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Social Media Marketing (SMM) as Networking Technique Essay

Introduction, reasons behind the popularity of smm, advantages and disadvantages of smm to entrepreneurs, how smm helps pepsi gain more customer insight, two businesses that have used smm to their advantage, future expectations of sm on marketing.

The business realm across the world has been undergoing enormous transformations and one of the foremost changes in the entrepreneurship sector is the emergence of technological advancements (Evans & McKee, 2010).

Modern technologies have been integral in supporting the growth and development of rapturous networking activities and Social Media (SM) has presently become one of the main issues in the business paradigm. Social media platforms, which comprise the actively consumed websites that are enhanced by the advent of the net 2.0 technologies, have been significantly influential in major business undertakings.

Social Media Marketing (SMM) is a modern marketing technique that uses social networking platforms like Facebook, Twitter, MySpace, LinkedIn, and Google+ among other social networks (Neti, 2011). The use of social media marketing strategies is increasingly becoming viral across the world, thus becoming renowned as viral marketing. This study examines the overall impact of SMM on modern entrepreneurship.

Marketing as a business strategy entails activities such as advertisement, announcement, promotion, and these marketing components require effective communication. As one of the best contemporary innovations, social networks are exceedingly becoming the focal point of human social interaction, thus presenting unique entrepreneurial experiences (Evans & McKee, 2010).

Social media is one of the modern communication tools that have recently proven to be powerful in influencing both informal and formal communication, where millions of potential consumers interact freely. Having the ability to engage consumers in an active interaction where sharing of information is effectual through powerful technological devices, social media is gradually receiving attention from businesses of all sizes.

Social media platforms currently tend to associate with flexibility, effectiveness, convenience, and efficiency in business communication where investors are capable of sharing important information with their stakeholders, who include potential business clients (Bailyn, 2012).

By using social networks in business communication, flexibility is achievable via devices that support these platforms, which mainly include accessible technological devices like mobile phones, computers, and tablets (Pradiptarini, 2011). Users access these social networks at their convenient time, hence communication consistency.

Businesspersons consider social networks as convenient communication and information-sharing tools for they involve instant feedback ability where messages reach users expediently. Most recently, researchers have found a great correlation between the use of social networks in businesses with enhanced sales output following increased connection between consumers and entrepreneurs (Pradiptarini, 2011).

Communities, which form the most excellent composure of active consumers of products and services of businesses, are the leading cause of rapid growth of social media marketing.

Since social media involves engaging in diverse online social networking communities, it potentially has substantial influence on attracting enormous consumer population (Pradiptarini, 2011). More specific, as all modern businesses in the markets are targeting the youthful consumers, vibrant youths in social networks offer business boom.

Social media marketing emerged with numerous experiences and brought several opportunities as well as challenges that entrepreneurs have encountered. Being a hotly business contested subject, social networks seem to have presented some potential benefits that have encouraged businesses in their performance (Evans & McKee, 2010).

Social media platforms are essential facets of exposing the business to the competitive markets and reaching out potential consumers. The foremost advantage of social networks in businesses is the enhancement of communication, information exchange, and knowledge sharing.

This aspect of social networks enhances a mutual connection between consumers and entrepreneurs that subsequently boosts the productivity of the businesses. During promotion, advertising, and announcement of social networks like Facebook, Twitter, and MySpace are progressively becoming essential communication tools (Bailyn, 2012).

These social media networks play a pivotal role in boosting marketing campaigns for organization’s products and services, which subsequently influences positive business outcomes. Modern consumers currently rely more on social networks to access relevant information concerning new products and services offered by companies.

Marketing also involves procedures of unveiling and introducing new products to the market in a process commonly known as product launching. Research considers social media as a great platform useful in creating the accurate market buzz before launching a new product or service (Vries et al., 2012).

As consumers are currently demanding exemplary services, social media enhances marketing campaigns, which are becoming useful in strengthening product and service information within the markets.

Consumers tend to concentrate and associate with famous market products and since social media are assisting in communication of information pertaining to product and service, they help in building brand reputation.

Companies using social networks are capable of attracting public attention more easily and marketing effectiveness of these networks becomes eminent when business performance enhances through increased market share and revenue performance (Vries et al., 2012).

Using powerful videos, photographs, audio streaming, widgets, and other social media features, companies create and share quality content to communicate their services and products to the business community.

Notwithstanding its ability to transform almost every aspect of modern business activity through sophisticated technologies, social media has also received criticism from entrepreneurs (Pradiptarini, 2011). The foremost challenge that comes with the modern technologies and the social media networks is the presence of insecurity within these platforms.

Entrepreneurs have little potency to control the growing cyberspace and Internet security, possess little control over the entire conservation, and can barely manipulate the pessimistic perceptions of clients expressed in public social websites (Vries et al., 2012). There is a high possibility of getting negative feedback from ill-motivated clients due to bad opinions from competitors expressed on social media.

The vibrant cyber activities that engage genuine civilians and illegitimate users across networks with little control strategies placed by the organizations, places companies at high risk of encountering fraud (Bailyn, 2012).

Tracing the route of negative conversation and fraudulent activities is a challenge and as many companies continually engage in social media marketing, the fears about losing billions of finances in fraud cases remain unsettled.

Since its inception as a potential marketing tool, social media marketing has been the most anticipated practice among several small, medium, and even multinational corporations. As Neti (2011) postulates, “Adult beverage companies, exotic automobile manufacturers, pastry shops have been using social media tool” (p.8).

Alongside other major multinational companies, PepsiCo is one of the world-leading international companies famous for producing carbonated Pepsi soft drinks, and currently, its Pepsi Refresh project has provided a new breakthrough in its marketing strategies.

Pepsi Refresh project is responsible for turning individuals’ dreams into realities by producing and researching about marketing possibilities of PepsiCo and funding amazing ideas that support the notion of Pepsi as a soft drink that refreshes the world.

Being responsible for managing resources of the billion dollars worth corporation PepsiCo, Pepsi Refresh project has recently associated with social media marketing as one of its major strategy to make Pepsi a world refreshing drink.

In marketing their major business, going to the market, unveiling, and launching new brands from the company, Pepsi undertook an online consumer-based study in which it concluded that social media was essential in creating and sharing ideas.

Results on the investigations from the 2009 Pepsi Optimism Project about the means of communication of the research impelled PepsiCo to develop off and online forums where social innovation could help in marketing the Pepsi Refresh Project. According to Neti (2011), “Pepsi Coke, Nokia and many of the top brands have effectively used social media for achieving their business objectives” (p.8).

During the process of establishing and informing people about the Pepsi Refresh project, PepsiCo consistently used social media tactics to access multitudes of people.

PepsiCo used Facebook, Twitter, and refresheverything.com to market its idea, and this viral marketing through text messages and video clips captured a huge public attention successfully. In using social media in marketing the Pepsi Refresh Project, the company spent less money.

Through refresheverthing.com, Pepsi became the most publicly renowned Super Bowl brands. In its impact, approximately 37% of Americans familiarized with Pepsi compared to 12% for the same marketing program. Over eighteen million exceptional consumer-oriented visitors engaged in activities of the refresheverthing.com website.

In online brand rating and voting, more than 12,000 projects received consumer votes, with approximately 2 million online comments from 76 million votes cast.

Benefiting from the newly developed refresheverthing.com website, brand attributes increased exponentially with products receiving favorability, trust, and attention among other important public interests. Online communities and consumers’ fan pages concerning the Pepsi products started developing following the initial influence.

Demonstrated by a continuum of studies, investigations on the marketing rivalry among the major U.S corporations have continuously identified Pepsi and Coke as the biggest rivalries. However, most American companies, including those in the fortune 500, have already integrated social media marketing. Apart from the beverage companies, exotic automobile manufacturers are using social media marketing.

Retail companies as well as other major corporations have noticed the positive impact of social media in marketing. Microsoft and Wal-Mart are two major fortune 500 companies that have demonstrated exemplary approach towards social media marketing, with Facebook, Twitter, MySpace, LinkedIn being the most used social websites in marketing their products.

The two companies have established social media as one of the most effectual communication and interaction tool where millions of active potential consumers made of optimistic youthful population meet freely. Pradiptarini (2011) acknowledges a substantial influence of these social networks in enhancing marketing strategies, boosting sales revenues, and subsequently influencing financial growth.

As social media gives marketers opportunities to communicate to the public and more specifically, the vibrant peers, consumers and potential customers, both Coke and Pepsi have been lively in promoting their brands through major social networks renowned in the US.

Microsoft and Wal-Mart have been using social media marketing as a strategy in building brand awareness, launching new products from the company, and generating leads. As Vries et al. (2012) affirm, “Companies can place brand posts (containing videos, messages, quizzes, information, and other material) on these brand fan pages” (p.83).

Wal-Mart and Microsoft have engaged their potential consumers in advertisements, promotional activities, and other marketing activities that reveal product information to the consumers.

Regarding their involvement in brands and companies on the social media, Pradiptarini (2011), noticed that approximately 71.14% of the Facebook users proved to be fans of brands produced by the two companies. In addition, 38.63% of their Twitter fans and followers, followed brands advertised by the two companies.

People have awakened towards a contemporary business world that requires present and forthcoming technologies to improve their overall effectiveness and social media is the probable marketing tool whose influence may never cease (Vries et al., 2012). The world is currently experiencing a shortage of youth employment and social interactions have been one of the leisure activities that keep youths engaged.

With the emergence of powerful technological devices like tablets, upgraded laptops, and the Smartphone technologies, social media continue to be a trendy business and social tool. The number of Facebook accounts, Twitter users, and other social media users following new brands and products online is rapidly increasing (Neti, 2011).

Coupled with unique and captivating experiences that social networks provide to the active youths and businesspersons, online working opportunities like brand advertising, social media may continue influencing social and business life for millions of decades to come (Pradiptarini, 2011).

Social media is an all-inclusive advancing technology where all age groups have their portion and as the present population gets weary, new generating still admire these experiences.

Notwithstanding its great potential in influencing millions of users, emerging issues in the cybercrime and related cyberspace insecurity may continue posing significant challenges to the social network users. Internet hacking activities, cyberbullying, and several other unethical practices associated with social networking are subjecting users to frustrations (Evans & McKee, 2010).

Since few policies and rules are currently present in curbing cyberspace insecurity and users are becoming used to social networks unaware of the mushrooming dangers, use of social media may face drastic collapse in the occurrence of a significant concern (Evans & McKee, 2010). Organizations themselves do not have governance and processes for controlling the use of social media within and outside the company.

Both Facebook and Twitter have reported criminal challenges and even in their privacy terms, abuse-related concerns are not addressed appropriately (Bailyn, 2012). In other websites, cases pertaining to social media crime remain undermined, go unreported, or even lack enough evidence to capture and prosecute the offenders.

The US is among the nations experiencing a rapid influx of social networking activities with this rise in social media networking, providing companies and consumers a gateway to enhanced communication. Advanced beverage companies, exotic automobile companies, and even pastry shops have recognized social media as a powerful marketing tool that connects companies with consumers.

Pepsi, Coca Cola, and Nokia technologies are among the American multinational companies that have embraced the use of social media in marketing and successfully witnessed impressive results. They have reported increased brand positive reputation, increased product attention, enhanced customer relationship, and boosted sales turnover.

Although most likely to continue facing high risks related to cybercrime, social media marketing may continue influencing marketing strategies of companies as present and maybe future generations will still continue anticipating for these experiences.

Bailyn, E. (2012). Outsmarting Social Media: Profiting in the Age of Friendship Marketing. Upper Saddle River, NJ: Pearson.

Evans, D., & McKee, J. (2010). Social Media Marketing: The Next Generation of Business Engagement . New York, NY: Wiley Publishing Inc.

Neti, S. (2011). Social media and its role in marketing. International Journal of Enterprise Computing and Business Systems, 1 (2), 1-15.

Pradiptarini, C. (2011). Social Media Marketing: Measuring Its Effectiveness and Identifying the Target Market. Journal of Undergraduate Research, 14, 1-11.

Vries, L., Gensler, S., & Leeflang, P. (2012). Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing. Journal of Interactive Marketing, 26 (1), 83–91.

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The future of social media in marketing

  • Conceptual/Theoretical Paper
  • Open access
  • Published: 12 October 2019
  • Volume 48 , pages 79–95, ( 2020 )

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  • Gil Appel 1 ,
  • Lauren Grewal 2 ,
  • Rhonda Hadi 3 &
  • Andrew T. Stephen 3 , 4  

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Social media allows people to freely interact with others and offers multiple ways for marketers to reach and engage with consumers. Considering the numerous ways social media affects individuals and businesses alike, in this article, the authors focus on where they believe the future of social media lies when considering marketing-related topics and issues. Drawing on academic research, discussions with industry leaders, and popular discourse, the authors identify nine themes, organized by predicted imminence (i.e., the immediate, near, and far futures), that they believe will meaningfully shape the future of social media through three lenses: consumer, industry, and public policy. Within each theme, the authors describe the digital landscape, present and discuss their predictions, and identify relevant future research directions for academics and practitioners.

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Introduction

Social media is used by billions of people around the world and has fast become one of the defining technologies of our time. Facebook, for example, reported having 2.38 billion monthly active users and 1.56 billion daily active users as of March 31, 2019 (Facebook 2019 ). Globally, the total number of social media users is estimated to grow to 3.29 billion users in 2022, which will be 42.3% of the world’s population (eMarketer 2018 ). Given the massive potential audience available who are spending many hours a day using social media across the various platforms, it is not surprising that marketers have embraced social media as a marketing channel. Academically, social media has also been embraced, and an extensive body of research on social media marketing and related topics, such as online word of mouth (WOM) and online networks, has been developed. Despite what academics and practitioners have studied and learned over the last 15–20 years on this topic, due to the fast-paced and ever-changing nature of social media—and how consumers use it—the future of social media in marketing might not be merely a continuation of what we have already seen. Therefore, we ask a pertinent question, what is the future of social media in marketing?

Addressing this question is the goal of this article. It is important to consider the future of social media in the context of consumer behavior and marketing, since social media has become a vital marketing and communications channel for businesses, organizations and institutions alike, including those in the political sphere. Moreover, social media is culturally significant since it has become, for many, the primary domain in which they receive vast amounts of information, share content and aspects of their lives with others, and receive information about the world around them (even though that information might be of questionable accuracy). Vitally, social media is always changing. Social media as we know it today is different than even a year ago (let alone a decade ago), and social media a year from now will likely be different than now. This is due to constant innovation taking place on both the technology side (e.g., by the major platforms constantly adding new features and services) and the user/consumer side (e.g., people finding new uses for social media) of social media.

What is social media?

Definitionally, social media can be thought of in a few different ways. In a practical sense, it is a collection of software-based digital technologies—usually presented as apps and websites—that provide users with digital environments in which they can send and receive digital content or information over some type of online social network. In this sense, we can think of social media as the major platforms and their features, such as Facebook, Instagram, and Twitter. We can also in practical terms of social media as another type of digital marketing channel that marketers can use to communicate with consumers through advertising. But we can also think of social media more broadly, seeing it less as digital media and specific technology services, and more as digital places where people conduct significant parts of their lives. From this perspective, it means that social media becomes less about the specific technologies or platforms, and more about what people do in these environments. To date, this has tended to be largely about information sharing, and, in marketing, often thought of as a form of (online) word of mouth (WOM).

Building on these definitional perspectives, and thinking about the future, we consider social media to be a technology-centric—but not entirely technological—ecosystem in which a diverse and complex set of behaviors, interactions, and exchanges involving various kinds of interconnected actors (individuals and firms, organizations, and institutions) can occur. Social media is pervasive, widely used, and culturally relevant. This definitional perspective is deliberately broad because we believe that social media has essentially become almost anything—content, information, behaviors, people, organizations, institutions—that can exist in an interconnected, networked digital environment where interactivity is possible. It has evolved from being simply an online instantiation of WOM behaviors and content/information creation and sharing. It is pervasive across societies (and geographic borders) and culturally prominent at both local and global levels.

Throughout the paper we consider many of the definitional and phenomenological aspects described above and explore their implications for consumers and marketing in order to address our question about the future of marketing-related social media. By drawing on academic research, discussions with industry leaders, popular discourse, and our own expertise, we present and discuss a framework featuring nine themes that we believe will meaningfully shape the future of social media in marketing. These themes by no means represent a comprehensive list of all emerging trends in the social media domain and include aspects that are both familiar in extant social media marketing literature (e.g., online WOM, engagement, and user-generated content) and emergent (e.g., sensory considerations in human-computer interaction and new types of unstructured data, including text, audio, images, and video). The themes we present were chosen because they capture important changes in the social media space through the lenses of important stakeholders, including consumers, industry/practice, and public policy.

In addition to describing the nature and consequences of each theme, we identify research directions that academics and practitioners may wish to explore. While it is infeasible to forecast precisely what the future has in store or to project these on a specific timeline, we have organized the emergent themes into three time-progressive waves, according to imminence of impact (i.e., the immediate, near, and far future). Before presenting our framework for the future of social media in marketing and its implications for research (and practice and policy), we provide a brief overview of where social media currently stands as a major media and marketing channel.

Social media at present

The current social media landscape has two key aspects to it. First are the platforms—major and minor, established and emerging—that provide the underlying technologies and business models making up the industry and ecosystem. Second are the use cases; i.e., how various kinds of people and organizations are using these technologies and for what purposes.

The rise of social media, and the manner in which it has impacted both consumer behavior and marketing practice, has largely been driven by the platforms themselves. Some readers might recall the “early days” of social media where social networking sites such as MySpace and Friendster were popular. These sites were precursors to Facebook and everything else that has developed over the last decade. Alongside these platforms, we continue to have other forms of social media such as messaging (which started with basic Internet Relay Chat services in the 1990s and the SMS text messaging built into early digital mobile telephone standards in the 2000s), and asynchronous online conversations arranged around specific topics of interest (e.g., threaded discussion forums, subreddits on Reddit). More recently, we have seen the rise of social media platforms where images and videos replace text, such as Instagram and Snapchat.

Across platforms, historically and to the present day, the dominant business model has involved monetization of users (audiences) by offering advertising services to anyone wishing to reach those audiences with digital content and marketing communications. Prior research has examined the usefulness of social media (in its various forms) for marketing purposes. For example, work by Trusov et al. ( 2009 ) and Stephen and Galak ( 2012 ) demonstrated that certain kinds of social interactions that now happen on social media (e.g., “refer a friend” features and discussions in online communities) can positively affect important marketing outcomes such as new customer acquisition and sales. More recently, the value of advertising on social media continues to be explored (e.g., Gordon et al. 2019 ), as well as how it interacts with other forms of media such as television (e.g., Fossen and Schweidel 2016 , 2019 ) and affects new product adoption through diffusion of information mechanisms (e.g., Hennig-Thurau et al. 2015 ).

Although the rise (and fall) of various kinds of social media platforms has been important for understanding the social media landscape, our contention is that understanding the current situation of social media, at least from a marketing perspective, lies more in what the users do on these platforms than the technologies or services offered by these platforms. Presently, people around the world use social media in its various forms (e.g., news feeds on Facebook and Twitter, private messaging on WhatsApp and WeChat, and discussion forums on Reddit) for a number of purposes. These can generally be categorized as (1) digitally communicating and socializing with known others, such as family and friends, (2) doing the same but with unknown others but who share common interests, and (3) accessing and contributing to digital content such as news, gossip, and user-generated product reviews.

All of these use cases are essentially WOM in one form or another. This, at least, is how marketing scholars have mainly characterized social media, as discussed by Lamberton and Stephen ( 2016 ). Indeed, online WOM has been—and, we contend, will continue to be—important in marketing (e.g., in the meta-analysis by Babić Rosario et al. 2016 the authors found, on average, a positive correlation between online WOM and sales). The present perspective on social media is that people use it for creating, accessing, and spreading information via WOM to various types of others, be it known “strong ties” or “weak ties” in their networks or unknown “strangers.” Some extant research has looked at social media from the WOM perspective of the consequences of the transmission of WOM (e.g., creating a Facebook post or tweeting) on others (e.g., Herhausen et al. 2019 ; Stephen and Lehmann 2016 ), the impact of the type of WOM content shared on others’ behavior (e.g., Villarroel Ordenes et al. 2017 ; Villarroel Ordenes et al. 2018 ), and on the motivations that drive consumer posting on social media, including considerations of status and self-presentation (e.g., Grewal et al. 2019 ; Hennig-Thurau et al. 2004 ; Hollenbeck and Kaikati 2012 ; Toubia and Stephen 2013 ; Wallace et al. 2014 ).

While this current characterization of WOM appears reasonable, it considers social media only from a communications perspective (and as a type of media channel). However, as social media matures, broader social implications emerge. To appropriately consider the future, we must expand our perspective beyond the narrow communicative aspects of social media and consider instead how consumers might use it. Hence, in our vision for the future of social media in marketing in the following sections, we attempt to present a more expansive perspective of what social media is (and will become) and explain why this perspective is relevant to marketing research and practice.

Overview of framework for the future of social media in marketing

In the following sections we present a framework for the immediate, near, and far future of social media in marketing when considering various relevant stakeholders. Themes in the immediate future represent those which already exist in the current marketplace, and that we believe will continue shaping the social media landscape. The near future section examines trends that have shown early signs of manifesting, and that we believe will meaningfully alter the social media landscape in the imminent future. Finally, themes designated as being in the far future represent more speculative projections that we deem capable of long-term influence on the future of social media. The next sections delve into each of the themes in Table 1 , organized around the predicted imminence of these theme’s importance to marketing (i.e., the immediate, near, and far futures).

The immediate future

To begin our discussion on the direction of social media, in this section, we highlight three themes that have surfaced in the current environment that we believe will continue to shape the social media landscape in the immediate future. These themes—omni-social presence, the rise of influencers, and trust and privacy concerns—reflect the ever-changing digital and social media landscape that we presently face. We believe that these different areas will influence a number of stakeholders such as individual social media users, firms and brands that utilize social media, and public policymakers (e.g., governments, regulators).

Omni-social presence

In its early days, social media activity was mostly confined to designated social media platforms such as Facebook and Twitter (or their now-defunct precursors). However, a proliferation of websites and applications that primarily serve separate purposes have capitalized on the opportunity to embed social media functionality into their interfaces. Similarly, all major mobile and desktop operating systems have in-built social media integration (e.g., sharing functions built into Apple’s iOS). This has made social media pervasive and ubiquitous—and perhaps even omnipotent—and has extended the ecosystem beyond dedicated platforms.

Accordingly, consumers live in a world in which social media intersects with most aspects of their lives through digitally enabled social interactivity in such domains as travel (e.g., TripAdvisor), work (e.g., LinkedIn), food (e.g., Yelp), music (e.g., Spotify), and more. At the same time, traditional social media companies have augmented their platforms to provide a broader array of functionalities and services (e.g., Facebook’s marketplace, Chowdry 2018 ; WeChat’s payment system, Cheng 2017 ). These bidirectional trends suggest that the modern-day consumer is living in an increasingly “omni-social” world.

From a marketing perspective, the “omni-social” nature of the present environment suggests that virtually every part of a consumer’s decision-making process is prone to social media influence. Need recognition might be activated when a consumer watches their favorite beauty influencer trying a new product on YouTube. A consumer shopping for a car might search for information by asking their Facebook friends what models they recommend. A hungry employee might sift through Yelp reviews to evaluate different lunch options. A traveler might use Airbnb to book future accommodation. Finally, a highly dissatisfied (or delighted) airline passenger might rant (rave) about their experience on Twitter. While the decision-making funnel is arguably growing flatter than the aforementioned examples would imply (Cortizo-Burgess 2014 ), these independent scenarios illustrate that social media has the propensity to influence the entire consumer-decision making process, from beginning to end.

Finally, perhaps the greatest indication of an “omni-social” phenomenon is the manner in which social media appears to be shaping culture itself. YouTube influencers are now cultural icons, with their own TV shows (Comm 2016 ) and product lines (McClure 2015 ). Creative content in television and movies is often deliberately designed to be “gifable” and meme-friendly (Bereznak 2018 ). “Made-for-Instagram museums” are encouraging artistic content and experiences that are optimized for selfie-taking and posting (Pardes 2017 ). These examples suggest that social media’s influence is hardly restricted to the “online” world (we discuss the potential obsolescence of this term later in this paper), but is rather consistently shaping cultural artifacts (television, film, the arts) that transcend its traditional boundaries. We believe this trend will continue to manifest, perhaps making the term “social media” itself out-of-date, as it’s omni-presence will be the default assumption for consumers, businesses, and artists in various domains.

This omni-social trend generates many questions to probe in future research. For example, how will social interactivity influence consumer behavior in areas that had traditionally been non-social? From a practitioner lens, it might also be interesting to explore how marketers can strategically address the flatter decision-making funnel that social media has enabled, and to examine how service providers can best alter experiential consumption when anticipating social media sharing behavior.

The rise of new forms of social influence (and influencers)

The idea of using celebrities (in consumer markets) or well-known opinion leaders (in business markets), who have a high social value, to influence others is a well-known marketing strategy (Knoll and Matthes 2017 ). However, the omnipresence of social media has tremendously increased the accessibility and appeal of this approach. For example, Selena Gomez has over 144 million followers on Instagram that she engages with each of her posts. In 2018, the exposure of a single photo shared by her was valued at $3.4 million (Maxim 2018 ). However, she comes at a high price: one post that Selena sponsors for a brand can cost upwards of $800,000 (Mejia 2018 ). However, putting high valuations on mere online exposures or collecting “likes” for specific posts can be somewhat speculative, as academic research shows that acquiring “likes” on social media might have no effect on consumers’ attitudes or behaviors (John et al. 2017 ; Mochon et al. 2017 ). Moreover, Hennig-Thurau et al. ( 2015 ), show that while garnering positive WOM has little to no effect on consumer preferences, negative WOM can have a negative effect on consumer preferences.

While celebrities like Selena Gomez are possible influencers for major brands, these traditional celebrities are so expensive that smaller brands have begun, and will continue to, capitalize on the popularity and success of what are referred to as “micro-influencers,” representing a new form of influencers. Micro-influencers are influencers who are not as well-known as celebrities, but who have strong and enthusiastic followings that are usually more targeted, amounting anywhere between a few thousand to hundreds of thousands of followers (Main 2017 ). In general, these types of influencers are considered to be more trustworthy and authentic than traditional celebrities, which is a major reason influencer marketing has grown increasingly appealing to brands (Enberg 2018 ). These individuals are often seen as credible “experts” in what they post about, encouraging others to want to view the content they create and engage with them. Furthermore, using these influencers allows the brand via first person narration (compared to ads), which is considered warmer and more personal, and was shown to be more effective in engaging consumers (Chang et al. 2019 ).

Considering the possible reach and engagement influencers command on social media, companies have either begun embracing influencers on social media, or plan to expand their efforts in this domain even more. For example, in recent conversations we had with social media executives, several of them stated the growing importance of influencers and mentioned how brands generally are looking to incorporate influencer marketing into their marketing strategies. Further, recent conversations with executives at some globally leading brands suggest that influencer marketing spending by big brands continues to rise.

While influencer marketing on social media is not new, we believe it has a lot of potential to develop further as an industry. In a recent working paper, Duani et al. ( 2018 ) show that consumers enjoy watching a live experience much more and for longer time periods than watching a prerecorded one. Hence, we think live streaming by influencers will continue to grow, in broad domains as well as niche ones. For example, streaming of video game playing on Twitch, a platform owned by Amazon, may still be niche but shows no signs of slowing down. However, live platforms are limited by the fact that the influencers, being human, need to sleep and do other activities offline. Virtual influencers (i.e., “CGI” influencers that look human but are not), on the other hand, have no such limitations. They never get tired or sick, they do not even eat (unless it is needed for a campaign). Some brands have started exploring the use of virtual influencers (Nolan 2018 ), and we believe that in coming years, along with stronger computing power and artificial intelligence algorithms, virtual influencers will become much more prominent on social media, being able to invariably represent and act on brand values and engage with followers anytime.

There are many interesting future research avenues to consider when thinking about the role of influencers on social media. First, determining what traits and qualities (e.g., authenticity, trust, credibility, and likability) make sponsored posts by a traditional celebrity influencer, versus a micro-influencer, or even compared to a CGI influencer, more or less successful is important to determine for marketers. Understanding whether success has to do with the actual influencer’s characteristics, the type of content being posted, whether content is sponsored or not, and so on, are all relevant concerns for companies and social media platforms when determining partnerships and where to invest effort in influencers. In addition, research can focus on understanding the appeal of live influencer content, and how to successfully blend influencer content with more traditional marketing mix approaches.

Privacy concerns on social media

Consumer concerns regarding data privacy, and their ability to trust brands and platforms are not new (for a review on data privacy see Martin and Murphy 2017 ). Research in marketing and related disciplines has examined privacy and trust concerns from multiple angles and using different definitions of privacy. For example, research has focused on the connections between personalization and privacy (e.g., Aguirre et al. 2015 ; White et al. 2008 ), the relationship of privacy as it relates to consumer trust and firm performance (e.g., Martin 2018 ; Martin et al. 2017 ), and the legal and ethical aspects of data and digital privacy (e.g., Culnan and Williams 2009 ; Nill and Aalberts 2014 ). Despite this topic not seeming novel, the way consumers, brands, policy makers, and social media platforms are all adjusting and adapting to these concerns are still in flux and without clear resolution.

Making our understanding of privacy concerns even less straightforward is the fact that, across extant literature, a clear definition of privacy is hard to come by. In one commentary on privacy, Stewart ( 2017 ), defined privacy as “being left alone,” as this allows an individual to determine invasions of privacy. We build from this definition of privacy to speculate on a major issue in privacy and trust moving forward. Specifically, how consumers are adapting and responding to the digital world, where “being left alone” isn’t possible. For example, while research has shown benefits to personalization tactics (e.g., Chung et al. 2016 ), with eroding trust in social platforms and brands that advertise through them, many consumers would rather not share data and privacy for a more personalized experiences, are uncomfortable with their purchases being tracked and think it should be illegal for brands to be able to buy their data (Edelman 2018 ). These recent findings seem to be in conflict with previously established work on consumer privacy expectations. Therefore, understanding if previously studied factors that mitigated the negative effects of personalization (e.g., perceived utility; White et al. 2008 ) are still valued by consumers in an ever-changing digital landscape is essential for future work.

In line with rising privacy concerns, the way consumers view brands and social media is becoming increasingly negative. Consumers are deleting their social media presence, where research has shown that nearly 40% of digitally connected individuals admitted to deleting at least one social media account due to fears of their personal data being mishandled (Edelman 2018 ). This is a negative trend not only for social media platforms, but for the brands and advertisers who have grown dependent on these avenues for reaching consumers. Edelman found that nearly half of the surveyed consumers believed brands to be complicit in negative aspects of content on social media such as hate speech, inappropriate content, or fake news (Edelman 2018 ). Considering that social media has become one of the best places for brands to engage with consumers, build relationships, and provide customer service, it’s not only in the best interest of social media platforms to “do better” in terms of policing content, but the onus of responsibility has been placed on brands to advocate for privacy, trust, and the removal of fake or hateful content.

Therefore, to combat these negative consumer beliefs, changes will need to be made by everyone who benefits from consumer engagement on social media. Social media platforms and brands need to consider three major concerns that are eroding consumer trust: personal information, intellectual property and information security (Information Technology Faculty 2018 ). Considering each of these concerns, specific actions and initiatives need to be taken for greater transparency and subsequent trust. We believe that brands and agencies need to hold social media accountable for their actions regarding consumer data (e.g., GDPR in the European Union) for consumers to feel “safe” and “in control,” two factors shown necessary in cases of privacy concerns (e.g., Tucker 2014 ; Xu et al. 2012 ). As well, brands need to establish transparent policies regarding consumer data in a way that recognizes the laws, advertising restrictions, and a consumer’s right to privacy (a view shared by others; e.g., Martin et al. 2017 ). All of this is managerially essential for brands to engender feelings of trust in the increasingly murky domain of social media.

Future research can be conducted to determine consumer reactions to different types of changes and policies regarding data and privacy. As well, another related and important direction for future research, will be to ascertain the spillover effects of distrust on social media. Specifically, is all content shared on social media seen as less trustworthy if the platform itself is distrusted? Does this extend to brand messages displayed online? Is there a negative spillover effect to other user-generated content shared through these platforms?

The near future

In the previous section, we discussed three areas where we believe social media is immediately in flux. In this section, we identify three trends that have shown early signs of manifesting, and which we believe will meaningfully alter the social media landscape in the near, or not-too-distant, future. Each of these topics impact the stakeholders we mentioned when discussing the immediate social media landscape.

Combatting loneliness and isolation

Social media has made it easier to reach people. When Facebook was founded in 2004, their mission was “to give people the power to build community and bring the world closer together... use Facebook to stay connected with friends and family, to discover what’s going on in the world, and to share and express what matters to them” (Facebook 2019 ). Despite this mission, and the reality that users are more “connected” to other people than ever before, loneliness and isolation are on the rise. Over the last fifty years in the U.S., loneliness and isolation rates have doubled, with Generation Z considered to be the loneliest generation (Cigna 2018 ). Considering these findings with the rise of social media, is the fear that Facebook is interfering with real friendships and ironically spreading the isolation it was designed to conquer something to be considered about (Marche 2012 )?

The role of social media in this “loneliness epidemic” is being hotly debated. Some research has shown that social media negatively impacts consumer well-being. Specifically, heavy social media use has been associated with higher perceived social isolation, loneliness, and depression (Kross et al. 2013 ; Primack et al. 2017 ; Steers et al. 2014 ). Additionally, Facebook use has been shown to be negatively correlated with consumer well-being (Shakya and Christakis 2017 ) and correlational research has shown that limiting social media use to 10 min can decrease feelings of loneliness and depression due to less FOMO (e.g., “fear of missing out;” Hunt et al. 2018 ).

On the other hand, research has shown that social media use alone is not a predictor of loneliness as other factors have to be considered (Cigna 2018 ; Kim et al. 2009 ). In fact, while some research has shown no effect of social media on well-being (Orben et al. 2019 ), other research has shown that social media can benefit individuals through a number of different avenues such as teaching and developing socialization skills, allowing greater communication and access to a greater wealth of resources, and helping with connection and belonging (American Psychological Association 2011 ; Baker and Algorta 2016 ; Marker et al. 2018 ). As well, a working paper by Crolic et al. ( 2019 ) argues that much of the evidence of social media use on consumer well-being is of questionable quality (e.g., small and non-representative samples, reliance on self-reported social media use), and show that some types of social media use are positively associated with psychological well-being over time.

Managerially speaking, companies are beginning to respond as a repercussion of studies highlighting a negative relationship between social media and negative wellbeing. For example, Facebook has created “time limit” tools (mobile operating systems, such as iOS, now also have these time-limiting features). Specifically, users can now check their daily times, set up reminder alerts that pop up when a self-imposed amount of time on the apps is hit, and there is the option to mute notifications for a set period of time (Priday 2018 ). These different features seem well-intentioned and are designed to try and give people a more positive social media experience. Whether these features will be used is unknown.

Future research can address whether or not consumers will use available “timing” tools on one of many devices in which their social media exists (i.e., fake self-policing) or on all of their devices to actually curb behavior. It could also be the case that users will actually spend less time on Facebook and Instagram, but possibly spend that extra time on other competing social media platforms, or attached to devices, which theoretically will not help combat loneliness. Understanding how (and which) consumers use these self-control tools and how impactful they are is a potentially valuable avenue for future research.

One aspect of social media that has yet to be considered in the loneliness discussion through empirical measures, is the quality of use (versus quantity). Facebook ads have begun saying, “The best part of Facebook isn’t on Facebook. It’s when it helps us get together” (Facebook 2019 ). There have been discussions around the authenticity of this type of message, but at its core, in addition to promoting quantity differences, it’s speaking to how consumers use the platform. Possibly, to facilitate this message, social media platforms will find new ways to create friend suggestions between individuals who not only share similar interests and mutual friends to facilitate in-person friendships (e.g., locational data from the mobile app service). Currently there are apps that allow people to search for friends that are physically close (e.g., Bumble Friends), and perhaps social media will go in this same direction to address the loneliness epidemic and stay current.

Future research can examine whether the quantity of use, types of social media platforms, or the way social media is used causally impacts perceived loneliness. Specifically, understanding if the negative correlations found between social media use and well-being are due to the demographics of individuals who use a lot of social media, the way social media works, or the way users choose to engage with the platform will be important for understanding social media’s role (or lack of role) in the loneliness epidemic.

Integrated customer care

Customer care via digital channels as we know it is going to change substantially in the near future. To date, many brands have used social media platforms as a place for providing customer care, addressing customers’ specific questions, and fixing problems. In the future, social media-based customer care is expected to become even more customized, personalized, and ubiquitous. Customers will be able to engage with firms anywhere and anytime, and solutions to customers’ problems will be more accessible and immediate, perhaps even pre-emptive using predictive approaches (i.e., before a customer even notices an issue or has a question pop into their mind).

Even today, we observe the benefits that companies gain from connecting with customers on social media for service- or care-related purposes. Customer care is implemented in dedicated smartphone apps and via direct messaging on social media platforms. However, it appears that firms want to make it even easier for customers to connect with them whenever and wherever they might need. Requiring a customer to download a brand specific app or to search through various social media platforms to connect with firms through the right branded account on a platform can be a cumbersome process. In those cases, customers might instead churn or engage in negative WOM, instead of connecting with the firm to bring up any troubles they might have.

The near future of customer care on social media appears to be more efficient and far-reaching. In a recent review on the future of customer relationship management, Haenlein ( 2017 ) describes “invisible CRM” as future systems that will make customer engagement simple and accessible for customers. New platforms have emerged to make the connection between customer and firm effortless. Much of this is via instant messaging applications for businesses, which several leading technology companies have recently launched as business-related features in existing platforms (e.g., contact business features in Facebook Messenger and WhatsApp or Apple’s Business Chat).

These technologies allow businesses to directly communicate via social media messaging services with their customers. Amazon, Apple, Facebook, and Google are in the process, or have already released early versions of such platforms (Dequier 2018 ). Customers can message a company, ask them questions, or even order products and services through the messaging system, which is often built around chatbots and virtual assistants. This practice is expected to become more widespread, especially because it puts brands and companies into the social media messaging platforms their customers already use to communicate with others, it provides quicker—even instantaneous—responses, is economically scalable through the use of AI-driven chatbots, and, despite the use of chatbots, can provide a more personalized level of customer service.

Another area that companies will greatly improve upon is data collection and analysis. While it is true that data collection on social media is already pervasive today, it is also heavily scrutinized. However, we believe that companies will adapt to the latest regulation changes (e.g., GDPR in Europe, CCPA in California) and improve on collecting and analyzing anonymized data (Kakatkar and Spann 2018 ). Furthermore, even under these new regulations, personalized data collection is still allowed, but severely limits firm’s abilities to exploit consumers’ data, and requires their consent for data collection.

We believe that in the future, companies will be able recognize early indications of problems within customer chatter, behavior, or even physiological data (e.g., monitoring the sensors in our smart watches) before customers themselves even realize they are experiencing a problem. For example, WeWork, the shared workspace company, collects data on how workers move and act in a workspace, building highly personalized workspaces based on trends in the data. Taking this type of approach to customer care will enable “seamless service,” where companies would be able to identify and address consumer problems when they are still small and scattered, and while only a small number of customers are experiencing problems. Customer healthcare is a pioneer in this area, where using twitter and review sites were shown to predict poor healthcare quality (Greaves et al. 2013 ), listen to patients to analyze trending terms (Baktha et al. 2017 ; Padrez et al. 2016 ), or even predict disease outbreaks (Schmidt 2012 ).

Companies, wanting to better understand and mimic human interactions, will invest a lot of R&D efforts into developing better Natural Language Processing, voice and image recognition, emotional analysis, and speech synthesis tools (Sheth 2017 ). For example, Duplex, Google’s latest AI assistant, can already call services on its own and seamlessly book reservations for their users (Welch 2018 ). In the future, AI systems will act as human ability augmenters, allowing us to accomplish more, in less time, and better results (Guszcza 2018 ).

For marketers, this will reduce the need for call centers and agents, reducing points of friction in service and increasing the convenience for customers (Kaplan and Haenlein 2019 ). However, some raise the question that the increased dependence on automation may result in a loss of compassion and empathy. In a recent study, Force (2018) shows that interacting with brands on social media lowered people’s empathy. In response to such concerns, and to educate and incentivize people to interact with machines in a similar way they do with people, Google programmed their AI assistant to respond in a nicer way if you use a polite, rather than a commanding approach (Kumparak 2018 ). While this might help, more research is needed to understand the effect of an AI rich world on human behavior. As well, future research can examine how consumer generated data can help companies preemptively predict consumer distress. Another interesting path for research would be to better understand the difference in consumer engagement between the various platforms, and the long-term effects of service communications with non-human AI and IoT.

Social media as a political tool

Social media is a platform to share thoughts and opinions. This is especially true in the case of disseminating political sentiments. Famously, President Barack Obama’s victory in the 2008 election was partially attributed to his ability to drive and engage voters on social media (Carr 2008 ). Indeed, Bond et al. ( 2012 ) have shown that with simple interventions, social media platforms can increase targeted audiences’ likelihood of voting. Social media is considered one of the major drivers of the 2010 wave of revolutions in Arab countries, also known as the Arab Spring (Brown et al. 2012 ).

While social media is not new to politics, we believe that social media is transitioning to take a much larger role as a political tool in the intermediate future. First evidence for this could be seen in the 2016 U.S. presidential election, as social media took on a different shape, with many purported attempts to influence voter’s opinions, thoughts, and actions. This is especially true for then-candidate and now-President Donald Trump. His use of Twitter attracted a lot of attention during the campaign and has continued to do so during his term in office. Yet, he is not alone, and many politicians changed the way they work and interact with constituents, with a recent example of Congresswoman Alexandria Ocasio-Cortez that even ran a workshop for fellow congress members on social media (Dwyer 2019 ).

While such platforms allow for a rapid dissemination of ideas and concepts (Bonilla and Rosa 2015 ; Bode 2016 ), there are some, both in academia and industry that have raised ethical concerns about using social media for political purposes. Given that people choose who to follow, this selective behavior is said to potentially create echo chambers, wherein, users are exposed only to ideas by like-minded people, exhibiting increased political homophily (Bakshy et al. 2015 ). People’s preference to group with like-minded people is not new. Social in-groups have been shown to promote social identification and promote in-group members to conform to similar ideas (Castano et al. 2002 ; Harton and Bourgeois 2004 ). Furthermore, it was also shown that group members strongly disassociate and distance themselves from outgroup members (Berger and Heath 2008 ; White and Dahl 2007 ). Thus, it is not surprising to find that customized newsfeeds within social media exacerbate this problem by generating news coverage that is unique to specific users, locking them in their purported echo chambers (Oremus 2016 ).

While social media platforms admit that echo chambers could pose a problem, a solution is not clear (Fiegerman 2018 ). One reason that echo chambers present such a problem, is their proneness to fake news. Fake news are fabricated stories that try to disguise themselves as authentic content, in order to affect other social media users. Fake news was widely used in the 2016 U.S. elections, with accusations that foreign governments, such as Iran and Russia, were using bots (i.e., online automatic algorithms), to spread falsified content attacking Hillary Clinton and supporting President Trump (Kelly et al. 2018 ). Recent research has furthermore shown how the Chinese government strategically uses millions of online comments to distract the Chinese public from discussing sensitive issues and promote nationalism (King et al. 2017 ). In their latest incarnation, fake news uses an advanced AI technique called “Deep Fake” to generate ultra-realistic forged images and videos of political leaders while manipulating what those leaders say (Schwartz 2018 ). Such methods can easily fool even the sharpest viewer. In response, research has begun to explore ways that social media platforms can combat fake news through algorithms that determine the quality of shared content (e.g., Pennycook and Rand 2019 ).

One factor that has helped the rise of fake news is echo chambers. This occurs as the repeated sharing of fake news by group members enhance familiarity and support (Schwarz and Newman 2017 ). Repetition of such articles by bots can only increase that effect. Recent research has shown that in a perceived social setting, such as social media, participants were less likely to fact-check information (Jun et al. 2017 ), and avoided information that didn’t fit well with their intuition (Woolley and Risen 2018 ). Schwarz and Newman ( 2017 ) state that misinformation might be difficult to correct, especially if the correction is not issued immediately and the fake news has already settled into the minds of users. It was also shown that even a single exposure to fake news can create long term effect on users, making their effect larger than previously thought (Pennycook et al. 2019 ).

Notably, some research has found that exposure to opposing views (i.e., removing online echo chambers) may in fact increase (versus decrease) polarization (Bail et al. 2018 ). Accordingly, more work from policy makers, businesses, and academics is needed to understand and potentially combat political extremism. For example, policy makers and social media platforms will continually be challenged to fight “fake news” without censoring free speech. Accordingly, research that weighs the risk of limited freedom of expression versus the harms of spreading fake news would yield both theoretical and practically meaningful insights.

The far future

In this section, we highlight three emerging trends we believe will have a have long-term influence on the future of social media. Note that although we label these trends as being in the “far” future, many of the issues described here are already present or emerging. However, they represent more complex issues that we believe will take longer to address and be of mainstream importance for marketing than the six issues discussed previously under the immediate and near futures.

Increased sensory richness

In its early days, the majority of social media posts (e.g., on Facebook, Twitter) were text. Soon, these platforms allowed for the posting of pictures and then videos, and separate platforms dedicated themselves to focus on these specific forms of media (e.g., Instagram and Pinterest for pictures, Instagram and SnapChat for short videos). These shifts have had demonstrable consequences on social media usage and its consequences as some scholars suggest that image-based posts convey greater social presence than text alone (e.g., Pittman and Reich 2016 ). Importantly however, a plethora of new technologies in the market suggest that the future of social media will be more sensory-rich.

One notable technology that has already started infiltrating social media is augmented reality (AR). Perhaps the most recognizable examples of this are Snapchat’s filters, which use a device’s camera to superimpose real-time visual and/or video overlays on people’s faces (including features such as makeup, dog ears, etc.). The company has even launched filters to specifically be used on users’ cats (Ritschel 2018 ). Other social media players quickly joined the AR bandwagon, including Instagram’s recent adoption of AR filters (Rao 2017 ) and Apple’s Memoji messaging (Tillman 2018 ). This likely represents only the tip of the iceberg, particularly given that Facebook, one of the industry’s largest investors in AR technology, has confirmed it is working on AR glasses (Constine 2018 ). Notably, the company plans to launch a developer platform, so that people can build augmented-reality features that live inside Facebook, Instagram, Messenger and Whatsapp (Wagner 2017 ). These developments are supported by academic research suggesting that AR often provides more authentic (and hence positive) situated experiences (Hilken et al. 2017 ). Accordingly, whether viewed through glasses or through traditional mobile and tablet devices, the future of social media is likely to look much more visually augmented.

While AR allows users to interact within their current environments, virtual reality (VR) immerses the user in other places, and this technology is also likely to increasingly permeate social media interactions. While the Facebook-owned company Oculus VR has mostly been focusing on the areas of immersive gaming and film, the company recently announced the launch of Oculus Rooms where users can spend time with other users in a virtual world (playing games together, watching media together, or just chatting; Wagner 2018 ). Concurrently, Facebook Spaces allows friends to meet online in virtual reality and similarly engage with one another, with the added ability to share content (e.g., photos) from their Facebook profiles (Whigham 2018 ). In both cases, avatars are customized to represent users within the VR-created space. As VR technology is becoming more affordable and mainstream (Colville 2018 ) we believe social media will inevitably play a role in the technology’s increasing usage.

While AR and VR technologies bring visual richness, other developments suggest that the future of social media might also be more audible. A new player to the social media space, HearMeOut, recently introduced a platform that enables users to share and listen to 42-s audio posts (Perry 2018 ). Allowing users to use social media in a hands-free and eyes-free manner not only allows them to safely interact with social media when multitasking (particularly when driving), but voice is also said to add a certain richness and authenticity that is often missing from mere text-based posts (Katai 2018 ). Given that podcasts are more popular than ever before (Bhaskar 2018 ) and voice-based search queries are the fastest-growing mobile search type (Robbio 2018 ), it seems likely that this communication modality will accordingly show up more on social media use going forward.

Finally, there are early indications that social media might literally feel different in the future. As mobile phones are held in one’s hands and wearable technology is strapped onto one’s skin, companies and brands are exploring opportunities to communicate to users through touch. Indeed, haptic feedback (technology that recreates the sense of touch by applying forces, vibrations, or motions to the user; Brave et al. 2001 ) is increasingly being integrated into interfaces and applications, with purposes that go beyond mere call or message notifications. For example, some companies are experimenting with integrating haptics into media content (e.g., in mobile ads for Stoli vodka, users feel their phone shake as a woman shakes a cocktail; Johnson 2015 ), mobile games, and interpersonal chat (e.g., an app called Mumble! translates text messages into haptic outputs; Ozcivelek 2015 ). Given the high levels of investment into haptic technology (it is predicted to be a $20 billion industry by 2022; Magnarelli 2018 ) and the communicative benefits that stem from haptic engagement (Haans and IJsselsteijn 2006 ), we believe it is only a matter of time before this modality is integrated into social media platforms.

Future research might explore how any of the new sensory formats mentioned above might alter the nature of content creation and consumption. Substantively-focused researchers might also investigate how practitioners can use these tools to enhance their offerings and augment their interactions with customers. It is also interesting to consider how such sensory-rich formats can be used to bridge the gap between the online and offline spaces, which is the next theme we explore.

Online/offline integration and complete convergence

A discussion occurring across industry and academia is on how marketers can appropriately integrate online and offline efforts (i.e., an omnichannel approach). Reports from industry sources have shown that consumers respond better to integrated marketing campaigns (e.g., a 73% boost over standard email campaigns; Safko 2010 ). In academia meanwhile, the majority of research considering online promotions and advertisements has typically focused on how consumers respond to these strategies through online only measures (e.g., Manchanda et al. 2006 ), though this has begun to change in recent years with more research examining offline consequences to omnichannel strategies (Lobschat et al. 2017 ; Kumar et al. 2017 ).

Considering the interest in integrated marketing strategies over the last few years, numerous strategies have been utilized to follow online and offline promotions and their impacts on behavior such as the usage of hashtags to bring conversations online, call-to-actions, utilizing matching strategies on “traditional” avenues like television with social media. While there is currently online/offline integration strategies in marketing, we believe the future will go even further in blurring the lines between what is offline and online to not just increase the effectiveness of marketing promotions, but to completely change the way customers and companies interact with one another, and the way social media influences consumer behavior not only online, but offline.

For brands, there are a number of possible trends in omnichannel marketing that are pertinent. As mentioned earlier, a notable technology that has begun infiltrating social media is augmented reality (AR). In addition to what already exists (e.g., Snapchat’s filters, Pokémon Go), the future holds even more possibilities. For example, Ikea has been working to create an AR app that allows users to take photos of a space at home to exactly , down to the millimeter size and lighting in the room, showcase what a piece of furniture would look like in a consumer’s home (Lovejoy 2017 ). Another set of examples of AR comes from beauty company L’Oréal. In 2014 for the flagship L’Oréal Paris brand they released a mobile app called Makeup Genius that allowed consumers to virtually try on makeup on their phones (Stephen and Brooks 2018 ). Since then, they have developed AR apps for hair color and nail polish, as well as integrating AR into mobile ecommerce webpages for their luxury beauty brand Lancôme. AR-based digital services such as these are likely to be at the heart of the next stage of offline/online integration.

AR, and similar technology, will likely move above and beyond being a tool to help consumers make better decisions about their purchases. Conceivably, similar to promotions that currently exist to excitse consumers and create communities, AR will be incorporated into promotions that integrate offline and online actions. For example, contests on social media will advance to the stage where users get to vote on the best use of AR technology in conjunction with a brand’s products (e.g., instead of users submitting pictures of their apartments to show why they should win free furniture, they could use AR to show how they would lay out the furniture if they were to win it from IKEA).

Another way that the future of online/offline integration on social media needs to be discussed is in the sense of a digital self. Drawing on the extended self in the digital age (Belk 2013 ), the way consumers consider online actions as relevant to their offline selves may be changing. For example, Belk ( 2013 ) spoke of how consumers may be re-embodied through avatars they create to represent themselves online, influencing their offline selves and creating a multiplicity of selves (i.e., consumers have more choice when it comes to their self-representation). As research has shown how digital and social media can be used for self-presentation, affiliation, and expression (Back et al. 2010 ; Gosling et al. 2007 ; Toubia and Stephen 2013 ; Wilcox and Stephen 2012 ), what does it mean for the future if consumers can create who they want to be?

In addition, when considering digital selves, what does this mean for how consumers engage with brands and products? Currently, social media practice is one where brands encourage consumer engagement online (Chae et al. 2017 ; Godes and Mayzlin 2009 ), yet the implications for how these types of actions on the part of the brand to integrate online social media actions and real-life behavior play out are unclear. Research has begun to delve into the individual-level consequences of a consumer’s social media actions on marketing relevant outcomes (Grewal et al. 2019 ; John et al. 2017 ; Mochon et al. 2017 ; Zhang et al. 2017 ), however much is still unknown. As well, while there is recent work examining how the device used to create and view content online impacts consumer perceptions and behaviors (e.g., Grewal and Stephen 2019 ), to date research has not examined these questions in the context of social media. Therefore, future research could address how digital selves (both those held offline and those that only exist online), social media actions, and if the way consumers reach and use various platforms (i.e., device type, app vs. webpage, etc.) impact consumer behavior, interpersonal relationships, and brand-related measures (e.g., well-being, loyalty, purchase behaviors).

Social media by non-humans

The buzz surrounding AI has not escaped social media. Indeed, social bots (computer algorithms that automatically produce content and interact with social media users; Ferrara et al. 2016 ) have inhabited social media platforms for the last decade (Lee et al. 2011 ), and have become increasingly pervasive. For example, experts estimate that up to 15% of active Twitter accounts are bots (Varol et al. 2017 ), and that percentage appears to be on the rise (Romano 2018 ). While academics and practitioners are highly concerned with bot detection (Knight 2018 ), in the vast majority of current cases, users do not appear to recognize when they are interacting with bots (as opposed to other human users) on social media (Stocking and Sumida 2018 ). While some of these bots are said to be benign, and even useful (e.g., acting as information aggregators), they have also been shown to disrupt political discourse (as mentioned earlier), steal personal information, and spread misinformation (Ferrara et al. 2016 ).

Of course, social bots are not only a problem for social media users but are also a nagging concern plaguing marketers. Given that companies often assess marketing success on social media through metrics like Likes, Shares, and Clicks, the existence of bots poses a growing threat to accurate marketing metrics and methods for ROI estimation, such as attribution modelling (Bilton 2014 ). Similarly, when these bots act as “fake followers,” it can inflate the worth of influencers’ audiences (Bogost 2018 ). This can also be used nefariously by individuals and firms, as shown in a New York Times Magazine expose that documented the market used by some influencers to purchase such “fake” followers to inflate their social media reach (Confessore et al. 2018 ). As discussed above in relation to influencer marketing, where it has been commonplace for influencers to be paid for posts at rates proportionate to their follower counts, there have been perverse incentives to game the system by having non-human “fake” bot followers. This, however, erodes consumer trust in the social media ecosystem, which is a growing issue and a near-term problem for many firms using social media channels for marketing purposes.

However, there are instances when consumers do know they are interacting with bots, and do not seem to mind. For example, a number of virtual influencers (created with CGI, as mentioned earlier) seem to be garnering sizeable audiences, despite the fact they are clearly non-human (Walker 2018 ). One of the most popular of these virtual influencers, Lil Miquela, has over 1.5 million followers on Instagram despite openly confessing, “I am not a human being... I’m a robot” (Yurieff 2018 ). Future research might try to understand the underlying appeal of these virtual influencers, and the potential boundary conditions of their success.

Another category of social bots gaining increasing attention are therapy bots. These applications (e.g., “Woebot;” Molteni 2017 ) aim to support the mental health of users by proactively checking in on them, “listening” and chatting to users at any time and recommending activities to improve users’ wellbeing (de Jesus 2018 ). Similar bots are being used to “coach” users, and help them quit maladaptive behaviors, like smoking (e.g., QuitGenius; Crook 2018 ). Interestingly, by being explicitly non-human, these agents are perceived to be less judgmental, and might accordingly be easier for users to confide in.

Finally, the Internet of Things revolution has ushered in with it the opportunity for a number of tangible products and interfaces to “communicate” via social media. For example, in what started as a design experiment, “Brad,” a connected toaster, was given the ability to “communicate” with other connected toasters, and to tweet his “feelings” when neglected or under-used (Vanhemert 2014 ). While this experiment was deliberately designed to raise questions about the future of consumer-product relationships (and product-product “relationships”), the proliferation of autonomous tangible devices does suggest a future in which they have a “voice,” even in the absence of humans (Hoffman and Novak 2018 ).

Going forward, we believe the presence of bots on social media will be more normalized, but also more regulated (e.g., a recent law passed in California prevents bots from masquerading as humans; Smith 2018 ). Further, consumers and companies alike will be become increasingly interested in how bots communicate and interact with each other outside of human involvement. This brings up interesting potential research questions for academics and practitioners alike. How will the presence of non-humans change the nature of content creation and conversation in social media? And how should companies best account for the presence of non-humans in their attribution models?

Future research directions and conclusion

This article has presented nine themes pertinent to the future of social media as it relates to (and is perhaps influenced by) marketing. The themes have implications for individuals/consumers, businesses and organizations, and also public policymakers and governments. These themes, which represent our own thinking and a synthesis of views from extant research, industry experts, and popular public discourse, are of course not the full story of what the future of social media will entail. They are, however, a set of important issues that we believe will be worth considering in both academic research and marketing practice.

To stimulate future research on these themes and related topics, we present a summary of suggested research directions in Table 2 . These are organized around our nine themes and capture many of the suggested research directions mentioned earlier. As a sub-field within the field of marketing, social media is already substantial and the potential for future research—based on identified needs for new knowledge and answers to perplexing questions—suggests that this sub-field will become even more important over time. We encourage researchers to consider the kinds of research directions in Table 2 as examples of issues they could explore further. We also encourage researchers in marketing to treat social media as a place where interesting (and often very new) consumer behaviors exist and can be studied. As we discussed earlier in the paper, social media as a set of platform businesses and technologies is interesting, but it is how people use social media and the associated technologies that is ultimately of interest to marketing academics and practitioners. Thus, we urge scholars to not be overly enticed by the technological “shiny new toys” at the expense of considering the behaviors associated with those technologies and platforms.

Finally, while we relied heavily (though not exclusively) on North American examples to illustrate the emergent themes, there are likely interesting insights to be drawn by explicitly exploring cross-cultural differences in social media usage. For example, variations in regulatory policies (e.g., GDPR in the European Union) may lead to meaningful differences in how trust and privacy concerns manifest. Further, social media as a political tool might be more influential in regions where the mainstream media is notoriously government controlled and censored (e.g., as was the case in many of the Arab Spring countries). While such cross-cultural variation is outside the scope of this particular paper, we believe it represents an area of future research with great theoretical and practical value.

In reviewing the social media ecosystem and considering where it is heading in the context of consumers and marketing practice, we have concluded that this is an area that is very much still in a state of flux. The future of social media in marketing is exciting, but also uncertain. If nothing else, it is vitally important that we better understand social media since it has become highly culturally relevant, a dominant form of communication and expression, a major media type used by companies for advertising and other forms of communication, and even has geopolitical ramifications. We hope that the ideas discussed here stimulate many new ideas and research, which we ultimately hope to see being mentioned and shared across every type of social media platform.

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The authors thank the special issue editors and reviewers for their comments, and the Oxford Future of Marketing Initiative for supporting this research. The authors contributed equally and are listed in alphabetical order or, if preferred, order of Marvel superhero fandom from highest to lowest and order of Bon Jovi fandom from lowest to highest.

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Appel, G., Grewal, L., Hadi, R. et al. The future of social media in marketing. J. of the Acad. Mark. Sci. 48 , 79–95 (2020). https://doi.org/10.1007/s11747-019-00695-1

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Protecting Confidentiality in the Digital Ecosystem of Humanitarian Aid

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INTRODUCTION

Social media, news headlines, and podcasts implicitly and explicitly remind us of the digital misinformation maelstrom we navigate every day to understand the truth of current events. Misinformation feeds off the topics that impact our lives and draw our attention – war, health, politics, identity, fear, and empathy. Misinformation has a digital reach faster and wider than true information based on its nature of novelty and emotional instigation. [1] It draws from data leakages, twists the truth, incites emotional responses, and can undermine real efforts to protect and aid vulnerable communities. Many of the places and events targeted by misinformation are sites of humanitarian crises such as Gaza, Yamen, and Ukraine among many others. Humanitarian groups conceived to provide relief to vulnerable communities are susceptible to personal harm and impeded aid because the organizational structure is not equipped for misinformation and data security breaches. While propaganda and misinformation did not emerge in the contemporary, their scope, speed, and impact have exponentially increased as the world’s use of digital media for communication developed. The current state of misinformation and data leakages are threats to humanitarian efforts, especially the vital and nuanced task of humanitarian medical aid that now simultaneously relies on the digital information ecosystem.

Humanitarian efforts center on the four main principles of humanity, neutrality, impartiality, and operational independence. The United Nations Refugee Agency specifies that ‘humanity’ refers to addressing human suffering wherever it is found to ensure health and respect, ‘neutrality’ is to not engage in political, racial, religious, or ideological controversies, ‘impartiality’ is to provide aid based on need alone without bias and priority, and ‘operational independence’ is to conduct aid autonomous from agendas or actors in sectors such as political, economic, or military. [2] Medecins Sans Frontieres explicitly states neutrality, impartiality, independence, bearing witness, and accountability in their code of principles. Their statement on medical ethics is much more vague. It aims to “carry out our work with respect for the rules of medical ethics, in particular the duty to provide care without causing harm to individuals or groups. We respect patients’ autonomy, patient confidentiality, and their right to informed consent.” [3] Confidentiality is mentioned, but in the nondescript sense that could refer to confidentiality outlined in any number of medical ethics contexts.

Three most commonly referred to ethical codes in Western medicine are the Declaration of Helsinki, the Belmont Report, and the Code of the American Medical Association (AMA). The Declaration of Helsinki places confidentiality in the context of research and was written pre-digital age in the 1960s. [4] The Belmont Report does not mention confidentiality or patient privacy in its summation of medical ethics from 1978. [5] Lastly, the AMA’s Code of Medical Principles upholds that physicians “shall respect the rights of patients, colleagues, and other health professionals, and shall safeguard patient confidences and privacy within the constraints of the law.” [6] This AMA principle was adopted in 1957 and revised in 2001, still before the onset of widely accessible digital media. These three medical ethics codes are the standard of Western medicine, and yet they are decades obsolete when facing the harm of digital misinformation and data leakages. Humanitarian aid organizations cannot afford to rely on outdated medical ethical codes amid digital misinformation and data leakages.

Medical humanitarian relief groups such as Medecins Sans Frontiers, the International Medical Corps, the WHO Global Health and Peace Initiative, and the International Committee of the Red Cross, rely on the medical ethics defined in the aforementioned guides in addition to their humanitarian foundation. These codes, while useful, were written prior to the digital age. And, as our methods of communication, medical delivery, and global action have evolved and digitized, the ethics guiding medical practice should be updated to reflect this dramatic change. Humanitarian medical organizations need the digital ecosystem to store metadata for medical services such as patient history, blood type, metrics on locations in need of aid, missing person searches, and funding. The levels of data vulnerable to misconstruction and hacking exist on the personal and organizational levels. Individual providers and the organizational body should prioritize confidentiality. Thus humanitarian medical ethics should adapt to the reality of the digital age to not endanger the populations receiving aid and to not propagate harm.

Misinformation and data leakage can lead to microtargeting, defamation, provider endangerment, and other harms preventing medical service. The European Data Protection Supervisor details how the personal information collected by organizations, such as medical, can be stolen or misconstrued to affect microtargeting, placing individuals in the direct path of echo chambers, digital tracking, and manipulation. [7] The International Broadcasting Trust released a report in 2018 detailing the extent to which misinformation was impacting the humanitarian aid groups it broadcasts to. [8] For example, the report shared that rumors spread by right-wing political groups in 2017 falsely circulated that humanitarian groups in the Mediterranean were collaborating with child trafficking rings. [9] After causing defamation, the right-wing group sent a boat to block and detain the humanitarian group’s search and rescue boat. This was one incident among many where providers and patients were put in harm’s way through misinformation and the misuse of location data. Other disinformation campaigns can be carried out by governments as well; in Syria and Ukraine, the Russian government has been specifically targeting Red ross and White Helmets. [10] Beneficial medical services cannot be delivered if providers and patients are targeted. In January of 2022, the International Association of the Red Cross was hacked. Approximately 515,000 vulnerable persons’ data was leaked and became inaccessible to the IARC providers. [11] If an organization cannot protect access to its digital ecosystem, humanitarian medical aid efforts can be rendered ineffective.

Additionally, misinformation and breached data cause the less immediate but more widely impactful harm of distrust. Stakeholders and funding sources can withdraw from supporting medical humanitarian aid organizations. Beneficial medical services cannot be offered if there is no monetary backing. Providers and patients also have their own digital devices and means of communication which can lead to sensitive information being shared online or with non-neutral parties. If a patient cannot trust their provider or the organization a provider acts in the name of, medical services can be refused. Beneficial medical service cannot be conducted if the trust of the patient is compromised by humanitarian groups failing to prioritize patient confidentiality. Confidentiality should be prioritized in humanitarian medical aid to safeguard against the extended harms of data leakage, misinformation, and malintent.  

Some critiques may postulate that due to the uniqueness of each community aided by medical humanitarian organizations, over-standardization from rigid ethical codes may occur, that standardization can lead to inflexibility with communities and render aid strategies ineffective. However, the reality is that ethical frameworks make sure that individual actors are not monolithic – they allow for collaboration and joint work. The WHO Global Health and Peace Initiative’s recent adoption of conflict sensitivity, along with other organizations’ additions of similar language, ensure that there is a feedback loop incorporated into the ethical code to mitigate unintended harm. Thus, ethical codes are helping providers to respond in unprecedented situations with consciousness to harm propagation. In events of limited time and of crisis, comprehensive ethical codes are especially beneficial because we rely on habits and pre-established information banks.

Humanitarian medical ethics should include a specific guide for confidentiality. Without forethought and the integration of traditional and digital confidentiality as a main tenant, medical humanitarian organizations will continue to act retrospectively. Trust in stakeholder-provider-patient relationships will continue to disintegrate. The current status quo of medical ethics in the humanitarian aid sector poses multiple risks for providers and patients whereas adopting stronger confidentiality language is a tangible step towards the protection of vulnerable communities from the harms of digital misinformation and data leakage.

[1] Vosoughi, Soroush, Deb Roy, and Sinan Aral. “The Spread of True and False News Online.” Science 359, no. 6380 (2018): 1146–51. https://doi.org/10.1126/science.aap9559 . 

[2] “Conflict Sensitivity and the Centrality of Protection.” The Global Portection Cluster , March 2022. https://www.globalprotectioncluster.org/sites/default/files/2023-03/220318_gpc_-_conflict_sens.pdf . 

[3] “Our Charter and Principles.” MEDECINS SANS FRONTIERES - MIDDLE EAST. Accessed December 24, 2023. https://www.msf-me.org/about-us/principles/our-charter-and-principles#:~:text=We%20give%20priority%20to%20those,of%20governments%20or%20warring%20parties.&text=The%20principles%20of%20impartiality%20and%20neutrality%20are%20not%20synonymous%20with%20silence . 

[4] “WMA - The World Medical Association-WMA Declaration of Helsinki – Ethical Principles for Medical Research Involving Human Subjects.” The World Medical Association. Accessed December 24, 2023. https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/ .

[5] Office for Human Research Protections (OHRP). “ The Belmont Report.” United States Department of Health and Human Services , September 27, 2022. https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/read-the-belmont-report/index.html .

[6] American Medical Association . “The Code .” AMA principles of Medical Ethics. Accessed December 24, 2023. https://code-medical-ethics.ama-assn.org/principles .

[7] “EDPS Opinion on Online Manipulation and Personal Data .” European Data Protection Supervisor. Accessed December 24, 2023. https://edps.europa.eu/sites/edp/files/publication/18-03-19_online_manipulation_en.pdf . 

[8] Robin. “Faking It: Fake News and How It Impacts on the Charity Sector.” International Broadcasting Trust, March 13, 2020. https://www.ibt.org.uk/reports/faking-it/ . 

[9] Reed, B. “Charities Colluding with Traffickers? Fake News.” The Guardian, February 15, 2018. https://www.theguardian.com/global-development/2018/feb/15/charities-aid-agencies-fake-news-says-report . 

[10] Sant, Shannon Van. “Russian Propaganda Is Targeting Aid Workers.” Foreign Policy, August 1, 2022. https://foreignpolicy.com/2022/08/01/russia-disinformation-ukraine-syria-humanitarian-aid-workers/ .

[11] International Committee of the Red Cross. “Hacking the Data of the World’s Most Vulnerable Is an Outrage.” International Committee of the Red Cross, October 27, 2022. https://www.icrc.org/en/document/hacking-data-outrage .  

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A generative AI reset: Rewiring to turn potential into value in 2024

It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .

With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.

Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.

Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.

Figure out where gen AI copilots can give you a real competitive advantage

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.

To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.

Copilot examples across three generative AI archetypes

  • “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
  • “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
  • “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.

Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

Upskill the talent you have but be clear about the gen-AI-specific skills you need

By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.

A sample of new generative AI skills needed

The following are examples of new skills needed for the successful deployment of generative AI tools:

  • data scientist:
  • prompt engineering
  • in-context learning
  • bias detection
  • pattern identification
  • reinforcement learning from human feedback
  • hyperparameter/large language model fine-tuning; transfer learning
  • data engineer:
  • data wrangling and data warehousing
  • data pipeline construction
  • multimodal processing
  • vector database management

The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).

It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.

While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.

Form a centralized team to establish standards that enable responsible scaling

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.

While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).

For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.

Set up the technology architecture to scale

Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.

Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:

  • Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
  • Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
  • Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.

Ensure data quality and focus on unstructured data to fuel your models

The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI:

  • Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
  • Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
  • Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.

Build trust and reusability to drive adoption and scale

Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.

One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.

Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.

Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.

While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.

In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.

Eric Lamarre

The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

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