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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In this Enterprise Automation Excellence episode, hosts Dan Twing and Tom O'Rourke discuss "automation myopia" - the problem of defining automation needs too narrowly, leading to suboptimal tool selection. They explore how development teams often focus on solving a narrow set of automation requirements, rather than considering end-to-end processes. The hosts advocate for broader thinking that considers operational requirements, business needs, and regulatory concerns beyond just a project’s technical requirements. They recommend wrapping specialized tools within enterprise-wide orchestration systems to maintain visibility of the complete business process while still leveraging the capabilities of specialized automation tools.
Key Points
Development teams often define automation problems too narrowly, leading to isolated point solutions that can't satisfy broader business requirements

• Data pipeline tools like Airflow and Dagster work very well for their targeted data automation tasks but are less suitable for general automation needs
• Such tools can have limited support for enterprise requirements such as business calendars
• Enterprise orchestration tools provide critical capabilities like SLA management, alerting, business calendar integration, and audit logging
• A hybrid approach using specialized tools wrapped within broader orchestration frameworks offers the best of both worlds
• Central automation teams should establish checkpoints in design reviews to help development teams find solutions that balance

• Establish an end-to-end process mapping requirement - Before approving any automation project, require teams to map the complete process from initial trigger to final business outcome. This forces consideration of upstream dependencies, downstream impacts, and the actual business value being delivered rather than just focusing on a narrow technical solution. The mapping should identify all handoffs, potential failure points, and dependencies on other systems.
• Implement automation governance checkpoints - Position the automation team as a required checkpoint in the design review process for all automation initiatives, similar to security or privacy reviews. Use these checkpoints to ensure enterprise-wide requirements like operational support, business calendars, SLA monitoring, and regulatory compliance are properly addressed, while ensuring that the development project requirements are all satisfied.
• Create a hybrid architecture strategy - Develop and communicate an automation architecture strategy that allows specialized tools (like data pipelines or RPA) to exist within a broader orchestration framework. This gives development teams the flexibility to use tools they're comfortable with while ensuring visibility across the entire process. Provide clear examples for how to "wrapper" point solutions within enterprise orchestration tools to maintain end-to-end visibility without sacrificing specialized capabilities.
  

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In this episode, hosts Dan Twing and Tom O'Rourkediscuss the relationship between observability and automation in enterprise systems. They explore how observability tools and standards like OpenTelemetrycan improve automation orchestration by providing visibility into the entire business process ecosystem. The hosts note that while observability technology is still in early stages for automation, it represents a significant opportunity to enhance orchestration capabilities, reduce downtime, and provide actionable business insights beyond technical metrics. They emphasize the need for standards development and collaboration between automation and observability teams.

Key Findings

• Observability is defined as "the collection and organization of data from the whole enterprise ecosystem," providing visibility into business processes

• Orchestration and observability are highly linked - effective orchestration requires awareness of the systems being automated

• Current observability solutions are still in early stages for automation and lack standardization

• OpenTelemetry currently lacks standards specifically for automation data

• Automation systems need to both consume observed data and be observable themselves

• Rich data models in automation tools make standardization challenging

Key Takeaways

• Automation teams often troubleshoot problems outside their systems - in applications, middleware, or external integrations

• Automation is becoming "too critical to be left in the dark" as its importance continues to rise

• Automation and observability teams will likely be separate but need to collaborate closely

• The EMA RADAR report for 2025 will include new metrics for observability capabilities

Action Items

1. Initiate a pilot project by first identifying what observability platforms your organization is already using, then selecting a specific automation problem to address using that platform.
2. Advocate for open standards rather than accepting proprietary or in-house solutions. Push for OpenTelemetry standards for automation data to increase interoperability and create end-to-end views of your processes.
3. Build collaboration between automation and observability teams focusing on developing relationships where both teams understand how their systems need to interact — with automation tools both consuming observability data and making their own data observable to other systems.

References

EMA Webinar: Unlocking the Future of Observability: OpenTelemetry's Role in IT Performance and Innovation

In this episode, hosts Dan Twing and Tom O'Rourke discuss the complex relationship between change management and automation. They explore how these two control functions must work together despite their inherent tensions--automation requires stability to function reliably, while modern business environments demand frequent changes. The conversation examines how automation teams navigate changes from multiple sources: application updates, infrastructure changes, automation tool updates, citizen developers, and external vendors. The hosts emphasize the need for balanced governance that enables business agility while maintaining system integrity, noting that emerging technologies like AI will further complicate this balance while potentially offering future solutions.

Key Findings

• Change management and automation are both control functions designed to minimize risk, but they approach this goal differently
• The complexity of today's technical environments has increased faster than the tools to manage changes have improved
• Modern business processes often span multiple organizations and geographical locations, creating more complex change management scenarios
• Smaller changes that don't rise to the level of change committee review can still have significant impacts on automation systems
• The pool of people making changes to automation has expanded beyond dedicated automation teams to include various IT roles and citizen developers
• External changes from cloud providers and SaaS vendors can impact internal systems without advance notice
• Automation systems themselves require change management as they receive updates and patches
  

Recommendations

1. Develop a Change Classification Framework - Create a system to categorize different types of changes (application updates, infrastructure changes, tool updates, etc.) and establish appropriate governance for each category.
2. Implement Robust Monitoring Systems - Deploy monitoring solutions that can detect anomalies in automation performance to quickly identify impacts fromunannounced changes.
3. Establish Knowledge Sharing Protocols - Schedule regular knowledge transfer sessions between automation teams and citizen developers to educate on potential system-wide impacts of local changes.
4. Define Clear Governance Boundaries - Document which types of changes require formal change management review versus those that can be implemented with lighter governance.
5. Implement Version Control for Automation - Apply version control practices to automation definitions to track changes and enable rollbacks when necessary.
6. Create Reusable Automation Components - Develop standardized, reusable automation patterns that can be centrally managed but locally configured to reduce the proliferation of unique automations.
7. Review Vendor Change Notifications - Establish a process to proactively review and assess change notifications from external vendors and cloud providers.
8. Maintain Testing Environments - Set up sandbox environments where even small changes can be tested before being deployed to production systems.
9. Conduct Regular Governance Reviews - Schedule periodic evaluations of your change management practices to ensure they remain effective as automation capabilities expand to more teams.
   

Questions and Comments

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