AI’s dis­rup­tive rep­u­ta­tion be­lies its po­ten­tial for pos­i­tive change


Leads Thom­son Reuters’ Toronto-based Cen­tre for AI and Cog­ni­tive Com­put­ing and is head of its global cor­po­rate R&D team

Ar­ti­fi­cial in­tel­li­gence (AI) and ma­chine learn­ing are al­ready chang­ing how we work, how we shop and how we con­nect with each other. But their “dis­rup­tive” po­ten­tial has caused much con­cern. A re­port by McKin­sey Global In­sti­tute es­ti­mates that in about 60 per cent of oc­cu­pa­tions, one-third of the tasks could be au­to­mated – glob­ally. The re­port states that AI can trans­form some busi­ness ac­tiv­i­ties and has the po­ten­tial to fun­da­men­tally change others. How­ever, the AI story is just be­gin­ning to un­fold and we do not yet fully com­pre­hend the po­ten­tial and/ or im­pact of these tech­nolo­gies.

This im­pact, to the ex­tent that it ma­te­ri­al­izes, is mas­sive in terms of how work gets done and what new jobs will be cre­ated in the AI econ­omy. This op­por­tu­nity is driv­ing global in­vest­ments in AI, and gov­ern­ments here in Canada are fund­ing var­i­ous AI ini­tia­tives such as the Vec­tor In­sti­tute and its Su­per­clus­ter Ini­tia­tive. The lead pri­vate-sec­tor fun­ders of Vec­tor rep­re­sent var­i­ous sec­tors in­clud­ing the banks, re­tail­ers, man­u­fac­tur­ers and in­for­ma­tion com­pa­nies. Con­cur­rently, busi­nesses are rac­ing to cre­ate their own AI teams.

As prac­ti­tion­ers who build AIen­abled ap­pli­ca­tions, like most others our ob­jec­tive has never been to dis­rupt how work is done, rather to re­move cus­tomer pain­points, to an­a­lyze com­plex tasks and ask how to achieve more with less while de­liv­er­ing an in­tu­itive user ex­pe­ri­ence. With this in mind, we see three broad types of op­por­tu­ni­ties for ar­ti­fi­cial in­tel­li­gence.

Con­tent au­toma­tion: AI is al­ready driv­ing scale and au­toma­tion in how in­for­ma­tion providers col­lect and ag­gre­gate con­tent, how it’s en­hanced, or­ga­nized and de­liv­ered. Most cre­ative work (con­tent au­thor­ing) re­mains a man­ual and time-con­sum­ing ex­er­cise, but “func­tional writ­ing,” such as con­tracts, will likely be au­to­mated.

Dig­i­tal busi­ness: Dig­i­tal busi­ness is the trans­for­ma­tion of busi­ness ac­tiv­i­ties and mak­ing them data-driven. Ex­am­ples of where ma­chine learn­ing is heav­ily uti­lized in­clude busi­ness in­tel­li­gence and ad­ver­tis­ing. The future where com­pa­nies can per­son­al­ize their mar­ket­ing pro­grams to in­di­vid­ual shop­pers through their loy­alty pro­grams is closer than you think. Once re­tail­ers “know” their cus­tomers they will be able to use dy­namic pric­ing and per­son­al­ized mar­ket­ing to achieve max­i­mum re­turns. The trans­for­ma­tive po­ten­tial of AI with re­gard to sup­ply chain man­age­ment and fore­cast­ing is in­trigu­ing. Imag­ine the ef­fi­cien­cies achieved when man­u­fac­tur­ers can ac­cu­rately pre­dict de­mand.

En­abling knowl­edge work: At a high level, knowl­edge work­ers do three things: (1) they search for in­for­ma­tion. AI is al­ready heav­ily uti­lized in “find” tech­nolo­gies such as search en­gines. The chal­lenge will be to en­able ma­chines to “learn” from a se­ries of ques­tions and to de­liver in­tu­itive con­text-pre­serv­ing ex­pe­ri­ences while tack­ling in­for­ma­tion overload. (2) As work­ers find in­for­ma­tion they try to an­a­lyze it. Tools al­ready ex­ist to an­a­lyze and process data, but the real op­por­tu­nity is the evo­lu­tion from sta­tis­ti­cal anal­y­sis to deeper un­der­stand­ing of doc­u­ments and data sets. Fi­nally, (3) knowl­edge work­ers make de­ci­sions. To date, AI has been limited to play­ing a de­ci­sion-sup­port role. While likely a good thing, there are many in­stances where real-time re­quire­ments do not al­low a hu­man in the loop, such as fraud de­tec­tion.

The op­por­tu­ni­ties are sig­nif­i­cant. How­ever, in the near term we should ex­pect broad yet in­cre­men­tal change. Bos­ton Con­sult­ing Group sur­veyed more than 3,000 com­pa­nies last year and found that while 85 per cent of them be­lieved AI would be­come a com­pet­i­tive ad­van­tage in the future, only a quar­ter were im­ple­ment­ing it now, and only 5 per cent were im­ple­ment­ing it ex­ten­sively.

What we do know is that big data, com­put­ing power and con­nec­tiv­ity are chang­ing the in­dus­trial land­scape. The vol­ume and di­ver­sity of in­for­ma­tion con­tin­ues to in­crease. At Thom­son Reuters, we process more data in a sin­gle day than we did in a month just five years ago. The ex­plo­sion of in­for­ma­tion is in­creas­ing the de­mand for au­toma­tion and AI. The op­por­tu­nity rests in ac­cel­er­at­ing the dig­i­ti­za­tion of busi­nesses, mak­ing them more data driven by build­ing ap­pli­ca­tions that de­liver ma­chine-as­sisted in­sights.

We also know AI works best when com­bined with hu­man ex­per­tise, to aug­ment us, not to re­place us. This re­quires in­tu­itive, task-fo­cused and user-cen­tric AI ap­pli­ca­tions and work­ers trained in how to use AI tech­nol­ogy.

Will AI dis­rupt the work force? Change, yes. Dis­rupt? Not yet. Yes, some­time in the future, when driver­less cars are com­mon­place, there will be no need for taxi and bus driv­ers – but un­til then, the op­por­tu­nity and the chal­lenge for com­pa­nies will be to iden­tify where AI can solve the real prob­lems of to­day that of­fer a com­pet­i­tive ad­van­tage. But the tech­nol­ogy will only be part of the so­lu­tion. The ex­tent that we are ready for the change and en­able our work­ers to thrive through this change may be the big­ger chal­lenge.

Big data, com­put­ing power and con­nec­tiv­ity are chang­ing the in­dus­trial land­scape.

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