The AI-savvy operating model - Matthew Skelton, Conflux - Agile to Agility conference

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 opening keynote at Agile to Agility on 04 December 2024 | K38

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 Agile to Agility conference | 2024-12-04 K38 Photo by Barbara Zandoval on Unsplash
2. 2 ❤
3. 3 Matthew Skelton holistic innovation Originator of Adapt Together™ by Conflux Co-author of Team Topologies matthewskelton.com
4. Small, scrappy startups that use GenAI well will be able to out-innovate larger incumbents [initially[. So what would an effective “AI-savvy” operating model look like that used AI to reduce time-to-value for larger organizations? 4
5. (we already know) 5
6. 6 • Empowered teams • No hand-offs • Ongoing stewardship • Clear boundaries • Active knowledge diffusion
7. The near-future AI advantage (?) My perspective on AI (and work) How to trust and organize AI The AI-savvy operating model 7
8. The near-future AI advantage (?) 8
9. 9 “AI-savvy” operating model
10. 10 Traditional AI: pattern matching, huge data volumes, temporal correlation Generative AI: next-token guess, content generation, option generation
11. 11 “Learning” but no understanding with AI
12. 12 Savvy: don’t pretend or forget that AI doesn’t understand ⚠
13. 13 Savvy use cases for AI: • Speed up content generation (incl. code) and workflows • Augment human decision-making and options • Agent-based processes • …
14. 14 Do like AI and look backwards!
15. 15 Cloud birthed SaaS, leaving older models behind (likewise GenAI)
16. 16 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!
17. 17 but…
18. 18 LLMs approaching limits?
19. 19 Serious security problems
20. 20 Data Science Data Eng ML Ops
21. 21 https://web.devopstopologies.com/ 2013
22. My perspective on AI (and work) 22
23. 23 1998 My first “AI” system: backpropagation neural network BSc, University of Reading
24. 24 https://www.linkedin.com/in/promarkbishop/
25. 25 2000 MSc in Neuroscience 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
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 175k copies sold to date 30
32. 32 Delivering at speed means we need to organize for fast flow - “sociotechnical”
33. How to trust and organize AI 33
34. 34 Assume we have a collection of AI agents responding to … some kind of input from humans…
35. 35 Savvy: don’t pretend or forget that AI doesn’t understand ⚠
36. 36 Humans set the context, goals, guardrails, and execution constraints in a repeatable and traceable way (programming)
37. 37 How would we be able to trust a set of AI agents?
38. 38 How are we be able to trust a set of people?
39. 39 • Guardrails • Ongoing domain context • Good boundaries • Ongoing stewardship and responsibility
40. 40 Teams of humans and AI agents with context, guardrails, stewardship, boundaries, etc…
41. 41 Good boundaries for: • Context windows • Security • Resilience
42. The AI-savvy operating model 42
43. 43 Cloud removed IT infrastructure as a blocker
44. 44 AI removes typing, generating options, trying out combinations, etc. as blockers
45. 45 Humans understand the organizational intent and specify guardrails & goals
46. Principles from Team Topologies provide guidance for knowledge work 46
47. 47 Multiple, independent flows, fractally
48. Respect Conway’s Law (aka ‘sociotechnical mirroring’) 48
49. Clear ongoing stewardship of services and systems 49
50. Stream-aligned teams have end-to-end responsibility for a service (You Build It, You Run It) 50
51. Platforms improve flow and reduce extraneous cognitive load 51
52. Teams are small (~9), slowly changing, with ‘aligned autonomy’ 52
53. Teams are empowered to sense and adjust boundaries to improve flow on a frequent basis 53
54. 54 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.
55. 55 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.
56. 56 3EO: Entrepreneurial Ecosystem Enabling Organizing https://www.boundaryless.io/3eo-framework/
57. How might AI tools help leadership decision-making? 57
58. TeamOS 58 teamos.is Disclosure: Matthew Skelton has invested personally in the company behind TeamOS
59. 59 TeamForm teamform.co
60. 60 CodeScene codescene.com
61. 61 “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)
62. If we have clear boundaries for flow, with limited interactions, how do we create alignment? How do we learn from each other at pace? 62
63. 63 Multiple, independent flows, fractally
64. 64 Active diffusion of knowledge across team boundaries
65. 66 Future: AI-powered tools to detect duplication, uncertainty, waste, innovation, etc. and diffuse learning
66. 67 internal conferences guilds Communities of Practice lunch & learn public blogs
67. 68 https://internaltechconf.com/ Internal Tech Conferences Victoria Morgan-Smith and Matthew Skelton
68. 69 “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)
69. 70 https://internaltechconf.com/ Internal Tech Conferences Victoria Morgan-Smith and Matthew Skelton It’s o p te !
70. 71 “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
71. 72 Thriving organizations, delivering at speed™ Create alignment, trust, and engagement across your organization whilst delivering at pace with fast flow. adapttogether.info
72. 73 Fast flow organizations need active knowledge diffusion across flow boundaries to create trust, alignment, and learning
73. The near-future AI advantage (?) My perspective on AI (and work) How to trust and organize AI The AI-savvy operating model 74
74. Small, scrappy startups that use GenAI well will be able to out-innovate larger incumbents [initially[. So what would an effective “AI-savvy” operating model look like that used AI to reduce time-to-value for larger organizations? 75
75. 76 • Empowered teams • No hand-offs • Ongoing stewardship • Clear boundaries • Active knowledge diffusion
76. 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. 77
77. 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.