A Framework For Calculating Roi For Agentic Ai Apps Microsoft

Bonisiwe Shabane

-Dec 27, 2025, 9:45 PM

a framework for calculating roi for agentic ai apps microsoft

This module introduces you to practical frameworks for quantifying and communicating the ROI of AI agents, even if you don't have a background in finance. Business leaders learn how to evaluate both quantified financial impact and strategic/intangible value, and how to apply these insights to prioritize use cases, build compelling business cases, and guide investment decisions. AI agents are rapidly transforming how organizations operate, automating tasks, enhancing decision-making, and improving customer experiences. But to scale adoption and secure stakeholder buy-in, it's not enough to showcase innovation; leaders must clearly demonstrate the business value these agents deliver. This module introduces you to practical frameworks for quantifying and communicating the ROI of AI agents, even if you don't have a background in finance. Business leaders learn how to evaluate both quantified financial impact and strategic/intangible value, and how to apply these insights to prioritize use cases, build compelling business cases, and guide investment decisions.

By the end of this module, you're able to: Assess your understanding of this module. Sign in and answer all questions correctly to earn a pass designation on your profile. Today we’re announcing new capabilities in Azure AI Foundry that make it easier for developers to build, observe, and govern multi-agent systems, while helping organizations close the trust gap in AI. As agentic AI adoption accelerates—eight in ten enterprises now use some form of agent-based AI, according to PwC1—the complexity of managing these systems is increasing. Developers face fragmented tooling, and organizations struggle to ensure agents behave responsibly.

Our latest updates to Azure AI Foundry address these challenges head-on. The Microsoft Agent Framework, now in public preview, is the open-source SDK and runtime that simplifies the orchestration of multi-agent systems. It converges AutoGen, a former Microsoft Research project, and the enterprise-ready foundations of Semantic Kernel into a unified, commercial-grade framework—bringing cutting-edge research to developers. With Microsoft Agent Framework, developers can: This framework is designed to help developers stay in flow. According to an industry study2, 50% of developers lose more than 10 hours per week due to inefficiencies like fragmented tools, highlighting the need for solutions that reduce complexity and improve the developer experience.

Over the last year, developers have been experimenting with AI agents in every imaginable form. Agents are not just chatbots or copilots — they are autonomous software components that can reason about goals, call tools and APIs, collaborate with other agents, and adapt dynamically. Whether it’s a retrieval agent for research, a coding agent embedded in a dev workflow, or a compliance agent ensuring policy enforcement, agents are becoming the next layer of application logic. Yet despite the excitement, the path from prototype to production has been fraught with obstacles. Many of the most popular open-source frameworks are fragmented, each with their own APIs and abstractions. Local development rarely maps cleanly to cloud deployments.

And most importantly, enterprise readiness is missing: observability, compliance hooks, security, and long-running durability are table stakes in OSS frameworks. At Microsoft, we’ve had a front-row seat to this problem. With Semantic Kernel, we gave developers a stable SDK with connectors into enterprise systems, content moderation, and telemetry. With AutoGen, pioneered in Microsoft Research, we opened the door to experimental multi-agent orchestration patterns that inspired the community. Both had passionate users — but each had gaps. Developers asked us: why can’t we have both — the innovation of AutoGen and the trust and stability of Semantic Kernel — in one unified framework?

That’s exactly why we built the Microsoft Agent Framework. 🚀 Beyond Productivity: Rethinking KPIs for Agentic AI Success 🔍 Based on my experience working with partners and customers in MS365 Copilot adoption programs, I've seen firsthand that KPIs and AI impact measurement are... And there’s a wide range of opinions on which KPIs truly matter. My approach is to start with two layers: 1️⃣ Tangible vs. Intangible Benefits: While tangible benefits—like cost savings and revenue growth—are straightforward and easy to measure, we need not to overlook the intangible ones. These include enhancements in brand reputation, employee morale, and customer satisfaction, all of which play a vital role in driving long-term success.

2️⃣ Tailored Value for Specific Groups: AI agents are deeply tied to business processes, meaning their impact isn't universal. The benefits they bring often differ from department to department, team to team, and even role to role. A one-size-fits-all KPI often fails to fully capture these unique, context-driven outcomes, making a tailored approach the best option! 👉 Why does this matter? Here are some key points that underscore the transformative potential of Agentic AI apps: 🔺 Automation & Efficiency: These apps automate tasks, streamline processes, and improve decision-making, which leads to substantial cost savings and... 🔺 Balanced ROI: A balance between tangible metrics and intangible benefits 🔺 Comprehensive Cost Analysis: When calculating ROI, we need to consider development costs, data acquisition, and ongoing maintenance of these agents.

🔺 Pricing Models: Flexible pricing models, such as usage-based or outcome-based pricing, can often be more effective than traditional SaaS pricing structures. 🔺 Short-Term & Long-Term Impact: A robust ROI assessment should capture both immediate gains and long-term strategic impacts to ensure sustained value creation. 🔺 Beyond Productivity: Quite often, when talking to executives, I've found that the concept of "productivity" alone isn’t enough to secure an AI investment. They need a broader value proposition that connects productivity gains to tangible financial impacts. As I mentioned earlier, this requires analyzing improvements at the people, group, or division level to truly demonstrate value. 💡 This is precisely why I found the article https://lnkd.in/dbq_82Jq so interesting—it proposes a simple and clear framework for calculating ROI for agentic AI scenarios.

It’s an excellent starting point to define your own model and tailor it to your organization’s unique needs. How are you tackling KPI measurement and ROI assessment in your AI initiatives? #MS365Copilot #AgenticAI #DigitalTransformation #ROI #BusinessInnovation #TechStrategy 🚀 Salesforce AI has the power to transform how organisations sell, service, and connect, but only if it’s adopted the right way. With over 80% of AI projects missing their goals, success takes more than just great technology. From choosing the wrong tools to skipping guardrails or scaling too fast, there are common pitfalls that can slow down or even derail your AI journey.

We’ve broken down the ten biggest mistakes to avoid, and how to build a smarter, more confident rollout using Salesforce AI, Einstein, and Agentforce. 🔍 Discover the top 10 AI adoption pitfalls and how to avoid them 👉 https://bit.ly/3KJfAQO #Salesforce #Agentforce #AI #ArtificialIntelligence #DigitalTransformation CRM isn't the center of your business anymore. Autonomous AI agents are. And Salesforce just made that shift official with Agentforce 360 👇 And while everyone's fixated on the "360" branding, they're missing the architectural earthquake underneath. It's the difference between building agents and building an agentic enterprise.

Here's what changed 👇 Agentforce 360 is built on 4 ingredients that fundamentally change how enterprises approach AI: 1️⃣ 𝗔𝗴𝗲𝗻𝘁𝗳𝗼𝗿𝗰𝗲 𝟯𝟲𝟬 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 Not just agent building. Agent orchestration on scale. ✅ 𝗔𝗴𝗲𝗻𝘁 𝗦𝗰𝗿𝗶𝗽𝘁: A new language for controlling agent behavior with deterministic precision ✅ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗕𝘂𝗶𝗹𝗱𝗲𝗿: Design agents in natural language, no code gymnastics ✅ 𝗛𝘆𝗯𝗿𝗶𝗱 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴: Balances LLM creativity with business logic certainty... • Intelligent Context extracts meaning from PDFs, diagrams, unstructured chaos • Tableau Semantics ensures everyone speaks the same data language • Partners with Databricks, dbt Labs, & Snowflake for standardized semantics 3️⃣ 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝟯𝟲𝟬... Slack isn't just messaging anymore. It's where humans and agents collaborate in real-time.

• Model Context Protocol (MCP) integrates third-party AI • Enterprise Search across Google Drive, GitHub, & Jira • Reimagined Slackbot as an actual AI companion Here's why this matters: We've moved from "let's build... Agentforce 360 gives you an OS. And that's the shift every CIO needs to understand: → It's not about agent features → It's about agent fabric → It's about making AI work at enterprise scale with enterprise governance The companies... The real question is: How do you orchestrate hundreds of agents across vendors, ensure they speak the same language, and govern them without creating chaos? That's what Agentforce 360 answers. And paired with MuleSoft Agent Fabric?

You get the full stack: Build, Connect, Govern, & Scale. Is your enterprise ready for this level of agent orchestration? Or are we still in the "let's test a chatbot" phase? #Agentforce #Dreamforce #AgenticEnterprise #EnterpriseAI MuleSoft Community Zoho launches free agentic AI features across its core business apps to ease AI adoption by automating tasks without added costs for users. If you think AgentKit replaced N8n, you probably never built a real automation.

After building 50+ production AI systems with N8n, here’s my honest, field-tested view 👇 🏃🏻Ease of Use, AgentKit Wins for Speed: - AgentKit lets you build in seconds, drag, drop, done. No API keys or setup hassle. - N8n takes more effort with manual configurations, but it teaches you how real systems work, a skill that pays off long-term. ⚔️Automation Power, N8n Still Leads: - AgentKit triggers only when you talk to it. It’s great for chat-based use cases. - N8n goes beyond that.

It can trigger from Gmail, Slack, webhooks, schedules, and many other sources. It builds true automation that runs in the background, even while you sleep. 🔌Integrations: N8n Has the Advantage: - AgentKit keeps things simple with MCP servers and limited integrations. - N8n offers more than 500 native integrations and can connect to almost any API. You can create modular, scalable systems that work together seamlessly. ⛓️💥Model Flexibility: Freedom vs Ecosystem: - AgentKit is built around OpenAI’s ecosystem, which is great if that’s where your stack lives.

- N8n gives you flexibility, you can switch between OpenAI, Anthropic, Google, or local models as needed. That kind of adaptability becomes crucial when costs or performance change. 📱Interface: AgentKit’s Hidden Strength: - AgentKit’s ChatKit is genuinely impressive. It lets you create clean, dynamic chat interfaces without writing frontend code. - N8n’s interface, in comparison, feels dated. It works, but it’s not something that excites you visually.

The Real question is What are you actually Building? 📌AgentKit is designed for teams that need to deploy conversational agents quickly customer support bots, internal copilots, or lightweight tools. N8n is for deeper systems that run independently, CRM automations, multi-agent orchestration, and backend workflows that scale with your business. Stop chasing hype. Pick the tool that fits your workflow, not the trend. AgentKit isn’t replacing N8n, they solve different problems for different users.

A Framework For Calculating Roi For Agentic Ai Apps Microsoft

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