AI has finally come of age, and the race is on

Technology

Despite the hype around Arti­fi­cial Intel­li­gence today, the con­cept of AI has been around for decades. The Log­ic The­o­rist, the first com­put­er pro­gramme to repli­cate human prob­lem solv­ing skills, was intro­duced in the US back in 1955.

And yet, until recent­ly we treat­ed AI as the stuff of sci­ence fic­tion, ref­er­enc­ing it in futur­is­tic films or pit­ting com­put­ers against chess grand­mas­ters to enter­tain our­selves.

This has all changed in the past few years. Arti­fi­cial Intel­li­gence has sud­den­ly emerged as one of our most trans­for­ma­tion­al tech­nolo­gies, with busi­ness­es in every indus­try invest­ing in AI to get clos­er to cus­tomers and gain more con­trol over their data.

This sec­ond point is cru­cial. The rea­son AI has final­ly hit the main­stream is that brands now col­lect so much data that they need intel­li­gent soft­ware to help them make sense of this infor­ma­tion. The dig­i­tal age is rel­a­tive­ly young, and even 10 years ago com­pa­nies were bare­ly col­lect­ing enough data to jus­ti­fy invest­ing in AI or Machine-Learn­ing algo­rithms. Today, busi­ness­es live and die by the bil­lions of data points at their dis­pos­al and the insight they can draw from this infor­ma­tion.

Com­put­ing pow­er has also caught up to the promise of AI. It’s no small feat to analyse huge vol­umes of infor­ma­tion in real-time, espe­cial­ly when it’s col­lect­ed in var­i­ous forms from an end­less array of sources. Only recent­ly have we devel­oped tech­nolo­gies that allow us to man­age all this data effec­tive­ly and bring con­text to our deci­sion-mak­ing.

Brands are feel­ing a real sense of urgency around AI. Research by Adobe reveals that 88% of com­pa­nies are on track to use AI for cus­tomer or busi­ness ana­lyt­ics by 2020. This is an aggres­sive time­line for imple­men­ta­tion, which shows busi­ness­es appre­ci­ate that they have entered a fourth indus­tri­al rev­o­lu­tion where mass per­son­al­i­sa­tion will be an impor­tant dif­fer­en­tia­tor.

This sense of urgency is ampli­fied by the suc­cess of dis­rup­tive com­pa­nies such as Spo­ti­fy (in the music space) or Stich­Fix (in the fash­ion sec­tor). Both com­pa­nies are using AI to re-imag­ine the dynam­ic between cus­tomers and their offer­ing, and turn­ing two well-estab­lished indus­tries on their head. Lead­ing busi­ness­es are think­ing more like sub­scrip­tion providers, putting peo­ple at the cen­tre of their deci­sion-mak­ing and deliv­er­ing expe­ri­ences in a way that pro­motes loy­al­ty.

This rais­es three chal­lenges for her­itage play­ers, who must find a way to inno­vate more quick­ly while also ensur­ing their approach is sus­tain­able:

  • Chal­lenge 1: Hav­ing access to the RIGHT data, and the RIGHT to use that data

Brands need access to infor­ma­tion that will tell them some­thing valu­able about cus­tomers, which is why many are tak­ing back con­trol of their data. The race for dif­fer­en­ti­a­tion com­bined with a reg­u­la­to­ry push for greater trans­paren­cy into data prac­tices will force brands to recon­sid­er what data sets they real­ly need and see own­er­ship shift back in-house over the next few years.

  • Chal­lenge 2: Gain­ing a sin­gle cus­tomer view

Com­pa­nies under­stand that a key step in mak­ing the most of AI is to inte­grate all their data onto a sin­gle cen­tral plat­form. After years of rely­ing on dis­parate process­es and sys­tems, it has become clear these bar­ri­ers need to come down if they want to devel­op com­plete cus­tomer view. The more data AI soft­ware has to work with, the more informed its analy­ses and the more accu­rate its rec­om­men­da­tions for how to per­son­alise expe­ri­ences for indi­vid­ual cus­tomers.

  • Chal­lenge 3: Build­ing an AI-cen­tric skillset

With­out the skills to work with data and AI, com­pa­nies have lit­tle hope of suc­ceed­ing. Brands need to build data lit­er­a­cy across their organ­i­sa­tion so peo­ple under­stand what’s pos­si­ble with AI and speak in a com­mon lan­guage. Encour­ag­ing­ly, they are hir­ing and train­ing staff in equal mea­sure to close their skills gap. Data sci­en­tists will help with lead­ing-edge tech­nol­o­gy needs, but it is the inter­nal teams who best under­stand the busi­ness and what cus­tomers want.

AI is bring­ing about rapid change and will increas­ing­ly enhance the way we work. It’s not a case of peo­ple vs machines, as many have spec­u­lat­ed. When done right, AI aug­ments our poten­tial. Take the health­care sec­tor, where it’s been found that machines are high­ly adept at iden­ti­fy­ing tumors in patient scans while humans are much bet­ter at deter­min­ing whether these are can­cer­ous. This is a win-win sce­nario, not only mak­ing diag­noses more effi­cient but also ensur­ing patients receive a high­er qual­i­ty of care.

The same is true in the world of mar­ket­ing and adver­tis­ing. AI helps brands inno­vate and work more effi­cient­ly, which in turn makes more space for greater cre­ativ­i­ty. This means they can deliv­er a bet­ter qual­i­ty of expe­ri­ence to their cus­tomers, and con­tin­ue to improve as people’s needs evolve. The race is on, and ear­ly adopters are already begin­ning to pull ahead.

Check out my blog to learn more about AI and Machine Learn­ing and see how lead­ing brands are adapt­ing to this new indus­tri­al rev­o­lu­tion, and check out Adobe’s AI readi­ness tool to see how you com­pare to your indus­try peers when it comes to being AI ready.


Technology
Bernard Marr

Posted on 08-11-2018


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