Customer-Centric AI: the Major Paradigm Shift in AI Governance (Part 2)
How to be a customer-centric AI company
In part 2 of this essay series, we will discuss the HOW question: how you can practice customer centricity for your AI products. I highly recommend reading part 1 first, the WHY question, to better grasp the context.
Achieving customer-centricity means making your customers feel like VIPs, the main characters in your company's story. It's not just about tossing around buzzwords like "customer-first" or "customer-centric"; it's about rolling up your sleeves and truly putting your customers at the heart of everything you do. Think of it as a mindset shift, a cultural revolution within your organization. It's about instilling a deep-rooted belief that every decision, every action, every product or service offered should ultimately benefit the customer. And this mindset starts at the top.
Leadership plays a pivotal role in setting the tone. They need to be the torchbearers, leading by example and demonstrating a genuine commitment to customer satisfaction. This means actively championing the cause of customer-centricity and empowering every member of the organization to do the same. But it's not just the responsibility of the C-suite; it's a team effort. Every employee, from the frontline staff to the back-office teams, plays a crucial role in delivering exceptional customer experiences. It's about fostering a culture where everyone is empowered to think creatively, problem-solve proactively, and go above and beyond to delight customers - the HOW question of customer-centricity.
The HOW Question Answered
All the ways how your organization can build customer-centric AI products:
Awaken to emerging tendencies and rebuild company culture - in the previous post, we talked about new trends that contribute to the need for customer centricity for AI companies. These changes aren’t just about AI, consumers are changing their attitudes toward purchasing decisions and AI is amplifying this change (just like AI tends to amplify anything else). The challenge is to gather all the complexities of the market/society, mix them, mash them, and build a new culture more systematically. Does Systems Theory ring a bell? It's not just one cause and effect that linearly impacts customer behavior, interactions are far more complex and you need to see the big picture: practices of doing business have evolved, especially in the technology field experiencing rapid advancements; customers are becoming more and more demanding, critical, informed and empowered. The market has become more competitive globally and standards for doing business have risen. You need to be far more focused, produce higher quality products, and care about your customers more. Once you truly realize this, you are ready to implement change at every function in your organization.
Whether you are an executive or ML engineer reading this, you can make this shift happen from your level as it needs both top-down and bottom-up leverage. Leadership has to rebuild governance mechanisms, decision-making processes, and structures that empower company employees and accommodate the evolving expectations of their customers. Allow seamless communication between departments so that everyone involved has insights about relevant customer insights, regulations, ethical guidelines, and educational materials. As a Machine Learning engineer or anyone involved in AI product development, you have to play your part by communicating with different departments to gather all the information possible helping you build the most useful, ethical, and compliant product. You are the only people in the organization who understand all the workings of the product best - it is up to you to learn to ask the right questions and source the right information at the organizational level or beyond. What is the specific purpose of the product you build? How it may or may not benefit the customer today, next year, and 10 years from now? How it may be misused? Which features are less reliable needing to communicate its limitations to the customers? These and similar questions have to be constantly integrated at every stage of the product life-cycle.
Understand customers’ context, and ensure AI solutions address real problems: AI algorithms rely heavily on the quality and relevance of the data they are trained on. Taking a customer-centric approach ensures that AI solutions are grounded in a deep understanding of customer pain points and needs, ensuring they are designed to solve genuine customer challenges. You need to know what makes your customers tick, what they like, and what they don't like. To truly embrace customer-centricity, you need to anticipate their needs before they even realize them. This involves understanding the context in which they operate and the challenges they face. Personalizing customer experiences is all about treating each customer as an individual.
For example, if you are building a recommendation algorithm for a personalized diet, you need to face several vital questions: do you want to personalize based on historical data even if the customer has a habit of eating unhealthy fast food (that's what the customer likes) or do you want to personalize based on what is generally considered healthier. How do you know what the customer wants or needs, how do you decide what's best for the customer? In such cases, all you need to do is (a) directly ask the customer what her purpose of using your AI tool is; and (b) develop customization features to allow the customer to modify algorithm recommendations based on her purpose. Your AI models have to be fit for the purpose, but that of customers. That's when you know your customer-centric AI efforts are effective.
Collect and process customer feedback: With AI technologies at hand, it is easier to fall into the trap of all-knowing: While you try your best to collect and process customer data, anticipate their needs, and offer personalized services, do not forget that the customer herself is the only person who knows her true desires, challenges, and entire context. No matter how much data you collect about the customers, directly listening to them is the only way to get an entire context. You need to make it easy for customers to tell you what they think, whether it's through surveys, social media, meetups, and conferences, or just talking to your customer service team. Do not just ask multiple-choice questions or ratings - ask open-ended questions to truly grasp what’s in their mind. By listening to their feedback and acting on it, you show that you value their opinion and are committed to making things right. You need to also make it clear to the customers that their feedback is heard and acted upon. There are multiple customer feedback tools available that can help you get started.
Additionally, it is important to consider that you'll need to collect different kinds of feedback at the different stages of your product life cycle. For example, in the development stage, you may beta-test your product with a couple of customers which allows you to collect truly targeted and comprehensive feedback. In the deployment stage, you already have a somewhat full-fledged product and you are gradually acquiring customers - you may want to ask them how the basic features work and what value (if at all) it creates for them. While you iterate and improve your product and acquire more customers you may want to ask them what additional features they'd like to have. In the scaling stage of your product, as you may have acquired hundreds and thousands of customers, your feedback collection strategies will change, you may want to understand how your customers experience your product not only individual level but also on the wider societal scale - how it changes interactions between peers, friends, colleagues and various groups of people. This is important because AI products evolve together with your customers who operate in different contexts.
Adopt customer service tools: You need to streamline not only customer feedback with technologies but also all the processes involved in a customer-centric culture. Whether it’s AI tools themselves or other digital tools, one simple technology solution may make your efforts far more effective. However, not every problem needs to be solved with technologies, you need to first ask yourself whether the bottleneck is in organizational culture or elsewhere, make sure you employ all human capabilities possible, and then you'll know more digitization and automation can bring more benefits. A simple survey bot or simple CRM tool could make processes far more fruitful. Natural Language Processing (NLP) technologies are extremely useful for customer sentiment analysis too. I've been often amazed by how many times companies (even high-tech companies themselves) do not integrate new tools and continue functioning with a backlog, silos, and inefficient processes. Making sure that all your customer-centric strategy is streamlined will pay off both in the short term and long term.
Co-create with customers: Collaborating with customers means involving them in decision-making, including organizational policies. Existing tools and systems are not yet quite ripe to comprehensively implement such processes but it is still possible to get creative and get started. One of the early initiatives, Metagov incorporates various projects for digital governance and community management. If you already have a community built, you can draft policy templates and let your community contribute and vote. This means, that before embarking on implementing any tool, you need to build community, the very early step for improved customer engagement - those customers are the ones most interested in your product and most likely to contribute to its improvement. Why not give them an opportunity to contribute? Even if you prefer to implement conservative customer engagement methods such as focus groups, beta testing, or surveys, building community will make these processes far more effective. Suppose you want to take these methods a little further. In that case, you can use easy-to-use tools, for example, Slack + Geekbot integration to conduct polling for certain policies, or Discord which has its integrated apps for voting, and even Google Docs could work. The most important thing is that customer centricity runs in your organization’s veins and flows unimpeded in every direction: company culture and decision-making, internal governance mechanisms, customer relations, product development, legal department, marketing, and sales. Everyone in your organization should try their best to ensure your AI product meets and exceeds customers’ expectations.
Be transparent and communicate clearly: Collaboration is nothing without meaningful transparency. Transparency is the basis for trust and trust is the basis for collaboration. Customers want to know that their data is safe and that you're using it responsibly. Customers also want to know how their contributions matter: how they make a difference in the company’s policies, and product development and how it affects their own life. With clear communication to the customers about the values, goals, purposes, and processes of the company you can build trust and loyalty. It is also important not to confuse transparency with explainability (XAI). Transparency is organizational level, how you communicate the AI model’s strengths and limitations with the customers or general public; Explainability is a technical challenge, how much you understand the inner workings of the model and can demonstrate it to anyone interested such as auditors.
Iterate and continuously improve: AI solutions differ from traditional products or services in that they require continual customization, monitoring, and optimization to align with evolving customer needs. Implementing iterative processes is essential at all levels, including policy-making and review processes. You may need to rewrite your policies, ethical principles, governance strategy, or the entire AI system. This ensures that AI solutions remain effective, compliant, and aligned with organizational goals. For example, if you offer a personalized recommendation system for online shoppers, your AI system initially recommends products based on basic factors like purchase history and browsing behavior. However, as customer preferences or economic situations change over time, the system needs to adapt. This is why collecting customer feedback once is not enough, you need to do it regularly to identify new patterns and trends. Additionally, you’ll need to continuously monitor the performance of the AI system, looking for areas where it can be improved. This could involve tweaking the algorithm to make it more accurate or adjusting parameters to enhance the relevance of recommendations. Furthermore, you’ll integrate these iterative processes into policy-making and review processes. This ensures that decisions regarding the AI system are based on current data and customer feedback, rather than outdated assumptions.
So, whether it's designing a new product, refining a service offering, or simply interacting with a customer, the question should always be: "How can we make this better for our customers?" Because at the end of the day, happy customers are loyal customers, and loyal customers are the lifeblood of any successful business.
In part 3 we will discuss the WHAT question. Stay tuned!
Ana Chubinidze is the founder/CEO of AdalanAI, building a novel approach to AI Governance.
email: ana.chubinidze@adalanai.com