitate a brand-new customer experience and help the manufacturer differentiate from competitors.

Horizon 3: Transforming (change market position)

Horizon 3 (H3) involves disruptive and innovative new business models. These are revolutionary applications that might cross industry boundaries or even create new customer needs.

For example, the same electronics manufacturer could sell “electronics-as-a-service” which means they use AI models to predict which electronic devices work best for your current system and needs. Ultimately, the company is selling a personalized service rather than a single product, creating new revenue streams and opportunities.

Next, let’s take a look at how to use a prioritization grid to apply a horizon-based framework.

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After learning the basics of an AI-centric organization, it’s important to understand that AI adoption is a journey. In their collaboration and discussion with business leaders, Microsoft is discovering insights on how organizations can achieve AI success.

 Note

For this purpose, Microsoft has developed a leader’s guide to build a foundation for AI success.

This model is based on five pillars that drive organizations to AI success:

• Business strategy.
• Technology strategy.
• AI strategy and experience.
• Organization and culture.
• AI governance.

In the following video, Jessica Hawk, Corporate Vice President of Azure Data, AI, and Digital Applications and Innovation Product Marketing, explains in detail this model and its five pillars.

This whitepaper includes a model of the stages of AI success. This five-tiered chart is a tool to help organizations take AI to the next level and evaluates their AI maturity.

1. Exploring stage: Companies at this initial stage of AI adoption are just starting their AI journey yet. They’re still learning about AI and experimenting with it in some parts of the organization.
2. Planning stage: Organizations at this stage are actively assessing, defining, and planning an AI strategy across the company.
3. Formalizing stage: At this point, companies are formalizing, socializing, and executing on AI strategy across the organization. These AI initiatives take place in multiple business units. AI is starting to generate value.
4. Scaling stage: Organizations are now in position to think bigger. AI initiatives deliver both incremental and new value across the company.
5. Realizing stage: At this final stage, AI achieves consistent AI value across the organization and in multiple business units.

However, enormous AI acceleration took place in the last few years. Great breakthroughs in generative AI and prebuilt models, such as the large language models (LLM) offered by Azure AI Studio, are disrupting the field. This new context has two major implications:

• Need to be up to date: Now, even mature companies need to reinvent themselves and adopt new waves of AI to avoid losing their competitive edge. Their AI strategy must reflect and leverage the impact brought by recent technologies.
• Mainstream AI: Generative AI is changing the rules of AI adoption by empowering business users at an unprecedented level. It might be easier than ever to implement AI in business. Many companies are working hard to rank higher in the maturity assessment model.

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