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.

EAE Podcast Home: https://em360tech.com/podcast-series/enterprise-automation-excellence

Feedback & Questions: mailto:eaepodcast@emausa.com

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.

EAE Podcast Home: EM360Tech – EAE Series
Feedback & Questions: eaepodcast@emausa.com