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INTRODUCTION 1
The Essential CIO Guide to AI
Index
Chapter 1: Why is AI trending now?
CIO: “Why is my CEO asking me this?”
TheevolutionoftheCIOrole
SurfingtheWaves
Chapter2:AframeworkforunderstandingAI
FormulatingaFrameworkforAI
AI≠Machines>Humans
AI≠BestAlgorithm
AI=TD+ML+HITL
ImportanceofHuman-in-the-loop
Chapter 3: Applying AI to your business
EngagingtheBusiness
FocusonOutcomesnotTechnologies
Explaining AI
SystemsofRecordwithUnstructuredData
EvaluatingyourVendors
Conclusion
Introduction
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5
7
8
14
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20
23
25
27
29
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32
36
3
ThetopicofAIhasreachedsuchafeverpitchin
the mediawiththecoverageofdriverlesscars,
conversationalbots and even movies made by AI that
it’sonlyamatteroftimebeforeeveryCEOstartsasking
their CIO “What’s our AI strategy.” For many CIOs this
willbea“deerintheheadlights”momentsincethe
topicofAIissomulti-facetedit’shardtoknowwhereto
start.Weputtogetherthise-bookasaprimerforCIOs
wantingtogettogripswiththetopicofAI.
Introduction
INTRODUCTION 3
Inthise-book,westartbygivingsomeinsightand
contextintowhyyourCEOisaskingthisquestion,why
now,andwhyyou.Then,wewillgiveyouafoundational
frameworktothinkaboutAIsoyoucangiveyourCEO
athoughtfulresponse.Finally,wewilldiscusshowyou
asCIO,canengagethebusinessonthetopicofAIand
importantconsiderationswhenevaluatingAIvendors.
INTRODUCTION 4 4
Why is AI trending now?
C H A P T E R 1
WHY IS AI TRENDING NOW? 5
So, why is the CEO asking you this now? CEOs are
humanstooandtheyreacttotheirenvironment.Their
environmentisoftendominatedbyotherCEOs,their
board,andtheoutsideworld.AIasatopichasrisentothe
boardroomandthepopularpresswithevenVanityFair
recentlypublishinganarticletitled“Suddenly Everyone is
Obsessed with AI.”SoifyourCEOhasn’tbroachedtheAI
topicyet,theysoonwill.
CIO: Why is my CEO asking me this?
AIisadisruptivetechnology.AccordingtoClayton
Christensen,authorofTheInnovator’sDilemma,a
disruptivetechnology“enablesnewmarketstoemerge”
anddisruptsanexistingmarket.
YourCEO’ssuccessdependsonhowwelltheyanticipate
thesetechnologydisruptions,andusethemtoyour
company’sadvantage.Attheendoftheday,your
companywilleitherbepartoftheneweconomythat
arisesfromthedisruption,orpartoftheoldorderthat
fadesintooblivion.AndifAIisthenextbigtechnological
disruptiontohitcompanies,yourCEOwantstobeonthe
rightsideofthisdisruption.
WHY IS AI TRENDING NOW? 6
NowthatwehaveabriefideaastowhyAIis
suddenlybecomingsuchabigdeal,andwhyyour
CEOcaresaboutit,let’slookattheroleoftheCIO
andhowyou’reexpectedtodealwiththisnewtrend.
AsCIOyoumaybethinkingwhyisthisquestion
directedatme?Whyisn’theaskingtheCFO,CTO,
CMO,orCOO?Toanswerthatquestionweneedto
putAIinthehistoricalcontextoftheroleoftheCIO.
A disruptive technology “enables new markets to emerge” and disrupts an existing market.CLAYTONCHRISTENSENAUTHOR,THEINNOVATOR’SDILEMMA
TheEvolutionoftheCIOrole
WilliamSynnott,formerSeniorVicePresidentof
theBankofBoston,andWilliamGruber,former
professorattheMITSloanSchoolofManagement,
firstformallydefinedtheroleoftheCIOin1981.
TheydefinedtheCIOasthejobtitlecommonly
giventothemostseniorexecutiveinanenterprise
responsiblefortheinformationtechnologyandcomputer
systems that support enterprise goals.
Upuntilabout2005theCIOmandatewasclear.
Deploythe6orsokeytechnologiestosupportthe
business:AccountingforFinance,Payroll&Benefitsfor
HR,CRMforsales,CallcentersforSupport,Emailand
Securityforallemployees.TheITfunctioncollectedthe
businessrequirementsandthenselectedanddelivered
theapplication.Itwasverylinear,typicallytook12+
monthsandwouldonlychangeevery5years.
ThisnewCIOrolewasnotconsideredaglamorousor
evenmission-criticalroleinitially.Itwaspredominantly
measuredasacostcenterandatonepointtherolewas
sounappreciatedthatthejokestartedcirculatingthat
CIOstoodfor“CareerisOver.”
WHY IS AI TRENDING NOW? 7
SurfingtheWaves
Butlikemanyexecutiveroles,CIOshavehadtoevolve
inresponsetotheirchangingenvironment.ForCIOs
thebiggestchangeintheirenvironmentwasthe
natureoftechnologyavailabletotheenterprise.These
changeswouldappearaswavestotheITfunction,
startingfromfarawaybutgaininginpowerasthey
approached.EitherCIOshadtolearntoridethewave,
orbeoverpoweredbythewave.Upuntilabout2005
therewasjustonewave,whichwasthetransition
frommainframestoclient-servertechnology, but in the
last15yearsthefrequencyandpowerofthewaveshas
increased.AgreatCIOislikeagreatsurfer.Theycansee
thewavefromfarawayandtheycantimehowandwhen
to ride it.
After20+yearsofcalm,glassyseathefirstbigwavehit.
BiggerandfasterthananythingaCIOhadexperienced
previously.Drownorride,thosewereyourchoices.
WHY IS AI TRENDING NOW? 8
WHY IS AI TRENDING NOW? 9
The Three Waves
On premise to Cloud & Mobile & Social
Small Data toBig Data
Unintelligent applications to AI
WHY IS AI TRENDING NOW? 9
The first wave: On premise to Cloud & Mobile & Social Salesforcewasthefirstcloudvendortoreach$1Bin
revenuein2009andpavedthewayforthemainstream
adoptionofcloudapplicationsintheenterprise.
ServiceNowandWorkdayhavesincejoinedSalesforce
inthe“$1billionrevenue”clubforpure-playcloud
applicationscompanies.
Thecloudwavewasalsoamplifiedbythesocialand
mobiletechnologies,whichincreasedthevolumeof
datasharedacrosscompanyboundaries.Thelaunchof
theiPhone,andtheevenmoreprolificAndroidmobile
ecosystemsbeganthemobilerevolution.Atthesame
time,socialnetworkingplatformslikeFacebook,Twitter,
andLinkedInweregainingmassadoptionglobally,
connectingnotjustconsumers,butprofessionals.This
fundamentallychangedthewayenterprisesfunction,and
CIOshadtoadaptaccordingly.
This cloudwaveprofoundlychangedtheroleoftheCIO
intwomajorways.First,ITswitchedfrombeingacapital
costtoavariableoperatingcostwherethepremium
was on speed and agility. This stressed the IT org that
hadbeenbuiltforstabilityandgovernance.Ratherthan
planforperiodicserverhardwareandlegacysoftware
upgrades,CIOsshiftedtoSaaStoolstosupporttheir
businessfunctions,andtopubliccloudvendorstorent
serversandcomputingresourcesbythehour.
Second,theenduserprovisioningofcloudandBYOD
mobiledynamicchangedthebalanceofpower.Previously,
IT would tell employees what they would use. Now,
employeeschoosewhatappstheywanttouseand
bypassIT.Ratherthandealwithjust6-8majorbusiness
applicationsfortheentirecompany,theynowhadto
supportatleasta10Xincreaseinapplications.Asan
exampleoftheexplosivegrowthjusttheMarketing
functionhad4,000cloudapplicationsavailabletothem.
WHY IS AI TRENDING NOW? 10
TheCIOhadtodevelopnewpoliciesandframeworks
toco-optratherthanignorethistrend.JustastheCIOs
werecatchingtheirbreath,asecondwavehitthem.
The second wave: Small Data to Big Data
TheconstantduringthefirstwavewasthatIT’sprimary
responsibilitywasstillenablingbusinessprocesses.This
forcedITtofocusalmostexclusivelyontheapplications
runningunderneaththosebusinessprocesses.Asthe
conceptofthedataitselfbeingvaluableandneeding
managinghitthemainstreamin2011withtheMcKinsey
GlobalInstitutepublicationof“Bigdata:Thenext
frontierforinnovation,competition,andproductivity,”
theroleoftheCIOchangedonceagain.
CIO’shadtobecomefamiliarwiththe5Vs(Volume,
Velocity,Variety,Variability,andVeracity)ofbigdata.
Theyhadtounderstandnewstorageandcompute
approachessuchasMapReduce,Hadoop,Spark,and
Cassandra.Theyhadtobuildanalyticscapabilitiesto
presentthedatatobusinessowners.Thescopeoftheir
worlddramaticallyexpanded.
Thiswaveisstillinmotion.Ithasn’tcrashedonthe
shoreyet.Butonthehorizonthebeginningsofathird
wave are emerging.
The third wave: Unintelligent applications to AI
Duringtheexplosionofthenumberofcloudapplications
beingusedinsideanorganization,theseapplicationswere
stillunintelligent.Namely,theapplicationitselfdidn’tdo
anythingunlessahumantookanaction–respondedto
asupportticket,updatedasalesopportunity–ormaybe
preprogrammedadefinedruletofollow–suchasnurture
aleadbasedontheleadactivities.Theseapplications
werepredominantlybackwardlooking.Theytoldyouwhat
WHY IS AI TRENDING NOW? 11
hadalreadyhappened.Butwiththecollectionofsuch
bigdatasetsfromthepreviouswave,therewasnow
abusinessimperativetogleanmorevaluefromthem.
Thismeantonething.Applicationsneededtobecome
intelligentandpredictwhathadnotyethappened.
ForAItobecomepossibleweneededadvancesin4
areas:dataavailability,processingpower,machine
learningtalent,machinelearningcloudofferings.All4
advanceshaveoccurredinthelast24monthssoAIfor
the enterprise is now possible.
AIrequiresstructureddata.Whilestructureddata
referstotextfilesthatareneatlylabeledwithrowsand
columnsthatcaneasilybeorderedandprocessedby
dataminingtools,80%ofbusinessrelevantinformation
isstillunstructured.Thisincludestexts,e-mails,digital
images,audio,video,andsocialmediaposts.Tomake
thisunstructureddatausefultothetaskathand,you
needhumanstoapplytheirintelligenceincategorizing
andlabellingthedatasoitbecomesstructuredandcanbe
ingestedbyamachinelearningmodel.
AIrequiresmassiveprocessingpower.Thisprocessing
powerisnowavailable,throughadvancesinmulti-corechip
designsandtheapplicationofGPUs.Ascientistworkingon
anAIprogramatGooglefiguredoutthatifyouuseGPUs
insteadofCPUsyoucangetcomputingtasksdonealot
quickerwithalotlessresources.Ittookjust64GPUstodo
thejoboriginallydesignatedfor2000CPUs.
AIrequiresmachinelearningtalentandmachinelearning
platforms.AccordingtoLinkedIn,therearenowatleast
180,000datascientistsinNorthAmerica and the big 5
technologyplayers–Amazon, Google, IBM, Microsoft, and
Salesforce–areactivelytargetingthemwithcloudmachine
WHY IS AI TRENDING NOW? 12
learningofferings.Google’sCEOhasstatedtheyare
now an AI-first(ratherthanmobile-first)companyand
Microsoft’sCEOhasarticulated10rulesforAI.
Thefirstexamplesofintelligenceapplicationsemergedin
marketingandsales,withcompaniessuchasQuantcast
doingpredictivetargetingandEverstring and Infer doing
predictiveleadscoring.Butnowwe’reseeingAIstartto
emergeinfieldsasdiverseasautomotive, healthcare, and
retail.
Sothewavesareappearingonthehorizon.It’sonlya
matteroftimebeforetheyhittheenterprise.Nowthat
youknowwhythewavesareappearing,whatcanyoudo
toprepare?What’stheequivalentoflearningtosurfwhen
itcomestoAI?
Next,wewillgiveyouafoundationalframeworktothink
aboutAIandhowitrelatestoyourbusiness,soyoucan
giveyourCEOathoughtfulresponse.
WHY IS AI TRENDING NOW? 13
A FRAMEWORK FOR UNDERSTANDING AI 14 14
Aframeworkforunderstanding AI
C H A P T E R 2
A FRAMEWORK FOR UNDERSTANDING AI 15
InChapter1,wegavesomeinsightandcontextintowhy
yourCEOisaskingthisquestion,whynow,andwhyyou.
InChapter2,wewillgiveyouafoundationalframework
tothinkaboutAIsoyoucangiveyourCEOathoughtful
response.
InthelastchapterweintroducedthemetaphoroftheCIO
beingabletoridethewavesoftechnologydisruption.
Toavoidbeingknockedoffyourboardandsweptoutto
sea,firstyoumustdevelopasolidconceptualframework
forunderstandingAI.Onceyouhavethat,youcanengage
withthebusiness,andevaluateyouroptionsforworking
withvendorsprovidingAIsolutions.
Formulating a Framework for AI
Let’sstartoffwithsomedefinitionsofwhatAIisnot.
ThisisnecessarybecausethemediacoverageofAIisnot
thoughtfulbutoftenhyperbolic.
AI ≠ Machines > Humans
For the last 30 years the media has loved to portray AI
asthereplacementofhumansbymachines,whether
it’sSchwarzeneggerintheTerminatororAliciaVikander
in ExMachina.ThisisthewrongmentalmodelforAIin
theenterprise.Therightframingishowcanmachines
augmenthumans,notreplacethem.Eventherecentthe
mediacoverageofGoogle’sDeepMind/AlphaGovictory
overLeeSedolwassimplisticallyportrayedasmachine
defeatshuman.Themoreaccuratedescriptionwouldbe
machineplusmanyhumansdefeatssinglehuman.
Machineshaveadvantagesthathumansdonot:speed,
cost,andconsistency.Humanshaveadvantagesthat
machinesdonot:taskcomplexityandbreadthoftask
capability.Thechallengeistofindtherightwaytoblend
humansandmachines,notreplacehumanswithmachines.
Asareminderthatmachinesaren’treadytotakeoverfrom
humansjustyet,anumberofrobotsinDARPA’srobotics
challenge last year struggled to open a door.
The more accurate description would be machine plus many
humans defeats single human.
M Y T H # 1
A FRAMEWORK FOR UNDERSTANDING AI 16
AI ≠ Best Algorithm
For many people the terms AI and algorithm are
synonymous.ThebestalgorithmcreatesthebestAI
solution.Facebookhasthebestnewsfeedalgorithm,
Netflixhasthebestmovierecommendationalgorithm,
andGooglehasthebestadplacementalgorithm.
Wethinkthisisincomplete.AIandalgorithmare
notsynonymousterms.Algorithmsareanecessary
componentofAI,butnotadefiningone.Manyleading
expertssuchasAlexanderWissner-Grossnowclaim
data–andnotalgorithms–arethekeylimitingfactor
todevelopmentofhuman-levelartificialintelligence.
M Y T H # 2
A FRAMEWORK FOR UNDERSTANDING AI 17
HereviewedthetimingofthemostpublicizedAI
advancesoverthepast30years,withtheevidence
suggestingmanymajorAIbreakthroughshaveactually
beenconstrainedbytheavailabilityofhigh-quality
training datasets.
Tofurtherillustratethis,let’stakealookatsomeof
the researchpublishedbyGoogleonartificialneural
networks(ANN)thatuselearningalgorithms.Google
wastraininganartificialneuralnetwork–acomputational
modelthatdrawsinspirationfromhowbrainswork–in
imageclassification.Oneofthetaskswasidentifying
dumbbells. The ANN was trained by showing them many
examplesofwhatGooglewantedthemtolearn,hoping
theyextracttheessenceofthematterathand(e.g.,a
dumbbellneedsahandleand2weights),andlearnto
ignorewhatdoesn’tmatter(adumbbellcanbedifferent
weights,sizes,colors,ororientation).
A FRAMEWORK FOR UNDERSTANDING AI 18
SOURCE:SPACEMACHINE
UnfortunatelytheANNfailedtocompletelydistillthe
essenceofadumbbell.
Maybeit’sneverbeenshownadumbbellwithoutan
arm holding it. These mistakes highlight that we need
tobecarefulbeforecompletelyremovinghumansfrom
theprocess.Ahumancanimmediatelyseethemistake
whileamachinecannot.ThiselementiscalledHuman-
in-the-loop(HITL).
Sothisbringsustoamorecompleteworking
definitionofAIfortheenterprise.
A FRAMEWORK FOR UNDERSTANDING AI 19
WebelievethisistheessentialequationthattheCIO
needstounderstandifAIistobeacommercialsuccess
inside an enterprise. So, let’s break it down and imagine
acompanyistryingtocreateanAIsolutionthatcan
categorizecustomersupportticketsbyseveritylevelbased
ontheunstructuredtextshowinganexchangebetween
acustomerandacustomersupportrepdiscussinga
particulartopicorproblemwithinthesupportticket.
TD isTrainingData.TrainingDataisasetofinputswiththecorrectoutputsorexamples
withthecorrectlabelsthatcanbeusedtotrainthe
machine.Inthisexample,theinputistheunstructured
textinsideasupportticket.Theoutputoransweristhe
label“severitylevel,”whichhasbeenappliedbyhumans
accordingtodefinitionsofseveritylevelsspecifictothe
companyinquestion.Anautomotivemanufacturerwill
wanttodefinetheseseveritylevelsdifferentlyfroma
retailbankerorawearabletechnologycompany.
AI = TD + ML + HITL
Training Data Machine Learning Human-in-the-loop
AI = TD + ML + HITL
A FRAMEWORK FOR UNDERSTANDING AI 20
ML is Machine Learning.TheMachineLearning
capabilityistheabilitytoconvertTraining
Dataintoapredictivemodelthatcanbeappliedtonew
inputs–inthiscasenewsupportticketswithunstructured
text.YouwanttheMachineLearningmodeltoapplyits
predictivepowertocreatenewoutputs–inthiscasethe
“severitylevel”label.Oneoftheadvantagesofmachines
comparedtohumansistheirabilitytounderstand
theirownconfidencelevel.Humansarenotoriously
overconfidentatevaluatingtheirownjudgments. So you
canacceptorrejectthepredictionbasedontheMachine’s
ownassessmentofitsconfidencelevel.Forexample,ifa
supporttickethaswordsandphrases,whichhaven’tbeen
seeninthetrainingdata,orseenveryinfrequently,then
themachinewillobjectivelyassessitsownconfidence
levelasbeinglowforthatparticularprediction.
AI ≠ Machines > Humans
M Y T H
M Y T H
T R U T H
AI ≠ Best Algorithm
AI = TD + ML + HITL
A FRAMEWORK FOR UNDERSTANDING AI 21
HITL is Human-in-the-loop. This is the
criticalthirdcomponentofcommercially
viableAI.IftheMachineLearningmodelisnotconfident
initspredictionitcanrouteittohumanstoreviewand
answer.Inthisblendedmodel,youtakeadvantageofthe
speedandscaleofMachineLearningtoaddresstheless
difficulttasks,whilethehumanshandlethehardertasks.
Theresult–anautomatedbusinessprocessfasterand
moreaccuratethanhumansormachinesalone.Humans
andmachinesarebettertogether.
A FRAMEWORK FOR UNDERSTANDING AI 22
Earlierthisyear,Facebookfacedcriticismsthatits
Trendingfeaturewassurfacingnewsstoriesbiasedagainst
conservatives.Inresponsetothiscriticismthecompany
firedallthehumaneditorsforTrending,replacingthem
withanalgorithmthatpromotesstoriesbasedentirelyon
whatFacebookusersaretalkingabout.
Importance of Human-in-the-loop ImagineaDataScientistgoingtotheVPCustomer
Supportandsaying“Ihaveamachinelearningmodel
thatisright70%ofthetime.Ithinkweshould
deployitintoproductionforclassifyingoursupport
ticketsandstopusinghumans.”TheVPCustomer
SupportwilllaughattheDataScientistandsay,“I
cannotaffordtobewrong30%ofthetime.SoI
can’tuseyourmodel.”
Sohowcancompaniesmovebeyondthisimpasse?
ThesolutionisanapproachcalledHuman-in-the-
loopwherethemodelhandlespredictionswhere
it’sconfidentbuthandsoffpredictionsforhuman
reviewwhereit’snotconfident.Ifyoudeploy
MachineLearningwithoutHuman-in-the-loopthen
youaresayingyouhave100%confidenceinallthe
predictionsfromthemodel.Ifyoudothis,youwill
haveavoidablebadoutcomes.
A FRAMEWORK FOR UNDERSTANDING AI 23
MACHINELEARNINGWITHOUTHUMAN-IN-THE-LOOPLEADSTOBADOUTCOMES
Within72hours,accordingtotheWashingtonPost,the
topstoryonTrendingwasafakestoryabouthowFox
NewsiconMegynKellywasapro-Clinton“traitor”who
hadbeenfired.AHuman-in-the-loopapproachwould
havepreventedthisobviouslyflawedoutcome.
IfyouapplyHuman-in-the-loopthenyouhavestarted
toautomateabusinessprocess.Initiallythatbusiness
process–sayclassifyingsupporttickets–is100%human.
ThentheMachineLearningmodelhandlesthehigh
confidentcases,whichismaybe10-20%ofthevolume,
buthumansstillhandlethevastmajoritybecausethe
modelisnotyetconfidentenough.Overtimethemodel
continuestoingestnewtrainingdata–thehumanoutput
–andbecomesmoreaccurateandmoreconfident,so
thepercentageofworkdonebythemodelincreases.
Additionally,thevolumeofworkthatcanbehandledby
thissemi-automatedprocessdramaticallyincreases.
AIismachinesaugmentinghumans,notreplacinghumans.
Inthethirdandfinalchapter,wewilltalkabouthow
to engage the business and think through important
considerationsforevaluatingvendors.
A FRAMEWORK FOR UNDERSTANDING AI 24
Machine → High Confidence PredictionsHuman → Low Confidence Predictions
A FRAMEWORK FOR UNDERSTANDING AI 25 25
Applying AI toyour business
C H A P T E R 3
APPLYING AI TO YOUR BUSINESS 26
Inthefirstchapter,wegavesomeinsightandcontextinto
whyyourCEOisaskingthisquestion,whynow,andwhyyou
theCIO.Inthesecondchapter,wegaveyouafoundational
frameworktothinkaboutAIsoyoucouldgiveyourCEOa
thoughtfulresponse.Inthischapterwewilldiscusshowyoucan
engagethebusinessonthetopicofAIandconsiderimportant
criteriawhenevaluatingAIvendors.
APPLYING AI TO YOUR BUSINESS 27
Engaging the Business
So now you have the AI=TD+ML+HITLconceptual
modeltoapplytoyourbusiness,it’stimetostart
engagingwithyourexecutivecounterpartsin
marketing,product,sales,andcustomersupport.
Inbusiness,theexplosivegrowthofcomplexand
time-sensitivedataenablesdecisionsthatcangiveyou
acompetitiveadvantage,butthesedecisionsdepend
onanalyzingataspeed,volume,andcomplexitythatis
toogreatforhumans.AIisfillingthisgapasitbecomes
ingrainedintheanalyticstechnologyinfrastructurein
industrieslikehealthcare,financialservices,andtravel.
TosuccessfullyapplyAItoyourbusinessthefirstplace
tolookiswhereverdataisbeinganalyzed.Whenever
dataneedstobeanalyzed,AIistheperfectvehicle
toachievethis.Withcompaniescollectingmassesof
APPLYING AI TO YOUR BUSINESS 28
informationthanksmostlytosocialmedia,makingsense
ofthisinformationandfindingvalueisperfectforAI.
Salesisacommonfunctionofanyenterpriseand
Salesforcepredictsthatnearly60%ofbusiness’sales
teamswillincreasetheiruseofsalesanalyticsthisyear.
Forexample,companiescannowuseanalyticstodecide
whichsalesrepresentativesshouldgetwhichleads,what
timeofdaytocontactacustomer,andwhetherthey
shoulde-mailthem,textthem,orcallthem.
Marketingisanotherfunctionoftheenterprisethatcould
begreatlyenhancedbytheuseofAI.AIisdrivinghuge
changeinthewayenterprisescantargetaudiencesfor
marketingandadvertising-evenforsmallercompanies.
This means that businesses are able to target their spend,
increaseROIandallowadvertisingtodowhatitshould
-givepeopleadvertstheywanttosee.Forexample,in
programmaticadbuyingontheWeb,computersdecide
whichadsshouldruninwhichpublishers’locations.
Massivevolumesofdigitaladsandanever-endingflowof
clickstreamdatadependonmachinelearning,notpeople,
todecidewhichwebadstoplacewhere.FirmslikeDataXu
usemachinelearningtogenerateupto5,000different
modelsaweek,makingdecisionsinunder15milliseconds,
sothattheycanmoreaccuratelyplaceadsthatcustomers
aremorelikelytoclickon.
CustomersupportisalsoripeforanAImakeover,asmany
oftherepetitiveaspectsofcustomerservicescouldbe
handledbyanartificialintelligence.Whetherconsumers
willbehappytospeaktoamachineisanothermatter
entirely,asautomatedswitchboardscontinuetobea
majorpressurepointforconsumerswhencontacting
businessesandorganizations.
APPLYING AI TO YOUR BUSINESS 29
Focus on Outcomes not Technologies
Avoidthetrapoftakingtheconversationwithyour
executivecounterpartsinMarketing,Product,Salesand
CustomerSupporttooquicklyintotechnospeak.Focus
theinitialconversationontheoutcomesthatmatterto
them.Agoodwaytofocustheconversationistostart
with2questions:
1. What are you already doing that you want to do faster/cheaper/better?
2. What are you not currently doing that you want to start doing?
Forexample,youmayhavetheSVPofCustomerSupport
saythats/heisnothappywiththequalityandconsistency
ofhowtheyclassifytheseveritylevelofsupporttickets
sinceit’stheindividualcustomersupportrepsdoing
theclassifyingandtheincentivesaremisaligned.Or
youmighthavetheVPProductsaytheyneedtostart
collectingrelevancedataforsearchqueriesandresults
tounderstandhowwelltheyaremeetingcustomer
demand.OryoumighthavetheCMOsaytheyneedto
startcollectingsentimentdatafromTwittertoestablish
abaselineforhowcustomersviewtheirbrandand
productssotheyarenotflyingblindwhentheyembarkon
expensivecampaigns.
Explaining AI
Onceyou’veestablishedtheoutcomesthatmatterto
yourexecutivecounterparts,youcanthenintroducethe
conceptofAI.Don’tleadwithit.ThegoalisnotAIforthe
sakeofAI,butAIthathelpsthebusinessmeetitsbusiness
objectives.Withtheclarityofthebusinessoutcomes
youwanttohelpdeliveragainst,youcannowintroduce
theconceptualframeworkwiththeAI=TD+ML+HITL
equation.Stepthroughtheequationandtranslatewhat
eachconceptmeanintermstheyunderstandtothem.
APPLYING AI TO YOUR BUSINESS 30
Forexample,iftheCMOwantstostartdoingsentiment
analysisatscalethenwalkthroughthefollowing:
Training Data:DownloadthetweetsfromTwitter
basedonthe#hashtagsand@mentionsthatmatter.
Definethequestionsandsentimentscaleyouwantto
use.Findhumanstoapplytheclassification.
Machine Learning: Createamodelthatcanpredict
sentimentbasedontheTrainingData.Applythe
predictivemodeltonewtweets.Modelassignsa
confidenceleveltoeachindividuallevelsentiment
prediction.
Human-in-the-loop: Usehumanstooverridethe
modelpredictionswithlowconfidence.Addthenew
humanlabeleddatatocontinuetotrainandimprove
the model.
Systems of Record with Unstructured Data
OnceyourexecutivecounterpartsinMarketing,
Product,SalesandCustomerSupporthaveasolid
foundationalunderstandingofhowAIcouldapply
totheirbusinesspriorities,thenyoucanmoveonto
takingstockofthenatureofthedatainthemission-
criticalcustomerandproductsystemsofrecord.
Specifically,youwanttounderstandwhatarethe
sourcesofunstructureddata(text,images,audio,
video)thatifstructuredcouldbetheinitial
training data.
Ifyou’relikethetypicalenterprisethenprobably
yourunstructureddatavolumeis4Xyourstructured
data volume.
APPLYING AI TO YOUR BUSINESS 31
SOURCE:IDCTHEDIGITALUNIVERSE,DEC2012
EXABYTES
APPLYING AI TO YOUR BUSINESS 32
Examples of unstructured data could include:
Tweetsfromcustomersdiscussing
yourproductsorbrand
Customerattributeswrittenintoasales
opportunityinSalesforceSalesCloud
orMicrosoftDynamicsCRM
Productdescriptionsandimagesinyour
productcataloguesystemofrecord
Relevancelabelsinyoursearchalgorithm
Emailconversationsbetweencustomersand
supportrepsinsupportticketsinyourSalesforce
ServiceCloud,MicrosoftDynamicsCRM
or Zendesk
Onceyouhaveunderstoodhowandwhereyourbusiness
iscollectingunstructureddata,andthepathtoturnthat
intostructuredtrainingdata,thenmachinelearningisa
possibility.
Evaluating your Vendors
ThethirdandfinalpartofhowtorespondtoyourCEO’s
question“WhatisourAIstrategy?”afterformulating
aframeworkandengagingthebusiness,isevaluating
yourvendors.Managingvendorriskhasbeenpartofthe
CIO’smandatesincetheroleemerged.Developingthis
competencehasbeenmoreimportantasthenumberof
vendorsexplodedwiththeemergenceofthecloudwave.
Ratherthanrehashallthetypicalconsiderationswhen
evaluatingvendors,let’scalloutthreefactorsworthyofextra
focusspecificallyforvendorsclaimingtodeliverAIsolutions.
APPLYING AI TO YOUR BUSINESS 33
Single Domain vs. General Purpose
SomevendorssuchasWise.iohavechosenasingle
domain(customersupport)whileotherssuchas
CrowdFlower and Sentienthavechosentobuildageneral
purposeplatformthatcanfulfillmanymachinelearning
usecasesfortext,images,audio,andvideodata.
Whileoneapproachisnotinherentlybetterthanthe
other,thereisalong-termtrendanddesireforCIOsto
rationalizethenumberofvendorstheymanagesothere
isaslightbiastowardsageneralpurposeplatform,if(and
it’sabigif)thatgeneralpurposeplatformcanmeetthe
businessneedsofthedifferentfunctions.
A
B
A
B
C
?
Sentiment Analyisis
Data Categorization
Data Validation
Transcription
Search Relevance
Data Collection & Enrichment
Image Anotation
Content Moderation
APPLYING AI TO YOUR BUSINESS 34
Black Box vs. White Box
Withtheemergenceof180,000datascientists
inNorthAmerica over the past 5 years, the
organizationalmodelofwheretheyfitisstill
developing.Somemorematureorganizationshave
aChiefDataOfficerwithacentralizeddatascience
function,butthemorecommonmodelseemstobe
adecentralizedmodelwithdatascientistssprinkled
acrossanorganizationinsidedifferentfunctions.
Inthedecentralizedscenariotherewillinevitablybe
abroadspectrumintheskillsetofthedatascientists
withinasinglecompany.Somewillhaveadvanced
machinelearningbackgrounds,whilesomewillhave
progressedtothedatasciencerolefrombeinga
softwareengineeroradataanalyst.Thosewiththe
advancemachinelearningchopsprobablywon’tbe
satisfiedwithablackboxapproachwhereit’snot
transparenthowanalgorithmworksandtheycan’t
finetunetheparameterweightings.Bearthisinmindwhen
consideringsolutionsthatareblackboxonly.
Point Solution vs. Integrated Platform
ManyvendorsclaimtobeAIsolutions,butasyounow
knowtheyareconflatingAIwithMachineLearning.
CommerciallyviableAIneedsthe3criticalcomponents
ofTrainingData,MachineLearningandHuman-in-the-
looptobeintegratedtogether.Soyouhaveachoice.
Eitherselect2or3differentvendorsforTrainingData,
MachineLearningandHuman-in-the-looporintegrate
themyourself.Evenwithwell-definedRESTfulAPIsthere
isstillacosttodothat.Orlookforasolutionthathas
TrainingData,MachineLearningandHuman-in-the-loop
integratedintoasingleplatform.
Training Data MachineLearnin g
Human-in -the-Loop
CONCLUSION 35
Conclusion
Nowthatyou’rearmedwithanapproachforhowto
engageyourbusinessonthetopicofAI,whywaitforyour
CEOtoask,“What’sourAIstrategy?”Insteadbeproactive
andtellyourCEO“Here’sourAIstrategy.”ThedaysofCIO
meaning“careerisover”arelonggone.It’stimeforthe
bestCIOstoseizetheinitiativeandridetheAIwave.
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