The AI-savvy operating model - Matthew Skelton, Conflux - DevOpsDays Singapore 2025

Generative AI has “laid down the gauntlet”: product discovery, user testing, and hypothesis validation will soon take only hours, not days or weeks, resulting in an order-of-magnitude reduction in lead time, similar to what public cloud did in the late 2000s. Small, scrappy startups that use GenAI well will be able to out-innovate larger incumbents, at least initially.

So what would an effective operating model look like that used GenAI to reduce time-to-value for larger organizations? We already have the answer: organizations that organize for fast flow, promote psychological safety, nurture community-based decision making, and use ideas from Team Topologies (such as bounded team cognitive load, and internal platforms) are already more high-performing compared to others, and these principles form the foundation of an AI-savvy operating model.

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From the keynote talk at DevOpsDays Singapore 2025

Transcript

1. The AI-savvy operating model towards a humane, effective, financially-transparent way of working for AI-enhanced knowledge work Matthew Skelton, Conflux - co-author of Team Topologies DevOpsDays Singapore 2025 | 2025-05-14 K38 Photo by Barbara Zandoval on Unsplash
2. 2 Matthew Skelton holistic innovation Originator of Adapt Together™ by Conflux Co-author of Team Topologies matthewskelton.com CEO/CTO at Conflux - confluxhq.com
3. How can we empower teams of humans + AI agents to deliver quickly, safely, and compliantly with high-fidelity domain knowledge and visibility of the data sources & results, plus the agency to address problems caused? 3
4. (we already know) 4
5. 5 • Empowered teams • No hand-offs • Ongoing stewardship • Clear boundaries • Defined guardrails and specifications • Active knowledge diffusion
6. The near-future AI advantage (?) My perspective on AI (and work) How to trust and organize AI The AI-savvy operating model 6
7. The near-future AI advantage (?) 7
8. 8 “AI-savvy” operating model
9. 9 Traditional AI: pattern matching, huge data volumes, temporal correlation Generative AI: next-token guess, content generation, option generation
10. 10 “Learning” but no understanding with GenAI
11. 11 Savvy: don’t pretend or forget that GenAI doesn’t understand ⚠
12. 12 Savvy use cases for AI: • Pattern extraction from huge data • Speed up content generation (incl. code) and workflows • Augment human decision-making • Agent-based processes • …
13. 13 Do like AI and look backwards!
14. 14 Cloud birthed SaaS, leaving older models behind (likewise GenAI)
15. 15 Agentic AI - devise 230 new product variations and: • Generate code, deploy • Test with synthetic users • Find the best combination • Data-driven product fit in hours A/B testing on steroids!
16. 16 but…
17. 17 LLMs approaching limits?
18. 18 Serious security problems
19. 19 Data Science Data Eng ML Ops
20. 20 https://web.devopstopologies.com/ 2013
21. End-to-end responsibility for service outcomes is essential to avoid harm and deliver effectively: use empowered teams 21
22. My perspective on AI (and work) 22
23. 23 1998 My first “AI” system: backpropagation neural network for weather data (no, it didn’t work properly!) BSc, University of Reading
24. 24 https://www.linkedin.com/in/promarkbishop/
25. 25 2000 MSc in brain science at the University of Oxford Research into dyslexia and Alzheimer’s disease
26. 26 2001 Software for MRI brain imaging machines
27. 27 2011 Organizational architecture and new techniques & tools for adopting cloud and Continuous Delivery at Trainline (UK)
28. 28 https://blog.matthewskelton.net/2013/10/22/what-team-s tructure-is-right-for-devops-to-flourish/ 2013 DevOps Topologies
29. 29 https://web.devopstopologies.com/ 2013
30. Team Topologies Organizing business and technology teams for fast flow Matthew Skelton & Manuel Pais IT Revolution Press, September 2019 Order via stores worldwide: teamtopologies.com/book 200k+ copies sold to date in 5 languages 30
32. Team Topologies paraphrased: “Given the need for ongoing stewardship of long-lived services, how can we realistically arrange the flow of value to be rapid, safe, sustainable?” 32
33. Start with the need for ongoing evolution of long-lived digital services meeting user needs… … and work backwards from there (don’t start with the technology) 33
34. How to trust and organize AI 34
35. 35 Assume we have a collection of AI agents responding to some kind of input from humans…
36. 36 Savvy: don’t pretend or forget that GenAI doesn’t understand ⚠
37. 37 Humans set the context, goals, guardrails, and execution constraints in a repeatable and traceable way (this is programming)
38. 38 “JSON Structure is a data structure definition language that enforces strict typing, modularity, and determinism.” json-structure.org { "$schema": "https://json-structure.org/meta/extended/v0/#" , "$id": "https://example.com/schemas/product" , "$uses": ["JSONStructureAlternateNames" , "JSONStructureUnits" ], "type": "object", "name": "Product", "properties": { "id": { "type": "uuid", "description": "Unique identifier for the product" }, "name": { "type": "string", "maxLength": 100, "altnames": { "json": "product_name" , "lang:en": "Product Name" , "lang:de": "Produktname" } }, "price": { "type": "decimal", "precision": 10, "scale": 2, "currency": "USD" }, ... "required": ["id", "name", "price", "created" ] }
39. 39 Cursor / Cline rules Constrain and guide LLM-based code generation ... 3. Consistency Across Codebases - Maintain uniform coding conventions and naming schemes across all languages used within a project. Project Context & Understanding 1. Documentation First - Review essential documentation before implementation: - Product Requirements Documents (PRDs) - README.md - docs/architecture.md - docs/technical.md - tasks/tasks.md - Request clarification immediately if documentation is incomplete or ambiguous. 2. Architecture Adherence ... https://gist.github.com/ruvnet/7d4e1d5c9233ab0a1d2a66bf5ec3e58f
40. 40 How would we be able to trust a set of AI agents?
41. 41 How are we be able to trust a set of people?
42. 42 RRR: regular, repeatable results
43. 43 plus: shared language, dashboards, effective boundaries, data provenance, decision heuristics, safe-to-optimize metrics, nimble governance, psychological safety
44. 44 • Guardrails • Ongoing domain context • Good boundaries • Ongoing stewardship and responsibility
45. 45 Teams of humans and AI agents with context, guardrails, stewardship, boundaries, etc…
46. 46 Good boundaries for: • Context • Security • Resilience
47. Define guardrails and boundaries for code gen and service specs: domains, security, nuances of terminology, assumptions, algorithms, biases, etc. (Hint: this has always been needed!) 47
48. The AI-savvy operating model 48
49. 49 Cloud removed IT infrastructure as a blocker
50. 50 AI removes typing, generating options, and prototyping as blockers
51. 51 Humans understand the organizational intent and specify guardrails & goals
52. 52 How can we define and align responsibility for service outcomes when the code for a service is generated by AI?
53. Team Topologies paraphrased: “Given the need for ongoing stewardship of long-lived services, how can we realistically arrange the flow of value to be rapid, safe, sustainable?” 54
54. 55 Multiple, independent flows, fractally
55. Respect Conway’s Law (aka ‘sociotechnical mirroring’) 56
56. Clear ongoing stewardship of services and systems 57
57. Stream-aligned teams have end-to-end responsibility for a service (You Build It, You Run It) 58
58. Platforms improve flow and reduce extraneous cognitive load 59
59. Teams are small (~9), slowly changing, with ‘aligned autonomy’ 60
60. Teams are empowered to sense and adjust boundaries to improve flow on a frequent basis 61
61. 62 With knowledge work, we’re fundamentally concerned with the fidelity of representation of intent (in code, writing, etc.), so ongoing domain knowledge, clear boundaries, and stewardship are essential.
62. 63 Architecture for fast flow resembles an ecosystem of loosely-coupled independently-viable services with clear boundaries and ownership aligned to the flow of business value.
63. 64 3EO: Entrepreneurial Ecosystem Enabling Organizing https://www.boundaryless.io/3eo-framework/
64. How might AI tools help leadership decision-making? 65
65. TeamOS 66 teamos.is Disclosure: Matthew Skelton has invested personally in the company behind TeamOS
66. 67 TeamForm teamform.co
67. 68 CodeScene codescene.com
68. 69 “The work is delivered in many small changes that are uncoordinated to enable flow. … Management’s job is to provide context, prioritization and to coordinate across teams. Lending resources if needed across teams to unblock things. … It works well within a high trust culture.” Adrian Cockcroft https://mastodon.social/@adrianco/111174832280576410 Technology strategy advisor, Partner at OrionX.net (ex Amazon Sustainability, AWS, Battery Ventures, Netflix, eBay, Sun Microsystems, CCL)
69. If we have clear boundaries for flow, with limited interactions, how do we create alignment? How do we learn from each other at pace? 70
70. 5 DevOps principles: CALMS • Culture • Automation / AI • Lean • Measurement • Sharing 71
71. 72 Multiple, independent flows, fractally
72. 73 Active diffusion of knowledge across team boundaries
73. 75 Future: AI-powered tools to detect duplication, uncertainty, waste, innovation, etc. and diffuse learning
74. 76 internal conferences guilds Communities of Practice lunch & learn public blogs
75. 77 https://internaltechconf.com/ Internal Tech Conferences Victoria Morgan-Smith and Matthew Skelton
76. 78 “This initiative around internal conferences has been the single most effective thing to align business and technology that I have seen in this organization” – Murray Hennessey, CEO, (UK retail co)
77. 79 https://internaltechconf.com/ Internal Tech Conferences Victoria Morgan-Smith and Matthew Skelton It’s o p te !
78. 80 “The way that the Conflux crew used their active knowledge diffusion approach to seek out and champion good practices was a real revelation to us at TELUS and helped to shift thinking around how we innovate and share successes.” – Steven Tannock, Director, Architecture (Platform Technology & Tools) at TELUS Digital
80. 82 Thriving organizations, delivering at speed™ Create alignment, trust, and engagement across your organization whilst delivering at pace with fast flow. adapttogether.info
81. Combine ‘architecture for flow’ (via Team Topologies) with explicit ‘active knowledge diffusion’ across flow boundaries to create trust, alignment, and learning. 83
82. The near-future AI advantage (?) My perspective on AI (and work) How to trust and organize AI The AI-savvy operating model 84
83. How can we empower teams of humans + AI agents to deliver quickly, safely, and compliantly with high-fidelity domain knowledge and visibility of the data sources & results, plus the agency to address problems caused? 85
84. End-to-end responsibility for service outcomes is essential to avoid harm and deliver effectively: use empowered teams 86
85. Start with the need for ongoing evolution of long-lived digital services meeting user needs… … and work backwards from there (don’t start with the technology) 87
86. Define guardrails and boundaries for code gen and service specs: domains, security, nuances of terminology, assumptions, algorithms, biases, etc. (Hint: this has always been needed!) 88
87. Combine ‘architecture for flow’ (via Team Topologies) with explicit ‘active knowledge diffusion’ across flow boundaries to create trust, alignment, and learning. 89
88. The clarity of mission and transparency resulting from the defined guardrails needed for AI-augmented organizations may be the most useful aspect of AI (!) 90 Matthew Skelton
89. Let’s do what it takes to empower teams of humans + AI agents to make decisions quickly, safely, and compliantly with high-fidelity domain knowledge and visibility of the data sources & results, plus the agency to address problems caused. 92
90. let’s work together confluxhq.com Copyright (c) 2017-2024 Conflux group of companies. All Rights Reserved. The name “Conflux” and the filled C device are Registered Trademarks ® in multiple jurisdictions.