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Reconstructed Slides: Moving away from Agile: What's Next – Martin Harrysson & Natasha Maniar, McKinsey & Company

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Moving away from Agile: What's Next – Martin Harrysson & Natasha Maniar, McKinsey & Company

Method

This deck is reconstructed from the existing video frame captures by detecting likely slide regions with OpenCV, cropping/upscaling those regions, deduplicating similar crops, and OCRing the cropped slide images locally. It is a cleaner companion to the full-stage frame deck.

Reconstructed Slides

slide-002.jpg

Slide text:

New technologies have given rise to new software dev methodologies

Pre-2000s 2000s 2010s 2020s

Tech breakthrough PCs Mainframes, Web, client-server APls,mobile Cloud, Al coding assistants

methodologies Software dev Waterfall Agile dev platform dev Product and Al-native dev

CEEE

MciOnsey&Comgany

Coaesummit

slide-003.jpg

Slide text:

New technologies have given rise to new software dev methodologies

Pre-2000s 2000s 2010s 2020s

Tech breakthrough PCs Mainframes, Web, client-server APls, mobile Cloud, Al coding assistants

methodologies Softwaredev Waterfall Agile dev Product and platform dev Al-nativedev

3

McKinsoy&Company

slide-004.jpg

Slide text:

New technologies have given rise to new software dev methodologies

Pre-2000s 2000s 2010s 2020s

Tech breakthrough PCs Mainframes, Web, client-server Cloud, APls, mobile Al coding assistants

methodologies Software dev Waterfall Agile dev platform dev Product and Al-native dev

McKsay&Comgany

AIE/LEAD MOVING AWAY FROM AGILE: WHAT'S NEXT?

Google DeepMind PRESENTEDBY Senior Partner MARTINHARRYSSON Business Analyst NATASHA MANIAR McKinsey & Company

slide-005.jpg

Slide text:

MOVING AWAY FROM AGILE: WHAT'S NEXT?

How can you go from "10x engineers" to a "10x team"?

How can you scale a "10x team" to a "10x company"?

slide-006.jpg

Slide text:

However, bottlenecks in processes may prevent high individual developer productivity to translate to equally high team productivity

Output(featuresper year) (due to increasing coding demand) model intelligence and increasing feature Output potential Bottlenecks amongteammembers Collaboration overhead

processing change Cognitive limits on

Bottlenecks (features delivered Output reality withintimeconstraint) Communication gaps among team members Manual review and debugging time

Increased complexity of

Years morecodegenerated

McKimeyCompa

AIE/ LEAD ex McKinsey Martin Harrysson &Company Katelyn Lesse ANTHROPIC EVERY Dan Shipper Asaf Bord Northwestern Mutual' Michele Catasta replit

Google DeepMind PRESENTED BY elastic Red Hat greptile comet CopilotKit Go gle Deep

slide-007.jpg

Slide text:

Bottlenecks within current operating model and team setup

Botleneck

Dolaysfromincreasedcomplexoty and security vulnerabaites

Refinement. OayO DoyB Retro.

thard.to-interprot pue siodoponop! Inoiciont task. agonts duo to spocifications Ussignment among Sprint planning Doyl Development: Oay23 Sprint reviewz Day4

Comeury

AIEngineer Engineering thefuture of Al

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Slide text:

For each tech function, there may be a different operating model

Types of work Future example operating models

Modernization Humans supervise factory of agents modernising legacy continuously Agentic factory

Maintenance ticketswith minimal humansupervision Agents process lowest complexity

Brownfield products Greenfield products needs,generatedesigns,codeand tests Factories of agents discover customer with human supervision Al co-creator innovation lab

Infrastructure &operations Agents process lowest complexity tickets with high level of human supervision due to higher risk of impact on critical services Human-led with co.pilots

AlEngineer Engineering the future of Al

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Slide text:

Global survey of 300+ enterprises showing what differentiates top performers

performers X-Likelihood of shift compared tobottom 7x Shifts 6x Outcomes

(E2Eimplementaton ofAlof4+usecases) workflows Al native + (broader skillsets, Al native newroles) roles 5-6x Faster time to market 2-3weclo 1.2 weeks

Enablers

2x 2x 7x 3-4x Higher quality artifacts

Upskilling measurement Impact Performance reviews

McAesry &Company

AIE/ LEAD MOVING AWAY FROM AGILE: WHAT'S NEXT?

Google DeepMind PRESENTEDBY Senior Partner MARTIN HARRYSSON Business Analyst NATASHA MANIAR McKinsey & Company

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Slide text:

Besides adopting tools what needs to shift?

Al native workflows Operating model: Al-native roles Talent:

Team ceremonies (continuousplanning with instead of two.pizza pod) Team size (one-pizza

Unit of work Team configuration

(spec-drivendevelopment instead of story-driven) from specialized roles) (definers and builders

Ways of working (code-based prototypes from long PRDs) (agent managers instead of specialized practitioners) Roles and skills

Monry & Compeny 10

AIE/LEAD MOVING AWAY FROM AGILE: WHAT'S NEXT?

Google DeepMind PRESENTED BY Senior Partner MARTINHARRYSSON Business Analyst NATASHA MANIAR McKinsey & Company

slide-013.jpg

Slide text:

Operating model: Positive impact of interventions

Adoption Agent consumption, # average ACUs Active users of Al coding assistant tool

60x vs end of Q2 +28% Highly active users vs. end Q2

Speed Number of code merges, biweekly average per frontrunner squad

+51% Vs end of Q2

Efficiency Business impact efficiency, avg hours per unit of work for each frontrunner squad

-60% vs. end o! Q2 -34% vs. avg Q3

McKescy&Company 12

AIE/LEAD MOVING AWAY FROM AGILE: WHAT'S NEXT?

Google DeepMind PRESENTEDBY SeniorPartner MARTINHARRYSSON Business Analyst NATASHA MANIAR McKinsey & Company

slide-014.jpg

Slide text:

Talent: Top performers are creating new AI-native roles and pivoting responsibilities of existing roles

Top 3 roles most shifted by AI1, % of respondents

Software engineer 71%

Product manager 69%

Testing / QA 57%

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Slide text:

and higher feature throughput in large international airline Talent: Smaller but higher number of teams enabling higher capacity

From

Pod # 1 PM Pod # 2 PM

Tech Lead 2 Toch lead 二

222 Devs Devs

QA 22 QA

Two pizza team of 8-10 people

Examplesharedfunctionalroles

Devops SREI TPM anslyst Business analysts/ Data engineers

MckGesay&

AlEngineer Engineering the future of Al

slide-016.jpg

Slide text:

Deeper organizational shifts required for large enterprises

Rewiring the champions

Change management

Rewiring 100+ teams

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Slide text:

Case study of scaling productivity in top 10 technology company 01010 10101 10101

Top 10 technology company for long term behavior change Unlocks needed to scale adoption

一GenAl tool DailyActive Usors(#)一DAU retention(%)

450 400 350 000 250 200 during training Usage spiked weeks... Nr 35 30 25 20 40 45 and coaching Al and agents Hands-on upskilling Resetexpectations of

150 100 50...but many unlocks sustain behavior were nceded to change 15 10 Spark grassroots movement

Oct Nov Dec Jan Feb Mar Ap May Jun Jul system Build measurement

McXrsoy &Company 18

AIE/LEAD MOVING AWAY FROM AGILE: WHAT'S NEXT?

Google DeepMind PRESENTED BY Senior Partner MARTIN HARRYSSON Business Analyst NATASHA MANIAR McKinsey & Company

slide-019.jpg

Slide text:

Holistic measurement system to evaluate enterprise-wide change

Outcomes

Velocity Capacity Security Quality Resiliency

sndnos Breadth and depth of adoption Number of people upskilled Developer NPS and attrition rate

Inputs SinvestmentinAcoding/devtools Sandtimeinvestedintraining/ upskilling programs Sand time inchange managemeny op model ransformation

McKrsny & Company 21

AIE/ LEAD MOVING AWAY FROM AGILE: WHAT'S NEXT?

Google DeepMind PRESENTEDBY Senior Partner MARTIN HARRYSSON Business Analyst NATASHAMANIAR McKinsey & Company

Hidden Non-Slide Evidence

Classification audit: raw/sources/slide-ai-classification/reconstructed/SZStlIhyTCY/audit.json