Home Technology Artificial Intelligence (AI) and the New Business space; Challenges, Opportunities and Threats

Artificial Intelligence (AI) and the New Business space; Challenges, Opportunities and Threats

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Survival in the next normal will rest heavily on the integration of artificial intelligence and machine learning into everyday workplace activities, how companies go about doing this will separate winners, losers, and laggards. “If companies are going to scale past the recent business shocks caused by the COVID-19 pandemic they must keep an eye on rapid evolution; while old ways have been attractive at different points in time, new ways must be adopted by corporations if they want to stay healthy. History is lovely but it is not a perfect measure of the future” noted  the Rotman School of Management, University of Toronto’s David R. Beatty recently.

Professor Beatty made the remarks at a webinar hosted by the Institute of Directors (IoD) Nigeria in 2020. Beatty’s admonition was timely. Not only are companies finding themselves increasingly on the narrow ledge of business sustainability but they are equally discovering that consumers do not pity businesses unwilling to rethink, reimagine and restrategize their operations, products, and services.

Understanding future consumer preferences is no longer a luxury but a critical requirement for corporate existence. Contemporary organisations require a deeper knowledge of who they provide products or services to than in the past because consumer expectations have grown along a geometric curve of product and service needs shaped by dynamically changing preferences. The younger generation of consumers have a different temperament from their older counterparts, they are plugged into a reality that is fast-paced, crushingly selective, and unapologetically social.

Indeed, today’s consumer is environmentally conscious, impatient, and savvy. The cash-conscious younger buyer requires speed, efficiency, effectiveness, and ease. The further away a company is from achieving these expectations the closer it is from being, in gen Z and Y contemporary lingo, ‘deleted’ (see illustration below).

From AI Zero to Hero; Managing Market Challenges

Evolving an appropriate AI business response to consumer market expectations is as tough as nails. The main challenges to AI adoption and implementation include but are not restricted to the following:

  • Hyper-personalization of products and services
  • Difficulty in assessing and scoring propensity-to-buy
  • Growing need for product and service speed and flexibility
  • Reduced loyalty and high portability of customers (i.e. high customer bounce rates)
  • Regularity and consistency in customer creditworthiness assessments
  • High fulfillment costs (for online trading transactions)
  • High agency costs (for online retail channels)
  • High breakeven margins
  • Cost of data storage, analytics, and strategic deployment (data prediction)

Corporate analysts note that organisations need to bring greater focus into the business game with a larger dependence on data gathering, predictive analytics, and product or service personalization. Gone is the Model T Ford concept of product standardization where customers could buy every colour of Ford’s Model T as long as it was black. In the new business order, every customer’s demand is unique, and even though demand can be ‘bunched’ the sub-categories of preferences still represent a rainbow of choices.

With customers becoming increasingly choosy and intolerant of attempts to mass supply standardized products or services corporations need to have the data and computer processing power to identify the nuances in product or service demand and see how best to meet such demand without leaning into a major spike in operating costs. The balance between the personalization of products and services and higher product or service costs remains a hi-wire trapeze act for many corporations.

The need to balance customer service or product satisfaction and higher costs may require quick-fix AI strategic tools that may include, but are not limited to, the following:

  • Regular consumer data touchpoints (primary data survey would provide the base data to be refined after customer onboarding)
  • Customer satisfaction analysis to drill down customer’s immediate and prospective needs
  • Customer spending analysis
  • Customer income analysis
  • Customer aspiration analysis
  • Customer brand loyalty analysis (assessing product or service fungibility)

Going forward, data would no longer be a fancy set of numbers designed to intimidate the uninitiated but would represent the lifeblood of understanding market trends and appropriate corporate responses to fast-paced  market changes

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