Generative AI has the ability to revolutionize the missions of public sector organizations and transform many aspects of our everyday work and life.
Generative AI holds a lot of potential for helping public sector organizations accomplish their missions. But it’s important to understand what generative AI can and can’t do so you can identify appropriate areas and ways where the technology can have the highest impact. The four main capabilities of generative AI are:
Content creation: Creating a human-like output, including textual, visual, or multimedia content based on input data or natural language prompts
Summarization: Extracting key themes and insights from a longer piece of text, including answering natural language queries
Code production: Generating code based on a prompt, translating code from one programming language to another, or reviewing and improving existing code
Semantic search: Going beyond traditional keyword matching by understanding the meaning behind a query and retrieving relevant search results that are semantically related to the user’s intent
In public sector organizations, there are three key areas where generative AI can drive transformation:
Enhanced productivity
Augmented cognition
Accelerated discovery
Enhanced productivity
Enhanced productivity through Al can enrich employee experiences by helping to unlock the best out of workforce talent. From content summarization to automating routine tasks, Al can positively affect productivity by reducing tedious tasks.
Augmented cognition
Another way Al use can help an organization is through augmented cognition. Augmented cognition provides teams with a digital sidekick that helps them think better and handle complex tasks with technology’s help. Data collection and extraction, intel analysis and interpretation, and anomaly detection are some of the ways Al can help increase a team’s productivity.
Accelerated discovery
Lastly, Al can empower every organization to bend the curve on innovation by modernizing internal processes, accelerating the discovery process, and understanding and simulating complex situations and processes.
This module explores each of these areas by sharing the following use cases and highlighting technologies and tools to support this revolution.
The availability of sophisticated AI models can help organizations reduce significantly the intimidating amount of resources a data science project can require. Let’s see how organizations can tackle machine learning challenges and operations with Azure Machine Learning.
Machine learning challenges and machine learning operations
Maintaining AI solutions typically requires machine learning lifecycle management to document and manage data, code, model environments, and the machine learning models themselves. You need to establish processes for developing, packaging, and deploying models, as well as monitoring their performance and occasionally retraining them. And most organizations are managing multiple models in production at the same time, adding to the complexity.
To cope effectively with this complexity, some best practices are required. They focus on cross-team collaboration, automating and standardizing processes, and ensuring models can be easily audited, explained, and reused. To get this done, data science teams rely on the machine learning operations approach. This methodology is inspired by DevOps (development and operations), the industry standard for managing operations for an application development cycle, since the struggles of developers and data scientists are similar.
Azure Machine Learning
Data scientists can manage and execute machine learning DevOps from Azure Machine Learning, a platform by Microsoft to make machine learning lifecycle management and operations practices easier. Such tools help teams collaborate in a shared, auditable, and safe environment where many processes can be optimized via automation.
Machine learning lifecycle management
Azure Machine Learning supports end-to-end machine learning lifecycle management of pretrained and custom models. The typical lifecycle includes the following steps: data preparation, model training, model packaging, model validation, model deployment, model monitoring and retraining.
The classic approach covers all the usual steps of a data science project.
Prepare dataset. AI starts with data. First, data scientists need to prepare data with which to train the model. Data preparation is often the biggest time commitment in the lifecycle. This task involves finding or building your own dataset and cleaning it so it’s easily readable by machines. You want to make sure the data is a representative sample, that your variables are pertinent for your goal, and so on.
Train and test. Next, data scientists apply algorithms to the data to train a machine learning model. Then they test it with new data to see how accurate its predictions are.
Package. A model can’t be directly put into an app. It needs to be containerized, so it can run with all the tools and frameworks its built on.
Validate. At this point, the team evaluates how model performance compares to their business goals. Testing may return good enough metrics, but still the model may not work as expected when used in a real business scenario.
Repeat steps 1-4. It can take hundreds of training hours to find a satisfactory model. The development team may train many versions of the model by adjusting training data, tuning algorithm hyperparameters, or trying different algorithms. Ideally the model improves with each round of adjustment. Ultimately, it’s the development team’s role to determine which version of the model best fits the business use case.
Deploy. Finally, they deploy the model. Options for deployment include: in the cloud, on an on-premises server, and on devices like cameras, IoT gateways, or machinery.
Monitor and retrain. Even if a model works well at first, it needs to be continually monitored and retrained to stay relevant and accurate.
This unit discusses the prebuilt AI models that are available in Azure AI Services. They are a solid alternative to developing internal custom AI models.
What is Azure AI Services?
When considering adopting AI into your business, you should consider prebuilt AI services first. Azure AI Services is a Microsoft product that delivers AI as SaaS. It includes pretrained models developed by Microsoft global researchers and data scientists to solve common problems. To avoid reinventing the wheel, businesses can leverage prebuilt services to achieve quality and accelerate delivery of technology solutions.
It’s better to use the Azure AI Services that offer prebuilt AI services in vision, speech, language, search, or generative AI to solve common scenarios. This brings AI within reach of every developer and organization without requiring machine learning expertise. As a result, it enables developers of all skill levels to easily add intelligence to new or existing business applications.
Using Azure AI Services can:
Save costs: Since AI Services is serverless, they’re usually less costly than developing and training custom models from scratch internally.
Give deployment flexibility: You can export AI Services models and run them wherever you need, in the cloud, on-premises, or on the edge.
Provide enterprise-level security: AI services provide a layered security model, including authentication with Microsoft Entra credentials, a valid resource key, and Azure Virtual Networks.
Connect to an ecosystem of products: AI services are part of a broad ecosystem that includes automation and integration tools, deployment options, Docker containers for secure access, and tools for big data scenarios.
Azure AI Services capabilities
Azure AI capabilities include: vision, language, speech, document intelligence, search, and generative AI. You can build solutions with these capabilities using a suite of Azure AI services, including:
Azure AI Vision: includes models that analyze images and videos. Beside more generic models, there are specific ones for extracting text from images (optical character recognition or OCR), for recognizing human faces. Another option is Azure Custom Vision, which lets users build their own AI models to recognize objects or classify images. Keep in mind that face recognition services are highly restricted due to Microsoft responsible AI policies.
Azure AI Language: focuses on processing and analyzing text. They’re trained to understand natural language and extract insights. For example, models recognize language, intent, entities, and sentiment in a text. Besides, they can find answers to the questions put to them.
Azure AI Speech: provides models that deal with oral conversation. They can transform speech to text and vice-versa. It’s also possible to translate what the speaker says and identify each speaker. Models can even suggest pronunciation corrections to the speakers.
Azure AI Document Intelligence: incorporates OCR and text analytics models to extract data from invoices, receipts, and other documents. Document intelligence relies on machine learning models that are trained to recognize data in text.
Azure AI Search: provides secure information retrieval at scale over user-owned content in traditional and generative AI search applications. Azure AI Search can index unstructured, typed, image-based, or hand-written media. The indexes can be used for internal only use, or to enable searchable content on public-facing internet assets.
Azure OpenAI Service: enables users to leverage generative AI models via Azure AI Services. In other words, it allows you to access OpenAI models directly from Azure instead of the public API. Keep in mind that Azure OpenAI Service isn’t the only Microsoft product delivering this kind of models to users. In previous units, we’ve already discussed generative AI included in Microsoft Copilot for Microsoft 365 and Copilot in Power Platform. These copilot features are powered by GPT, an OpenAI model for text generation.
Azure AI Foundry: a Microsoft cloud platform that brings together multiple Azure AI-related services into a single, unified development environment. Developers can use these services to build end-to-end AI solutions. Specifically, Azure AI Foundry combines:
The model catalog and prompt flow development capabilities of Azure Machine Learning service.
The generative AI model deployment, testing, and custom data integration capabilities of Azure OpenAI service.
Integration with Azure AI Services for speech, vision, language, document intelligence, and content safety.
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.
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:
Business function
Example scenario
Commerce
Users can use AI insights to help them more effectively manage cashflows using payment recommendations, intelligent budget proposals, and cashflow forecasting. They can even use AI to better protect their e-commerce business—and their customers—against fraud.
Customer service
Customer service users can gain insights to address increasing volumes and manage efficient agent distribution. They can also create virtual agents that identify and resolve customer issues quickly—all without having to write code.
Finance
Analysts are provided a range of AI-powered tools for real-time reporting, embedded analytics, and insights. For example, AI can predict when or whether their customers will pay their invoices.
Human Resources
Workforce data can be transformed into actionable insights and next-best-action guidance. AI can also be used to automate HR tasks for employees, making procedures more agile.
Marketing
AI-powered customer insights give marketing users a single view of their customers to optimize engagements and discover insights that drive personalized and meaningful experiences.
Project management
Embedded analytics can provide insights based on project sales and financial data. The solution proposes an AI-powered scheduling to anticipate needs. Operations users gain insights into how their customers use their products and services.
Sales
Sellers can sell smarter with embedded AI-powered insights fueled by customer data.
Supply Chain
Business users can use AI for predictive maintenance in factories. AI is also helpful to optimize inventory.
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.
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.
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.
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.
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.
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.
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.
