Dan Twing and Tom O'Rourke discuss the evolution from traditional agents to AI-powered agents in enterprise automation software. While automation agents originally served as proxies for remote system operations, AI agents now bring capabilities that enables generative and adaptive responses. Their discussion positions AI agents as an evolution rather than revolution in automation, offering enhanced decision-making capabilities while following similar architectural patterns as traditional agents. Automation leaders should prepare for integration with enterprise systems that have adopted AI agent capabilities, requiring the same coordination, governance and control ofsystems that may have unanticipated behaviors.

Key Points

• Traditional vs. AI Agents: Traditional automation agents operated as delegates for managing remote operations, while AI agents add probabilistic capabilities, LLM intelligence, and generative abilities that enable adaptive decision-making
• Orchestration Evolution: As enterprise software systems adopt AI agent capabilities, automation must evolve to coordinate and orchestrate these intelligent systems while maintaining process integrity.
• Observability: AI-enabled agents can excel at identifying anomalies previously unseen scenarios that may lead to automation service failure
• Integration: Automation teams will need to learn new protocols like Model Context Protocol (MCP) to build integrations with AI-enabled systems where vendors do not offer packaged solutions

Takeaways for Automation Leaders

1. Anticipate Inevitable Adoption: Even organizations that adopt new technologies slowly will eventually wind up with systems that use AI capabilities
2. Start Small: Create a pilot project where AI Agents bring capabilities that aren't available in traditional agents
3. Implement Guardrails: Create testing frameworks around AI agents that canidentify where the agent is operating outside expected behaviors
4. Build Trust Over Time: Expect that it will take time for staff and users to trust AI, begin using AI agents to provide observations and recommendationsthen gradually increase agent independence to allow autonomous execution

Request for Listeners

If you're starting to integrate AI agents into your automation environment, we would really love to hear from you. We want to hear from some automation team who are using AI agents, so if you're doing interesting things, please reach out to us.

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In this episode, hosts Dan Twing and Tom O'Rourke explore the challenges of migrating automation software. They discuss the business and technical drivers for switching to a different automation tool, what makes these projects difficult, and why migration is occurring more frequently. Key Points

• Migration frequency is increasing: Average time on workload automation tools has dropped from 12-15 years to just 4-5 years, reflecting market changes and rapid technology evolution
• Multiple drivers force migration decisions: Key catalysts include cloud capabilities, pricing changes, product limitations, integration gaps, vendor roadmap misalignment, and operational consolidation needs
• Integration complexity and heavy API use creates "stickiness": Enterprise software becomes difficult to replace due to extensive connections to other systems and staff training investments
• Vendor consolidation creates imposed migrations: Acquisitions and end-of-life announcements force unwanted migration decisions, though large customers can sometimes negotiate timeline extensions

Five Learnings

• Production continuity is paramount: Business operations must continue during migration, requiring careful staging and sequencing of which systems to migrate first
• Conversion tools have limitations: Automated migration tools typically handle only basic configurations, while integrations, complex scripting and custom workflows require manual recreation
• Scope decisions are critical: Organizations must decide whether to migrate everything at once, implement in phases, or leave some legacy systems in place permanently
• Cultural resistance must be addressed: Staff resistance to change represents a significant challenge that requires dedicated change management attention

• Professional expertise is essential: Successful migrations typically require either internal team augmentation or external professional services from vendors or specialized partners

Takeaways for Automation Leaders Considering Migration

1. Assess the current automation environment, inventorying workflows and integrations, analyzing the quality of schedule and configuration data, and understanding what knowledge gaps exist in the automation staff and users.
2. Establish migration scope and strategy, considering whether the conversion should include redesign of business and operational workflows, whether all applications should be migrated, and how to maintain production SLAs while building and deploying a new automation system.
3. Evaluate the business continuity, technical, resource and operational risks, creating risk mitigation strategies in advance for scenarios that have a significant likelihood of occurring and which would have a substantial impact on the business or the project.
4. Develop an implementation plan, defining the project timeline, resources and costs, testing and rollback procedures, progress metrics, and how stakeholders and users will be engaged through the project.

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Roy Dreyfus, Senior Director of IT at Market America, discusses his 14-year automation journey with Dan Twing and Tom O'Rourke on the Enterprise Automation Excellence Podcast. Roy shares how Market America has evolved from basic, disconnected automation tools to a sophisticated orchestration platform using HCL Workload Automation (HWA). Market America is now transitioning to HCL's new AI-powered Uno platform, which combines automation with artificial intelligence capabilities.

Key Points

• Evolution from Basic to Advanced Automation: Market America transformed from using simple automation tools starting in 2016 to implementing enterprise-wide orchestration platforms
• Strategic Vendor Partnership: - After their licensing costs tripled, Market America selected HCL HWA based on exceptional pre-sales support and ongoing customer engagement
• Cross-Process Orchestration Value: Roy highlighted a use case building automated metrics and analytics that examine system performance data to provide meaningful insights for business owners and executive leadership

Takeaways for Automation Leaders

• Fear is the Main Barrier: The biggest obstacle to automation adoption is fear rather than technical limitations. People who avoid automation typically haven't invested time in learning about or using these tools
• Start with Partnership: When exploring new automation technologies, work closely with vendors to access their expertise and provide feedback that shapes product development. This collaborative approach ensures better outcomes
• Embrace Experimentation: Companies that want to stay competitive must allow their teams to experiment with automation and AI tools, recognizing that failed experiments still provide valuable learning experiences
• AI Enhances Rather Than Replaces: Successful automation implementation focuses on making human workers more efficient and capable of deeper thinking, rather than eliminating jobs entirely
• Executive Buy-in is Essential: Advanced automation initiatives require top-down support and investment, as sophisticated AI and orchestration tools typically require significant financial commitment and organizational change
• CI/CD Pipeline Automation Opportunity: There's significant potential to automate software development processes, with 6-7% of enterprise automation jobs already focusing on CI/CD pipeline automation

Show Links

Roy Dreyfuss discussing HCL automation on YouTube: https://www.youtube.com/watch?v=1F6Z_3-xfGo

Roy's LinkedIn: https://www.linkedin.com/in/leroy-dreyfuss-cio/

Dan's LinkedIn: https://www.linkedin.com/in/dantwing/

Tom's LinkedIn: https://www.linkedin.com/in/tomorourkeEAE Podcast Home: https://em360tech.com/podcast-series/enterprise-automation-excellence

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A successful automation strategy requires moving beyond technology-focused thinking to business outcome-driven approaches, with AI serving as an enabler rather than an end goal.

In this Enterprise Automation Excellence episode, hosts Dan Twing and Tom O'Rourke present a strategic framework that moves beyond technology-focused approaches to business outcome-driven automation planning.

Key Points

• Operational Maturity - Improving your organization's process maturity will help shift from a reactive, task-focused model to a predictive and dynamic automation that will contribute to business transformation.
• AI as an Enabler - Artificial intelligence should be treated as a tool within the automation toolkit, not as the primary objective or strategy.
• Automation Strategy Ownership - Automation leaders are best positioned to own automation strategy.
• Focus on Business Value - Communicate automation benefits in terms of business solutions rather than technical features .
• Plan for AI Tool Integration - Anticipate - integrating external AI tools and supporting their data requirements.

Takeaways for Automation Leaders

• Focus on Intelligent Orchestration, Not AI - The goal is better end-to-end business process execution. AI is simply one tool to achieve more intelligent orchestration and consistent task execution.
• Apply Product Thinking for Strategic Decision-Making - Evaluate automation opportunities through the lens of customer value, business impact, and resource requirements.
• Prioritize Based on Business Impact - Use structured evaluation criteria to prioritize automation initiatives, considering factors like customer value, implementation effort, security risks, and timeline for business impact.

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In this Enterprise Automation Excellence episode, hosts Dan Twing and Tom O'Rourke discuss AI's impact on enterprise automation and orchestration. Dan takes a more optimistic stance while Tom adopts a pragmatic perspective on AI adoption timelines. They recognize that AI technologies like neural networks and machine learning are already deployed in enterprise environments, and that the current focus is on new capabilities like Large Language Models (LLMs) and agentic AI.

The hosts agree that current AI implementations in automation tools are being driven by vendor marketing rather than customer demand, with many products adding AI features as competitive necessities rather than selecting customer-requested solutions. They emphasize that meaningful AI adoption in enterprise automation will require years, not months, and success depends heavily on organizational maturity, data quality, and process standardization.

Key Points

• Current AI adoption is vendor-driven – Software providers are adding AI labels to products based on market pressure rather than customer requests, creating "me-too" product management.

• Limited real-world validation – Claims about productivity gains (such as reducing 300 L1 support staff to 6) remain largely unproven with insufficient deployment data.

• Basic AI features dominate – Most current implementations focus on simple chatbots and natural language interfaces rather than advanced automation capabilities.

• Integration challenges persist – AI's value in core automation functions like system integration and orchestration remains unclear and undemonstrated.

• Adoption timeline is extended – Similar to containerization (which took 15 years to reach 50% adoption), AI integration will be a multi-year journey.
• Success requires organizational maturity – Effective AI implementation depends on having well-curated data, standardized processes, and clear problem-to-solution mappings.

Takeaways for Automation Leaders

1. Audit and Improve Data Quality and Process Maturity

Conduct a comprehensive review of your current automation processes and data managementpractices. Focus on standardizing how problems are documented, solutions arerecorded, and processes are executed.

2. Develop a Strategic Partnership Approach with Vendors

Select 1-2 key vendors to work with as strategic partners for adopting AI into the automation portfolio. Establish pilot programs with clear success metrics.

3. Adopt Governance and Validation Frameworks

Learn more about your organization's AI governance and validation models. Review your existing processes and adjust them to address potential risks introduced by the introduction of AI capabilities.

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In this episode of the Enterprise Automation Excellence podcast, Dan Twing and Tom O’Rourke dive into the 2025 EMA Radar for Workload Automation and Orchestration—the most significant overhaul in the Radar’s 16-year history.

They explore how three foundational technology shifts—orchestration, observability, and AI/agentic capabilities—are reshaping the automation landscape. Vendors are advancing unevenly across these areas, creating a patchwork of strengths that reflect both customer priorities and technical readiness. From data pipelines and container orchestration to AI-driven workflows and the evolving role of legacy capabilities, this conversation maps where the market is going—and what leaders should be watching.

Key Topics:

• Why orchestration, observability, and AI now define best-in-class WLA

• What’s changed in the 2025 Radar measurement criteria—and why it matters

• Challenges in adopting multiple complex technologies simultaneously

• How cloud platforms are changing automation architecture priorities

• The market’s journey from fragmented experimentation to standardization

Takeaways for Automation Leaders:

• Integration of the "automation triad" is a competitive advantage—but also a challenge

• Customer-vendor collaboration is key to success in emerging capability areas

• Legacy functionality still matters: don’t lose focus on what’s already working

• Product roadmaps are increasingly shaped by Radar cycles and timing pressures

Listen now to understand where enterprise automation is heading—and how to get ahead of the curve.

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In the second half of their focus on product thinking hosts Dan Twing and Tom O'Rourke discuss how automation teams can apply product thinking principles to shift from operating reactively as service providers into a strategic-minded,customer value-focused organization.

Key themes include managing automation asa product portfolio, developing strategic roadmaps, implementing iterative planning processes, and building compelling business cases for automation investments. The episode emphasizes the importance of understanding customer needs through frameworks like "Jobs to Be Done" and Value Proposition Canvas, while providing practical guidance on piloting product thinking initiatives and securing funding for automation improvements.

Learnings

• Portfolio Management Approach - Automation teams should manage their offerings as a curated portfolio of products and services, including automation software, integrations, APIs, operations, and support services.
• Curation is Critical - Teams must deliberately choose which automation capabilities to offer and which to exclude, avoiding the trap of exposing all available product features to users.
• Communication Drives Adoption - Success requires building capabilities to communicate offerings, share success stories, and provide clear pathways for users to request help.
• Strategy as Planning Tool - Effective automation strategy involves understanding what needs to change (why), defining target state (what), and outlining execution approach (how) through roadmaps and resource plans.
• Iterative Planning Process - Product thinking encourages quarterly strategy updates and monthly adjustments rather than annual planning cycles, enabling faster response to changing business needs.
• Pilot-Based Implementation - Organizations should start with small, low-risk pilots like providing dashboard access to business users or establishingdeveloper office hours.
• Investment Framework - Automation funding requests are evaluated using defend/extend/upend categories, with "extend" and "upend" projects having better approval chances than basic operational improvements.
• Proactive vs. Reactive Positioning - By anticipating needs and providing standardized solutions (like data pipeline tools), teams can reduce ad-hoc requests and gain strategic control.

Action Items for Piloting Product Thinking

1. Identify and Define Your First Customer Segment
2. Design and Launch a Low-Risk Pilot Service
3. Create Your First Product-Style Communication

Key Success Factor: Start small and focus on learning rather than perfection. The goal is to test whether product thinking approaches resonate in your organization and build momentum for broader adoption.

Questions & Comments

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In this Enterprise Automation Excellence episode, hosts Dan Twing and Tom O'Rourke explore how automation teams can adopt "product thinking" to better serve business needs and stakeholders. Rather than focusing solely on technology delivery, product thinking shifts the emphasis to understanding customer problems and working backward to solutions. This approach helps automation leaders move from reactive, ad-hoc service delivery to strategic, value-driven automation portfolios that align with business outcomes and demonstrate the importance of automation to business activities.

Key Takeaways

• Start with the job to be done, not the requested tool or technology.

• A request for "Airflow" might really mean "avoid failed reports on Monday morning."

• Use the Value Proposition Canvas to align automation services to real customer pains and gains.

• Different internal customers (such as HR, ERP, and DevOps teams) need tailored automation approaches.

• Mapping your automation portfolio to customer needs exposes both gaps and unused offerings.

Recommendations for IT Leaders

• Start with the real problem—don’t just deliver what was requested.

• Ask “What are you hiring this automation to do?” before committing resources.

• Map automation offerings to each customer segment you serve.

• Balance demand with budget and staffing realities.

• Justify automation investments by showing business impact—not technical features.

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