Microsoft Generative AI Use Cases for Businesses

In 2022, the world was stunned by the emergence of the highly advanced, publicly available generative AI tool, ChatGPT. By 2023, businesses were beginning to question how AI could be used in the workplace. In 2024, generative AI adoption has continued to increase, with 65% of organisations now regularly using it in their business functions.¹

If you’re considering implementing Microsoft’s AI services, you’re already on the right track; in my opinion, Microsoft’s generative AI suite is one of the most comprehensive and technically advanced on the market. 

The two primary services available are Microsoft Copilot and Azure AI Services. Copilot is a general AI assistant that can be integrated into Office 365 programmes,² while Azure AI Services allows users to access a number of different AI models and fine-tune them using their own data.³

In this article, I’ll explore some of the key use cases for Microsoft generative AI in businesses, and give you pointers on how to successfully implement it in yours. Let’s dive in.

Suggested reading: If you work in the education sector, check out the eBook ‘AI Use Cases for Education Publishers’ for suggestions tailored to improving educational outcomes.

1. Customer service and support

According to recent studies, those working in the tech, telecom, and media industries are the most likely to regularly use generative AI for work.⁴ AI’s popularity in these industries is likely due to the high pressure on customer support services, which can be effectively mitigated through partial intelligent automation. 

There are a number of natural language tools you can choose from within the Microsoft suite:

• Azure OpenAI: Fine-tune the AI model of your choice using your own data for accurate, relevant, and actionable insights about your CS interactions.

• Azure AI Bot Service: Integrated with Power Virtual Agents, this service allows you to rapidly build your own conversational bots in multiple channels and languages. Provide a better customer experience with advanced AI that can effectively solve customer queries upon first contact.

• Azure Cognitive Services: Delivers language, speech, vision, and decision tools in the form of cloud-based APIs that can be easily integrated into your existing applications. This is especially useful for performing sentiment analysis, voice verification, and real-time translation.

• Dynamics 365 Contact Center: Powered by Microsoft Copilot AI, the Dynamics 365 Contact Center enables streamlined human-assisted customer service, helping to route conversations and deliver real-time reporting. 

Microsoft’s AI-powered chatbots can provide customers with more personalised, relevant, and helpful responses compared to traditional automation software, while AI sentiment analysis informs wider service and product improvements.

2. Sales and CRM enhancement

34% of businesses in 2024 are already using generative AI for marketing and sales purposes.⁵ This number has more than doubled since 2023,⁶ as studies reveal that this is one of AI’s most valuable use cases.⁷ The central way in which generative AI delivers value in sales is through forecasting and analysis, using business data to:

• Predict sales trends
• Analyse customer behaviour
• Identify high-value leads and opportunities
• Tailor sales pitches to a customer’s unique wants and needs

For most businesses, the best choice for this use case is Microsoft’s Dynamics 365 for Sales, which delivers highly accurate AI scoring, sales forecasts, and automated call reports. Dynamics 365 for Sales also integrates with Copilot for Sales to cut the amount of time that salespeople have to spend on repetitive customer relationship management (CRM) tasks. It does this by:

• Summarising customer details to enable salespeople to quickly access the information they need.
• Automatically prioritising conversations on Outlook that require more urgent attention.
• Preparing meeting notes to reduce the amount of time that needs to be spent gathering information before a sales call.
• Drafting email responses based on the contents of the initial customer email.
• Notifying sellers about overdue replies, close dates, and recent activity.⁸

By streamlining the processes of data entry, scheduling, call summarisation, and follow-up activities, Microsoft’s generative AI technology can increase the likelihood of sales, boost lead development, and drive long-term revenue.⁹

Pro tip: Keeping your customers’ data secure through AI implementation is a top priority. Read my recent article, ‘AI Integration Challenges: Common Risks and How to Navigate Them’, to learn more about common security risks and solutions.

3. Operational efficiency

So far, I’ve focused specifically on generative AI’s potential to improve productivity and efficiency in sales and customer-oriented industries. However, AI’s potential likewise extends to B2B and public sector businesses, from the financial and legal sectors to the education industry.

In fact, the generative AI tool Azure OpenAI is already being used by over half of Fortune 500 companies. ¹⁰Many of these businesses will be making the most of Azure OpenAI’s ‘fine-tuning’ capabilities, using an existing generative AI model, such as ChatGPT or DALL-E 3, and training it on individual company data. With Azure OpenAI Service, businesses can use the generative AI model of their choice while still enjoying the security and privacy of the Microsoft service. 

Once the model has been fine-tuned, it will be more effective at:

• Intelligently automating workflows to eliminate inefficiencies and reduce pressure on employees. One 2024 study found that Copilot for Microsoft 365 users were almost 30% faster at tasks like writing, searching, and summarising content.¹¹
• Real-time data processing, analysing data to provide specific required outputs or identify system weaknesses.
• Working seamlessly alongside other technology such as Azure Logic Apps, Power Automate, or existing automation tools.

Fine-tuning your Microsoft generative AI model does require some technical expertise, as you will need to integrate the LLMs with RAG techniques to extract information from your business data. Nonetheless, the process of adapting a Microsoft generative AI model is still significantly more cost-effective than developing one from scratch.

4. Research and development

Generative AI is a powerful tool for one quite simple reason: it can see the ‘bigger picture’. Researchers, marketers, analysts, and developers like myself will all spend long periods of time creating data, analysing it, and making decisions based on it. But the amount of data that we can process in one go is relatively small — no match for the increasing quantities of big data being produced by businesses every day. Generative AI, on the other hand, rapidly analyses:

• Large data sets
• Unstructured data sets
• Data from different sources

Microsoft’s Azure AI and Cognitive Services are especially relevant to this use case, having been created by Microsoft’s own AI and research teams for the purpose of R&D. The Azure AI Service includes a few excellent and unique research features:

• Azure AI Document Intelligence: Extract tables, structures, text, and more from any document to support streamlined knowledge management.
• Azure AI Search: Locate information, wherever it’s stored, using keywords or vectors.
• Azure AI Vision: Read text and analyse images to search for specific information.¹²

These insights can then feed into the development process, inspiring new product ideas and prototypes across a range of industries. I expect that as usage grows, Microsoft’s Azure AI Services will pave the way for improved organisation, development, and, ultimately, innovation.

Seamless implementation for your Microsoft Generative AI solution

To enjoy the many benefits of Microsoft’s generative AI, businesses will need to take certain steps to prepare their data prior to implementation. Otherwise, they may be at risk of challenges such as data inaccuracy, poor cybersecurity, or compliance issues.¹³ Pre-implementation preparations could include:

• Employee training
• Internal process reviews
• Data cleaning and preparation

If you do not have internal expertise in Microsoft’s AI suite, I would recommend working with a technology partner through the implementation process. This will ensure that you have chosen the right solutions for your business, and have the systems and processes in place to enable a smooth transition to AI-assisted operations.

Learn more about essential AI implementation principles in my recent article, ‘The Business Leader’s Guide to Responsible AI Use’.

Getting started with Talk Think Do

Microsoft’s generative AI solutions are highly customisable, secure, and user-friendly. As such, they’re a great choice if you’re looking to implement AI in your business for the first time, but want the flexibility to grow and fine-tune your own models in the future.

Here at Talk Think Do, we work closely alongside businesses of all sizes to implement tailored Microsoft AI solutions. We are a trusted Microsoft Solutions Partner, CCS Digital Outcome Supplier, and Learnosity Services Partner, and prioritise building deep working relationships with each of our clients. 

Whether you are just looking to learn more about what AI solutions are available to you, or are ready to get started with implementation, we can help. Book a free consultation with a member of our team to discuss your needs today.

_{1 The state of AI in early 2024: Gen AI adoption spikes and starts to generate value | McKinsey}

_{2 Copilot}

_{3 Azure OpenAI Service – Advanced Language Models}

_{4 The state of AI in 2023: Generative AI’s breakout year | McKinsey}

_{5 The state of AI in early 2024: generative AI adoption spikes and starts to generate value | McKinsey}

_{6 The state of AI in 2023: Generative AI’s breakout year | McKinsey}

_{7 Economic potential of generative AI | McKinsey}

_{8 Guided tour | Sales Acceleration | Microsoft}

_{9 Economic potential of generative AI | McKinsey}

_{10 Copilot by the Numbers: Microsoft’s Big AI Bet Paying Off — Visual Studio Magazine}

_{11 Copilot by the Numbers: Microsoft’s Big AI Bet Paying Off — Visual Studio Magazine}

_{12 Azure AI Services – Using AI for Intelligent Apps | Microsoft Azure}

_{13 The state of AI in 2023: Generative AI’s breakout year | McKinsey}