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/)

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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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While much of the focus on the value of automation systems is on the consistent and reliable execution of the schedule, the data within the automation system can also be valuable to the organization. In this Enterprise Automation Excellencepodcast, hosts Dan Twing and Tom O'Rourke explore how automation systems serve as critical sources of truth for business operations. They explain that automation teams manage key data repositories that become the singleauthoritative record for business processes. The discussion covers the challenges of maintaining data quality, the security risks involved, and the growing importance of this data for AI and analytics initiatives. The hosts emphasize that automation leaders need to recognize their role as data stewards and actively engage with other business leaders to share the valuable information they maintain.

Key Points

• Automation systems are primary sources of truth for business process execution
• Business process knowledge is often only recorded in automation tools
• Historical records of process execution serve as proof of compliance, which can be important in regulated industries

The historical and operational data in automation systems can provide valuableinput for corporate AI and analytics initiatives

Takeaways for Automation Leaders

• Automation teams should understand they are keepers of critical business data and implement proper governance, change management, and security controls
• Engage Chief Data Officers and Chief AI Officers to discuss how your data can support broader organizational initiatives and AI programs
• Implement robust data security controls since sources of truth are prime targets for cyber attacks that could disrupt entire business operations
• Instead of talking with business leaders about "scheduling data", start talking about "operational intelligence" and "process optimization."

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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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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'Rourke discuss 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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Dan Twing and Tom O'Rourke discuss EMA's latest research report titled "The Future of Workload Automation and Orchestration: Driving Digital Transformation with Orchestration and Generative AI". The hosts explore the evolving landscape of workload automation, highlighting how it has become a central hub for orchestration across enterprises. The conversation covers key trends including growth in the number of jobs, staffing and skill challenges, the rise of observability, and the impact of AI on automation strategies. The research shows workload automation tools are evolving into enterprise-wide orchestration platforms that connect business processes beyond just technical automations.Key Findings

• Continued job growth in workload automation, with 20-25% of organizations seeing about 10% growth and another 30% growing in the 10-25% range, though 16-17% of organizations now report staying at the same level
• Lack of skilled resources has risen to become the #1 or #2 challenge for implementing automation, even surpassing security and compliance concerns
• 75-85% of organizations are now focusing specifically on orchestration as a strategic objective
• Organizational structures are becoming more diverse, moving from centralized teams to a mix of centralized oversight with decentralized teams and specialized groups
• Observability has become increasingly important as automation expands to business outcome processes
• 97% of organizations expect AI to significantly impact their workload automation strategy within 2-3 years, with 18% saying it already has

Recommendations

• Build a comprehensive automation skills development strategy
• Develop a structured approach to address the critical skills gap in workload automation and orchestration. This should include creating formal training programs, establishing mentorship opportunities, and potentially partnering with vendors and professional services organizations for specialized training.
• Integrate observability into your automation strategy
• Invest in robust observability capabilities that provide visibility across all automated processes and systems. Implement monitoring solutions that collect telemetry data in standardized formats (such as OpenTelemetry) and integrate this data with your orchestration platform.
• Develop an AI adoption roadmap for automation_Create a strategic roadmap for incorporating AI into your automation initiatives over the next 12-24 months. Start by identifying specific use cases where AI can provide immediate value, such as anomaly detection, predictive analytics for job performance, or intelligent workflow routing. Allocate resources for AI experimentation and establish metrics to measure the business impact of AI-enhanced automation.

Show Links:Enterprise Management AssociatesEMA Future of Workload Automation and Orchestration ReportEMA webinarContact Us: mailto:eaepodcast@emausa.com

This episode discusses the role and evolution of Automation Centers of Excellence (CoE) in enterprises. The hosts, Dan Twing and Tom O'Rourke, explore how CoEs have become critical organizational structures for managing automation initiatives, discussing how CoEs serve as knowledge-sharing hubs, bridge gaps between technical teams and citizen developers, and help standardize automation practices.

 Key Points

-CoEs are an organizational pattern found in large enterprises to coordinate important cross-group initiatives by streamlining the sharing of technical and organizational knowledge

-The majority of automation organizations have either formal or informal CoEs

-CoEs' roles vary from controlling all automation changes to consultative support of citizen developers

-Automation CoEs are expanding, with 84% reporting moderate to significant growth

-Automation CoEs can become a bottleneck if they are under-resourced or their work is deprioritized 

-CoEs can provide essential governance and risk management function, assisting citizen developers in complying with business policies and technology standards

-Looking beyond automation, CoEs will be crucial for integrating new technologies like AI with existing systems

Dan and Tom discuss Tom's observations and learnings from the 2024 Gartner IT Infrastructure, Operations & Cloud Strategies conference in Las Vegas. 
AI and improving Infrastructure & Operations (I&O) leadership were key themes, with sessions addressing strategic and tactical approaches for technology, business, and staffing issues. The I&O outlook for 2025 and beyond was optimistic, while acknowledging executive frustrations with the increasing costs of cloud, failures of major projects, and the disruptions generated from CVEs and other security events. 
Key Ideas

- **Generative AI**: Gartner believes that we are still in the early days with respect to AI, with many immediate opportunities for productivity improvements that need to be balanced with potential risks and unpredictable costs, highlighting the lack of best practices and challenges moving from a POC to production.
- **Automation & Orchestration**: Automation teams need to focus on value to their customers, prioritizing efforts to orchestrate business processes over tactical task automation. Automation leaders should adopting Agile development processes and metrics for managing their team's efforts.

- **I&O Agile Adoption**: Highlighting enterprises that have introduced Infrastructure Platform Engineering (IPE) and Site Reliability Engineering(SRE), Gartner recommends restructuring the infrastructure organization around these disciplines, looking holistically at the infrastructure as a managed platform, and adopting product management thinking as a framework for managing the evolution of the environment.
- **Digital Employee Experience (DEX)**: Building on feedback from corporate boards, Gartner highlighted staffing and skill shortages as a workforce risk and impediment to business transformation initiatives, which is leading to an evolution in employee tools and rethinking about management approaches to improve employee engagement job satisfaction. 
- **I&O Leadership**: I&O leaders need to become more strategically-focused and proactive in managing their portfolios, aligning their priorities with key business initiatives and building up the confidence and trust of the executive team in I&O. 
Takeaways
- Prepare for integrating the new AI-driven tools and data flows integrating into the infrastructure
- Find ways to use AI tools that help with day-to-day activities, but make sure that there are checks of the AI-generated results
- Shifting to Infrastructure as Code approaches to make updates to the environment can reduce risks and improve security
- Collect and review metrics for the customer experience, not just technology operations
- Agile retrospectives can be a powerful learning mechanism for improving staff skills and performance
- Allocate time to skill development for yourself and your team, consider where upskilling is more effective and quicker than hiring
- Be prepared to discuss the value of I&O projects in the context of business initiatives, not just the technical results that they provide
Show Links
[Gartner IT Infrastructure, Operations & Cloud Strategies Conference](https://www.gartner.com/en/conferences/na/infrastructure-operations-cloud-us)
[AI and the Future of Work, Paul Redmond](https://paul-redmond.co.uk/speaking/)
[Transformational Leadership, Carla Harris](https://www.carlaspearls.com)
[Super-Communicators, Charles Duhigg](https://www.charlesduhigg.com/supercommunicators)

Dan has returned from Italy where he participated in an HCL Automation customer event focused on orchestration of business processes as an integrated element of a centralized automation solution.
HCL has embraced orchestration as encompassing business process automation as an extension of traditional job scheduling and workload automation, leveraging the centralized automation team and controls that exist in many organizations.

HCL announced the 2nd generation of their Universal Orchestrator product, integrating with the other elements of their Automation Orchestrator Suite.
Key Ideas:
- Automation continues to increase in strategic importance, over 40% of IT executives are now measured on automation expansion (up from 30%)
- Use of automation tools by _citizen developers_ outside of the centralized automation team continues to increase as well, allowing the process experts to shape the automations they rely on and expanding the number of processes that are automated.
- The empowerment of citizen developers raise challenges with change control and governance, sometimes interfering with the automation team's efforts to maintain reliability and resilience
  - 28% of enterprises have significant citizen developer contributions
  - 52% limit citizen developers to experimental work
  - 20% use citizen developers primarily for requirements definition
- Automation tools need additional work to support citizen development effectively
Show Links:
- HCL Universal Orchestrator: https://www.hcl-software.com/automation-orchestration/uno

We're most of the way through 2024 and it's time to start thinking about plans for next year. In many cases, the budget has already been set, but it's still important to think through what your priorities will be and how to allocate time for yourself and your team.
Important Trends:
* Digital Transformation – 70% of organizations are in the midst of transformation efforts, relying heavily on automation services to achieve their digitization goals
* Cloud Migration – Increasing push toward multi-cloud and hybrid cloud, strategic focus on SaaS applications
* Expanding Automation User Base – Growing emphasis on citizen developers, business teams taking on more automation responsibilities
* AI Integration – new integrations, increased need for data management

* Data Analytics – more production applications, growing importance in business decision making

* Cybersecurity – continued focus on keeping automation software updated, access management controls
* Legacy systems – slow retirement of legacy hardware and software, requiring maintenance of specialized knowledge of automation integrations
While many automation leaders feel that their teams have no time to take on additional work, the payback on service and skill enhancements can be high.  We advocate being proactive in selecting areas where they can make improvements.
- Organization Alignment – understand your organization's business goals and make sure that your team's efforts are supporting the business plans
- Service Improvement – find an area where you can improve the way you serve a user group, such as reducing the time to fulfill a user request.
- Team Development – determine what skills you and your automation team need to support the business goals and evolving technology portfolio of your organization 
- Communication Plans – develop communication strategies to keep you team, management, stakeholders and users aware of what you're doing to deliver automation services to the organization
To get started, allocate a couple of hours a week of your time to an improvement initiative. Identify a project that will address immediate operational needs or a strategic effort that will help you get to where you need to be in the long term. Share your improvement plan with your team and manager so that they can support you in the project.
Good luck, and let us know if you have any questions.

Chris Steffen joins the EAE podcast to discuss how automation teams can collaborate with security teams to maintain a secure, resilient environment.

Enterprise automation is expected to orchestrate critical processes 24x7x365. Automation teams must address risks from infrastructure failures and security vulnerabilities in their tools and environments.

Key Ideas

- Automation systems carry high risk due to their critical role and extensive integrations across business, analytics, and operations.

- Cloud and SaaS foundations still require automation teams to understand configurations for reliability.

- Business-critical automation systems often demand 99.999% availability ("five nines").

- Risk assessment is the first step to address cybersecurity, examining implementation, integrations, operations, and access controls.

- Limiting access privileges and eliminating unused accounts reduces vulnerability.

- Changes to systems can impact availability and security, requiring careful change management proportional to risks.

- Security teams and automation teams share the goal of a reliable, resilient environment.

Takeaways for Automation Leaders

- Regularly assess risks from human error, software defects, and third-party failures. Test updates in non-production environments before rollout.

- Build relationships with security teams to prioritize risks and improve team knowledge.

- Audit access management to identify and limit unused or excessive privileges.

- Review change processes for automations, software, and infrastructure to identify mitigations for significant risks.

Show Links

- Chris Steffen - Cybersecurity Awesomeness podcast - Zero Trust Working Group for the Cloud Security Alliance - "Five Nines" High Availability (Wikipedia) - NIST Cybersecurity Framework - SANS Institute⁠