Enterprise Automation Excellence

AI is reshaping the observability market, bringing greater ability to detect patterns and predict issues that may occur in the future. In this episode, Arturo Oliver (Head of Market Strategy, ScienceLogic) joins Dan Twing and Tom O'Rourke to discuss three major shifts: from reactive problem diagnosis to proactive prevention; from noise-filtering to AI-driven recommendations; and from scripted automation to agents that can take action independently. The episode tackles the harder, long-term challenge of connecting AI agents together across different tools and systems.

Key Points

• Observability shifting from retrospective root-cause analysis to forward-looking prediction and prevention
• AI agents organized in layered, specialized roles reflecting real enterprise IT structures
• Trust and transparency as primary barriers to adoption
• Traditional telemetry insufficient; intent and expected outcomes required for meaningful context
• The “orchestrator of orchestrators” as an unresolved challenge in aligning intent and policy across systems

Recommendations for Automation Leaders

1. Don't be a late adopter – look for quick wins with current agentic AI technology, don't wait for the technology to fully mature

2. Prioritize prevention over recovery – focus on preventing incidents, not fighting fires

3. Deliver intelligence to the right people at the right time – agentic systems should make your team more productive, not bury them in more data

4. Trust and transparency are non-negotiable – AI insights and recommendations must be verifiable, traceable and auditable

5. Focus on building institutional knowledge - capture and share your learnings as you apply agents and evolve new solutions

Links

Arturo Oliver: https://www.linkedin.com/in/arturooliver/

ScienceLogic: https://sciencelogic.com 

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Transforming the business does not require expensive technology changes, it can be accomplished through business partnership and better use of existing systems and information. In this episode, Rich Corbridge (CIO, SEGRO plc) joins Dan Twing and Tom O'Rourke to discuss how they are using automation and AI to significantly improve their ERP capabilities, without replacing their ERP systems. Focusing on business value, SEGRO is piloting AI-enable orchestration to create workbenches that tie together disparate systems for timely access to information, improving decision making.

Crucially, this approach introduces agentic AI within a centralized orchestration model, ensuring governance, consistency, and controlled deployment rather than uncoordinated agent sprawl.

Key Points

• Business value drove technology selection and the decision to supplement existing ERP systems rather than replacing them

• A centralized approach balances consistency with localized customizations

• Partnering with two vendors with different engagement styles provided valuable insights

• ROI was defined upfront, each pilot process had specific targets before testing began

• Technology change is happening quickly, SEGRO's deliberate approach is making them culturally ready for rapid change.

• Agentic AI is being deployed within a centralized orchestration framework to maintain control and avoid fragmented, independent agent development

Takeaways for Automation Leaders

• Build alliances with business leaders before starting the technology selection process

• A structured evaluation of AI-enabled orchestration outperforms giving everyone tools to built their own agents

• Cultural change in the IT team is as important as cultural change in the business

• Momentum comes from demonstrating what is possible

• Success is defined as business impact, SEGRO is using orchestration to grow the business without increasing staff or operational costs

Links
Rich Corbridge on LinkedIn
https://www.segro.com/

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Enterprise automation leaders must maintain reliable services supporting business and operational processes while also driving change in their organizations. In this episode, Dan Twing and Tom O'Rourke discuss the challenges and opportunities of becoming a change agent, and why it is important to step forward rather than waiting for direction from above. They advocate behaving like an entrepreneur in identifying problems to be solved, building business cases, and actively selling their proposals to leadership.Key Points

• Many automation groups only get attention when something is broken, functioning as a service rather than being recognized as a strategic asset
• Senior managers often do not understand how automation enables the business
• Automation leaders often wait for someone above them to provide direction on when to act
• No matter where you are in the organization, you have a role to play in moving change forward
• Automation leaders need to behave like entrepreneurs, driving change to improve their business impact
• Marketing and selling are not dirty words; automation leaders must frame proposals in business terms, understand executive and stakeholder priorities, and actively communicate the value automation delivers.

Takeaways for Automation Leaders

1. Create a "State of Automation" briefing that your management and non-technical stakeholders can read to understand why automation matters to the business
2. Meet with your management to understand: - their expectations of what the automation team needs to work on, - what business initiatives automation should be performing, and - whether the leadership team feels the automation team is moving quickly enough
3. Design and propose a small pilot where automation can deliver a measurable business improvement

References

1. Intrapreneurship (Wikipedia): https://en.wikipedia.org/wiki/Intrapreneurship
2. Intrapreneuring: Why You Don't Have to Leave the Corporation to Become an Entrepreneur, Gifford Pinchot III, (Harper & Row, 1985) - out-of-print

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The current hype about AI has left many enterprise automation leaders feeling anxious about how they will integrate these rapidly evolving AI solutions whilemaintaining reliability and resilience. Hosts Dan Twing and Tom O'Rourke recommend taking a pause to recognize that there are more similarities than differences with past waves of integrations of new technologies into automation platforms. These integrations succeeded by following proven change management practices: start with small pilots, iterate based on learnings, collaboration with stakeholders, and maintaining production discipline. 

Key Points

• AI agents are just another process type to be managed
• Core automation principles remain unchanged, ensuring that the right actions happen at the right time applies equally to AI workloads
• Technology adoption fails more frequently from coordination failures than from technical issues
• Choose pilots based on readiness and suitability for an iterative, learning approach
• Assess your observability capabilities, AI can introduce non-deterministic execution and outcomes, requiring a higher level of monitoring than traditional applications 

Takeaways for Automation Leaders

• Invest in expanding your observability capabilities now, even there's no immediate need for AI integration.
• Improve your teams cross-functional collaboration skills, focusing on business, technical and operations teams.
• Be prepared, tart looking for your AI "learning candidate" automation use cases and platform touch points.

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Enterprise IT continues to evolve rapidly as organizations look for effective ways to use AI and other automation tools to streamline their processes. In this episode, Dan Twing and Tom O'Rourke discuss Tom's learnings from December's Gartner IT Infrastructure, Operations & Cloud Strategies Conference. Topics include the emerging pragmatism around AI, continuous operations, managing the automation portfolio as a platform, and the importance of communicating automation value in business terms. The episode reinforces the need for automation leaders to focus on business outcomes and maintain reliability as the technology landscape changes.

Key Points

• Top-down pressure to adopt AI is solution-driven rather than problem-focused
• Real autonomy in agentic AI remains elusive, with many solutions underperforming or costing much more than projected
• Platform-centric I&O represents the next maturity stage for infrastructure management, automation leaders should adopt this approach as they evaluate which tools belong in their portfolio
• While AI tools continue to improve rapidly, I&O leaders should anticipate that costs will rise to reflect the large amounts of money being invested.

 Takeaways for Automation Leaders

• AI is automation, as the hype dissipates the operation of these tools will wind up in the automation center of excellence.
• Addressing tech debt must be framed in business enablement, enabling faster delivery of new capabilities, while improving reliability and resilience.
• AI will transform expectations around automation, increasing demand and requiring more agility to satisfy business needs.

 Links

Episode 8 - Five Key Trends from Gartner I&O 2024: 

https://em360tech.com/podcasts/episode-8-five-key-trends-gartner-io-2024-ai-automation-and-evolution-technical-leadership

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

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Dan Twing and Tom O'Rourke conduct a retrospective of EAE 2024 and 2025 episodes. Using Anthropic Claude to analyze the podcasts, they identify five dominant themes that merged from their conversations. They discuss their learnings fromlooking back at the episodes, plans for the podcast in 2026, and ideas for tools to help listeners navigate the growing library of episodes to explore specific topics.  

RetrospectiveThemes

1. Don't Automate, Orchestrate
2. AI is a Catalyst for the Evolution of Enterprise Automation
3. The Skills Gap is a Bottleneck to Improving Business Impact
4. Communicate Business Value to Stakeholders
5. Adapt Change Management and Governance Process

 Takeaways for Automation Leaders

• Adopt Orchestration as Your Strategic Framework
• Invest in Observability and Data Quality
• Help Your Stakeholders Understand the Business Value of Automation

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Dan Twing and Tom O'Rourke are joined by Pete Goldin (DevOps Digest). Dan and Pete discuss their recent survey of AI development and DevOps tools. The research shows high adoption rates of AI-native development tools with broad use of AI integration in core processes. This early success has created cautious optimism, however, significant governance gaps exist in many organizations. Automation leaders must prepare for changes to CI/CD and release management processes, as well as different automation usage profiles as AI tools are integrated. Key Points

• AI development tools have become a competitive requirement
• AI introduces changes that are not addressed through existing change controls and governance
• Integrations with automation
• Automation integration use cases will shift as AI tools change development processes and utilize a greater set of automation tool capabilities
• Developer and automation team skillsets must evolve, while AI may handle more basic activities, deep knowledge of the environment and business processes will be needed to meet requirements for critical business systems

Takeaways for Automation Leaders

1. AI SRE tools will automatically make changes to enterprise infrastructure and processes:

• Document current integration points and systems
• Establish change detection to generate alerts of changes made outside of existing controls
• Measure current performance in order to identify where AI tools impact KPIs

These precautionary actions have value for cybersecurity as well.2. Build governance partnerships before a crisis occurs

• Establish relationships with development, DevOps, security and IT governance teams
• Discuss how AI changes will be reviewed, tested and promoted through the environments
• Create a shared knowledge base

Episode Links

AI in DevOps On-Demand Webinar:https://www.enterprisemanagement.com/product/ai-in-devops-transforming-the-developer-experience-and-expanding-the-security-perimeter/AI in DevOps Report:https://www.enterprisemanagement.com/product/ai-in-devops-adoption-outpaces-governance-as-developer-role-evolves/DEVOPSdigesthttps://devopsdigest.comPete Goldin, DEVOPSdigest Editor and Publishermailto:[email protected]FooterEAE Podcast Home: https://em360tech.com/podcast-series/enterprise-automation-excellence

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Getting approval for new automation investments requires building a business case with a cost analysis, expected business benefit, and assessment of project complexity and risk. In this episode, Dan Twing and Tom O'Rourke provideguidance on how to build a successful business case for automation initiative, highlighting the need to speak in terms of business value, particularly revenue growth, profit expansion, and customer acquisition. Their conversation includes real-world examples ranging from expanding API access to transforming business operations.

Key Points

• Describe technical capabilities in terms of business services and outcomes
• Understand who is involved in approving your proposal and what their concerns are
• Match project cost and effort to payback expectations
• Strategic alignment is key, initiatives tied to revenue growth, profit expansion, and customer acquisition receive highest priority
• Don't take rejection personally, executive leadership must assess hundreds or thousands of proposals with finite resources

Takeaways for Automation Leaders

• Build a solid financial framework by partnering with Finance to build the initiative's cost and benefits model
• Explain initiative outcomes through business impact, not technical improvements
• Realistically access risk, complexity and strategic alignment
• Think like a shareholder, asking "Is this truly the best use of company resources compared to other possible investments?"

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IBM TechExchange 2025 featured AI, Agents, Observability and other technologies that are changing the way that developers work. In this Enterprise Automation Excellence podcast, hosts Dan Twing and Tom O'Rourke discuss Dan's learnings from the event and what these trends mean for developers, as well as the implications for automation, development and DevOps teams. A key theme is howthese new tools require rethinking development processes, with a particular focus on the need for guardrails and check points for systems that are relying on non-deterministic AI as part of the solution. 

 Key Points

• AI-Assisted development tools continue to evolve, helping developers work faster, rather than replacing them 
• Many new AI-based development projects have not moved out of the experimentation stage
• Guardrails are essential at multiple levels, from the application itself to security monitoring and production operation checkpoints
• Agentic AI applications differ fundamentally from traditional apps and require completely different approaches to building, testing, and monitoring
• Observability tools are bringing together resource and performance monitoring with traditional systems operations solutions
• While TechXchange for focused on IBM's solutions, this evolution of development and operations reflects a fundamental shift that impacts every enterprise technology stack

Takeaways for Automation Leaders

• Prepare your automation workflows for AI-assisted development projects
• Consider what guardrails your automation solution can provide to address unexpected behaviors in AI-based applications 
• AI-based applications will operate differently and introduce new failure modeswhen unanticipated inputs provide unexpected outputs

Takeaways for I&O and DevOps Leaders

• AI-assisted development will change application architectures, behavior patterns, infrastructure usage and support models
• Observability platform development is accelerating, evaluate your vendor strategies to make sure that your tools fit your operations and business needs
• This isn't a passing trend, it reflects a new, faster trajectory of technology change

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IBM TechXchange 2025 (https://www.ibm.com/community/ibm-techxchange-conference/)

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

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Enterprise automation is entering a new era defined by convergence. AI, observability, and orchestration are reshaping how automation platforms operate and how IT organizations deliver reliability and agility. While vendors move rapidly toward orchestration control planes, enterprise adoption remains measured and pragmatic—focused on governance, integration, and operational stability. Drawing on insights from EMA’s ongoing 2025 Workload Automation and Orchestration Radar research, this episode explores how automation teams can evolve to lead in this new landscape.

Key Points

• Agentic AI expands—not replaces—centralized orchestration.

• The “silo first, then centralize” pattern continues to define automation maturity.

• Assess how your current automation platform aligns with AI, observability, and orchestration trends.

• Position your organization as the “Orchestrator of Orchestrators.”

• Pilot intelligent-automation features to build skill and institutional confidence.

• Leverage observability data to guide governance and drive automation-led optimization.

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