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How to Make AI Agents Execute Across the Enterprise: Orchestration, Governance, and Architecture

A Workato-hosted event, Work^AI, revealed that despite widespread AI deployment, most companies struggle to achieve ROI from AI agents due to structural execution gaps rather than model quality issues. Speakers from Workato, Snowflake, and Monte Carlo emphasized that robust enterprise AI requires a stable architectural foundation, comprehensive governance to prevent sprawl, and strong leadership from IT and GRC teams to drive successful transformation.

Tags

AI Research Gartner IDC Monte Carlo Snowflake Workato Adam Seligman Barr Moses Carter Busse F. Perri Okhtay Azarmanesh Rahul Dureja San Francisco AI Agents AI Infrastructure

Key points

Notable quotes

"Enterprises don't transform because AI can reason," Rahul Dureja said. "They transform when AI can execute. Can it access the right systems? Can it operate within governance boundaries? Can it understand the state of a business process? Can it take action reliably?"

— Rahul Dureja

"If every agent is a special project doing its own identity and integration and orchestration and connectivity and observability and governance and token management with every system," Seligman said, "those of you who have been in the IT landscape for a while know exactly how this turns out. It's super unpleasant. It adds a lot of risk. It adds a lot of complexity. You go slower."

— Adam Seligman

"The IT team needs to be that group. You need to step up and lead that now. Because if you don't become the leader, you won't own the operations, and they'll hire a CAIO around you. Go get it now if you don't own it, because it's powerful, and you'll become much more strategic if you own AI as an IT leader."

— Carter Busse

Structured claims — 34

  1. 1
    Monitor Workato's event schedule for future AI-focused gatherings, as this event brought together key enterprise stakeholders.
    “On June 10, Workato hosted Work^AI at itsAI Research Lab in San Francisco, bringing together enterprise architects, IT and data executives, integration engineers, business systems leaders, and AI practitioners for a half-day of technical depth and real talk on where enterprise AI actually stands.”
    Entities: Workato, Work^AI, AI Research Lab, San Francisco
  2. 2
    Track the perspectives of these key speakers on enterprise AI challenges and solutions, as they represent diverse expertise from leading technology companies.
    “Talks were given by Workato CTOAdam Seligman, Snowflake Principal AI ArchitectOkhtay Azarmanesh, Workato Chief Strategic ArchitectRahul Dureja, Monte Carlo CEOBarr Moses, and Workato CIOCarter Busse.”
    Entities: Workato, Adam Seligman, Snowflake, Okhtay Azarmanesh, Rahul Dureja
  3. 3
    Diligence AI project proposals to ensure they address execution capabilities, not just model development, to maximize ROI.
    “The gap between having agents and getting ROI from them is execution.”
    Entities: artificial_intelligence, ai_agents
  4. 4
    Benchmark current AI initiatives against this finding to assess the effectiveness of existing deployments in driving measurable business outcomes.
    Entities: artificial_intelligence
  5. 5
    Route AI strategy discussions towards architectural and operational improvements rather than solely focusing on model development or selection.
    “The reason is structural, not a model quality problem.”
    Entities: artificial_intelligence
  6. 6
    Monitor AI project success metrics to prioritize execution capabilities, such as system access and action reliability, over pure reasoning power.
    “"Enterprises don't transform because AI can reason," Rahul Dureja said. "They transform when AI can execute."”
    Entities: Rahul Dureja, artificial_intelligence
  7. 7
    Investigate current AI agent capabilities to identify gaps in end-to-end execution and integration with enterprise systems.
    “"They can draft an amazing email or PowerPoint, but they can't deliver it, get feedback on it, coordinate on it, pull real data, take action in a system on your behalf."”
    Entities: Adam Seligman, ai_agents
  8. 8
    Benchmark internal AI POC-to-production conversion rates against this industry statistic to identify potential bottlenecks in scaling AI initiatives.
    “Dureja cited IDC and Gartner data showing 88% of AI POCs never reach production”
    Entities: IDC, Gartner, artificial_intelligence
  9. 9
    Assess the risk profile of ongoing agentic AI projects and implement mitigation strategies to avoid cancellation, focusing on execution and governance.
    “more than 40% of agentic AI projects are projected to be canceled by 2027”
    Entities: ai_agents
  10. 10
    Identify opportunities to accelerate AI scaling initiatives within the organization, learning from best practices in architecture and governance.
    “two-thirds of companies haven't begun scaling their AI initiatives at all.”
    Entities: artificial_intelligence
  11. 11
    Prioritize the development of a stable AI infrastructure to prevent agent improvisation and ensure reliable operation within enterprise systems.
    Entities: ai_agents, ai_infrastructure
  12. 12
    Investigate the consistency of AI agent outputs across different tools and models, especially in critical business processes like healthcare scheduling.
    “The same patient request, routed through thirteen granular MCP tools, produced four different outcomes across different models and prompt phrasings.”
    Entities: MCP
  13. 13
    Evaluate the adoption of platform-side orchestration and composable skills to ensure consistent and reliable AI agent execution.
    “Routed through a single intent-based composable skill backed by platform-side orchestration, it produced the correct outcome every time.”
    Entities: artificial_intelligence
  14. 14
    Prioritize investment in enterprise AI infrastructure and orchestration capabilities over continuous model iteration to solve execution challenges.
    Entities: ai_agents, ai_infrastructure
  15. 15
    Benchmark the proportion of agent-driven data consumption against Monte Carlo's experience to understand the potential for AI automation in data workflows.
    “"Over half of our consumption is now done by agents pulling data through MCP."”
    Entities: Barr Moses, Monte Carlo, ai_agents, MCP
  16. 16
    Model future product consumption patterns to account for a growing proportion of AI agent users, impacting product design and support.
    “"When I think about what the future looks like, we're going to have more agents than humans consuming our products."”
    Entities: Barr Moses, Monte Carlo, ai_agents
  17. 17
    Assess the risk landscape associated with the rapid proliferation of AI tools and agents within the organization.
    Entities: ai_agents, artificial_intelligence
  18. 18
    Implement centralized management and governance frameworks for AI agents to mitigate complexity, reduce risk, and improve operational efficiency.
    Entities: Adam Seligman, ai_agents, ai_regulation
  19. 19
    Investigate the current landscape of MCP servers and data sources to identify governance challenges and potential vulnerabilities.
    “"You're starting to have hundreds of MCP servers from different sources, internally and externally. The governance of those is becoming an issue."”
    Entities: Okhtay Azarmanesh, MCP, ai_regulation
  20. 20
    Audit existing MCP setups for security vulnerabilities related to service account usage, lack of user-level permissions, and insufficient audit trails.
    “"Most MCP setups run on service accounts with admin access. No user-level permissions. No audit trail."”
    Entities: Rahul Dureja, MCP
  21. 21
    Benchmark Workato's runtime governance model for AI agents as a best practice for implementing secure and auditable AI operations.
    “Workato enforces governance at runtime: every agent action is authenticated, authorized, and auditable.”
    Entities: Workato, ai_agents, ai_regulation
  22. 22
    Implement these specific governance capabilities to establish a robust and secure framework for managing AI agent interactions and data access.
    Entities: MCP, ai_regulation
  23. 23
    Prioritize governance initiatives to ensure AI deployments contribute positively to business value and mitigate potential liabilities.
    “The governance is the difference between AI as a controlled business asset and AI as a compounding liability.”
    Entities: artificial_intelligence, ai_regulation
  24. 24
    Route AI leadership responsibilities to the IT team to ensure strategic alignment and effective operational control.
    “"The IT team needs to be that group. You need to step up and lead that now."”
    Entities: Carter Busse, artificial_intelligence
  25. 25
    Alert IT leadership to the strategic imperative of owning AI initiatives to maintain influence and control over enterprise technology.
    “"Because if you don't become the leader, you won't own the operations, and they'll hire a CAIO around you."”
    Entities: Carter Busse, artificial_intelligence
  26. 26
    Implement structured enablement programs to accelerate AI adoption and ensure employees are proficient in using new AI tools.
    Entities: Workato, artificial_intelligence
  27. 27
    Investigate the potential for mandatory AI adoption to streamline production pipelines and reduce staffing requirements, as demonstrated by Monte Carlo.
    Entities: Monte Carlo, artificial_intelligence
  28. 28
    Benchmark potential gains in feature shipping capacity through AI adoption against Monte Carlo's reported results to justify investment in AI tools.
    “and tripled to quintupled feature shipping capacity”
    Entities: Monte Carlo, artificial_intelligence
  29. 29
    Develop an AI transformation roadmap that incorporates tool provision, system integration, leadership alignment, and accountability mechanisms.
    Entities: artificial_intelligence
  30. 30
    Identify and empower leaders who possess both technical expertise and organizational influence to drive AI initiatives effectively.
    “But it requires someone with both the technical authority to build the right architecture and the organizational standing to enforce it.”
    Entities: artificial_intelligence

Source

Source
workato-company-blog
Record title
How to Make AI Agents Execute Across the Enterprise: Orchestration, Governance, and Architecture
Author
Perri Bronson
Published
Jun 15, 2026
URL
https://workato.com/the-connector/how-to-make-ai-agents-execute-across-the-enterprise-orchestration-governance-and-architecture
Manifest ID
1781872765183216491
Significance
high
Sentiment
mixed