Peer-Driven Insights

Architecture

Community Posts

Why do you think there are so few mature AI-driven autonomous pentesting solutions on the market, and why does this topic seem to generate more hype than in-depth technical discussion?

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Whilst there are high-quality, paid-for enterprise architecture applications in the market, with the increasing move to SaaS and dangers of vendor lock-in, it's prudent to have an alternative. Does anyone have experiences (good or bad) to share after moving from one of the market leaders to open source alternatives Essentials and/or Archi?

https://enterprise-architecture.org/products/essential-open-source/
https://www.archimatetool.com/

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Our organization is subject to data retention requirements over very long periods (50 years or more). Do your organizations face similar constraints? If so, what long-term retention strategy have you implemented for these regulated data, and what technologies or solutions do you use to ensure their durability and compliance?

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Which tech conference do you intend to attend in person next year or have already participated in this year, and what motivated your choice?

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How do you think AI will disrupt business across industries? Add to my list: 1. Content creation 2. Photos and video production 3. Basic coding and debugging 4. Strategic analysis to be highly complimented

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Is anyone working on automation of EA Practice activities such as design and/or roadmap development through the use of AI tools? This could include but not be limited to strategy, analysis, solution development, governance submissions, standards review, white papers etc. We are using ARIS as an EA repository and are considering the use of the AI companion or perhaps even standalone AI tools to import, process, analyses & publish. Happy to discuss ideas for future implementation.

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Looking at adopting Blazor for small to mid-sized projects (primarily the Server-Side Interactive mode). Any thoughts on Blazor Server-Side in production - whether development experience, user experience, support, etc? Have you had to support a user base that has less-than-ideal network conditions?

Has anyone successfully negotiated with a big hyperscaler partner to introduce a term into the contract that establishes service prices as public price at time of contract execution and holds those prices flat for the entire contract term? We're trying to think of ways to mitigate potential future cost increases if public prices change (due to tariffs or other unknown conditions) and dilute the impact of negotiated discounts.

We have a servicing organization within IT segmented by applications and has 3 different director teams supporting overall pieces which is creating confusion, misalignment and becoming harder to support one business area. Components considered within this org are: PEGA products (NCompass, G&A), Genesys Cloud, Google, Member Portal and Member Mobile. How should i look at proposing a new target state Org considering all these aspects/products? Any ideas?

Our organization is implementing an Internal Developer Platform (IDP) to enable self-service infrastructure provisioning, and we're evaluating Terraform Cloud workspace management strategies. We're debating between: - VCS-driven approach: Repository-based workspace management with GitOps workflows - API-driven approach: Programmatic workspace creation and management via Terraform Cloud APIs Specific Questions: - Which approach have you found more effective for true self-service at scale? - How do these approaches handle multi-tenancy and isolation requirements? - What integration challenges did you encounter with developer portals/service catalogs? - How did your choice impact developer adoption and platform team operational overhead? Would love to hear about your real-world experiences, particularly around the trade-offs you discovered post-implementation.

What is the greatest current cybersecurity challenge for your organization?

Stopping external cyberthreats (including organized cyber criminals, nation-state actors and ransomware attacks, etc.)33%

Stopping insider cyberthreats (including both deliberate actions and employee mistakes like clicking on bad links.)33%

Workforce rightsizing, including hiring, work from home challenges and other post-Covid workforce (HR) issues.14%

Incorporating new technologies, including the governance and implementation of genai, AI and other new toolsets. Includes retraining existing staff to take advantage of cutting-edge tech.12%

Something else.6%

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SAP ECC to S/4 upgrade. 2030 is fast approaching. For companies over $1b that have been on SAP for > 10 years. Still a lot of us out there. Where are you?

We are done! (Please add comment on success factors or mistakes)9%

We have plan in place and executing. (Please add comments on progress and any pitfalls)49%

Dabbling. We have some S/4 but not tackled ECC. (Please add where you dabbled in comments) 34%

We (IT) really wants to move, but struggling to find a business case.33%

Waiting to see if SAP caves and extends support beyond 2030. 10%

Other? 1%

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What is the biggest problem businesses will face in the next decade?

The pace of change72%

Unpredictability27%

What advice do you have for technology leaders that need help navigating the US administration's executive order to recalibrate the nation's AI strategy? Where do I begin?

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AI and Research in Marketing and Communications: How Are You Ensuring Quality and Truth Over Speed? As AI becomes embedded in our day-to-day marketing operations, from customer insight to content strategy, it’s clear we’re gaining speed and scale. But are we sacrificing veracity and quality of output in the process? One of the biggest challenges I'm seeing is the rise of hallucinated content in AI-generated research. - How do we ensure AI systems are surfacing trustworthy, contextually accurate, and well-sourced information? - Are we building enough verification layers into our AI-driven content and research workflows? - And how are you educating teams (and execs) to challenge AI outputs, rather than accept them at face value? - What frameworks, tools, or governance models have you adopted to maintain integrity and depth in AI-led research and insight creation? - Have you had success (or struggles) balancing speed vs. substance in AI-powered strategies? Your thoughts, examples, and recommendations would be incredibly valuable in helping others try to navigate this shift responsibly.

We are looking for a solution to implement a chatGPT-like solution on premise? The solution should propose a GUI & back end components, ability to connect to LLM on prem and in the cloud (routing according to sensitivity of the data). Feature examples: questions to document/knowledge base, simple agent creation, RAG, and multimodal.

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