Tag: Microsoft

  • Microsoft Build 2025: Bridging the Gap Between AI POCs and Agentic AI Success

    Microsoft Build 2025: Bridging the Gap Between AI POCs and Agentic AI Success

    The AI Acceleration Challenge

    The AI market has rapidly evolved from early chatbot failures to a landscape of simplified access to information driven by generative AI, with agentic AI coming soon. Yet, most organizations are still struggling to move beyond proof-of-concept projects to production deployments that deliver measurable business value. Over 80% of firms interviewed by Lopez Research report significant technical skills shortages and challenges with change management when deploying AI systems. The landscape has also become increasingly complex, with thousands of AI models and multiple approaches to designing, deploying, and managing AI solutions.

    Before enterprises had even fully embraced conversational interfaces within various SaaS solutions, the technology industry rapidly moved toward creating agentic AI products. Instead of AI that assists people, the industry has pushed toward systems that can operate autonomously, reason through complex problems, and take action without always requiring human guidance. While agentic systems represent the future of AI, the inherent risks in autonomous systems terrify all but the most fearless companies.

    Amplified Risks in an Autonomous Agentic AI World

    AI systems face significant reliability challenges, including unpredictable outputs, “hallucinations” where they generate false information, and brittleness when encountering unfamiliar scenarios. These systems can also experience model drift as underlying data patterns change over time, leading to degraded performance without obvious warning signs. From a security perspective, AI systems remain vulnerable to sophisticated attacks, including adversarial inputs designed to manipulate outputs, data poisoning that corrupts training datasets, and inference attacks that can extract sensitive information from trained models.

    These challenges become exponentially more serious with agentic AI. Reliability issues that may cause minor inconveniences when a human is in the loop will become critical safety concerns when agents can act autonomously. An agentic AI experiencing model drift or brittleness could make consequential decisions affecting business operations, financial transactions, or even physical systems without human oversight. Security vulnerabilities become particularly dangerous, as adversarial attacks can manipulate agents into taking harmful actions, while data poisoning can corrupt not only outputs but entire chains of autonomous decision-making.

    Companies Flock To Strategic Vendors For AI Platforms

    The AI sprawl of startup vendors and highly specialized solutions only creates increased anxiety. Today, most business leaders are looking to a handful of strategic technology vendors to offer more comprehensive systems for designing, securing, maintaining, and governing AI that supports many but not all of the AI functions. It’s crucial that these strategic vendors also provide ecosystem-friendly platforms that can connect with other third-party technology vendors when necessary.

    Microsoft Responds with Advances in Agentic AI 

    At Microsoft’s Build 2025 conference, CEO Satya Nadella advanced the company’s vision for an “open, agentic web.” Microsoft’s announcements encompass over 50 new AI tools and platforms, focusing on agentic AI capabilities that promise to transform how organizations develop software, conduct research, and manage business processes. 

    Microsoft calls its Microsoft 365 Copilot “the UI for AI,” providing a place where people and teams interact with agents in the flow of work. However, since last year’s Build conference, the company has moved well beyond Microsoft Copilot as a conversational interface to also offering a more comprehensive platform for designing and using agents. This is in addition to providing its own AI models and special-purpose AI hardware. The latest Microsoft Build announcements showcase several key updates that demonstrate Microsoft’s commitment to addressing enterprise concerns about the deployment of autonomous AI while maximizing its transformative potential.

    1. Evolving Software Development

    Microsoft Build has always focused on tools for software development. The evolution of GitHub Copilot from code suggestion to an autonomous coding agent represents a significant shift in software development lifecycle management. The new agent handles end-to-end programming tasks, including bug fixes, feature implementation, and code refactoring. Embedded directly into GitHub, the agent activates when developers assign a GitHub issue to Copilot or prompt it in VS Code, spinning up secure and fully customizable development environments powered by GitHub Actions.

    The autonomous handling of routine coding tasks allows development teams to focus on architecture and innovation, effectively multiplying the strategic impact of existing technical staff. Beyond productivity gains, the system ensures consistent application of best practices across entire codebases, reducing quality variations that often plague large development organizations. Perhaps most significantly for enterprise operations, the platform reduces dependency on individual developer expertise, creating more resilient and maintainable systems where institutional knowledge embedded in the AI agent ensures continuity when personnel changes occur.

    The advances in GitHub Copilot address one of the most persistent challenges facing technical leaders: the growing gap between the demands of software development and the available talent. At Build, Microsoft shared that Ramp, a spend management platform, saves approximately 30,000 hours of manual work per month by utilizing GitHub Copilot. Cathay Pacific, Hong Kong’s largest airline, similarly leveraged GitHub Copilot to save developer time and increase productivity across their development teams.

    2. Delivering Choice and Interoperability

    Microsoft’s Azure AI Foundry Agent Service represents the cornerstone of enterprise agentic AI deployment. Now generally available, the platform provides enterprise-grade infrastructure for production AI agent deployments, integrating Semantic Kernel and AutoGen into a unified SDK while supporting Agent-to-Agent (A2) communication and the Model Context Protocol (MCP). The support for A2A and MCP means it will be easier for companies to create Agentic AI systems where agents can connect and collaborate across various applications and data sources. 

    Microsoft’s commitment to the Model Context Protocol across its entire platform stack addresses a critical enterprise concern: avoiding AI vendor lock-in while enabling sophisticated integrations. MCP integration with Windows will provide a standardized framework for AI agents to connect with native Windows applications, allowing seamless agent-based interactions. The support for the A2A protocol, an open standard, also enables Microsoft to integrate with other major technology companies, including Google (the originator of the protocol), Atlassian, Box, Cohere, Intuit, LangChain, MongoDB, PayPal, Salesforce, SAP, ServiceNow, UKG, and many others.

    Organizations will also gain model flexibility with access to over 11,000 models through a Hugging Face integration in addition to the existing 1,900 models in the Microsoft catalog. These capabilities enable organizations to integrate best-of-breed AI models regardless of vendor and optimize costs by selecting the right model for the right workload. 

    3. Advanced Workflow Automation

    The introduction of multi-agent systems enables sophisticated workflow automation where specialized agents collaborate to handle complex business processes within the Microsoft ecosystem and across applications. This multi-agent orchestration transforms organizational thinking about business process automation, shifting from rigid, predetermined workflows to intelligent systems that adapt and collaborate in real time.

    Cross-platform integrations demonstrate the versatility of this capability. Adobe’s marketing agent, integrated with Microsoft 365 Copilot, enables marketers to access audience analysis without needing to switch applications. ServiceNow AI agents work with Microsoft 365 Copilot for service management. SAP integration through SAP Joule enables the creation of custom AI agents for SAP workloads using Azure AI services while also allowing SAP users to access data within Microsoft 365 applications.

    Financial operations benefit similarly, with compliance agents working alongside analysis agents to produce automated reporting that maintains accuracy while reducing manual oversight requirements. Supply chain management becomes more responsive as demand forecasting agents coordinate directly with inventory management agents, creating dynamic, self-optimizing systems that respond to market changes in real time.

    4. Observability for Agentic Systems

    For technology leaders grappling with the complexities of AI deployment, this platform addresses several fundamental concerns that have historically hindered the success of AI initiatives. In addition to supporting open protocols for agent communication and governed access to multiple types of AI, it also offers built-in observability. Observability provides real-time metrics for performance, quality, cost, and safety, giving executives the visibility needed to manage AI operations with confidence. 

    Rather than deploying AI as a black box, organizations can now monitor and optimize their AI investments with the same rigor applied to traditional enterprise systems. The platform’s integrated compliance controls help prevent “agent sprawl,” which many organizations fear as AI adoption accelerates, enabling centralized oversight while increasing accessibility through a centralized agent store.

    AI Can Deliver Impact

    Real-world implementations demonstrate measurable impact. Accenture has leveraged Azure AI Foundry for AI and agent-led business process transformations, achieving a 30% increase in efficiency and a 50% reduction in AI application development time. Carvana developed an agent that analyzes customer interactions, resulting in a 40% reduction in inbound sales calls. The Indiana Pacers created an in-arena real-time captioning system with error rates reduced to just 1 percent. 

    Early implementations highlight the potential of agentic AI in mission-critical environments. Stanford Medicine’s Healthcare Agent Orchestrator, built on Azure AI Foundry, transforms cancer care delivery across approximately 4,000 tumor board meetings per year. The system consolidates fragmented information from multiple sources, integrates patient history with radiology data, medical literature, and clinical trials, and generates comprehensive reports for clinicians, thereby reducing the time spent on manual information gathering. Deployed into Microsoft Teams with a foundation in specific clinical notes, the solution enhances patient decision-making by making it more efficient, faster, and potentially more accurate while enabling the sharing of advanced medical AI capabilities with community hospitals to democratize these capabilities. 

    The NFL’s implementation demonstrates the impact of agentic AI on data-driven decision-making. Azure AI Foundry revolutionized scouting operations by combining previously scattered data systems, enabling teams to ask specific player questions and receive immediate comparative analysis, complete data filtering in seconds rather than hours, access detailed player information in real-time during evaluations, and perform instant queries like “give me the fastest 40 times of a defensive lineman.” This transformation gave NFL teams significant competitive advantages in player evaluation by fundamentally changing how they process and analyze scouting data.

    Build Your Foundation Wisely

    For technology leaders, the strategic imperative is clear: organizations that successfully implement agentic AI will gain substantial competitive advantages in efficiency, innovation speed, and operational capability. The key to success lies not in the technology itself but in thoughtful implementation that addresses governance, security, and change management challenges while maximizing the transformative potential of autonomous AI systems. While organizations should proceed with caution, the latest offerings announced by Microsoft and others signal that the technology market is providing more mature offerings to mitigate risks, suggesting that the foundation for safe, effective agentic AI deployment is rapidly solidifying.

  • Microsoft’s Business Apps Division Shows Traction

    Business is booming for Microsoft and its business apps team. The company shared that Dynamics 365 had a breakthrough quarter with revenue increasing 45% during its recent earnings call. It also shared that the Power Platform is being used by nearly 16 million monthly active users, up 97% year over year and revenue increased 84% year over year. What’s behind the success?

    Part of this success reflects a multi-year journey where Microsoft embarked on integrating its business applications into a more comprehensive suite and adding extensions to Teams and the Power Platform. Microsoft referred to this as a unified collective of capabilities. Part of its success was driven by Microsoft’s renewed focus on creating more vertical-focused solutions to support the unique industry need. Another element contributing to its momentum is Microsoft’s success as an infrastructure cloud computing provider.

    Today, everything produces data. Microsoft asserts that, unlike its competition, Microsoft customers will have a leg up because Azure Cloud allows an organization to capture all of that information, analyze it and use it to predict the future. In interviews with Lopez Research, IT leaders have shared that Microsoft’s focus and success in the cloud computing business has created an environment where corporate buyers will more readily consider the company’s broader portfolio. Finally, timing is also essential. All of these previous steps were in motion when organizations had to embrace business and technology transformation rapidly.

    At Microsoft’s Business Applications Summit, the company shared how customers used its software to deliver a wide range of digital experiences and solutions that supported employees returning to work. The conference kicked off with Alysa Taylor, Microsoft’s Corporate Vice President of Business Applications & Global Industry, sharing how companies used Microsoft’s tech to support manufacturing, digital learning, and enhanced retail. James Phillips, the President of Microsoft’s Digital Transformation Group, shared how the company continued to invest in software certification and programs. But perhaps most interesting was the discussion of what’s changing in the business apps landscape. Phillips referred to the need to move from reactive, applications-first business processes to proactive data-driven workflows.

    The Next Application Shift Is Upon Us

    A few years ago, Lopez Research defined a category called Right-time Experiences, These are products, services and applications which provide the right information to the right person at the right time on their device of choice. Right-time experiences are personalized, contextual, adaptive, learning and predictive. These experiences break down data silos connecting information across a company as well as linking to third-party data sources to enhance these experience.

    To create RTEs, a company must connect a wide range of data (real-time data, partner and open data) and AI- driven analytics into applications. Taylor and Phillips described this shift as Microsoft’s vision for Dynamics 365 during the keynote. Philips said, “It’s a game-changer across every single business process, every single industry, and puts in the hands of your users now applications that don’t look anything like they did just a few short years ago….It allows a predictive, proactive business process. Later, he went on to say, “Customer Insights allows you to build a data-driven understanding of your customer, to predict their needs, to understand the opportunity, to deliver experiences that are very tailored, an experience of one, right down to the customer”.

    What’s different today? It’s actually happening.

    One could say, haven’t we been discussing this for years? While this is true, I would posit that certain things have changed. First, technology vendors are building more comprehensive solutions to support digital transformation. Second, advances in cloud technology and the availability of pre-trained AI models within applications, such as Dynamics365, make it easier to deliver on the vision of right-time experiences. Third, shifts in the business market have made every company up its digital game to provide new experiences. At the Microsoft Business Applications Summit, the company shared three great examples of Right-time Experiences that included how:

    • L’Oréal enabled experts to be anywhere and everywhere. L’Oréal makes a wide range of products that require very sophisticated machinery for production and packaging. L’Oréal used Microsoft Dynamics 365 Remote Assist and HoloLens 2 to enable employees in one location to see what employees in another site were looking at in real-time. These remote experts can react to the same image, use mixed-reality annotations, and share critical information, regardless of location. As a result, L’Oréal cut the time it spent resolving issues in half. It also shrunk travel expenses and improved employee productivity by eliminating unnecessary travel.
    • The LA Clippers delivered predictive fan engagement. The LA Clippers built a customer experience system using the Microsoft Cloud, Dynamics, and the Power Platform. The solution provides personalized and predictive experiences across all touchpoints, whether that’s as they walk into the stadium or talk to a call center agent within a mobile app. Using customer Insights, the LA Clippers can track how a fan interacted with the company and make predictions about products the fan would enjoy. It also uses the AI and ML algorithms within the app to identify common interests in its fan base.
    • Humana supported those in need during a pandemic.  Humana used Dynamics 365 and the Power Platform to help its crisis management team make sense of rapidly changing circumstances, help schedule vaccine appointments and used machine learning models to help deliver 1.4 million meals to those in need. Since Humana had already been using the Power Platform before the pandemic, it could create specific solutions in days.

    What should organizations be thinking about today?

    The market is designing the next wave of business applications that are cloud-native and created using microservices that can be updated and recombined to deliver new and enhanced experiences. As companies embark on digital transformation 4.0, technology buyers must:

    1. Get serious about creating agility businesses. Agility isn’t a new concept, but now it’s mandatory. The ability for teams to detect changes in customer behavior and act quickly on insight is key to remaining competitive. Additionally, every business must gracefully pivot to new models such as digital experiences and as-a-service delivery. Cloud infrastructure and application services provide agility in the face of rapid change.

    2. Design processes to meet changing experience expectations. Best-in-industry isn’t good enough. Every company needs to benchmark itself against best-in-class because that’s what your customers expect of you. Also, it’s not just against one company or one benchmark. It’s best-in-class by type of experience such as payments, logistics tracking, product configuration, and contact center response.

    3. Shift from reactive to proactive business processes.  Organizations that can collect, analyze and act on data in near real-time or real-time will predict opportunities and proactively eliminate potential issues. To do this, companies must ensure they are performing the correct data engineering to ensure clean data is entering the firm’s AI and analytics systems.

    Fortunately, every company, regardless of size or industry, can leverage new cloud infrastructure and applications to create better employee and customer experiences. The question is, “What will you build”?