IT University of Copenhagen
Copenhagen Business School
Omar A. El Sawy
Marshall School of Business,
University of Southern California
Businesses are increasingly using artificial intelligence (AI) to transform their processes and create new ways of engaging with customers. But leaders who project old assumptions about customer engagement onto the breathless use of AI risk creating a dangerous disconnect that will deprive their companies and their customers of value. Pernille Rydén, Torsten Ringberg, and Omar A. El Sawy present a strategic mindset framework to help leaders avoid this pitfall.
AI is currently one of the most in-demand digital technologies for driving businesses forward. Its allure to enterprises lies in its ability to execute tasks that previously could only be done by the human brain, and to do so faster and often better.1 Because of AI’s utility and efficiency, ever more businesses are adopting it. The global AI market is expected to reach $267 billion by 2027.2 And yet to derive full value from the use of AI, it is important that enterprise leaders consider carefully what mindset should govern their strategic decisions about AI’s use, including increasing customer engagement.3 AI can escape some human cognitive pitfalls but will never be bias-free since its usage is determined by the underlying strategic mindset of business leaders.
Strategic mindset determines how leaders answer the question, ‘why AI?’ The mindset defines how leaders understand the opportunity of AI and how they apply it to everyday business interactions. But a mindset is a human’s black box where habitual mental programming formed by past experiences and assumptions produce a take-it-as-a-given reality.
March and Simon4 found that leaders who rely on simplified mindsets are hampered by bounded rationality when they face complex problems in the market.5 Their results highlight not only why leaders are suboptimal decision-makers, but also why their decisions are heavily biased by their underlying mindsets.6 So leaders’ assumptions about AI deployment and future business scenarios are influenced by their existing mindsets, which may hinder their enterprises, efforts to explore the new opportunities of AI.7
Finding the right ‘why’ can be challenging. It depends on what leaders want to achieve, who their intended customers are, and what needs AI can help them meet. Many leaders tend to skip these steps and instead focus immediately on what AI they should use and how.
The strategic mindset framework helps leaders to identify which strategic mindset will best suit their use of AI to increase customer engagement by illustrating four ways of engaging with customers.
The left side of the model represents classical modes of marketing interaction. Quadrant A represents the cold sale, the analytical use of AI to push products and services to consumers. Quadrant B reflects observing consumers’ preferences, needs, and motivations, using AI to draw new insights to make customer engagement more meaningful. The right side of the model represents modern market thinking, with types of engagement that involve customers more. Leaders in Quadrant C use AI to glean new and visionary insights from consumers by inviting them to collaborate and innovate. And Quadrant D shows a more empowering and sustainable approach to business engagement with customers, using AI to entice consumers to make informed decisions that have a positive effect on their own lives and on their communities and the planet.
Enterprise leaders usually have an over-arching mindset that frames their customer engagement strategy, but aspects of the other three mindsets often support that governing mindset. This structure affects what AI tools the company uses and how. Since the mindsets are not mutually exclusive, their influence on decisions about AI should be aligned to serve a common strategic direction.
Leaders must therefore become consistently aware of how their mindset limits their outlook and, consequently, their ability to effectively use AI in new ways to engage with potential and existing customers.
For instance, the goal of using AI to deliver a great customer experience may be inadvertently overruled by other priorities such as using AI to save money, cut lead times, boost process optimization or data compliance, and build cyber security. Leaders must therefore become consistently aware of how their mindset limits their outlook and, consequently, their ability to effectively use AI in new ways to engage with potential and existing customers.
The Promote and Sell Mindset
Leaders who rely on a classic transactional business to customer interaction logic use AI to automate marketing and sales operations, increasing sales, enhancing marketing, improving operational efficiency, to reach more customers faster.9 AI can replace the early stages of the traditional sales funnel, fortifying the sales pipeline and online stores to boost efficiency, managing customer relationships in real time, identifying opportunities to cross-sell, and predicting customer behavior.10
Facebook and Google, for example have multibillion-dollar ad businesses that offer AI capabilities for ad management.11 AI support chatbots, algorithmic trading,12 and entertainment recommendations to make ongoing and smarter offers that increase customer purchases.13 Bots that directly evaluate and sell to customers reduce the need for expensive, commission- earning sales agents.14 Amazon has benefitted enormously from using AI applications across e-commerce, logistics, and warehousing, ranging from immediate recommendations to Alexa-enabled voice shopping.15
Walmart uses AI to help customers through its personal shopper program that suggests the best substitute for an outof- stock item.16 Macy’s is testing cognitive AI technology powered by IBM’s Watson AI to help customers navigate its stores. The smartphonebased assistant Macy’s on Call can answer questions about where to find products or brands, services, and facilities in the store.17 By using AI to replace some human shop assistants, companies can optimize their sales and make their service more reliable.
Enterprises driven by the promote and sell mindset can tap into myriad connected digital devices and platforms. AI can then use realtime behavioral data to understand what customers consume and when, rather than the deeper needs that drive their consumption. The resulting value chain is tightly controlled, which ensures consistency, security, and value, but it does risk misalignment between offers and what consumers truly need.
Table 1: Business-to-Customer AI Engagement
- Optimize production, distribution, and sales: Identify and anticipate product shortages and peaks due to bottlenecks in production or spikes in demand.18
- Reach more customers faster: Marketing campaigns using large sets of integrated customer and product data and employing analytical models can target large numbers of customers instantly.
- Increase product and service reliability: Deliver promotional and sales offers across a wide range of automated and face-to-face channels, such as the web, mobile, or call center agents.19
- Faster and cheaper products and services: Monitor customer behavior through instantly available live data feeds to improve services and to drive sales in enterprises.20
- Available offers and communication: Identify the priority of offers, based on factors such as customer lifetime value and offer value, and preassigned business rules, to identify the best offer for the customer.
- Less friction frees personal resources: Optimize self-service opportunities and enhance real-time, data-driven digital customer experience through virtual customer assistants and chatbots to drive marketing automation.
The Listen and Learn Mindset
To develop value propositions, creating offers that appeal to customers, it is increasingly vital that enterprises know what those customers really want.21 They can use sophisticated AI to listen to customers and learn about their needs and behavior in order to make targeted offers. AI can pick up and employ emotional cues and language in a human-like way that brings customers closer to the brand. AI can also analyze how customers feel by instantly identifying emotional cues in the words they use, which may generate insights into their underlying motivations.22
For example, Kore.ai is a conversational AI software company. Its omnichannel virtual assistant, BankAssist, uses AI-enabled interactive voice response to communicate with customers in their own personal context. It can also personalize and automate customers’ relationships with businesses through highly accurate natural conversations with maximum containment, that is, the fewest possible human interventions.23 Because the system is integrated with major core banking, cards, digital banking, and bill pay, customers can start conversations in one channel and complete them in a different channel without interruption or loss of context.
Companies can scrutinize the entirety of the customer experience using AI rating tools, which use survey questions specific to each company to rate the interactions for which there is no customer feedback. Enterprises like Home Depot, JPMorgan Chase, Starbucks, and Nike use AI to monitor customer experiences and behavior and to generate real-time insights which ensure personalized, seamless, omnichannel customer experiences. 24 They can also use this data to intervene quickly when things go wrong, achieving effective service recovery.
Facebook, Instagram, and Tik-Tok tap into personalized social media data streams and use AI to learn how to create relevant offerings that are rooted in both social and emotional needs and behavior. Siri and Google silently listen to users through smartphones and track movements and location. Telecom giant Comcast uses Pointillist, an AI and machine learning customer journey analytics software, to rapidly diagnose problems and identify how to increase customer satisfaction and recover quickly from failure.25
So, by ensuring that their internal capabilities and consumer insights are better than those of their competitors, enterprises can increase customer satisfaction, creating unique value propositions. Moreover, customers who receive relevant propositions are more likely to share their personal data and remain loyal.
The Connect and Collaborate Mindset
Customers who prefer to further engage with enterprises will appreciate a connect and collaborate approach, in which enterprises benefit from cocreative and crowd-sourced business-with-customer engagement. Here, AI builds knowledge networks as well as personalized and informal interactions, increasing the flow of ideas and knowledge between consumers and enterprises, which allows those enterprises to efficiently create, disseminate, and revise its offerings.
Table 2: Business-from-Customer AI Engagement
- Instant personal customer data insights: Identify, gain, develop, and retain profitable customers, learning what they need by generating, monitoring, and measuring their sentiments and preferences.
- Optimize and segment processes to fit customer preferences: Content recommendation algorithms personalize the information on social media, search engines, smart speakers, and other applications.
- Integrate immediate customer ratings: Automate rating processes with AI rating tools for more actionable analytics.
- Customize recommendations and digital products and services: Offer highly personalized predictions and recommendations.
- Customize engagement to strengthen customer relationships: More accurate segmentation and computation of individual preferences and anticipatory interaction.
- Fast feedback from customer input increases relevance: Respond to customers’ increasing demand for faster and better services.
AI allows enterprises to actively solicit customer’s input, further improving their offerings and deepening their relevance while making user engagement more real, impulsive, and meaningful. For instance, one of Sony PlayStation’s most popular games, “The Last of Us,” uses AI to create dramatic performances set in post-apocalyptic America. It gives digital characters the appearance of human intelligence by allowing them to respond to each player through interesting speech and behavior, presented through convincing animation.26
This AI reverses the traditional method of developing games. For instance, the producers framed their enemy design around developing adversary characters that seem real enough that players feel bad about killing them. And this type of innovative rethinking can be applied to a range of uses.
AI-driven language processing tools can help people, businesses, and creators to collaborate more effectively and rethink problems of tomorrow.
AI-driven language processing tools can help people, businesses, and creators collaborate more effectively and rethink problems of tomorrow. For example, AI is used by artists and business innovators to make groundbreaking discoveries by studying patterns in the arts and sciences. Meta Foresight demonstrates how emerging AI tools can fire the human imagination and expand access to creative works around the globe.27
So for those with the connect and collaborate mindset, customer engagement is much more profound than it is in the previous mindsets, and so requires enterprises to adopt new norms and values, relinquish control of their user communities, and increase the exchange of knowledge with them. This mindset challenges the traditional dominance of in-house research and design (R&D) expertise, instead leading companies to emphasize cocreation processes that result in innovations outside the house, with crowdsourcing fueled by customers’ inherent appreciation of being able to help to deliver what they value.28
The Empower and Engage Mindset
With its focus on people, planet, and profit, this mindset is often driven by a higher purpose of increasing human and societal involvement.29 Stakeholders increasingly call for this type of business engagement, so we elaborate upon it in the hope of inspiring leaders to explore the potential of AI in higher pursuits.
When enterprises make an effort to empower their customers and use AI to help them make smarter and better choices, they encourage customer advocacy while bringing new and better opportunities for people and enterprises to redefine social relations and take a proactive approach to pressing societal issues. Starting from a disruptive business-for-customer approach, leaders also use AI to solve dilemmas and explore sustainable solutions so they can avoid causing long-term social and environmental harm.
Tesla, for example, has revolutionized the transportation market with its AI-supported, self-driving electric cars, making mobility more efficient and sustainable. The vehicles process a wealth of real-time data from cameras, using computer vision to inform their full autonomy.30 Tesla uses the PyTorch framework, originally developed by Facebook’s AI Research (FAIR) group, for training and other support tasks such as automated workflow scheduler, model threshold calibration, and passive tests simulation.31
AI can also empower customers to engage with sociopolitical issues.32 The artist Stephanie Dinkins uses AI and media tools to foster conversations about race, gender, aging, and history.33
AI can also empower customers to engage with sociopolitical issues.
Enterprises can use augmented reality (AR) and virtual reality (VR) to dramatically increase educational training and entertainment across a variety of instances. Meta’s Oculus 2 use spatial AI applications for consumers to explore the hiding place of Anne Frank and her family or to learn how to play the piano or to dance with a robot instructor.34 Thus, the development of AI, AR, and VR may have broad social, political, and cultural implications for customer engagement using both digital assistants and physical robots.
Lemonade is the first peer-to-peer insurance carrier, and it is scaling up in different areas of insurance, including car, homeowners, pets, and life.35 This platform enterprise36 is pairing transferrable AI with cognitive and behavioral psychology37 to continuously improve customer engagement.38
The company’s AI is the foundation of its real-time engagement with millennial consumers, but it is Lemonade’s overarching empower and engage approach that is reversing the traditional insurance industry model. Meanwhile, its unique combinations of mindset with AI fuel its value propositions and boost its market position. The previous mindsets, by contrast, focus primarily on tactical uses of AI to optimize logistics, lower costs, offer targeted solutions, and improve customer engagement.
Lemonade’s AI-assisted risk assessment minimizes human errors, helping to customize insurance plans, allowing customers to pay only for what they need. The company has also built a transparent fee model based on trust, lower costs, fast claim settlement, and doing social good through peer-to-peer insurance and partial profit sharing.
Lemonade designed its three main bots to be playful, ease customer interactions, and offer real-time services for house renters. Lemonade’s AI Maya is a conversational virtual assistant that collects information, gives quotes, and handles payments. CX.AI answers customers’ questions, and AI Jim is the claims bot that handles about 30 percent of claims and does much of the work before passing the case on to a human.
Lemonade has also built a forensic graph, which uses AI and behavioral economics to predict, detect, and block fraud. By maintaining a direct relationship with the customer, rather than working through an agent, Lemonade has created a very different business model that boosts its ability to innovate.
Using AI and machine learning41 to settle claims considerably faster disrupts the often slow and reluctant practices of the traditional insurance industry.42 And Lemonade’s Giveback charity program involves customers directly by allowing them to choose which charities receive each year’s unclaimed money.43 Customers find the company’s designed customization services, fast processing, and easy navigation empowering and engaging, as well as entertaining.
As customer engagement preferences become just as important to future innovations as profit and tech, today’s leaders must learn to think and act differently.
Table 3: Business-with-Customer AI Engagement
- Stimulate global real-time collaboration and cocreation: Create dynamic learning and interactive AI platforms.
- Test the functions and add to product and service innovation: Prototype testing of entertainment platforms.
- Participate in cocreation processes and application testing: Return value to customers in terms of improved services and products.
- Creative partnerships to strengthen business-customer relations: Fast collection and processing of open-source, interactive, user-controlled, online experience and knowledge.
Table 4: Business-for-Customer AI Engagement
- Solve dilemmas and explore sustainable solutions: Use AI to avoid long-term negative social and environmental impact.
- Show immediate action in ethical and political issues: AI can help identify and integrate the interests of stakeholders and higher purposes for the individual and community wellbeing.
- Serve engaging political causes: Use AI to create a sustainable business model that satisfies community needs and supports responsible market development.44
- Enable customers to make smarter and better decisions faster: Use AI to improve the conditions for customers as well as their networks for impactful responsibility-taking.
- Educate customers to critically reflect and to increase self-dependency: Use AI to help people manage local and global issues on their own.
- Ease customers emotionally and practically: Increase their self-dependence and help them make a positive difference to themselves and others.
Developing the Right Strategic Mindset
It is of critical importance that enterprises be able to deploy AI to meet their customers’ expectations better than their competitors. The goals of leaders and the identities and needs of their customers determine which AI rationale is right.
However, many enterprises have large portfolios of products and services, so they serve different customer segments with a variety of preferences. In such cases, managers must match their strategic mindset to the customer segments they’re targeting, knowing that customer needs will probably change over time and could even conflict in some situations. When customers consider price and environmental impact to be equally important, for example, managers need to continually adjust their strategies and mindsets to succeed in fast-changing and competitive markets.
Table 5 can guide leaders in taking their first steps by showing various ways to apply AI to create relevant, real-time customer engagement. The four business/ customer categories show the different outcomes of AI engagement for businesses (e.g., real-time operations) and for customers (e.g., customer convenience) and how to achieve these results with AI.
The future benefits of AI depend more on the underlying mindset than on AI technologies per se. Two of the authors have analyzed the mindsets of more than 1,000 leaders in many companies and industries, using the information to facilitate the digital transformation processes. The following guidelines are rooted in what they learned, and will help leaders to define their own reasons, their why, for using AI in customer engagement.
Identify the mindset for effective customer engagement with AI that best fits you − and be true to your why. AI offers opportunities across a broad range of company processes as well as market interactions. But to take full advantage of AI, it is essential to ascertain the customer’s preferred type of interaction, be true to it, and align your mindset. Customers come with different needs, so tracking customer sentiment, preferences of engagement, and experiences in real time will help enterprises to manage potential frictions that would put customers off.
Meaningful engagement depends on context and time so customers’ expectations should be measured, communicated, and met. However, replacing human services with AI does not necessarily improve customer value or business revenue; it could instead result in bad service experiences. Not all consumers want AI engagement, so it is essential to find a balance between mindset, industry, and customer expectations. Be true to your why and don’t call customers kings while treating them as profit-generating servants.
Marketing guidelines in general recommend the connect and collaborate mindset and the empower and engage mindset, but a significant number of businesses still operate under either promote and sell or listen and learn. The classical marketing orientations are persistent, but many small and medium sized enterprises (SMEs) cannot incorporate customer relationship management (CRM) systems or the AI algorithms that would follow up intelligently. Likewise, many categories of products, such as fast-moving consumer goods, do not require extensive consumer engagement.
Develop real-time capabilities within the enterprise that bring value to the customers. In optimizing their dynamic AI, managers can cultivate a sensitivity to how each mindset functions in real time, adding value for their companies by matching a given mindset with their consumers’ preferences. To fully integrate their AI processes, enterprises need to bridge silos, democratize access to data, retrain employees, and identify customer needs.
Increasingly, customers demand that enterprises offer more personal and targeted engagement as well as corporate citizenship.45 As AI applications push enterprises into faster marketing cycles and shorter reaction times, both corporations and individuals are under immense pressure, making them increasingly difficult to manage.
The impact of AI on customer engagement may also depend on how well the AI tools, like ChatGPT, work. As it becomes more prevalent, AI, like other enhancement technologies, customers may soon come to take it for granted, making it an essential, rather than a competitive edge.
Leaders should therefore reflect critically on which mindset is most opportune and relevant for effective real-time engagement with their customers, taking into account what it would take to foster that mindset at all points of customer interaction with the enterprise.
the right strategic foundation for AI. While AI provides new opportunities to optimize customer engagement and value creation, it can easily become a nuisance if it oversteps moral and ethical boundaries. IT investments are often wasted, in part because managers do not identify and share the optimal mindset for customer engagement.46 It is therefore vital that enterprises explore how AI can best support the chosen mindset before they invest. Employees and leaders who share a mindset will find clearer and more concise ways to use AI, ensuring that the investments pay off. When enterprises rely on leaders’ existing mindsets to apply AI, they risk failing to develop the AI solutions preferred by their customers.47
Again, the strategic mindsets presented here are not necessarily mutually exclusive. Managers can combine them to build successful AI-enabled customer engagement. But remember that leadership teams with multiple strategic mindsets can create internal inefficiencies and may be seen as opportunistic by customers. It was this kind of inconsistency that gave rise to the concept of greenwashing.
While enterprises might wish to embrace multiple mindsets during a transitional phase, it would be an inefficient strategy for the long haul. Once leaders become aware of their latent mindset, they can effectively examine their own logic and reasoning which in turn will allow them to evaluate how they can best use AI to optimize customer engagement. This analysis will then allow them to assess the relevance and value of AI to their enterprise’s particular situation.48 In short, mindset awareness fuels better leadership for companies hoping to use AI to gain a competitive advantage through effective customer engagement.
Pernille Rydén is Dean of Education at the IT University of Copenhagen, Denmark. She earned her PhD in strategic cognition of new technologies from Copenhagen Business School. Her research investigates the influence of strategic sensemaking and decision-making involving business-consumer engagement using digital technologies such as social media, big data, and AI as well as real-time management of businesses and brands in digital business platform ecosystems. firstname.lastname@example.org
Torsten Ringberg is a professor of marketing at Copenhagen Business School, Denmark. He earned a PhD from The Pennsylvania State University. His interests include sociocognitive theories related to how mental and cultural models and embodied cognition influence perception and understanding, and how consumers and managers create identity. His empirical approaches range from deep-qualitative (ZMET) to experimental methods. He investigates topics such as service recovery, B2B and B2C decision making, and the (ab)use of digital technologies. email@example.com
Omar A. El Sawy is Kenneth King Stonier Chair in Business Administration and a professor of information systems in the Data Sciences and Operations Department at the Marshall School of Business, University of Southern California. He earned a PhD from Stanford Business School. He is a fellow of the Association of Information Systems. He is interested in digital business strategy in messy environments, real-time management, the management of AI, and digital platform business models. firstname.lastname@example.org
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- cf. https://www.fool.com/investing/2022/03/30/automation-reducing-sales-agentjobs-lemonade/
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- Light Detection and Ranging (LIDAR) is a remote sensing method that uses light in the form of a pulsed laser to measure ranges (variable distances) to the Earth. These light pulses, combined with other data recorded by the airborne system, generate precise, three-dimensional information about the shape of the Earth and its surface characteristics.
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- Established in 2016. Alois, J. D. 2017. Lemonade Updates on Platform Growth. Adds 14,300 Customers in 8 Months, Crowdfund Insider.com, June 1, 2017, www.crowdfundinsider.com/2017/06/101283-lemonade-updates-platform-growthadds-14300-customers-8-months/
- Insurtech refers to technological innovations that improve the efficiency of the insurance industry cf. https://www.pwc.com/gx/en/industries/financialservices/fintech-survey/insurtech.html
- Insurance video: https://www.youtube.com/watch?v=flSLI2JmWVE
- We do not use this case example to endorse Lemonade nor belittle its achievements. That its stock has been slumping since March 2021 and has reported net loss does not detract from our purpose of illustration, cf. https://finance.yahoo.com/news/lemonadedown-75-disruptive-growth-172008105.html
- Lemonade employs around 500 people for tasks that cannot be automated. https://rickhuckstep.com/lemonade-5yranniversary/
- Data scientists have modeled an optimal mix of internal and external ‘reinsurance’. The amount available for Giveback will average less than 40% due to claims. https://www.mymoneyblog.com/lemonade-vs-mutual-insurance.html
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