Case Study: The Leader’s Strategic Mindset for AI Success

Reprinted with permission from Management and Business Review. The original title is 'The Leader's Strategic Mindset: A Key Factor for AI Success.'

AI is currently one of the most in-demand digital technologies for driving businesses forward. Its allure to enterprises is the ability to execute tasks that previously could only be done by the human brain – and do so much faster and increasingly better. Because of its utility and efficiency, adoption has been rising: The size of the global AI market is expected to reach $267 billion by 2027. And yet to fully optimize the use of AI, it is important that enterprises consider carefully what mindset should govern their strategic decision-making for all use cases, such as enhancing customer engagement. AI can escape some human cognitive pitfalls but will never be bias-free as its usage is determined by the leaders’ underlying strategic mindset.

It is one’s strategic mindset that determines the answer to the question, ‘why AI?’ The mindset defines how leaders understand the opportunity of AI and how to apply it in the context of everyday business interaction. But a mindset is akin to a human ‘black box’ where habitual mental ‘programming’ formed by past experiences and assumptions give rise to a take-it-as-a-given reality.

Related:Case Study: AI for Customer Engagement at Google

March and Simon found that leaders rely on simplified mindsets that are hampered by bounded rationality when facing complex problems in the market. It highlights why leaders are not only suboptimal decision-makers, but decision-making is also heavily biased by the underlying mindsets. So, assumptions about AI deployment and future business scenarios are influenced by existing mindsets that hinder enterprises in fully exploring new opportunities embedded in AI.

Finding out the right ‘why’ can be challenging, as it depends on what the leaders want to achieve, who are the intended customers, and what are their needs that AI can help them meet. Many leaders prefer to skip that part and instead focus on the ‘what AI’ and ‘how AI’ questions.

Four mindsets

The Strategic Mindset Framework depicts four ways of engaging with customers to help leaders identify which strategic mindset fits them best for effective customer engagement with AI.

Left side of the model represents classical marketing interaction modes. Quadrant A represents the ‘cold sale’ − analytical use of AI as a tool to push products and services to consumers. Quadrant B represents being observant of consumers’ preferences, needs and motivations and using AI to bring new insights for more meaningful customer engagement. The right side of the model represents more customer-involving types of engagement reflected in modern marketing thinking. Quadrant C represents using AI to attain new and visionary insights from consumers by inviting them along on a collaborative and innovative journey, and Quadrant D represents a more empowering and sustainable approach to business engagement with customers, using AI to entice consumers to make informed decisions that make a positive impact on their own lives as well as on their communities and even the planet.

Related:Mastercard: Using AI to Spot Micro Trends for Effective Customer Engagement

Enterprises usually have an over-arching mindset that frames their customer engagement strategy, but aspects of the other three mindsets are often also in play in support of the governing mindset, which can affect what AI tools are used. As the mindsets are not mutually exclusive, their influence on decision-making regarding AI should be aligned to serve a common strategic direction.

For instance, the goal of using AI for delivering a great customer experience may easily be overruled by other priorities such as using AI to save money, cut lead times, for process optimization or data compliance and cyber security. A vital task for leaders is therefore to become aware of how their mindset limits their outlook and, consequently, their ability to use AI in new ways to engage with potential and existing customers.

Related:Case Study: Candy Giant Mars Uses AI to Test Which Ads Lead to Sales

The Promote & Sell mindset

Leaders who rely on a classic transactional ‘Business-to-Customer’ interaction logic use AI for automating marketing and sales operations to increase sales, enhance marketing, and improve operational efficiency and reach more customers faster. AI can replace the early stages of the traditional sales funnel and fortify the sales pipeline and online stores to boost sales efficiency, manage customer relationships in real time, identify cross-selling opportunities, and predict customer behavior.

For example, Facebook and Google are running multibillion-dollar ad businesses offering AI capabilities for ad management. Support chatbots, algorithmic trading, and entertainment recommendations enable ongoing and smarter offers that enhance customer purchases. Bots taking on the selling, evaluating, and closing of customers deals reduce the need for expensive, commission-based sales agents. Amazon has benefitted from this mindset of using AI applications across e-commerce, logistics, and warehousing, ranging from offering immediate recommendations to Alexa-enabled voice shopping.

Also, Walmart uses AI to help customers with its Personal Shopper program that suggests the best substitute for an out-of-stock item. Macy’s is testing cognitive AI technology powered by IBM's Watson AI tech to help customers navigate its stores. The smartphone-based assistant, Macy’s on Call, can answer questions such as where to find products or brands, services, and facilities in the store. AI replacing some of the human shopping assistants can optimize sales and increase service reliability.

Driven by the Promote & Sell mindset, enterprises can tap into myriad connected digital devices and platforms and use behavioral real-time data for AI to process what customers consume and when, rather than the deeper needs that drive their consumption. Consequently, the value chain is tightly controlled, which ensures consistency, security, and value − yet it is vulnerable to a misalignment between offers and what consumers truly need.

The Listen & Learn mindset

To develop value propositions, it is increasingly vital to know what customers really want. Enterprises can use sophisticated AI to listen to customers and learn about their needs and behavior to apply targeted offers. AI can pick-up and employ emotional cues and languages in a human-like way that bring customers closer to the brand. AI can also analyze how customers feel by identifying emotional cues in the words customers use in real-time, which may generate insights into underlying motivational drivers.

For example, Kore.ai is a conversational AI software company. Its omni-channel conversational virtual assistant (Kore.ai BankAssist) uses AI-enabled Interactive Voice Response to drive contextualized customer experiences via voice and other digital channels. It can also personalize and automate business customer journeys through highly accurate natural conversations with maximum containment. Pre-integrated with major core banking, cards, digital banking, and bill pay, banking customers can start conversations in one channel and complete them in a different channel without losing context.

Companies can gain full operational visibility into the customer experience using AI Rating tools, which uses company-specific survey questions to rate the interactions for which there was no customer feedback. Enterprises, such as Home Depot, JPMorgan Chase, Starbucks, and Nike successfully apply AI to monitor customer experiences and behavior and generate real-time insights to ensure personalized, seamless omnichannel customer experiences and to intervene in a timely manner for effective service recovery.

Facebook, Instagram, and TikTok tap into personalized social media data streams and use AI to build understanding of how to create relevant offerings that are based on both social and emotional needs and behavior. Siri and Google silently listen to users via smartphones and account for location and movements history while the telecom giant Comcast uses Pointillist, an AI and ML customer-journey analytics software to rapidly diagnose problems and identify how to increase customer satisfaction and employ a fast recovery when failing.

Thus, enterprises can create unique advantages with value propositions that increase customer satisfaction by ensuring that internal capabilities and consumer insights are better than those of competitors at satisfying consumer needs. It brings another advantage as customers who receive relevant propositions are more likely to share their personal data and remain loyal.

The Connect & Collaborate mindset

Customers who prefer to further engage with enterprises will appreciate a Connect & Collaborate approach where enterprises benefit from co-creative and crowd-sourced business-with-customer engagement. Here, AI facilitates knowledge networks as well as personalized and informal interactions, thereby boosting ideas and knowledge flows between consumers and enterprises − leading to efficient creation, dissemination, and revisions of offerings.

With AI, enterprises can actively elicit customer inputs in real time to further improve their offerings and deepen relevance through continuous and collaborative real-time feedback that make user engagement more realistic, impulsive, and meaningful. For instance, one of Sony PlayStation’s most popular games, “The Last of Us,” uses AI to create dramatic performances in post-apocalyptic America. It gives characters the illusion of human intelligence when characters respond to the player with convincing animation while sounding and behaving in interesting ways.

Here, the use of AI reverses the traditional way of developing games. For instance, what drives the entire design of the enemy AI is that the producers developed characters from the start that make players believe that their enemies are real enough so that they feel bad about killing them. This type of innovative rethinking can be applied to other use cases.

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 uncover new patterns in art and sciences for ground-breaking discoveries: Meta Foresight explains how emerging AI tools can accelerate human imagination and expand accessibility of creative works across the globe.

Hence, the customer engagement is much more profound than in the contexts of the two previous mindsets, so applying the Connect & Collaborate mindset requires that enterprises adopt new norms and values, relinquish control of user communities and enhance knowledge-sharing with them. This mindset challenges traditional in-house R&D expertise as co-creation processes inspire out-of-house innovation, with crowdsourcing benefitting from the inherent appreciation that customers are co-delivering what they value.

The Empower & Engage mindset

This mindset often reflects a higher purpose for human and societal involvement with its integral focus on People, Planet, and Profit. We see a trend towards stakeholders calling for this type of business engagement, so we elaborate on this mindset to inspire leaders to explore the potential of AI for higher pursuits.

When enterprises make an effort to empower their customers and use AI to help them make smarter and better choices, it encourages customer advocacy while bringing new and better opportunities for people and enterprises to redefine social relations and act proactively on pressing societal issues. Based on a disruptive business-for-customer engagement, leaders also use AI to solve dilemmas and explore sustainable solutions to avoid causing long-term negative social and environmental impact.

For instance, Tesla has revolutionized the transportation market with its AI-supported, self-driving electric cars that make mobility more efficient and sustainable. The vehicles process a considerable amount of real-time data from cameras and use computer vision for full autonomy. Tesla uses Pytorch, originally developed by Facebook’s AI Research lab (FAIR), for training and other supporting tasks such as automated workflow scheduler, model threshold calibration, and passive tests simulation.

AI can also empower and engage customers when facilitating engagement in socio-political issues. For example, the artist Stephanie Dinkins uses AI and media tools to help foster dialogue on race, gender, aging, and history.

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 (now Quest) 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. Thus, the development of AI, AR, and VR may have broad social, political, and cultural implications for customer engagement using digital assistants and physical robots.

Lemonade is the first peer-to-peer insurance carrier, and they are scaling up in different areas of insurance (such as car, homeowners, pets, and life.). This platform enterprise is pairing AI with cognitive and behavioral psychology for continuous innovation of customer engagement with transferable AI capabilities.

Its use of AI underpins real-time engagement with millennial consumers, but it is Lemonade’s over-arching Empower & Engage approach that is framing its customer engagement to reverse the traditional insurance industry model, while its unique mindset-AI combinations determine its value propositions and market positioning. (The other mindsets primarily encompass the tactical uses of AI to optimize logistics, lower costs, offer targeted solutions and enhance customer engagement.)

Lemonade’s AI-assisted risk assessment that minimizes human errors help customize plans, so customers only pay for what they need. Moreover, the company has 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 three main bots to be playful, ease customer engagement, and offer real-time AI services for house renters. Lemonade’s AI Maya is a conversational virtual assistant that collects information, gives quotes, and handles payments. CX.AI answers customer questions, and AI Jim is the claims bot that handles about 30% of the claims and does much of the work before a case is passed on to a human.

Lemonade has also built a Forensic Graph, which uses AI and behavioral economics to help predict, detect, and block fraud. The direct relationship with the customer, rather than using an agent, makes for a very different business model that enhances its ability to innovate. (We do not use this case example to endorse Lemonade nor belittle its achievements. That its stock has been slumping ... does not detract from our purpose of illustration.)

Using AI and machine learning to settle claims considerably faster disrupts the oftentimes slow and reluctant insurance industry practices. Also, Lemonade’s ‘Giveback’ charity program empowers and engages customers to select charities to receive unclaimed money. The charity option lets the customer select a cause from a limited list of local or nationwide choices, which enhances customer involvement and sense of doing good. Designed customization services, fast processing, and easy navigation are empowering and engaging, as well as entertaining.

Today, leaders should think and act differently as the focus is shifting to customer engagement preferences becoming just as important as the profit and tech focus for future innovations.

Developing the right strategic mindset

It makes a critical difference for enterprises when they can deploy AI to better meet customer expectations than their competitors. What is the right rationale about using AI depends on what the leaders want to achieve, who their customers are, and what needs they have that AI can help them meet.

However, many enterprises operate with large product and service portfolios, and consequently serve different customer segments with a variety of preferences. In such a case, match the strategic mindset to the targeted customer segments – with the caveat that customer needs will likely change over time and could even conflict in different contexts. (For instance, when price and environmental concerns are deemed equally important, ongoing strategic adjustments and mindset alignments are necessary in fast-changing and competitive markets.)

The table below shows the various ways that leaders apply AI for relevant real-time customer engagement, which may help other leaders take their first steps. 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 reach these results with AI.

Future benefits of AI depend more on the underlying mindset than on AI technologies per se, so we provide guidelines for leaders on how to define their ‘why AI’ in customer engagement, based on the experience of two of the authors who have analyzed more than 1,000 leaders’ mindsets across many  companies and industries, and facilitated digital transformation processes that started with the analysis of the leadership team’s mindset. 

1. Identify the mindset that best fits you for effective customer engagement with AI − and be true to your ‘why.’

AI obviously provides 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 mindsets. Customers come with different needs, so tracking customer sentiment, preferences of engagement, and experiences in real time helps enterprises manage potential frictions that would affect customer experiences negatively.

Meaningful engagement is context-time dependent, and expectations should be measured, communicated, and met. However, replacing human services with AI does not necessarily improve customer value or business revenue but instead could result in bad service experiences. Not all consumers want AI engagement, so it is essential to balance the type of mindset with the type of industry and customer expectations. Be true to your ‘why’ and don’t call the customer king if they are treated as profit-generating servants.

Marketing guidelines in general recommend the Connect & Collaborate mindset and the Empower & Engage mindset, but a significant number of businesses still operate either with the Promote & Sell or the Listen & Learn mindset. The classical marketing orientations are persistent, but many SMEs are also not able to incorporate CRM systems or AI algorithms that enable intelligent follow-up interaction, and many products categories do not require extensive consumer engagement, such as fast-moving consumer goods.

2. Develop real-time capabilities in the enterprise that bring value to the customers.

The benefits that can be reaped from optimizing the dynamic AI environments is a sensitivity to aspects of real-time context for each mindset and a focus on the value that is added when matching a particular mindset with consumer preferences. To achieve a full integration of AI processes, enterprises need to bridge silos, democratize access to data,  reskill employees, and identify customer needs.

Increasingly, customers demand more personal and targeted engagement as well as corporate citizenship from the enterprises. As AI applications push enterprises towards faster cycles and shorter reaction times, the human and corporate conditions and capabilities come under immense pressure, which make them increasingly difficult to manage.

The impact of AI on human customer engagement may also depend on how well the AI tools work, for example ChatGPT. As competitors catch on, like other enhancing technologies, it may soon be considered a ‘hygiene factor’ that customers take for granted rather than provide a competitive edge.

Thus, it requires leaders to critically reflect on which mindset is most opportune and relevant for effective real-time customer engagement, including what it takes to implement such a mindset across all points of interactions throughout the customer journey with the enterprise.

3. Keep the right strategic foundation for AI.

While AI provides new opportunities for optimizing customer engagement and value creation, it easily risks being perceived as a nuisance if it oversteps moral and ethical boundaries. Often, IT investments are wasted partly because the optimal mindset of customer engagement is not identified and shared. As such, it is vital to explore how AI will be best suited to support that mindset before investing. A shared mindset among employees and leaders enables a clearer and more concise way to use AI, so the investments lead to success. When enterprises rely on leaders’ tacit mindsets for applying AI, they risk missing the opportunity to develop better AI solutions preferred by their customers.

Again, the strategic mindsets presented here are not necessarily mutually exclusive. Rather, combinations can be used to build successful AI-enabled customer engagement. But be mindful that leadership teams with multiple strategic mindsets can create internal inefficiencies and risks being perceived as opportunistic − like green washing − by customers across touchpoints during the customer journey.

While enterprises might embrace multiple mindsets during a transitional phase, it would represent an inefficient strategy if such positioning persisted in the long haul. When leaders become aware of their tacit mindset, they can question its logic and reasoning and thus evaluate the use of AI to optimize customer engagement, and through that engagement, assess its relevance and value. Mindset awareness thus enables much better leadership for gaining competitive advantage through effective customer engagement with AI.