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Customer Strategy Empowering AI Leadership

Contents

Introduction | Examples | Responsibilities | Oversight | Agenda | Resources | Endnotes

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Introduction

Artificial intelligence (AI) has begun to dramatically change the relationship between an organization and its customers. If it is implemented responsibly and ethically, AI can be used by organizations to increase the value provided to customers, patients and citizens through building a better understanding of prospects, personalizing interactions with customers and customizing products and services. According to a recent report from IBM, 77% of outperforming organizations cite customer satisfaction as a crucial value driver for AI.[1] However, recent high-profile media and regulatory incidents emphasize the need for a responsible approach to both AI deployment and the underlying customer data. It is important that boards understand how radically customer expectations are shifting – both in terms of the service and personalization they are learning to expect from AI-powered firms and in terms of their sensitivity to what they see as corporate abuse of the same power.

Companies are using AI to improve their customer proposition by:

  • Targeting the right customer at the right time with the right product: Traditional mass marketing is reaching its limits. AI means segmented and personalized propositions can be offered at scale. Prediction tools powered by AI help companies reach out to those customers who are more likely to buy their products at the right time. Online advertising platforms use AI to optimize and refine their targeting, placement and pricing propositions – driving new revenue for them and superior outcomes for corporate marketing teams. H&M has launched an AI-powered chat platform where customers can receive clothing item recommendations based on completing a short questionnaire.[2]
  • New service and product offerings: AI enables the creation of new businesses – for example, those offering medical diagnosis or superior recommendation engines. It can radically improve existing businesses, such as taxi services or hotel bookings, and provides new customer interfaces, for instance, the voice interactivity of Alexa. The use of AI to increase efficiency and effectiveness – to detect fraudulent activity on customer accounts, for example – will provide faster services, better products and, ultimately, lower prices. This will help create significant business model challenges for organizations.
  • Augmenting workers: AI improves customer interactions through human and machine collaboration. Chatbots provide a low-cost opportunity to scale service (and sales) channels, but the customer experience is optimized by having human staff in the loop for more complex interactions. While AI is better at processing large amounts of data for analysis, human staffers are better at judgement and interaction with customers. As an example of AI supporting people, a Japanese clothing retailer improved its sales by 10–30% through using AI to optimize staff allocation.[3]

However, there are growing concerns over transparency and privacy: The very detailed insights that AI offers companies in regard to their customers inevitably also raise challenges. There are two elements to this. First, concerns are evident in certain markets when data about individuals is not used responsibly or fails to meet privacy expectations. Second, the outcomes from AI-driven processes can be problematic, particularly if the AI’s process and output are not readily explicable. For instance, a customer might imagine that his or her loan was denied because an AI algorithm was biased. An organization using AI should consider not only how to increase customer satisfaction but also how to build customer trust. Emerging regulation, such as the GDPR, is a reminder that ethical and legal questions are especially focused on this aspect of AI.

This module will help board directors ensure management are examining AI’s potential to build better customer propositions, target them more effectively, deliver stronger service experiences and achieve higher levels of customer satisfaction. It provides directors with frameworks and questions to evaluate management’s actions independently in response to AI’s customer opportunities and challenges, the need to behave ethically and responsibly, and to prioritize the actions the board can take.

Examples

Enhance product offering

Enhance product offering by using AI tools and big data – for example, to support personalized product creation. With the use of machine learning, a sportswear manufacturer has been able to reduce its existing timeframe for turning trends into commercially saleable shoes from 18 months to just 24 hours. It does so by letting consumers design their own shoes online and then makes and ships them immediately. By analysing the co-created designs using machine learning, the company gains insight into future trends and can efficiently anticipate future demand.

Automate sales conversations

Use automated sales text conversations through a chatbot to enable the potential for simplified, high-volume sales. For example, 1-800-Flowers launched its IBM Watson-powered concierge service, Gwyn (which stands for “gifts when you need”), to help customers get more personalized results and better engage them – and reported that 80% of users liked using the chatbot.[4] Benefits include improved sales effectiveness and a reduction in average staffing cost per sale.

Automate customer service conversations

Use automated customer service text conversations through a chatbot to enable high-volume, fast-reaction customer support. However, unexpected questions may “break” the chatbot system, so consumers need to understand that they are interacting with a machine. KLM automated responses to over 50% of customer enquiries on social media by implementing a machine-learning chatbot from DigitalGenius. The benefits include limiting staff scaling costs.[5]

Monitor customer sentiment

Use machine learning to predict and monitor the sentiment of customers across touchpoints to identify general trends of satisfaction. For example, Wunder2, a British cosmetics brand start-up, analysed 500,000 customer Facebook posts through natural language processing capabilities to better understand customer sentiment and concerns.[6] Potential benefits include stronger customer retention and enhanced data on appropriate customer service activity.

Automate customer service text communication

Use automated customer service conversations through a chatbot to enable wider reach and expedited response time. Avianca (Latin American Airlines) worked with Accenture to launch Carla, which enabled them to offer a connection to their 28 million customers in a “simple and intuitive” way. It was one of the first virtual assistants to be launched in Colombia.[7] Potential benefits of this use case include shorter customer waiting times and lower staffing costs.

Responsibilities

Understanding the AI landscape and its role in the company’s strategy is an emerging area of board responsibilities for strategy oversight.[8]

According to the G20/OECD Principles of Corporate Governance:[9]

The corporate governance framework should ensure the strategic guidance of the company, the effective monitoring of management by the board, and the board’s accountability to the company and the shareholders.

To fulfil their responsibility to the company and its shareholders:

Board members should act on a fully informed basis, in good faith, with due diligence and care, and in the best interest of the company and the shareholders. (Principle VI.A)

When considering questions about the role of AI in the company’s customer strategy, board members must make good faith efforts to be fully informed about:

  • Management’s use and plans for applying AI in its strategy for serving and interacting with customers, including anticipated benefits as well as progress in achieving those benefits and mitigating possible internal and external risks associated with such a strategy
  • The impact of customer data on its business outcomes, and
  • Transparency of customer data usage, and potential biased outcomes.
The board should fulfil certain key functions, including reviewing and guiding corporate strategy, major plans of action, risk management policies and procedures, annual budgets and business plans; setting performance objectives; monitoring implementation and corporate performance; and overseeing major capital expenditures, acquisitions and divestitures; overseeing the process of disclosure and communications. (Principle VI.D.1&8)

AI-related topics that boards should review and guide may include:

  • Management’s approach to using AI within the company’s business and overall customer strategy
  • The alignment with and performance of the company’s current usage of AI to advance its strategy
  • The major actions and expenditures planned for using AI to interact with customers or use customer data, and their progress towards successful implementation
  • Management’s awareness, plans and actions for ameliorating the risks 1) of using AI in the organization’s strategy and 2) of violating customers’ privacy
  • Whether the company’s acquisitions are strengthening or affecting its ability to use AI to advance its customer strategy, and whether they introduce new risks
  • Whether management anticipate compliance with upcoming laws and regulations, and possible impacts on individuals’ rights and society, and
  • Whether management effectively communicate with customers to reduce their anxiety surrounding AI usage.
In order to fulfil their responsibilities, board members should have access to accurate, relevant and timely information. (Principle VI.F)
  • Board members should be able to obtain the information needed to fulfil their oversight responsibilities relating to AI.
  • Board members should have access, at no cost to them, to timely advice from qualified advisers.

The analysis in this section is based on general principles of corporate governance, including the G20/OECD Principles of Corporate Governance, 2015. It does not constitute legal advice and is not intended to address the specific legal requirements of any jurisdiction or regulatory regime. Boards are encouraged to consult with their legal advisers in determining how best to apply the principles discussed in this module to their company.

Oversight

This section includes three tools to help directors oversee management’s competitive AI customer strategy and implementation.

The knowledge assessment tool helps board members rate whether they possess, or have access to, the knowledge required to independently judge management’s knowledge and leadership on AI customer strategy.

View Appendix 1 for the knowledge assessment tool here

The performance review tool consists of questions boards can ask management about their knowledge of AI and customer strategy and the progress and performance of their actions. It offers the SCEPTIC framework to help directors assess the answers they receive.

View Appendix 2 for the performance review tool here

The guidance tool offers possible suggestions for further action in an “If, then” format.

View Appendix 3 for the guidance tool here

Agenda

Discussion guide

The 5A framework will help directors set the agenda for the initial meeting, prepare for discussions and anticipate follow-ups.

  • Attention: Establish why artificial intelligence is important for the company to improve customer experience and customer satisfaction. Focus on customer data, value propositions and the advantage of both AI and staff employees in sales, marketing and customer service. Gather information about customers’ new lifestyles, competitors’ strategy and the company’s current AI implementation. Understand the central ethical, regulatory and legal issues relating to both data use and AI deployment.
  • Alignment: Build the case for why boards should focus on AI’s impact on customers and their company’s customer strategy. The Responsibility section explains why AI falls under the board’s oversight role. Ensuring that everyone is aligned around an ethical and responsible approach to both AI and customer data management should be a crucial focus.
  • Allies: Identify fellow board members, executives, internal experts and trusted partners who can credibly back the case for responsible use of AI, identify and overcome possible objections from other board members, plan to deal with some of the concerns that may arise on ethical and legal grounds, and share ways to gain backing from undecided directors and executives. In addition to heads of legal and technology, executives with customer strategy formation roles such as the chief strategy officer, chief marketing officer or senior vice-president of sales can be potent allies. Finding out about and communicating details of strong examples – ideally both internal and external – of ethical and responsible deployment of AI may well be central to this.
  • Action: Think ahead about the desired outcomes of the board discussion. Initial discussions may lead to establishing a flow of information required to provide independent oversight or identify issues that require action by management. Later meetings could focus on the progress of strategic AI initiatives, what has been achieved and the metrics of success. A vital part of this will be monitoring risk factors, including ethical or data privacy issues.
  • Assignment: Consider whether AI and customer strategy should be discussed by the entire board or by a particular board committee, such as the strategy or technology committee. Strong governance with oversight across all business, marketing, technological, ethical, risk and legal issues will be crucial to the ongoing transformation of the customer proposition that AI can deliver.

Ways to broaden board thinking

Feel the AI customer’s experience. Seeing/hearing is believing. After directors step outside the boardroom to discover what customers experience for themselves, hold a workshop on reimagining the customer experience.

Bring customers into the board meeting. Millennials could provide the boards with insights for the future.

Game alternative customer competitive scenarios. For example, what would the organization do if an AI-enabled, data-driven high technology firm (such as Google) targeted the customers in our industry (what would their playbook be? why would customers find this attractive? how would we react? what can we learn?). Alternatively, try to design the data-enabled “killer start-up” from a customer perspective.

Test for PR exposure. Consider what the worst-case scenarios would be for the organization in terms of data privacy and AI outcomes. How defensible – in front of a regulator, court or public opinion – are our positions? Bring in someone with journalistic or legal experience to see how these scenarios might play out.

Resources

(All links as of 4/8/19)

Books

  • Ajay Agrawal, Avi Goldfarb and Joshua Gans, Prediction Machines: The Simple Economics of Artificial Intelligence, Harvard Business Review Press, 2018.
  • Anastassia Lauterbach and Andrea Bonime-Blanc, The Artificial Intelligence Imperative: A Practical Roadmap for Business, Praeger, 2018.
  • Larry Downes and Paul Nunes, Big Bang Disruption: Strategy in the Age of Devastating Innovation, Portfolio/Penguin, 2014.
  • Paul R. Daugherty & H. James Wilson, Human + Machine: Reimagining Work in the Age of AI, Harvard Business Review Press, 2018.

Reports and articles

Executive education programmes

Endnotes

(All links as of 4/8/19)

Examples courtesy of Best Practice AI

Other modules:

Home | Audit | Brand Strategy | Competitive Strategy | Customer Strategy | Cybersecurity | Ethics | Governance | Operations Strategy | People and Culture | Responsibility | Risk | Sustainable Development | Technology Strategy | Glossary