AI is permanently changing how businesses operate

Arti­fi­cial intel­li­gence (AI) is com­plete­ly trans­form­ing the con­tem­po­rary busi­ness land­scape. It’s chang­ing how oper­a­tions are con­duct­ed, deci­sions are made, and growth is achieved across a wide range of industries.

Pre­vi­ous­ly, many busi­ness tasks required the intel­li­gent input of humans. How­ev­er, advance­ments in the last 2 years have result­ed in AI com­put­er sys­tems that are high­ly capa­ble. These sys­tems can learn, make deci­sions, and solve prob­lems – to name just a few abilities!

In the realm of busi­ness, AI’s appli­ca­tion extends to automat­ing process­es that not only stream­line oper­a­tions but also enhance the effi­ca­cy of deci­sion-mak­ing and fos­ter sig­nif­i­cant effi­cien­cies. Inte­grat­ing AI into every­day busi­ness prac­tices is not a pass­ing trend – this is a huge shift that will make busi­ness­es more intel­li­gent, agile, and competitive.

AI and automation

Facil­i­tat­ed and enhanced by AI tech­nol­o­gy, automa­tions are bet­ter than ever before. Busi­ness­es are tak­ing advan­tage, using these tools to exe­cute recur­ring tasks or process­es where man­u­al effort can be replaced or sig­nif­i­cant­ly reduced.

Automa­tions are designed to min­imise costs, increase effi­cien­cy, and stream­line process­es. Their cur­rent appli­ca­tions are numer­ous and high­ly cre­ative. In con­trast to pre­vi­ous automa­tions (that were not enhanced by AI), these new automa­tions bring a lev­el of smart deci­sion-mak­ing and pre­dic­tive analy­sis. Able to learn from out­comes and analyse large vol­umes of data, AI automa­tions are enabling busi­ness­es to cre­ate high­ly opti­mised oper­a­tions, clever strate­gies, and per­son­alised cus­tomer experiences.

Though AI automa­tions are often cost-effec­tive, they don’t scrimp on qual­i­ty. As demon­strat­ed lat­er in a few case stud­ies, the inte­gra­tion of these tools often improves how oper­a­tions are con­duct­ed and result in bet­ter out­comes than if they were han­dled by human staff.

By automat­ing tasks that are both rou­tine and com­plex, AI allows busi­ness­es to focus on strate­gic activ­i­ties that add greater val­ue. This shift not only reduces oper­a­tional costs but also accel­er­ates the pace of inno­va­tion and growth.

Read on to learn how AI automa­tions are ben­e­fit­ting oth­er busi­ness­es. The fol­low­ing case stud­ies demon­strate just a hand­ful of the numer­ous excit­ing appli­ca­tions offered by AI automa­tions. What­ev­er your busi­ness goals, this will like­ly inspire you to learn that any­thing is possible!

AI automations revolutionising businesses: 3 case studies

Devel­op­ments in AI are going to touch every­thing. This is demon­strat­ed below, with these case stud­ies com­ing from a wide range of indus­tries. In each of these cas­es, busi­ness­es were able to boost pro­duc­tiv­i­ty with­out scrimp­ing on oth­er areas:

Customer service at Klarna

Klar­na is the pop­u­lar “buy now, pay lat­er” ser­vice provider, with over 150 mil­lion cus­tomers worldwide.

Ear­li­er this year, Klar­na announced its new AI assis­tant and pub­lished some com­pelling­ly pos­i­tive sta­tis­tics about its suc­cess­ful start. Devel­oped with Ope­nAI tech­nol­o­gy, the AI assis­tant is designed to replace Klar­na’s cus­tomer ser­vice – and it has been huge­ly positive.

In just one month since going live, the AI assis­tant has:

• Engaged in 2.3 mil­lion con­ver­sa­tions with cus­tomers (two-thirds of Klar­na’s usu­al cus­tomer ser­vice workload).
• Done the work of 700 full-time cus­tomer ser­vice agents.

This may not sur­prise you, because we all know that cus­tomer ser­vice bots can work round the clock. How­ev­er, you’ll be impressed to learn how well the bot improved Klar­na’s KPIs. This new AI assis­tant was able to:

• Keep cus­tomer sat­is­fac­tion scores the same.
• Reduce repeat enquiries by 25%.
• Reduce prob­lem res­o­lu­tion time from 11 min­utes to 2 minutes.
• Com­mu­ni­cate in more than 35 languages.

Evi­dent­ly, the automa­tion of Klar­na’s cus­tomer ser­vice has dras­ti­cal­ly increased effi­cien­cy, cost-effec­tive­ness, and cus­tomer expe­ri­ence. Not only are cus­tomers get­ting their issues resolved faster, but more are being helped due to the bot’s many lan­guages. This has notably improved inter­ac­tions with local immi­grant and expat com­mu­ni­ties, demon­strat­ing Klar­na’s com­mit­ment to wide-reach­ing cus­tomer service.

With a 25% reduc­tion in return­ing enquiries, it’s clear that the assis­tant has a much high­er first-con­tact res­o­lu­tion rate – a desir­able thing for any com­pa­ny that cares about cus­tomer service.

Klar­na must be seri­ous­ly hap­py because their new cus­tomer assis­tant is esti­mat­ed to dri­ve a prof­it of $40 mil­lion in 2024. With this sig­nif­i­cant boost in prof­itabil­i­ty, Klar­na is sure to inspire count­less busi­ness­es to stream­line their cus­tomer ser­vice too. Though it has reduced labour costs sig­nif­i­cant­ly, Klar­na has been able to cre­ate supe­ri­or auto­mat­ed cus­tomer expe­ri­ences while yield­ing bet­ter returns for its stakeholders.

Recruitment decisions at Unilever

Unilever, the fast-mov­ing con­sumer goods com­pa­ny, is a house­hold name. This multi­na­tion­al is so vast that it faces sift­ing through approx­i­mate­ly 1.8 mil­lion job appli­ca­tions every year to recruit more than 30,000 employees.

A recruit­ment process as huge as this drains time, mon­ey, and resources. So, Unilever is har­ness­ing the pow­er of AI to stream­line and auto­mate its glob­al recruit­ment process. In part­ner­ship with Pymet­rics, they have devel­oped an online recruit­ment plat­form to eval­u­ate can­di­dates in the com­fort of their own homes.

Can­di­dates are invit­ed to play sev­er­al inter­ac­tive games that give away their apti­tude for log­ic, risk-tak­ing, and rea­son­ing. Machine learn­ing com­pares can­di­date results against pre­vi­ous suc­cess­ful can­di­dates and assess­es their suit­abil­i­ty for what­ev­er role they’re apply­ing for.

If suc­cess­ful, can­di­dates must sub­mit a video inter­view. Again, machine learn­ing assess­es their suit­abil­i­ty with nat­ur­al lan­guage pro­cess­ing (NLP) and body lan­guage analysis.

This auto­mat­ed approach has dras­ti­cal­ly improved effi­cien­cy, by cut­ting about 70,000 hours from the first inter­view stage.

What’s more, this auto­mat­ed process has enabled Unilever to pro­vide more detailed feed­back to every sin­gle can­di­date – some­thing unheard of in the world of multi­na­tion­als. The chief of HR at Unilever says this is “an exam­ple of arti­fi­cial intel­li­gence allow­ing us to be more human.”

By automat­ing the pre­lim­i­nary screen­ing process, Unilever has enhanced its abil­i­ty to make recruit­ment deci­sions, while ensur­ing a high-qual­i­ty tier of can­di­dates at the inter­view stage. While we don’t have access to exact fig­ures, we can con­fi­dent­ly guess that this automa­tion has dras­ti­cal­ly reduced the work­load on human staff and enabled resources and time to be direct­ed elsewhere.

Product listings on Amazon

There are close to 2 mil­lion third-par­ty sell­ers on Ama­zon. Pre­vi­ous­ly, these sell­ers had to input large vol­umes of infor­ma­tion to cre­ate prod­uct pages – list­ing ingre­di­ents, features,

attrib­ut­es, ben­e­fits, and details. This process can be tire­some and repet­i­tive, which is exact­ly right for auto­mat­ed solutions.

Ama­zon has made it its mis­sion to make things eas­i­er for sell­ers. For this rea­son, it has cre­at­ed AI list­ing tools for sell­ers to cre­ate high-qual­i­ty prod­uct pages with min­i­mal effort. This tool allows sell­ers to eas­i­ly con­vert their exist­ing web con­tent into ful­ly fleshed-out Ama­zon list­ings by pro­vid­ing a sim­ple URL.

The tool then enhances these pages to meet Ama­zon’s stan­dards, ensur­ing they are detailed and customer-friendly.

This new AI tool has only been embraced by 100,000 sell­ers, but it’s per­form­ing well. Out of all AI-gen­er­at­ed list­ings, 80% have been used with­out much human edit­ing. This shows that the tool is high­ly accu­rate and pro­duces rel­e­vant con­tent. It not only saves time but also improves the qual­i­ty of infor­ma­tion pre­sent­ed to customers.

This case study exam­ple is only part­ly auto­mat­ed, as human inter­ven­tion is still need­ed to input the URL and approve the copy. How­ev­er, this AI-gen­er­at­ed con­tent is out­per­form­ing tra­di­tion­al meth­ods in clar­i­ty, accu­ra­cy, and com­pre­hen­sive­ness and it won’t be long before more sell­ers start using it.

As a result of this automa­tion tool, prod­ucts will be more search-friend­ly with­in Ama­zon and sell­ers can con­cen­trate on oth­er excit­ing busi­ness oper­a­tions like growth and prod­uct innovation.

Challenges of using AI automations in business

Despite the impres­sive ben­e­fits of AI automa­tion illus­trat­ed through the case stud­ies above, it is equal­ly impor­tant to address the chal­lenges that busi­ness­es may face when imple­ment­ing these tech­nolo­gies. As organ­i­sa­tions seek to har­ness the pow­er of AI to stream­line oper­a­tions and enhance effi­cien­cy, they must also nav­i­gate sev­er­al poten­tial problems.

Here are some of the main chal­lenges we predict:

1. Workforce disruption and employee relations

The automa­tion of tasks that were pre­vi­ous­ly per­formed by human employ­ees can lead to sig­nif­i­cant work­force dis­rup­tion. Klar­na’s tran­si­tion to an AI assis­tant, for instance, while improv­ing cus­tomer ser­vice met­rics, also did the work of 700 full-time cus­tomer ser­vice agents.

This sce­nario rais­es ques­tions about job dis­place­ment and the need for busi­ness­es to man­age changes in employ­ee roles sen­si­tive­ly. Com­pa­nies must bal­ance the effi­cien­cy gains from automa­tion with the poten­tial impact on staff morale and job security.

2. Upskilling and training

For busi­ness­es want­i­ng to retain the staff whose roles are replaced by AI tech­nol­o­gy, retrain­ing and upskilling may be on the cards.

This is a press­ing issue in gen­er­al because in a few years, many jobs may be obso­lete. The shift towards more auto­mat­ed process­es requires work­ers to have dif­fer­ent skill sets, such as dig­i­tal lit­er­a­cy, the abil­i­ty to work with AI tools, and ana­lyt­i­cal skills to inter­pret AI-gen­er­at­ed insights.

Busi­ness­es must invest in train­ing pro­grams to equip their work­force with these nec­es­sary skills, ensur­ing that employ­ees can adapt to and thrive in an increas­ing­ly auto­mat­ed work envi­ron­ment. This not only helps in retain­ing tal­ent but also in get­ting the most from AI technologies.

3. Legal and ethical considerations

The use of AI in busi­ness oper­a­tions brings a few legal and eth­i­cal chal­lenges. For exam­ple, AI-dri­ven recruit­ment process­es, like those adopt­ed by Unilever, must nav­i­gate com­plex issues around data pri­va­cy, bias, and fairness.

There is a risk that AI sys­tems, if not care­ful­ly designed and mon­i­tored, could inad­ver­tent­ly intro­duce bias­es into deci­sion-mak­ing process­es, lead­ing to unfair treat­ment of candidates.

Sim­i­lar­ly, the deploy­ment of AI in cus­tomer ser­vice or con­tent gen­er­a­tion must com­ply with data pro­tec­tion reg­u­la­tions, such as GDPR in Europe, which impos­es strict rules on how per­son­al data is col­lect­ed, processed, and stored. Busi­ness­es must ensure that their use of AI is trans­par­ent, eth­i­cal, and com­pli­ant with all rel­e­vant laws and regulations.

Final thoughts

AI’s sig­nif­i­cance in boost­ing busi­ness effi­cien­cy is unde­ni­able, offer­ing busi­ness­es an unmatched chance to improve their oper­a­tional effec­tive­ness, deci­sion-mak­ing capa­bil­i­ties, and com­pet­i­tive edge. Com­pa­nies that embrace and cap­i­talise on AI are poised to lead their industries.

As AI tech­nol­o­gy advances, its role in busi­ness automa­tion and trans­for­ma­tion will only increase, cement­ing its sta­tus as an essen­tial asset for any com­pa­ny aim­ing to pros­per in the dig­i­tal era.

How­ev­er, achiev­ing suc­cess requires thought­ful nav­i­ga­tion of its chal­lenges. Busi­ness­es need to care­ful­ly assess the impli­ca­tions for their work­force, com­mit to upskilling ini­tia­tives, and adhere to eth­i­cal and legal frame­works. By proac­tive­ly address­ing these issues, com­pa­nies can unlock AI’s full poten­tial to fos­ter inno­va­tion, enhance effi­cien­cy, and dri­ve growth.