What was the last thing you did with a generative AI app? Create a cat-unicorn coloring book for your niece? Summarize that 42-page brief a colleague sent you? For me, it was using Microsoft Copilot to help my 9th grader with a history study session—I know more than you can believe about Mesopotamia.
Whatever it was for you, I bet it was something you wouldn’t have even considered a year ago. As fast as we’ve become comfortable with AI at our fingertips, our expectations for what it can do for us are growing just as fast. Companies are responding to those rising expectations by increasingly customizing AI to create apps and unique experiences that differentiate their brand in the marketplace.
When I say customers are customizing AI to create apps, I mean they are reshaping entire experiences with it. The NBA is redefining fandom with AI-powered personalization, delivering game highlights and stats tailored to each viewer. Meanwhile, the city of Buenos Aires has transformed urban living with ‘Boti,’ an AI chatbot managing over two million monthly queries, providing residents with instant assistance for things like driver’s license renewals, subway schedules, parking regulations, and even personalized tourism plans. These organizations are bending AI to their vision, pushing the limits of what’s possible. That is why I am happy to share a new MIT Technology Review Insights report that delves into how businesses are leveraging AI customization to stay ahead in the competitive market—DIY GenAI: Customizing generative AI for unique value. The report highlights the motivations, methods, and challenges faced by technology leaders as they tailor AI models to create net new value for their businesses.
While AI customization isn’t new, rapidly advancing AI platforms like Azure AI Foundry can make it easier and offer businesses greater opportunities to create unique value with AI. According to the MIT report, while boosting efficiency is a top motivation for customizing generative AI models, creating unique solutions, better user satisfaction, and greater innovation and creativity are equal motivations.
Improved efficiency is a top motivator here because it is the first clear-cut benefit businesses can realize quickly by customizing AI. As organizations gain experience, the learning curve flattens, and I think we’ll see the other motivators soar as companies focus more on customizing AI for top-line revenue impact than COGS (Cost of Goods Sold) savings.
Specializing with agents
When it comes to selecting models, half of the executives surveyed in the MIT report said they are prioritizing agentic and multi-agent capabilities in addition to multimodality (56%), flexible payment options (53%), and performance improvements (63%). AI agents that perform tasks and make decisions without the need for direct human intervention have broad utility. They lend themselves to autonomous problem solving in areas like data entry and retrieval for clinical operations in Healthcare, supplier coordination and maintenance tracking in manufacturing, and enhancing inventory and store operations in Retail.
Agents have the potential to disrupt the market with something unique beyond automating processes that humans find dull. Take Atomicwork, a newcomer to the service management space dominated by established industry players with decades of experience. Atomicwork stands out with an ITSM (IT Service Management) and ESM (Enterprise Service Management) platform centered around specialized AI agents that integrate into the flow of work, providing seamless, instant support without the need for multiple tools or complex integrations. According to Atomicwork, one of their customers achieved a 65% deflection rate (the percentage of issues resolved without human intervention) within six months.
Like other areas of AI development, agent-building tools are rapidly evolving to accommodate a wide variety of use cases. From creating simple low-code agents in Microsoft Copilot Studio to developing more complex, autonomous pro-code agents using GitHub and Visual Studio, the process is streamlined. For example, using the intuitive agent orchestration experience built directly into Azure AI Foundry, Azure AI Agent Service allows you to accomplish in just a few lines of code what originally took hundreds of lines. This makes it remarkably easy to customize and safely put agents to work in your operations.
Good data equals good AI
The potential of AI customization is immense but not without its challenges. Ironically, the greatest asset for AI customization often presents the biggest barrier customers run into: data. Specifically, data integrity—the safety, security, and quality of the data they use with AI. Half the participants in the MIT report cited data privacy and security (52%) and data quality and preparation (49%) as AI customization obstacles.
Generative AI is one of the best things to happen to data in a long time. It presents innovative ways for companies to interact with and use their data in solutions unique to them. Data is where the magic happens. AI models know a lot, but a model doesn’t know your company from your competitor until you ground it in your data.
Critical to empowering data-driven AI is an intelligent data platform that unifies sprawling, fragmented data stores, provides controls to govern and secure data, and seamlessly integrates with AI building tools. It’s why Microsoft Fabric is now the fastest-growing analytics product in our history and why we’re seeing AI-driven data growth of raw storage, database services, and app platform services as customers fuel their AI workloads with data. Fabric removes the data integrity obstacle. Together with Azure AI Foundry, data and dev teams are integrated and working in the same environment, removing any time-to-market drag due to data issues.
RAG is the customization starting point
One of the simplest and most effective methods for customization is retrieval-augmented generation (RAG). Two-thirds of those surveyed in the MIT report are implementing RAG or exploring its use. Grounding an AI model in data specific to an organization or practice makes the model unique and capable of providing a specialized experience.
In practice, RAG isn’t used alone to customize models. The report found it’s often used in combination with fine-tuning (54%) and prompt engineering (46%) to create highly specialized models. Dentsu, a global advertising and PR firm based in Tokyo, initially analyzed media channel contributions to client sales using general-purpose LLMs but found their accuracy lacking at 40-50%. To improve this, they developed custom data controls and structures and tailored models leveraging their expertise in retail and marketing data analysis. By integrating a customized RAG framework and an agentic decision layer, Dentsu reports about 95% accuracy in retrieving relevant data and insights. This AI-powered approach now plays a central role in shaping campaign strategies and optimizing marketing budget allocation for their clients.
Empowering development teams
Developing AI brings new dynamics, not the least of which is keeping pace with AI advancements. Model features and capabilities, along with developer tools and methods, are evolving rapidly, which makes empowering teams with the right tools crucial for successful AI customization.
For example, the pace of new model capabilities begs for model evaluation tooling automation. According to the MIT report, 54% of companies use manual evaluation methods, and 26% are either beginning to apply automated methods or are doing so consistently. I expect we’ll see these numbers flip soon. The report notes that playgrounds and prompt development features are also widely used to facilitate collaboration between AI engineers and app developers while customizing models.
Evaluation is a critical component not just for customizing an AI but also in managing and monitoring the app once it hits production. We built full lifecycle evaluation into Azure AI Foundry so you can continuously evaluate model capabilities, optimize performance, test safety, and keep pace with advancements.
We also see customization and growing AI portfolios ushering in next-generation AI development. The report reveals that more than half of the surveyed organizations have adopted telemetry tracing and debugging tools. AI tracing enhances the transparency needed to understand the outcomes of AI applications, and debugging helps optimize performance by showing how reasoning flows from the initial prompt to the final output.
Looking ahead with Azure AI
AI has high utility when it comes to creating services and experiences that can differentiate you in the marketplace. The speed of adoption, exploration, and customization is evidence of the value companies see in that utility. Models are continually advancing and specializing by task and industry. In fact, there are more than 1,800 models in the Azure AI Foundry catalog today – and they are evolving just as quickly as the tools and methods to build with them. We already see agents delivering new customer service experiences—something that might be a differentiator today, but I expect fast-follows will reshape customer service for most companies as consumers learn to expect an AI-powered experience. As that happens, what we see as AI customization today will lose the novelty of being custom and become standard practice for building with AI. What we won’t lose is the novelty of building something unique. It will become an organization’s IP.
What’s that unique experience for your business? What’s the next special thing you want to do for your customers? How do you want to empower your employees? You’ll find everything you need to bend the curve of innovation with Azure AI Foundry.
One final note: No matter where you are in retooling your organization to operationalize AI, I encourage you to read the MIT report. In addition to survey findings, the team spent quality time talking with technology leaders about creating value by customizing generative AI. Sprinkled throughout the report are some helpful, real-world examples and insights. Big thanks to the researchers and editors at MIT Technology Review Insights for helping put a focus on this exciting area of opportunity.
About Jessica Hawk
Jessica leads Azure Data, AI, and Digital Applications product marketing at Microsoft. Find Jessica’s blog posts here, and be sure to follow Jessica on LinkedIn.
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