How Systems Orchestrators Support Business Operations

Let’s say your support center is getting hammered with repeat calls about a new product feature. Historically, the team would escalate, create a task force, and maybe update a knowledge base weeks later. With the tech available today, you should be able to unify signals from tickets, chat logs, and social mentions instead. This helps you quickly interpret the root cause. Perhaps in this case it's a confusing update screen that’s triggering the same questions. Instead of just sharing the feedback with the task force that'll take weeks to deliver something, galvanize leaders and use your tech stack to orchestrate a fix in real time. Don't have orchestration in that stack? Start looking into this asap. An orchestration engine canauto-suggest a targeted in-app message for affected users, trigger a proactive email campaign with step-by-step guidance, and update your chatbot’s responses that same day. Reps get nudges on how to resolve the issue faster, and managers can watch repeat contacts drop by a measurable percentage in real time. But the impact isn’t limited to operations. You energize the business by sharing these results in a company-wide standup and spotlighting how different teams contributed to the OUTCOME. Marketing sees reduced churn, operations sees lower cost-to-serve, and leadership sees a team aligned around outcomes instead of activities. If you want your AI investments to move the needle, focus on unified signals, real-time orchestration, and getting the whole business excited about customer outcomes....not just actions. Remember: Outcomes > Actions #customerexperience #ai #cxleaders #outcomesoveraction

Everyone’s deploying AI agents. But most of them still can’t handle a refund. Because what’s missing is control. Let alone a cancellation. Or a churn save. Or a troubleshooting case. You need orchestration.  Clear logic. Human-defined rules. Let’s break it down: The customer says: “I want a refund.” Behind the scenes: - Check eligibility (CRM) - Pull invoice (NetSuite / Stripe) - Validate usage (telemetry) - Review policy (KB) - Escalate to finance You need: financial data + SOP logic + approval thresholds. And every piece has to be orchestrated in the right order. Now imagine doing that for: - Cancellations – Legal terms + risk scoring + routing - Retention – Usage data + plan logic + escalation paths - Troubleshooting – Product config + logs + warranty checks Every. Single. One. Requires: - Data from 3+ systems - Approvals and escalation logic - Custom workflows - Real-time context — You need AI that knows what to do — and when. That’s only possible if humans design the logic, define the paths, and control the complexity. If your agent can’t navigate across systems or apply business logic — it’s not ready for primetime. #EnterpriseAI #AIOrchestration #CustomerSupport

AI agents... Can do things like research, summarize calls, or automate outreach and are starting to show up everywhere. But getting them to work with each other and with your existing apps has been challenging. Enter MCP... Think of MCP like a standardized communication protocol where agents can exchange structured information about who they are, what capabilities they have, and what they need. Similar to how humans might introduce themselves and state their needs in a professional setting. Why this matters for you (and your customers): Fewer bugs, faster delivery. Instead of building 10 different point-to-point connections, you plug into MCP once. Clear roles and handoffs. Agents declare “I handle summaries,” “I handle translations,” or “I sync files,” so you don’t have to guess who does what. Easier updates. When a new AI tool launches, you just add it to the MCP network; you don’t write a whole new integration. Real-world HeyGen MCP examples: ✨ Videos syncing in Box and Dropbox and Google Drive MCP lets HeyGen automatically push new video assets into your folders. 🐘 Post-call video recaps with Gong, Fireflies.ai Otter.ai (all great tools) Your rep wraps up a demo; HeyGen’s agent grabs the recording, creates a short personalized recap, and sends it via any of those platforms. Need translation? An MCP-connected translation agent jumps in and HeyGen can handle it. 🔥 Automated video campaigns in HubSpot, Intuit Mailchimp or Outreach. Set a campaign goal, point HeyGen at your contact list, and watch it generate and send tailored videos directly through your existing platform. 🎆 Hex dashboard summaries in video form Swap out the daily Slack pings with a HeyGen-powered video that highlights key metric changes—explaining “why this matters," automatically. TLDR: Agents do one thing really well. Orchestrators (like MCP servers) coordinate them into a smooth workflow. Over time, (I believe) the orchestration layer will be where the real value sticks & everyone will have awesome single-purpose agents, but only a few will tie them all together seamlessly. At HeyGen, we’re building both an agent (for video creation) and embracing the orchestrator role, plugging into MCP so you can drop us into your tech stack with minimal effort. 🚀 🚀 🚀

AI Agents Are Here: The CIO’s Dilemma CIOs are facing a new kind of pressure. AI agents are everywhere, and everyone—from vendors to internal teams—is pitching their own flavor. Every SaaS platform now offers an AI agent or soon will. Open-source tools make DIY agents easier than ever. But more agents doesn’t mean more value. So if you are the CIO, here is the dilemma: Do you go deep with vendor-native agents or build your own cross-system intelligence? Here is a high level framework we use to think through this: 1 | Single-System Workflows → Let the SaaS Vendor Lead If a workflow lives mostly inside one system—customer service in Zendesk, specific HR operations in Workday then the native agent is your best bet. Vendors already understand their own schema, UI, and permissions. Their agents can hook directly into those layers, offering speed to value and tighter integration than anything you could build from scratch. 2 | Cross-System, High-Value Workflows → Orchestrate + Extend The real opportunity is in workflows that span systems. CRM to ERP to contract management or compliance reporting in HR Let vendor agents handle the system-native tasks. Insert your own agents where custom logic, IP, or policy lives. But glueing them together is the hard part. There is the need for an orchestration layer. Pick (or build) an orchestration layer with open APIs, function-calling standards, and shared observability so first-party and third-party agents can cooperate in one end-to-end flow—without duplicating every SaaS feature. 3 | The No-Regret Move → Build a Functional Knowledge Graph All agents—vendor, open-source, or custom—need business context they can trust. If you already centralize data in Snowflake, Redshift, Databricks, you are already ahead of the game. Expose your star schemas and shared dimensions as entities, enrich them with business ontologies and serve them via APIs that support RAG. Each agent can ground its reasoning in the same governed, up-to-date source of truth. This becomes the governed brain all agents up-to-date, and enterprise-safe. You can’t build everything. You can’t trust vendors with everything. The strategic move is to own the orchestration and knowledge layers—while letting SaaS partners own the plumbing inside their walls. #AgenticAI #EnterpriseAI

Your Enterprise needs to drop the Ai Factory naming and mindset and move to Orchestration yesterday..... Picture a roaring factory floor at the height of the Industrial Revolution. Steam hisses, pistons pound, and identical parts rattle down the conveyor. The brilliance of that age was scale, but its blindness was sameness. Today’s “AI factory” idea inherits that mindset: rack the GPUs, crank out tokens, brag about throughput. It treats foundation models and tokens as interchangeable bolts and screws. Useful, certainly. Differentiating? Not anymore. Cloud providers now sell inference like kilowatt‑hours. Open‑source checkpoints proliferate overnight. Fine‑tunes arrive pre‑packaged. In this landscape, models and tokens have become raw material, cheap and abundant. The boardroom still sees cost per million tokens, yet the frontline teams ask a sharper question: “Did I get the ROI I needed when I needed it?” That answer lives in workflows, not in warehouses of silicon. A fraud‑detection agent must ping a private graph, call an LLM for pattern reasoning, and trigger a payment hold inside ninety milliseconds. A procurement bot must sift contracts, calculate risk, and draft a renegotiation plan before the market opens. These are orchestrations, living score‑sheets that weave data, tools, and models into a single moment of value. True AI orchestration offers three big shifts: - From capacity to choreography Instead of booking GPU hours, you schedule outcomes. The orchestrator decides whether a small domain model at the edge or a giant cloud model in Singapore delivers the SLA, then routes the call accordingly. - From hardwired pipelines to adaptive playbooks Policies, guardrails, and optimization loops live in a control plane. When privacy law changes and DATA must be processed locally or a new model outperforms the old, the workflow rewires itself without a rewrite. - From hidden spend to visible impact Telemetry rolls up into metrics like Revenue per Inference and AI Leverage Index (being update on our website shortly). Leaders see which processes earn their keep and which merely burn tokens. Factories stamped out parts the same way every time. Orchestration composes a different symphony for every request, yet does so with repeatable governance and relentless efficiency. The factory mindset tells you how many tokens you can mint per second. The orchestration mindset tells you how fast you turn a question into cash. In short: models and tokens are today’s steel and copper. Workflows are the skyscrapers and railroads we build with them. To capture the value, stop counting ingots. Start conducting the orchestra. Keith Townsend Chet Kapoor KamiwazaAI Matthew Wallace

Automation is overrated. What your IT really needs is orchestration. Most enterprise IT teams have automated something—a ticketing workflow, a report, a backup job. But here’s the problem: real-world issues don’t show up in clean, isolated chunks. A job runs late → a payroll file doesn’t get processed → employees don’t get paid → someone scrambles manually → then a compliance issue gets flagged. That’s not an automation problem. That’s a coordination problem. 🚫 Automation handles individual tasks. ✅ Orchestration connects the dots between tasks, systems, data, and outcomes. Here’s what orchestration really looks like in a modern IT environment: - When a performance issue is detected, a ticket is automatically created → - That ticket triggers a diagnostic workflow based on past incident patterns → - The system fetches logs, runs scripts, and evaluates possible root causes → - Based on thresholds, it launches an automated remediation → - If human approval is needed, it routes the workflow and waits → Once resolved, the platform logs the incident, notifies stakeholders, and produces a compliance report. All of that? One orchestrated workflow. Not five disconnected tools duct-taped together. And the best part? It’s adaptive. If you move from SAP to Workday or AWS to Azure, the workflow still works. You just swap out a block. No need to rebuild the entire thing! In fast-moving IT environments, automation alone is like having a screwdriver when what you really need is an entire toolkit and a blueprint. 🧠 The takeaway: Automation is great. But without orchestration, you're just speeding up silos. If you’re still managing IT with patchwork tools and people-heavy processes, it's time to think differently. Have you experienced the difference between automation and orchestration in your organization? 👇 I’d love to hear what that looked like.

Multi-agent systems are starting to become mainstream. As this space evolves, here are my observations: New products generally fall into one of two categories: agents and orchestrators. Agents are single-purpose task executors. They are vertical-specific systems that excel at completing one specific task—like a deep research agent or an outbound sales agent. Orchestrators sit a layer above. Their role is to break larger goals into manageable tasks, assign them to specific agents, connect relevant data sources, and ensure appropriate guardrails and controls for enterprise environments. My take is long-term value and defensibility isn't in individual agents, they are probably going to be commoditized. The stickiness lies in orchestrators—platforms that can learn each business's unique characteristics and effectively manage multiple agents to deliver outcomes. Another way to put it is, are you an MCP client, or are you an MCP server? 

AI agents are redefining workflows Pilots alone won’t drive impact CMOs who treat agentic AI like plug-ins miss the enterprise value. Strategic deployment turns automation into orchestration. Here’s how to activate true ROI: Step 1. Map your friction points Audit repetitive, manual tasks across departments Morgan Stanley used AI to scan 100K+ financial documents, saving 2.5 hours/day per advisor Step 2. Define agent roles clearly Assign agents to solve specific business problems ServiceNow built virtual IT agents that resolved 75% of support tickets without escalation Step 3. Start narrow and scale Test in one function before expanding cross-org Unilever piloted AI onboarding tools in HR, cutting training time by 60% in five countries Step 4. Build governance from day one Align legal, compliance, and cross-functional inputs JP Morgan created an AI council to govern ethics, compliance, and rollout risk Step 5. Measure business outcomes Track KPIs like cost-per-task or cycle time, not just usage GE implemented AI for predictive maintenance and reduced unplanned downtime by 25% AI transformation doesn’t scale without orchestration. How are you operationalizing agents beyond pilots? #ExecutiveLeadership #AITransformation #Martech