The changing face of hr

1. The Changing Face of HR Peter Cochrane cochrane.org.uk ca-global.biz

2. A C O N N E C T E D W O R L D Unexpected behaviours and outcomes

3. ManAgement Struggle G e n e r a l l y c o n f o u n d e d b y c o m p l e x i t y Instinct and experience increasingly dangerous Knee jerk management is always dangerous Dynamic connectivity and multi-species non-linearity

4. L i m i t e d : - A s s u m p t i o n s - M e a s u r e s - T h i n k i n g - M o d e l s - D a t a E v o l v e d f o r a n s l o w c h a n g i n g a n a l o g u e w o r l d - l i n e a r i t y r u l e d & o u r m a t h w o r k e d - b u t i t a l l c r a s h e s a b o v e o r d e r ( 5 ) - c o m p l e x i t y n o w r u l e s w i t h 1 0 s t o 1 0 0 0 s o f v a r i a b l e s - a n d p e r h a p s w o r s e w e c o p e w i t h < 5 - 7 s i m u l t a n e o u s i d e a s “There are lots of simple solutions to complex problems and they are all wrong” tyranny of THE SIMPLE We are linear thinkers but the world is complex

5. p o l i t i c s M a n a g e r s E C O N O M I CS “People love simple - it is so obvious and doesn’t involve thinking”

6. T H E K i n g s o f s i m p l e ? Both are IT illiterate - they cannot operate a computer and ‘boast’ their inability and ignorance !!! These are not good leadership qualities..

7. Technology Ecology d e p e n d e n cy J I GS AW Economics Society Industry Care Markets Trade Education Policing Defence Commerce Security Logistics Finance Government Companies W e h a v e n o m o d e l o r u n d e r s t a n d i n g The BIG DATA DOMAIN links everything and records relationships and patterns of behaviour - in doing so it creates vast amounts of data vital to the modelling of the real world… Law & Order Defence Education Health Institutions

8. Technology Ecology d e p e n d e n cy J I GS AW Economics Society Industry Care Markets Trade Education Policing Defence Commerce Security Logistics Finance Government Companies W e h a v e n o m o d e l o r u n d e r s t a n d i n g The BIG DATA DOMAIN links everything and records relationships and patterns of behaviour - in doing so it creates vast amounts of data vital to the modelling of the real world… Law & Order Defence Education Health Institutions “ S o m e t i m e s W E m i g h t o b s e r v e o r i n f e r s o m e l i m i t e d r e l a t i o n s h i p s a n d typify some characteristics however AI can produce a comprehensive analysis and a much fuller picture and/ or model”

9. A X I O M S V e r y o b v i o u s ? H i g h l y o p t i m i s e d m a c h i n e s s y s t e m s a n d c o m p a n i e s a re brittle and prone to drastic a n d u n c o m p ro m i s i n g f a i l u re

10. H i g h l y o p t i m i s e d m a c h i n e s s y s t e m s a n d c o m p a n i e s a re brittle and prone to drastic a n d u n c o m p ro m i s i n g f a i l u re I n c re a s e d e f f i c i e n c y l e a d s t o increased br ittleness and m o re f a i l u re s … . …more data is a must for a c c u ra t e b u s i n e s s m o d e l s a n d wa r g a m e s … ….BIG and small DATA are key to a more predictable t h e f u t u re Stop Over Optim ising if you wa nt your c o m p a n y t o s u r v i ve a n d p ro s p e r Av oid erroneous KPIs and bonus driven cultures Embrace a data and business model driven approach

11. - P o l i s h i n g w h a t w e h a v e w i l l n o t r e a l i s e s u s t a i n a b i l i t y - N e w m a t e r i a l s & m a n u f a c t u r i n g m e t h o d s a r e r e q u i r e d - N e w d i s t r i b u t i o n , s u p p o r t a n d m a i n t e n a n c e r e g i m e s - E n e r g y a n d m a t e r i a l u s a g e h a s t o b e m i n i m i s e d - R e u s i n g , R e p u r p o s i n g , R e c y c l i n g , h a s t o b e r e a l - E c o n o m i c s y s t e m s h a v e t o b e f i t f o r p u r p o s e A X I O M S Not so obvious?

12. C O M P A N Y C O L L A P S E M a n a g e m e n t h u b r i s i g n o r a n c e A great world leading company - what could possibly go wrong ?? Did the board understand their technology Did the board understand their market Did they heed their own experts Did they ‘eat their own dog food’ Did management assumed the style of ‘Gods’ Did they think themselves infallible/all knowing Did they rely on instinct instead of facts and models D E S T R O Y E D

13. C O M P A N Y C O L L A P S E L o s s o f l e a d e r s h i p & m a n a g e m e n t Another great world leading company - what could possibly go wrong ?? They lost their leaders They lost sight of their core values They lost sight of their core mission They lost their grip on technology and markets They then drastically changed direction The company core competences were lost What is now left is smething much less than it was! D A M A G E D

14. 1884 - George Eastman patents photographic film on a roll 1888 - First film camera perfected 1892 - Kodak founded in Rochester, NewYork. 1900 - Brownie camera launched at a price of $1 1930 - Eastman Kodak launched on the Dow Jones. 1969 - Kodak film used on the Apollo 11 missions 1975 - The first company to build a working digital camera 1976 - 90% of photographic film and 85% of camera sales market 1994 - Kodak designs Apples first consumer digital camera 2004 - Stopped selling film cameras in the face of digital alternatives. 2005 - Largest seller of digital cameras in the US, revenue reaching $5.7Bn 2009 - Stopped selling 35mm colour film after 74 years of production. 2011 - Shares fell >80pc as it struggled to maintain market share 2012 - Files for chapter 11 bankruptcy. L E A d e r L a g g a r d B A N K C R U PT

15. 1884 - George Eastman patents photographic film on a roll 1888 - First film camera perfected 1892 - Kodak founded in Rochester, NewYork. 1900 - Brownie camera launched at a price of $1 1930 - Eastman Kodak launched on the Dow Jones. 1969 - Kodak film used on the Apollo 11 missions 1975 - The first company to build a working digital camera 1976 - 90% of photographic film and 85% of camera sales market 1994 - Kodak designs Apples first consumer digital camera 2004 - Stopped selling film cameras in the face of digital alternatives. 2005 - Largest seller of digital cameras in the US, revenue reaching $5.7Bn 2009 - Stopped selling 35mm colour film after 74 years of production. 2011 - Shares fell >80pc as it struggled to maintain market share 2012 - Files for chapter 11 bankruptcy. L E A d e r L a g g a r d B A N K C R U PT A s s i s t c o m p a n y t o m u t a t e f r o m p r o d u c t i o n & s u p p l y t o o i n n o v a t o r a n d m a r k e t l e a d e r w i t h n e w p r o d u c t s H R C H A L L E N G E ? H R C H A L L E N G E ? H e l p t h e c o m p a n y r e s p o n d t o t h e p e n d i n g d e s t r u c t i o n o f t h e i r p r i m a r y b u s i n e s s m a r k e t a n d m o d e l

16. -1889:Time-recording equipment maker Bundy incorporated -1896:The (punched card)Tabulating Machine Co incorporated. -1918: CEO Watson becomes business icon, pioneering worker benefits -1923: First electric key punch a big advance on mechanical systems -1928: 80-column IBM punched card, 2x previous capacity introduced -1931: IBM 400 accounting machines offer alphabetic data -1932: IBM 600 performs multiplication and division -1933: IBM acquires Electromatic Typewriters -1944: Mark I, first machine to accomplish long operations automatically -1946: IBM 603 first machine to offer electronic arithmetic circuits -1948: IBM the Selective Sequence Electronic Calculator -1952: IBM 701 first production electronic computer, featuring tape-drive -1957: FORTRAN introduces as the main language for technical work -1961: Selectric typewriter released later models offer memory and word processing. -1964: IBM 360 Solid Logic Technology microelectronics -1966: IBM's Robert Dennard invents the Dynamic Random Access Memory -1969: IBM onboard computer used in first manned flight to the moon L E A d e r S u rv i vo r i n n ovato r

17. -1971: Floppy disk is introduced to later becomes the PC data storage standard -1975: IBM 5100 Portable Computer enters the market, 50 pounds priced at $9 to $20k. -1981: IBM PC launched at $1,565, the lowest priced PC to date -1984: IBM second-generation PC, runs on a 6MHz Intel 80286 processor -1987: IBM PS/2is launched along with the OS/2 jointly developed with Microsoft -1990: IBM 390 family, midrange machines and supercomputers -1991:An annual loss of $2.82Bn, the ﬁrst of three annual losses in a row -1993: Louis Gerstner states his intention to keep IBM together as an integrated company -1995:Acquires Lotus and Notes making IBM the world's largest software company. -1995: Introduces the ThinkPad 701cm, which runs on the Intel 133MHz Pentium -1996: Launch of the DB2 Universal Database, capable of querying alphanumeric data -1997: Deep Blue supercomputer defeates Gary Kasparov Chess Master -2002: Further strengthens services business with a $3.5 billion acquisition PWC Tech unit -2005: Sold more than 20 million ThinkPads and sells business to Lenovo -2011: IBM Watson artiﬁcial intelligence defeats TV Jeopardy game show champions -2012: IBM Watson outguns medical doctors at diagnosis -2013: IBM Watson APIs made public L E A d e r S u rv i vo r i n n ovato r A s s i s t m u t a t i o n f r o m o n e i n n o v a t i v e b u s i n e s s t o t h e n e x t b y p r o v i d i n g f l e x i b i l i t y o f r e c r u i t m e n t a n d e m p l o y m e n t H R C H A L L E N G E ?

18. H E A D I N G Tow a r d a T e c h i m p a ss e Business cannibalisation Search business model based on clicks AI search business model based fewer clicks Revenue collapse projected Pending business collapse! Diversiﬁcation into new sectors Driverless vehicles Delivery drones AI as a service AI ofﬁce +++

19. H E A D I N G Tow a r d a T e c h i m p a ss e Business cannibalisation Search business model based on clicks AI search business model based fewer clicks Revenue collapse projected Pending business collapse! Diversiﬁcation into new sectors Driverless vehicles Delivery drones AI as a service AI ofﬁce +++ C o n t i n u a l l y a d j u s t e m p l o y m e n t a n d r e c r u i t m e n t m o d e l t o m e e t a v e r y w i d e & d y n a m i c r a n g e o f a c t i v i t i e s & b u s i n e s s e s H R C H A L L E N G E ?

20. Managing the resources of the planet by a single parameter ($$) is never going to work - it can only result in chaotic economies & eventually a total demise…we need far more sophistication! AX I O M AT I C W e h a v e B I G D A T A b u t w e n e e d B I G U N D E R S T A N D I N G Trying to manage the resources and capabilities by a limited number of KPIs and/or simple minded thinking now guarantees failure..we need to be far more sophisticated!

21. • Raw Data • Meta Data • Small Data • B I G D a t a E v e r y s e c t o r i s a w a s h / o v e r w h e l m e d W hat w e HAVE TODAY W h a t w e a c t u a l l y n e e d : K n o w l e d g e & U n d e r s t a n d i n g

22. DATA Big & SMALL Small Data Focussed Detail Big Data Macro-View Relationships Limited C o n t a i n e d C o n s t ra i n e d Expanding Tending to T h e I n f i n i t e Both important and dynamic

23. distribution Automated & intelligent

24. distribution Automated & intelligent BIG DATA Where has the package come from and where is it going ? Where is it now and what does it contain ? small DATA The detailed product history of how it was created; where it has been s t o re d a n d d e t a i l e d handling history including humidity, temperature, G- force, damage +++ Item Box Pallet Warehouse Container Supplier Customer

25. O B S E RV AT I O N S - Change is always a surprise - It tends to come from an unexpected direction - Mechanism tends to be novel and not anticipated - Responding quickly is absolutely essential - There is a BIG ﬁrst mover advantage F a v o u r s t h e A d a p t a b l e

26. W A R G A M I N G A r t i f i c i a l I n t e l l i g e n c e M odelling Com puter M at e r i a l S c i e n c e Clouds B I G D A T A BYOD BMOB DIY IT Sensors Robots C y b e r Security Nano Bio T H E B I G G A M E C H A N G E R S IoT + Auto Immunity Block Chain + Apps

27. EXPANDING HR FUNCTION In an ideal world HR would be ahead: Aware of tech progress & change Providing management updates Anticipating new applications Following new social trends Promoting new practices Monitoring competitors Using latest office tech Watching the market Identifying key staff Helping the board +++++++++++ ++++++++++

28. O n e S I z e D O ES n ot F I T a l l - Companies are seldom monolithic - Technology change is challenging - Competition never sleeps - Solutions are dynamic A D A P T A B I L I T Y I S K I N G !

29. Fa i l FA S T ! ! - Recognise failures quickly - Respond & repair immediately - Be ruthless in the action necessary - Mid-course corrections generally work - reboot if needs be - Evolution is not linear - it is chaotic with many false avenues “Doing something, making a decision is always better than doing nothing”

30. H R : P E O P L E S K I L LS c h a n g e Compensation Recruitment Compliance Legislation Training Welfare Safety Cognitive System Complexity Content Processes Social Resources Technical Physical Growing Stable Declining 2000 - 2025

31. E M P L OY M E N T: P R E M I U M S K I L LS At a premium: Leaders Analysts Specialists Innovators Generalists Negotiators Communicators Educated managers Complex problem solvers Hard to ﬁnd ﬁxers: Software Electrical Electronic Mechanical

32. E M P L OY M E N T: T H E N O M A DS Motivation: Lifestyle Freedom Burn out Excitement Higher earnings Self determination Second career: Do not want job ! Object to the routine Seeking challenging work! Do not want responsibility

33. A dva n c i n g t e c h n o l o g y AI v Human Defeats: Chess Poker Go Debating General Knowledge Medical Diagnostics +++ Robotics v Human Defeats An endless list D e m a n d s m o r e p e o p l e ‘Machines are good at dealing with vast quantities of the expected” “People are good at dealing with small quantities of the unexpected”

34. When robots do all the work, how will people live? Tom Watson U K I S S H O R T O F 2 0 0 k A N A L Y S T S

35. Analytics: Veracity Medical Security Big Data Political Networks Economic Genomic Proteomic Diplomatic Complexity ++++ We are way past the point where humans can analyse complexity even on a small scale ! Design: Chips Devices Systems Networks Behaviours ++++ Knowledge: Veracity Linking Creation Curation Searching Selection ++++ A I : A p r i m a ry to o l o f i n n ovat i o n

36. N E W M a n a g e m e n t To o ls - AI - The IoT - BIG DATA - small DATA - DATA Analytics - Business Modelling - War Gaming/Simulation - The Cloud ? - Block Chain ? - Social Networking ? - Behaviour Monitoring ? - +++

37. E M P L OY M E N T: I N /o u t s o u r c i n g Leaders VisionariesNiche Players Challengers Completeness of Vision AbilitytoDeliver “Do what you do best - outsource the rest”

38. The ability to solve problems now trumps pure and isolated academic attainment… HR IMPLICATIONS: Hiring ?? Collaboration helps us g e t a n i n c r e a s i n g l y difficult balance right dynamically ! AI and Robotics are the new tools of many professions Multi-skilled people are rare and at a premium

39. H R - B I G I M P L I C AT I O N S - Point of production change - Different business models - Green agenda drivers - Faster tech innovation - New manufacturers - New processes - New skill sets - New tools - Greater accessibility & freedoms - Changing people expectations - More autonomy/less control - Shorter projects/contracts

40. Thank You cochrane.org.uk ca-global.biz