This document provides guidance on how organizations can get ready for artificial intelligence (AI). It begins with definitions of common AI tasks like image recognition, games, and speech recognition. It then discusses challenges to AI adoption like expectations, talent shortages, and data issues. A checklist is provided for organizations to assess their AI readiness in areas like awareness, capability, data, plans, and getting external help. Key questions for executives are outlined. Case studies and approaches from companies that have successfully implemented AI strategies are referenced. The document aims to help organizations understand both the opportunities and challenges of AI, and develop plans to incorporate it effectively.

1. How$Orgs.$get$ready$for$
AI
SK#Reddy
Chief#Product#Officer#AI
skreddy99
skreddy99

2. Did$I$tell$you$that?$
AI$affects$every$org$and$every$process

3. https://www.logistics.dhl/content/dam/dhl/global/core/documents/pdf/glo7ai7in7logistics7white7paper.pdf

4. ImageNet(Large(Scale(Visual(Recognition(Challenge((ILSVRC)(
winners

5. https://www.eff.org/ai/metrics
Image&
Recognition

6. https://www.eff.org/ai/metrics
Games

7. https://www.eff.org/ai/metrics
Speech&
Recognition

8. https://www.eff.org/ai/metrics

9. Speed — Accuracy-trade off
https://arxiv.org/pdf/1611.10012.pdf
• Marker8shapes8indicate8meta<architecture8
• Colors8indicate8feature8extractor.8
• Each8(meta<architecture,8feature8
extractor)8pair8can8correspond8to8multiple8
points8on8this8plot8due8to8changing8input8
sizes,8stride,8etc.

10. Fowl%Language:%AI%Decodes%the%Nuances%of%Chicken%“Speech”
https://www.scientificamerican.com/article/fowl3language3ai3decodes3the3nuances3of3chicken3ldquo3speech3rdquo/

11. Availability)heuristic, Attentional)bias, Illusory)truth)effect, Mere)exposure)effect, Context)effect,)Cue9dependent)forgetting,)Mood9congruent)memory)
bias, Frequency)illusion,)Baader9Meinhof)Phenomenon, Empathy)gap, Omission)bias, Base)rate)fallacy,)Bizarreness)effect,)Humor)effect, Von)Restorff)
effect, Picture)superiority)effect, Self9relevance)effect, Negativity)bias,)Anchoring,)Contrast)effect,)Focusing)effect, Money)illusion, Framing)
effect, Weber–Fechner)law, Conservatism, Distinction)bias,)Confirmation)bias, Congruence)bias, Post9purchase)rationalization,)Choice9supportive)
bias, Selective)perception, Observer9expectancy)effect,)Experimenter’s)bias,)Observer)effect,)Expectation)bias, Ostrich)effect,)Subjective)
validation, Continued)influence)effect, Semmelweis)reflex,)Bias)blind)spot, Naïve)cynicism, Naïve)realism,)Confabulation, Clustering)
illusion, Insensitivity)to)sample)size, Neglect)of)probability, Anecdotal)fallacy, Illusion)of)validity, Masked)man)fallacy,)Recency)illusion, Gambler’s)
fallacy, Hot9hand)fallacy, Illusory)correlation,)Pareidolia, Anthropomorphism,)Group)attribution)error, Ultimate)attribution)error, Stereotyping,)
Essentialism, Functional)fixedness, Moral)credential)effect, Just9world)hypothesis, Argument)from)fallacy, Authority)bias, Automation)bias,)
Bandwagon)effect, Placebo)effect,)Halo)effect, In9group)bias, Out9group)homogeneity)bias, Cross9race)effect, Cheerleader)effect, Well9traveled)
road)effect, Not)invented)here, Reactive)devaluation, Positivity)effect,)Mental)accounting, Normalcy)bias, Appeal)to)probability)fallacy,)Murphy’s)
Law, Subadditivity)effect, Survivorship)bias, Zero)sum)bias,)Denomination)effect, Magic)number)7+92,)Curse)of)knowledge, Illusion)of)
transparency, Spotlight)effect, Illusion)of)external)agency, Illusion)of)asymmetric)insight, Extrinsic)incentive)error,)Hindsight)bias, Outcome)
bias, Moral)luck, Declinism, Telescoping)effect,)Rosy)retrospection, Impact)bias, Pessimism)bias, Planning)fallacy, Time9saving)bias, Pro9
innovation)bias, Projection)bias, Restraint)bias, Self9consistency)bias,)Overconfidence)effect, Egocentric)bias, Optimism)bias, Social)desirability)
bias, Third9person)effect, Forer)effect,)Barnum)effect, Illusion)of)control, False)consensus)effect, Dunning9Kruger)effect, Hard9easy)effect, Illusory)
superiority,)Lake)Wobegone)effect, Self9serving)bias, Actor9observer)bias,)Fundamental)attribution)error, Defensive)attribution)hypothesis, Trait)
ascription)bias,)Effort)justification, Risk)compensation,)Peltzman)effect,)Hyperbolic)discounting, Appeal)to)novelty, Identifiable)victim)effect,)Sunk)
cost)fallacy, Irrational)escalation,)Escalation)of)commitment, Loss)aversion, IKEA)effect,)Processing)difficulty)effect, Generation)effect, Zero9risk)
bias, Disposition)effect, Unit)bias, Pseudocertainty)effect, Endowment)effect,)Backfire)effect,)System)justification, Reactance, Reverse)
psychology, Decoy)effect, Social)comparison)bias, Status)quo)bias,)Ambiguity)bias, Information)bias, Belief)bias, Rhyme)as)reason)effect,)Bike9
shedding)effect,)Law)of)Triviality, Delmore)effect, Conjunction)fallacy,)Occam’s)razor, Less9is9better)effect,)Misattribution)of)memory,)Source)
confusion, Cryptomnesia, False)memory, Suggestibility, Spacing)effect,)Implicit)associations,)Implicit)stereotypes,)Stereotypical)bias, Prejudice,)
Negativity)bias, Fading)affect)bias,)Peak–end)rule, Leveling)and)sharpening, Misinformation)effect,)Duration)neglect, Serial)recall)effect,)List9length)
effect, Modality)effect,)Memory)inhibition,)Part9list)cueing)effect, Primacy)effect, Recency)effect,)Serial)position)effect, Suffix)effect,)Levels)of)
processing)effect, Testing)effect, Absent9mindedness, Next9in9line)effect, Tip)of)the)tongue)phenomenon, Google)effect
https://betterhum ans.coach.m e/cognitive5bias5cheat5sheet555a472476b18
A"few"human"biases…..

12. AI#Adoption#Challenges
• 80#20%Rule%of%adoption1:#Chasm#between#
ambition#and#adoption#
• Low#visibility#of#where#to#apply#AI
• High#expectations
• Shortage#of#talent
• Fear#of#AI#(job#loss,#new#learning,#etc.)
• Tech#challenges
• Costs
Pioneers# (Understood+and+Adopted)+1
Investigators(Understood+but+in+Pilot+stage)
Experimenters(Piloting+without+deep+understanding)+
Passives(No+adoption)
1M IT#Sloan#M anagem ent#Review;#https://www.bcg.com /Im ages/Reshaping%20Business%20with%20Artificial%20Intelligence_tcm 9Q177882.pdf
“Don’t#invest#in#AI.#We#don’t invest#in#natural#
language#processing. Don’t#invest#in#image
analytics.#Invest in#a#business#problem.”

13. 8"Critical"questions"for"CXOs
1. AI%ready%
2. Opportunities%/%Challenges
3. Obstacles
4. Awareness
5. Capability
6. Data
7. Plan
8. Help

14. • Are%you?%
• Committed?%
• Do%you%know%how%to%do%it?%
• What%are%you%missing%without%AI?%
• Have%listed%the%reasons%why%you%want%to%do?%
• Regulations?%
• Culture%of%organization%is%supportive%of%changes?
• Why%do%you%want%to%get%AI%
• Reasons,%Challenges,%Benefits%
• What%do%you%want%to%accomplish
• Competition?%Revenue?%Customer%value?%
Efficiency?
AI#ready
• How%do%you%want%to%go%
there
• AI%strategy
• Gap%analyses,%
• Priorities%of%focus%
areas%
• Barriers%to%change%
• Org%change%
• Train%people%
• Acquire%tools%
• Talent%hiring%
• Change%management%
• Metrics

15. M IT$Sloan$M anagem ent$Review1;$https://www.bcg.com /Im ages/Reshaping%20Business%20with%20Artificial%20Intelligence_tcm 9F177882.pdf
• Is$the$organization$aware$of$
‘how’$and$‘what’$of$AI?
Awareness

16. • Don’t'know'what'to'collect?
• Do'you'have'the'data?'
• Incomplete?'
• Large?'
• Diverse'type?'
• Diverse'sources'(machines,'mobiles,'man)?'
• Sparse?'
• Erroneous?'
• Distributed'storage?'
• Data'pipelines?'
• Streaming'data?'
• Data'ownership'(proprietary'data):'Do'you'have'it
• Where'to'get:'Orgs'that'own'may'have'little'incentive'to'
make'it'available'
• Data'is'fragmented'across'data'sources'or'orgs'
• ownership'of'important'data'may'be'uncertain'or'contested.'
• Data'privacy,'data'access'permissions'and'data'privacy'
rules
• Regulatory'requirements
• Negative'data'is'almost'never'published
Data
https://hbr.org/2017/09/only434of4companies4data4meets4basic4quality4standards
Only%3%%of%Companies’%Data%Meets%Basic%Quality%Standards%< HBR

17. Ref:%https://www.kaggle.com/surveys/2017
Obstacles:*Kaggle*survey*on*AI*state*of*affairs

18. • What&is&the&plan?&
• Risks?&
• SWOT?&
• Schedules,&effort&and&budget?&
• Geographically&distributed&groups?
• Role&play&as&start>ups,&Internet&companies,&
and&direct&competitors&to&get&ideas
Plan
• External)help?)
• Consultants?)
• Industry)Partners)/)consortium)/)
collaboration)etc.?
Help
• Middle)management)black)hole
• Is)there)a)buy>in)for)AI?)Are)they)scared?
• Job)loss
• Budget)constraints)for)other)departments
• Scared)of)centralization)of)decision)making?
• Culture
Change+Management• Do)you)have)teams)that)can)do)it?)
• Tools)and)Frameworks)identified?)
• Procured?)
• Current)infrastructure?)
• Compatible)tools?
• Centralized)vs)distributed)capability)
acquisition
Capability

19. https://www2.deloitte.com/us/en/pages/deloitte5analytics/articles/cognitive5technology5adoption5survey.html
What%to%focus
• Helping)Humans)(sift)through)vast)data6)speed)of)response)
• Loan)applications
• Fraud)detection
• Patient)diagnosis)from)reports
• Predict)the)right)stock
• Send)timely)discount)coupon
• Automating)Ops
• Detect)malware
• Shipping)options
• Ad)placement)dates
• Energy)consumption)in)data)centers
• Network)routing
• Placing)block)trades)in)stock)purchases
• Truck)routes)
• AutoGreplacement)of)servers)in)data)centers
• Make)products)smarter
• Suggest)the)right)movie
• Self)driving)autos
• Detect)Sentiment
• Develop)new)great)products
• Voice)assistants
• AutoGemail)reply
• DangerGcomprehending)Surveillance)cameras
AI:$Primary$benefits$to$companies

20. How$to…
Hexagon’s*master*approach
https://www.capgemini.com/wp6content/uploads/2017/09/dti6ai6report_final1.pdf
Step*1
Step$2
AI$Sprint

21. Checklist (in$addition$to$commonsense$change$management$ideas)
• AI$First$motto
• AI$health$check$of$infra,$skills,$processes
• "Brace$for$impact”$(failures$are$possible)$
• Chief$AI$/$Data$Science$/$Digital$Officer$/$Cognitive$Capability$Officer4
• AI$awareness$across$the$org.$
• Central$coordination$of$AI$movement$(preferably$from$higher$echelons)
• Multiple$pilots$– Jeff$Bezos5
• No$need$for$experimentation$(of$AI$projects)
• Take$external$help
• Customer$education$(example,$a$Patient$needs$to$be$assured$of$AI$results,$a$stock$investor$needs$evidence,$etc.)
• Privacy$and$Regulatory$compliance$(example,$who$is$“reading”$your$emailsU$GDPRU$)
• Keep$an$eye$on$your$organization$“Tay1”$projects$or$“Gorilla2”$projects$(especially$for$B2C$companies)
• Simultaneous$MultiZpronged$objectives:$
• Processes$(work$on$internal$processes,$customer$facing$and$Business$objectives)$(Example:$Pfizer3)
• Size$of$projects$(Low$hanging$fruits,$businessZimpacting)
• AI$talent$(train$internal,$hire$talent$from$market,$engage$consultancy$firms)
1https://www.theverge.com/2016/3/24/11297050/tay;microsoft;chatbot;racist
2https://www.usatoday.com/story/tech/2015/07/01/google;apologizes;after;photos;identify;black;people;as;gorillas/29567465/
3,4https://www2.deloitte.com/us/en/pages/deloitte;analytics/articles/cognitive;technology;adoption;survey.html
5https://www.cnbc.com/2017/05/08/amazon;jeff;bezos;artificial;intelligence;ai;golden;age.html

22. Thank&you
SK&Reddy
AI#blogs#at:#########skreddy99
YouTube#AI#Channel#at:##########skreddy99