160 pa ne na leydt iwc shaenndbsuasdi nlyecsas llleeaddiet rdsi gt oi toakl . b a by s t e p s i n v i s u a l Sa no ,dwbhuesri ne ews si l li nt hn eo vi na nt oorvsawt i oi l nl scpoamr ke nf reowmp? rNo ej ewc tsst aa nr td- u p s ideas. Initially, it will be slow (like an awkward waltz). The pace will then accelerate. Perhaps a boogie? What Is AI? Why Does It Matter? Let’s start with a clear definition. I define Artificial in - telligence (AI) as the computing ability to perform tasks rl eeaqruni irni ng ga hn ud mA aI na ri en tdeilflfiegreenncte: ma nadc hdiins ce el rena mr nei nn gt . iMs aa cwh ianyet o tseaamc he . tMh ea cchoimn ep ul etaerr.nAi nI ga ni sdammaecahni ns et ol eaanr neinndg. a r e n o t t h e The road forward is bumpy. AI outputs, like human think- ing, are often flawed. Within an organization, political bias can consume computer thinking. How wrong and biased depends on the inputs and the refinement of the mt h oe dseple. eCdo notfrcool lmi npguet ri nr og r. Ba un cdkbl ei aysoius ri ms epaot br teal nt : t AbIewc ai lul speuot f shue pl ppi lnygcmh aoi dn edl se cs iosri ot nt h- mr oaukgi hn gi noanc csut er raoc iydas n. Td hbei apsr. o b l e m i s The general AI models like ChatGPT are the buzz, but the most significant opportunity for the supply chain leader irso wi n At hI ei sneanr raobwl i nAgI iwn soi rglhdt sd rbiavseendboynddeiespp al eraart en idnagt. aN. Ma ry- rr ee mp l ao cr isnegi st rtahdaitt itoenc ha lnoopl ot igmi sitzsaat inodn bwuistihn enseswl ee ar dme or sd ea lrse based on narrow AI but not questioning the taxonomies or the current definitions of planning. My analogy is put - tIianmg apnleeawseednagtintheeininavnesotlmd ejanlot piny.semantic data models, or ontologies, to complement graph deployments. Using
a knowledge graph (versus a relational database model), coof ms epeai nngi eds actaani na ns ae lpyazrea st eh itfat bs liens r. eTlhaet i ok nn sohwi pl e wd geeb gs ri anps the a d ep no ai nbtl se st ht ah te wv iosuuladl ioz taht ei or nw oi sf ec bo ne nuencat bi ol en st ob be tewvei seunadl iaztead . Wcheinaerseaarelosneglfw-aawyafrreo.m superintelligence, where ma- Mt a yl i sftrsi eanrde sailnl aS-ibl iuczozn wVai tlhl euyswe oc ar ks ei nsg. Twhiet hp Vr oebnlteumr efCo ra pmi -e , au ns ed cma sa ensy i os ff emwy ar ne addf ea rr sb, ei st wt heaetnt.hTehwe of er ak roins tshuep pr el ya dc hi naei ns s ofafcatduoreprtsioanreofeaarllaygagdaordptienrdsu. stry where only 3% of manu- What Is the Value? St ou pa dp mo siet twhea ta trhe eocpuernr et on tt hs ue popplpy ocrht auinni tpi el asnanni dn ga rt ae xwo ni l ol i -n g mies and the use of data are legacy. In that case, compa- nies make the first step to drive value in in five areas: Sense and Respond. Today, companies are invest- iwn hg ei nn tnheet yw do or k, cs ot mo ipma np ireosv ea rsee dn rs oi nwg n. Ui nngf oi nr tduantaat ea lnyd, low on insights. The problem is the lack of a seman- tic layer. Traditional ERP and APS technology de - pp lrooyc me ses nl tast ei nn cc rye. aUsnef ot hr teubnua ltlewl yh, icpo emf fpe ac nt iaens ds pe leonndg a t e loanr gwe hbeut hc ke sr ti to wmaaskae gdoeoc di s idoencsi sl iaot ne . wT iht eh oh ui gt hc lcaor si tt yo f inflationary inventories and the need to work down pbeotstte-pr aunsdeenmetiwc worokrkdiantga.capital drive companies to
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