AI at Work
99: Using AI Automation to Build Smarter Workflows Across Your Organization with Marc Boscher
Why It Matters
Understanding how to scale AI from individual use to coordinated, cross‑functional workflows is crucial for companies that want to capture the full productivity boost AI promises. Without reliable context and trust mechanisms, AI deployments remain siloed and risky, leaving businesses behind competitors who embed AI into their operating systems.
Key Takeaways
- •Shift AI from personal tools to organization-wide agents.
- •Context is critical; agents need accurate, dynamic data sources.
- •Dynamic context libraries replace static prompt libraries for AI.
- •Bidirectional sync across systems eliminates friction and silos.
- •Trust and governance are essential for autonomous AI workflows.
Pulse Analysis
In today’s episode, Marc Boscher explains why enterprises must move beyond the "single‑player" mindset of AI—where individuals copy‑paste prompts—to a "multiplayer" approach that embeds intelligent agents across teams. This shift transforms AI from a novelty into a strategic operating system, unlocking cross‑functional productivity gains that far exceed isolated personal efficiencies. Listeners learn that the real competitive edge lies in orchestrating AI workflows that span sales, support, and project management, rather than keeping the technology confined to siloed use cases.
A central theme is the importance of context. Boscher argues that agents need the right, up‑to‑date information—what he calls a "context library"—to make reliable decisions. Static prompt collections quickly become stale; dynamic context pulls live data from CRMs, ticketing platforms, and knowledge bases, ensuring AI actions reflect the current state of the business. Establishing clear sources of truth and automated mechanisms to feed that data reduces friction, builds trust, and mitigates the risk of erroneous outputs.
Unido’s solution illustrates these principles in practice. By providing bidirectional synchronization between systems like Salesforce, Asana, and ServiceNow, the platform delivers the right context to agents exactly when needed, effectively eliminating the manual copy‑paste middleware that hampers adoption. This live sync not only keeps data consistent across departments but also enables autonomous ticket triage, deal advising, and real‑time coaching. Companies that implement such integrated AI workflows can expect faster decision cycles, lower operational costs, and a scalable foundation for future AI initiatives.
Episode Description
Most companies think they are “doing AI” but are still stuck in single-player mode.
In this episode Chris talks with Marc Boscher, Founder and CEO of Unito, a workflow integration platform, about why AI adoption breaks down at the organizational level. Marc explains that the real barrier is not model capability, but fragmented systems, missing context, and lack of trust. He introduces the shift from prompt engineering to context engineering, and why connecting systems and data is the key to unlocking AI that works across teams, not just for individuals.
The conversation explores how leaders can move from isolated productivity gains to true enterprise impact by building context libraries, enabling dynamic data access, and reducing operational friction. Marc also breaks down the importance of trust, deterministic vs non-deterministic systems, and why change management remains the biggest challenge. This episode gives leaders a practical lens for turning AI from a tool employees use into infrastructure the business runs on.
Chapters:
00:00:00 Introduction
00:00:36 Why Trust and Context Are Critical for AI Agents
00:01:00 Context vs Prompts: What Actually Matters
00:03:48 Single Player vs Multiplayer AI in Business
00:06:30 Why Context Unlocks Enterprise-Level AI Value
00:08:28 What “Context” Really Means in AI Systems
00:11:34 Building Context-Rich AI Use Cases (Sales Example)
00:13:42 Static vs Dynamic Context Explained
00:20:12 Why Context Engineering Replaces Prompt Engineering
00:24:04 From Human-in-the-Loop to Autonomous AI Systems
00:27:29 The Context Gap and Operational Inefficiency
00:36:01 Why Change Management Is the Real Bottleneck
00:42:03 Deterministic vs Non-Deterministic AI Systems
🔎 Find Out More About Marc Boscher:
LinkedIn: https://www.linkedin.com/in/marcboscher
Unito: https://unito.io
🛠 AI Tools and Resources Mentioned:
Unito – https://unito.io
Salesforce – https://www.salesforce.com
ServiceNow – https://www.servicenow.com
GitHub – https://github.com
HubSpot – https://www.hubspot.com
NetSuite – https://www.netsuite.com
Workday – https://www.workday.com
ChatGPT – https://chat.openai.com
Claude – https://claude.ai
Gemini – https://gemini.google.com
Copilot – https://copilot.microsoft.com
Comments
Want to join the conversation?
Loading comments...