The silent question - What if technology disrupts my role?

AI is disrupting the IT industry at a pace we have never experienced before. Tools, roles and required skills change faster than organizations can adapt. But just like previous waves, DevOps, Agile, Cloud, the hardest part is not the technology.

It’s the people.

When change becomes constant, fear quietly enters the system: fear of becoming irrelevant, fear of looking incompetent, fear of losing status or control. These fears shape behavior far more than strategy or tooling ever will. They lead to resistance, defensive decision-making, fixed thinking and a strong desire to “stick to what used to work”.

In this session, we look beyond AI capabilities and focus on how humans experience continuous change.

Drawing on lessons from years of DevOps transformations, and the psychology behind mindset, fear and motivation, this talk explores why a learning culture is no longer optional, why a growth mindset is essential when knowledge expires quickly, and why human connection becomes more important as technology becomes more powerful

This session is not about becoming an AI expert, it is about dealing with the transition.

https://sessionize.com/s/ReneVanOsnabrugge/the-silent-question-what-if-technology-disrupts-my/187837

Transcript

1. H07 - THE SILENT QUESTION WHAT IF TECHNOLOGY DISRUPTS MY ROLE? RENÉ VAN OSNABRUGGE
2. WHY DID I CHOOSE THIS TOPIC?
4. THE APPROACH-AVOIDANCE CONFLICT
5. SO WHAT IF? DO YOU THINK AI WILL DISRUPT YOUR ROLE?
6. SO WHAT IF? DO YOU FEEL "FEAR" / UNCERTAINTY / UNCOMFORTABOUT THAT?
7. ACT 1 THE CURRENT STATE OF THINGS
8. YES!
9. https://www.anthropic.com/research/labor-market-impacts - March 2026
10. WHAT WILL HAPPEN?
11. SWITZERLAND 1967 source: https://escapement.watch/journal/the-quartz-crisis
12. DO YOU SEE SIMILARITIES?
15. IS AI EVIL?
16. IS AI EVIL? WORKFORCE DISRUPTION SKILLS EROSION BIAS & FAIRNESS PRIVACY & SECURITY MISINFORMATION & TRUST GOVERNANCE & ACCOUNTABILITY ENERGY & SUSTAINABILITY
17. NOW WE KNOW THIS SHOULD WE ABANDON AI?
18. WHAT TECHNOLOGY WANTS TECHNOLOGY CONTINUOUSLY REORGANIZES WORK AROUND NEW POSSIBILITIES, AND AI IS SIMPLY THE LATEST EXAMPLE OF THAT PATTERN.
19. RESISTANCE IS FUTILE
20. ACT 2 OUR ROLE IN THIS NEW WORLD
21. AN EXPERIMENT
22. WE MOVE INTO THE FEAR ZONE
23. FEARS AND BIASES
24. 3 FEARS THE FEAR OFLOSING RELEVANCE THE FEAR OF INCOMPETENCE THE FEAR OF LOSING CONTROL
25. THE FEAR OF LOSING RELEVANCE
26. DISRUPTION IS NOT NEW
27. INDUSTRIES WERE DISRUPTED BY “US”
28. AI DISRUPTS TASKS WITHIN INDUSTRIES
29. THE FEAR OF INCOMPETENCE
30. AI CHANGES FASTER THAN OUR LEARNING SYSTEM
31. Recent studies indicate the average half-life of a skill has shrunk from 6 years to 2.5 years. AI Puts The Squeeze On The Shrinking Half-life Of Skills 40% of the workforce may require reskilling. Organizations struggle with adaptation speed, not willingness to learn. AI courses become outdated rapidly. Sources Google Forbes HBR Salesforce
32. THE REAL IMPACT IS FOR STARTERS
33. KNOWLEDGE TACIT KNOWLEDGE
34. WILL WE ALL BE BEGINNERS AGAIN?
35. THE FEAR OF LOSING CONTROL
36. MASTERY AUTONOMY PURPOSE
37. FEARS ARE KEPT ALIVE BY BIASES
38. Photo by Gibson Crafton on Unsplash BIASES
39. STATUS QUO BIAS
40. CONFIRMATION BIAS
41. THE INDUSTRY TELLS US WE ARE DOOMED
43. Block framed 4,000 layoffs as AI-driven efficiency, but the real cause was financial pressure from pandemic-era overhiring — employees say AI's actual productivity gains were minimal. Snap cut ~1,000 jobs in April, with Spiegel citing AI (including a claim that AI wrote 65% of new code) — but the layoffs followed activist-investor pressure over Snap's chronic losses and 2026 stock slide, and the cuts were concentrated in units like AR rather than the AI-exposed roles a genuine AIdriven layoff would target. Sources Forrester Resumetemplates 59% of u.s. hiring managers admitted they emphasize ai when explaining hiring freezes or layoffs because it plays better with stakeholders than citing financial constraints. Intuit cut 3,000 jobs in May and simultaneously struck deals with Anthropic and OpenAI, prompting press to frame it as AI-driven restructuring — but the CEO explicitly pushed back, saying the cuts targeted excess management layers and coordination-heavy roles, not AI substitution. “Many companies announcing A.I.-related layoffs do not have mature, vetted A.I. applications ready to fill those roles, highlighting a trend of ‘A.I.-washing’ — attributing financially motivated cuts to future A.I. implementation.”
44. LOSS AVERSION
45. BIAS OR FEAR FOR THE NEW?
46. ACT 3 A NEW REALITY
47. SO WHAT NOW?
48. DIFFICULT TO SEE. ALWAYS IN MOTIO IS THE FUTURE. - YODA
50. AN EXPERIMENT
51. AN EXPERIMENT
52. WHAT HAPPENS HERE?
53. GROWTH MINDSET
54. THE TWO MINDSETS
55. GROWING YOUR MINDSET • NOTICE YOUR FIXEDMINDSET VOICE • RECOGNIZE IT'S A CHOICE • REFRAME: "NOT GOOD AT THIS... YET" • ACT ON IT: EMBRACE CHALLENGES, PERSIST THROUGH SETBACKS, TREAT EFFORT AS THE PATH TO MASTERY • TREAT FAILURE AS DATA, NOT IDENTITY • LEARN FROM CRITICISM INSTEAD OF AVOIDING IT • PRAISE PROCESS (EFFORT, STRATEGY) OVER INNATE ABILITY • MINDSET IS TRAINABLE
56. WE HAVE BEEN HERE BEFORE
57. INDUSTRIAL REVOLUTION PREDICTIONS REALITY • HANDLOOM WEAVERS WOULD LOSE THEIR JOBS. • FACTORY AND MACHINE OPERATORS • SKILLED CRAFTSPEOPLE WOULD BE REPLACED BY MACHINES. • ENGINEERS AND MECHANICS • AGRICULTURAL WORKERS WOULD FACE MASS UNEMPLOYMENT. • RAILWAY AND CONSTRUCTION WORKERS • INDUSTRIAL MANAGEMENT • MASS PRODUCTION AND RETAIL • GROWING CITIES AND SERVICE INDUSTRIES
58. AUTOMOBILES PREDICTIONS REALITY • COACH DRIVERS WOULD LOSE THEIR JOBS. • AUTOMOTIVE MANUFACTURING • STABLE WORKERS AND HORSE BREEDERS WOULD BECOME UNEMPLOYED. • MECHANICS AND REPAIR SHOPS • BLACKSMITHS AND CARRIAGE MAKERS WOULD DISAPPEAR. • ROAD CONSTRUCTION • TRUCKING AND LOGISTICS • CAR DEALERSHIPS • PETROL STATIONS AND SERVICE AREAS
59. COMPUTER PREDICTIONS REALITY • TYPISTS AND SECRETARIES WOULD LOSE THEIR JOBS. • SOFTWARE DEVELOPMENT • BOOKKEEPERS AND ADMINISTRATIVE CLERKS WOULD • IT SUPPORT AND SYSTEM ADMINISTRATION BECOME OBSOLETE. • DATA ANALYSIS • FACTORY AND OFFICE WORKERS WOULD BE • CYBERSECURITY REPLACED BY AUTOMATION. • DIGITAL DESIGN • THE INTERNET AND -ECOMMERCE INDUSTRIES
60. INTERNET PREDICTIONS REALITY • RETAIL WORKERS WOULD LOSE THEIR JOBS. • E- COMMERCE AND DIGITAL MARKETPLACES • TRAVEL AGENTS WOULD BECOME OBSOLETE. • WEB DEVELOPMENT AND DESIGN • NEWSPAPER AND PUBLISHING JOBS WOULD DISAPPEAR. • DIGITAL MARKETING AND CONTENT CREATION • LOGISTICS AND FULFILMENT • SOCIAL MEDIA AND ONLINE COMMUNITIES • CLOUD COMPUTING AND CYBERSECURITY
61. WE ALREADY DELEGATED WORK
62. THERE IS RISE IN AI USAGE
63. THE JEVONS PARADOX
66. OUR JOBS WILL DISAPPEAR
67. AND WILL SHIFT TO TASKS
68. CLEAN TASKS VS. MESSY TASKS Three Ways to Think About AI and Jobs
69. CLEAN TASKS VS. MESSY TASKS CLEAN TASKS ARE PREDICTABLE, DATA - HEAVY, AND OBJECTIVE MESSY TASKS INVOLVE AMBIGUITY, SUBJECTIVE JUDGMENT, TACIT KNOWLEDGE, AND HUMAN RELATIONSHIPS (E.G., PROCESSING EXPENSE REPORTS OR UPDATING SPREADSHEETS). THESE ARE (E.G., CALMING AN ANGRY CLIENT, HIGHLY VULNERABLE TO AI. CHOOSING A BRAND STRATEGY, OR LEADING A TEAM)
70. STRONG BUNDLE VS. WEAK BUNDLES STRONG BUNDLE TASKS DEPEND HEAVILY ON SHARED CONTEXT, RELATIONSHIPS, JUDGEMENT, OR ACCOUNTABILITY. WEAK BUNDLE TASKS CAN BE SEPARATED WITH LITTLE LOSS OF VALUE. AI HELPS WITH TASKS, BUT THE HUMAN REMAINS AI CAN TAKE OVER INDIVIDUAL TASKS, LEAVING HUMANS WITH A SMALLER ROLE. RESPONSIBLE FOR THE COMPLETE SERVICE.
72. YOUR JOB IS BIGGER THAN EXECUTION. DECIDE What should we solve? EXECUTE Build it DELIVER Validate and own it
73. AI COMPRESSES THE MIDDLE OF THE SANDWICH. DECIDE What should we solve? EXECUTE Build it DELIVER Validate and own it
74. HUMAN VALUE SHIFTS
75. ACT 4 MOVING ON
76. CLAPPING EXPERIMENT Sound Decibel Meter - Sound Level Meter
77. RETHINK INSTEAD OF OPTIMIZE
78. RETHINK EDUCATION
79. WE NEED TO CREATE FRICTION!
80. TAXONOMY OF BLOOM CREATE Produce new or original work Design, assemble, construct, conjecture, develop, formulate, author, investigate EVALUATE Justify a stand or decision Appraise, argue, defend, judge, select, support, value, critique, weigh ANALYSE Draw connections among ideas Differentiate, organise, relate, compare, contrast, distinguish, examine, question, test APPLY Use information in new situations Execute, implement, solve, demonstrate, interpret, operate, schedule, sketch UNDERSTAND REMEMBER Explain ideas or concepts Classify, describe, discuss, identify, locate, recognize, report, select, translate Recall facts and basic concepts Define, duplicate, list, memorise, repeat, state
81. TAXONOMY OF BLOOM CREATE Produce new or original work Design, assemble, construct, conjecture, develop, formulate, author, investigate EVALUATE Justify a stand or decision Appraise, argue, defend, judge, select, support, value, critique, weigh ANALYSE Draw connections among ideas Differentiate, organise, relate, compare, contrast, distinguish, examine, question, test APPLY Use information in new situations Execute, implement, solve, demonstrate, interpret, operate, schedule, sketch UNDERSTAND REMEMBER Explain ideas or concepts Classify, describe, discuss, identify, locate, recognize, report, select, translate Recall facts and basic concepts Define, duplicate, list, memorise, repeat, state
82. TAXONOMY OF BLOOM CREATE Produce new or original work Design, assemble, construct, conjecture, develop, formulate, author, investigate EVALUATE Justify a stand or decision Appraise, argue, defend, judge, select, support, value, critique, weigh ANALYSE Draw connections among ideas Differentiate, organise, relate, compare, contrast, distinguish, examine, question, test APPLY Use information in new situations Execute, implement, solve, demonstrate, interpret, operate, schedule, sketch UNDERSTAND REMEMBER Explain ideas or concepts Classify, describe, discuss, identify, locate, recognize, report, select, translate Recall facts and basic concepts Define, duplicate, list, memorise, repeat, state
83. THE SOCRATIC WAY CLARIFYING THINKING & UNDERSTANDING Can you give me an example? Could you explain further? Are you saying...? What is the problem you are trying to solve? CHALLENGING ASSUMPTIONS Is that always the case? Are you assuming...? How could you verify or disprove that? What would happen if...? EXAMINING EVIDENCE & RATIONALE Why do you say that? How do you know? Why? What evidence is there that supports...? CONSIDERING ALTERNATIVE PERSPECTIVES Are there any alternatives? What is the other side of the argument? What makes your viewpoint better? Who would be affected and what would they think? CONSIDERING IMPLICATIONS & CONSEQUENCES What are the implications/consequences of...? How does that affect...? What if you are wrong? What does our experience tell us will happen? META QUESTIONS Why do you think I asked that question? What does...mean? What is the point of the question? What else might I ask?
84. ESPECIALLY FOR THE NEW GENERATION
85. RETHINK PRODUCTIVITY
86. MEASURE OUTCOMES INSTEAD OF ACTIVITY
87. DO NOT ACCELERATE BROKEN PROCES
88. TURN PRODUCTIVITY GAINS INTO GROWTH
89. RETHINK ORGANIZATIONS
90. MOVE TO 24 - HOURSPRINTS 9:00 AM 5:00PM Midnight 9:00 AM Night shift — 16 hours 5:00PM Midnight Day shift — 8 hours Night shift - agent factory Day shift - human-supported • Requirements: define business requirements from stakeholders • Sprint review: validate agent output vs acceptance criteria • Architecture: set up structure and design for new features • Build & test: build, test, and report results to humans https://www.mckinsey.com/capabilities/mckinsey-technology/our-insights/rewiringsoftware-delivery-for-the-agentic-era • Spec & code review: pair review of code paths and compliance • Optimization: refactor code, refine guardrails and prompts • Sprint planning: set inputs and align priorities for next shift
91. CREATE A KNOWLEDGE INFRASTRUCTURE Example insight data by source SharePoint — results of previous surveys (eg, drop-off drivers) Connections Agent Observability — recent customer usage data and drop-off points Jira — features or user stories already planned for implementation GitHub — existing services that support onboarding or related capabilities SMEs — implicit SME knowledge on the journey (not formally documented) https://www.mckinsey.com/capabilities/mckinsey-technology/our-insights/rewiring-software-delivery-for-the-agentic-era
92. RESIZE TEAMS AND REDESIGN PORTFOLIO https://www.mckinsey.com/capabilities/mckinsey-technology/our-insights/rewiring-software-delivery-for-the-agentic-era
93. RETHINK INNOVATION & PRODUCTS
94. STEP BEYOND THE GLUED - ON- AI
95. WE CAN DO MORE
96. FROM STANDARDIZATION TO PERSONALIZATION PERSONAL SHOPPERS AUTOMOUS SUPPORT AGENTS THAT SOLVE MULTICHAIN PROBLEMS WITH YOUR DATA PERSONAL UI AND SOFTWARE SELF HEALING IT SYSTEMS
97. THINK IN OPPORTUNITIES WORKFORCE DISRUPTION SKILLS EROSION BIAS & FAIRNESS PRIVACY & SECURITY MISINFORMATION & TRUST GOVERNANCE & ACCOUNTABILITY ENERGY & SUSTAINABILITY
98. AND SINCE NOBODY KNOWS
99. WE ARE PIONEERS (AGAIN)
100. WRAPPING UP AI WILL CHANGE YOUR ROLE FEAR IS NORMAL, BUT NOT ALWAYS WARRANTED. IT'S NOT YOUR JOB THAT DISAPPEARS, IT'S YOUR TASKS THAT SHIFT. RETHINK, DON'T OPTIMIZE. WE'RE PIONEERS AGAIN.
101. LAST EXPERIMENT Sound Decibel Meter - Sound Level Meter
102. RENÉ VAN OSNABRUGGE GLOBAL CONSULTING DIRECTOR @ XEBIA RENE.VANOSNABRUGGE@XEBIA.COM HTTPS://CULTURE-ENGINEERS.NL THANK YOU! Attributions Pictures: Unsplash
103. THANKS! PHOTO CREDITS (UNSPLASH) Slide 4 5 8 9 11 18 19 20 21 22 23 24 25 28 29 30 31 Photographer Profile Chris Stenger Profile Bankim Desai Profile Marc Wieland Profile Daniel Bernard Profile Viktor Vasicsek Profile Foto Micha Profile Phillip Flores Profile Guillaume Profile Archambault Inggrid Koe Profile James Toose Profile Gary Bendig Profile Michael Profile Williams Bernd Profile Dittrich Martin Sanchez Profile James Profile Wainscoat Mathew Profile Schwartz Peter Bromley Profile Photo Link Photo Photo Photo Photo Photo Photo Photo Slide Photo 49 50 Photo Photo Photo Photo Photo Photo 34 35 36 40 48 51 53 54 55 Photo 58 63 Photo 65 Photo 68 69 71 Photographer Profile Salvatore Profile Tonnara imsogabriel Profile stock Simon Hurry Profile Gibson Crafton Profile Connor Profile McSheffrey Mattia Poli Profile Hu Chen Profile Wolfgang Profile Hasselmann Jasper Garratt Profile Omar Profile Ramadan Courtney Profile Cantu Wes Warren Profile Jorge Coromina Profile John Mark Profile Arnold Florian Berger Profile Dawid Zawiła Profile THLT LCX Profile Photo Link Slide Photo 74 Photo 78 Photo Photo 79 Photo Photo Photo Photo Photo Photo Photo Photo Photo Photo Photo Photo Photo 80 81 82 83 87 90 91 92 96 98 99 101 103 Photographer Dmitry Grigoriev Damian Markutt Skyler Ewing Sandis Kovaļovs Lisa Yount NIR HIMI Kari Shea Rob Vesseur Pawel Czerwinski Everest Louis Meggyn Pomerleau Jay Sneade Wai Siew TC Photography Susan Q Yin Matt Seymour Profile Photo Link Profile Photo Profile Photo Profile Photo Profile Photo Profile Profile Profile Profile Photo Photo Photo Photo Profile Photo Profile Photo Profile Photo Profile Profile Photo Photo Profile Photo Profile Profile Photo Photo