Gen AI & Changing Nature of Consumer Expectations

“If you want to understand today you have to search yesterday”, Pearl S. Buck, American Novelist

There have been six technology inflection points over the last 25 years each building on what came before. Each of these inflection points significantly changed consumer behavior and expectations.? It is difficult for a 25 year old today to imagine how 25 years ago we communicated with one another, how we consumed news, how we listened to music, how we shopped and how we built and established communities.

Generative AI is and will be the 6th inflection point in this evolution. ?Companies that plan and prepare for the coming changing consumer behaviors and expectations will both be better prepared to defend their market share and be better positioned than their competitors to take advantage of new opportunities.?

Inflection Point 1: Welcome to the Worldwide Web

AOL, Microsoft and Netscape were the competitors in what was called the “browser wars” in the early 90s. ?kicking off the “.com” boom and popularizing the internet. The early 90s landscape and the “.com” boom that began with Netscape’s IPO in 1997 was the birthplace of Amazon, Blizzard’s Warcraft, Craigslist, eBay, Expedia, Linkedin, Match, Netflix and Yahoo to name a few of the surviving brands. eMail became free and more common, advertisers began thinking about and investing in digital, independent specialty retailers such as book and record stores started to experience from online competitors, the entertainment industry was taken aback by Napster and other peer-to-peer media sharing networks.

On the consumer side, customers began to become comfortable with online buying and consumption of digital products. The convenience of shopping online and buying with 1 or a few clicks began to become popular.

Along with advances in AI techniques, supporting technologies the internet provided a new greenfield that contributed to the end of what was known as the “AI Winter” that began in the 1980s.

Inflection Point 2: Fast Internet, Please??????????????????????????????????????????

In 2000, 1% or less of households had broadband internet access, by 2007, it was ~50% and as of 2021, it was ~71% (Pew Research).

In a June, 2002 report entitled “The Broadband Difference”, Pew Research noted that broadband users (at that time) took care of 7 tasks online a day compared with 3 tasks per day for dial-up internet users.? There was a positive feedback loop between consumer demand for online goods and services and a desire by both traditional companies and new companies to fulfill these desires. Once a consumer experienced broadband internet either at home or in the workplace their level of patience with dial-up fell very quickly.

This increasing level of consumer impatience will be a theme throughout this piece. This now common consumer characteristic is one of the least talked about drivers of AI adoption.

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Inflection Point 3: Social Media Comes to Town

In 2001, the “.com” boom ended in the “.com” bust. And while this burst the dreams and financial pocketbooks of many. It did not end the ongoing invention and innovation happening in the internet space. In 2003, MySpace was launched, in 2004, Facebook was launched, in 2007, Twitter was launched.? Prior to social media, there was certainly attention paid to and talk of “personal branding”. However, except for highly successful people your personal brand was local to your community, family and work. All of a sudden with it becoming a near requirement to have a digital footprint and present everyone had an online brand, whether they were an influencer or not. And it was public on a global scale. Each consumer began to have to make a conscious or unconscious decision about what their digital footprint and persona would look like at any given time.

Consumers could now look at crowdsourced reviews on the internet review sites. Ask online communities rather than having time consuming conversations with friends. Or as marketers found, find an influencer who they trusted and buy their recommendations. ?

These new buying patterns and markets created new business requirements for analytics. More of these requirements were initially more business intelligence (BI) based than AI based. However, as a desire to incorporate more disparate types of data into certain analytic workflows some of these requirements leant themselves to what was seen as the “emerging” field of AI in business.

Inflection Point 4: Thank You, Steve Jobs

Prior to 2007, there were mobile phones. Blackberry, Motorola, Nokia and Palm all had healthy smartphone businesses. The user experience of browsing the web on these phones and working with anything other than text content was “less than ideal” to put it politely. The first iPhone was introduced in 2007, combining an Apple designed user interface, with the ability to play music in MP3 format, and a camera along with some basic apps all of this running on 3G networks. In it’s first and only year of production Apple sold 6.1 million of iPhone 1. The new game in mobile was on.

The rapid adoption of smart phones, increased speed and quality of connectivity created massive demand for mobile apps in every category imaginable. This has given rise to 24/7/365 real time connected world where consumers expect and been trained that anything they want should be available nearly instantly. Increasing the standard which businesses need to live up to in order to exceed or meet consumer expectations.

The only way to effectively and efficiently deal with the increased number of both consumer and system interactions is as much as possible is to implement automated decision systems where possible and the more advanced of these leverage AI (rather than BI or rules based systems).

Inflection Point 5: The Hot, Long Big Data & AI Summer

The broad availability of broadband internet, rise of social media and rapid adoption of smart phones and associated apps gave rise to the term Big Data which initially referred to the new variety, velocity and volume of data that could no longer be cost effectively managed in traditional relational databases popularized by IBM’s DB2 and Oracle. In 2008, Hadoop the database that companies like Facebook and Yahoo were using to manage their consumer data was made available as open source to the public and this was the same year that Cloudera was founded. At the same time, Big Data was coming into it’s own so was advanced analytics and more common use of AI/ML not only by venture backed growth companies but by large traditional Fortune 2000 companies as well.? During the mid-2010’s at tech conferences it was not uncommon for skilled Data Scientists to be referred to as rock stars.

The reasons companies made investments in both Big Data and the accompanying advanced analytics was to deliver a more personalized experience to their customers. While, there is a wide disparity in where companies are on the spectrum of ability and quality of delivering mass personalization to their customers, once again, consumer expectations were raised with regards to ?“I don’t only want what I want now…I also want the experience to be take into account my personal preferences”.

Inflection Point 6: You Have Questions, We Have Answers

OpenAI launched ChatGPT 3.5 in Deecember, 2022. It was downloaded over a million times in five days. In April 2023, ChatGPT was being visited 60 million times a day. Generative AI captured the world’s imagination because the pattern of interaction is similar to human interaction. Ask a question and get an answer of varying accuracy and relevance. The quality of the answer depends on who or which Gen AI model you ask, what you ask and you ask. With humans this is called quality of communication. With Generative AI this is called prompt engineering.

One of many things that Generative AI does is change consumer expectations as to how acquired information from the internet should be presented.? Prior to ChatGPT that way most people found what they wanted on the internet was to prompt a popular search engine with a natural language question or statement. The search engine would than present relevant links that could be followed to get more detail on the results of the search.? This pattern was also often reflected in corporate knowledge management systems.

Going forward this pattern of providing relevant links will rather rapidly become quite dated. An analogy would be that when Yahoo was initially launched they had individuals and teams who would curate what links a search engine user saw when they did popular searches.? This way of curating search was made irrelevant by Google’s search algorithm which did a beter job of providing the most relevant links to an internet search. Generative AI will do to search what Google did to Yahoo’s original curation model.

More and more consumers will expect clear, concise, simple answers and standard tasks to be completed in response to their prompts.? Companies that do this well will expand market-share, strengthen brand loyalty and be seen as keeping up with a changing cultural landscape. Those that do not will struggle to be competitive.

How to Prepare

At Cognizant, we believe every company needs a well considered fit-for-purpose AI strategy that provides the foundation and framework to efficiently and effectively make business and technology decisions that are mission aligned. The rate of change of the rate of change will continue to increase in both the AI and technology space. The skillful response is to recognize this, accept it and to be prepared for the inevitable surprises that both today and years from now will make the saying “May you live in interesting times” more and more true every day.

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