The importance of customer-centricity in the digital age
In today’s dynamic business landscape, customer-centricity isn’t just a buzzword – it’s a strategic imperative. At Endava, we strive to meet the ever-evolving needs of our customers, and the role of artificial intelligence (AI) has become increasingly prominent. However, amidst the AI hype, it’s crucial to remember one essential truth: at its core, customer-centricity is about people, not technology, and business decisions are still taken by people. So, tailoring AI to augment your company’s services must be done in a way that adds value both for your business and your customers.
In this new stage of the digital transformation era, businesses are presented with an unprecedented opportunity to leverage AI in fostering deeper, more meaningful connections with their customers. Yet, the key to unlocking the true potential of AI lies not in its technological prowess alone but in its ability to complement and enhance human interactions. The question is, how can you leverage your AI expertise to support customers who rely on you?
Building trust through ethical AI practices and transparency
At the heart of this approach lies a commitment to ethical AI practices. As businesses integrate AI into their operations, it’s imperative to prioritise values such as fairness, transparency and accountability. By ensuring that AI systems are optimally designed and deployed with ethical considerations in mind, companies can build trust and credibility among their customer base, while keeping the flexibility between the level of automation and human factor that best suits their business needs.
Transparency is a cornerstone of people-centric AI. Our customers must know when AI is being used to inform decisions or personalise experiences. On the flipside, as an organisation, they want to have control over the information that is served, the costs generated as well as other metrics that may impact the relationship with their own customers. By providing clear explanations of how AI is employed and its implications for users, businesses can empower their customers to make informed choices and alleviate concerns about sensitive aspects like privacy and data usage.
Privacy protection measures, human oversight and continuous monitoring are essential for mitigating some of the risks and ensuring ethical outcomes both for our customers and their end customers. Additionally, businesses that strongly rely on AI should prioritise ethics training and stakeholder engagement to foster a culture of responsible AI adoption. By integrating these practical steps into our AI initiatives, we can uphold ethical standards, build trust and maximise the positive impact of AI on our stakeholders and society.
Anticipating needs and delivering personalised experiences
Moreover, the true power of AI in driving customer-centricity lies in its ability to anticipate needs and deliver personalised experiences at scale. By analysing vast amounts of data, AI algorithms can discern patterns, preferences and behaviours, enabling businesses to tailor their offerings to individual customers with unprecedented precision. From personalised product recommendations to proactive customer support, AI can elevate the customer experience to new heights.
Balancing AI and human interaction for optimal customer experience
However, it’s essential to strike the right balance between automation and human touch. While AI can streamline processes and enhance efficiency, it seems that, for now, both our customers as well as end users prefer AI to complement rather than replace human interactions. In complex or sensitive situations, the human capacity for empathy and broader context understanding is irreplaceable.
Furthermore, fostering a culture of continuous learning and adaptation is essential in harnessing the full potential of AI for customer-centricity. As technologies evolve and customer preferences shift, businesses must remain agile and responsive. By embracing a mindset of experimentation and innovation, you can stay ahead of the curve and deliver exceptional experiences that anticipate and exceed your own customer expectations.
Generative AI: a new approach to personalisation
One of the most promising developments in AI for customer-centricity is the rise of generative AI. This innovative technology has opened up new avenues for approaching personalisation differently.
Generative AI models have made significant strides in understanding textual descriptions and visual depictions of products or content. This advancement allows businesses to estimate similarities between products directly, without having to decipher them from numerous user interactions.
Furthermore, large language models (LLMs) have become increasingly proficient at imitating human behaviour. By providing descriptions of different customer personas or even personalities, these models can generate accurate ‘guesstimates’ of their preferences, enabling businesses to tailor their offerings more effectively.
Proactive AI: engaging customers in conversation
AI can also take a much more proactive approach. Instead of trying to guess what the user might want by monitoring their clicks and views, the models can simply engage them in conversation and ask them what they are looking for. In a way, that is strangely similar to the work that sales clerks have been doing in brick-and-mortar stores.
These conversations between AI systems and customers are set to have an even bigger impact on customer support, particularly when supported by voicebots. We’ve seen huge progress in the accuracy of speech-to-text, the quality of text-to-speech and all kinds of improvements leading to natural-sounding responses in time frames that feel very similar to those in conversations between humans.
An early example of successful large-scale deployment of the next generation of voicebots in customer support was recently revealed by fintech company Klarna. They reported that their voicebots, which are powered by OpenAI’s models, have had 2.3 million conversations in their first month of operation, which corresponds to the amount of work that would require 700 full-time human agents. While the deployment is set to create an additional $40 million in profit for the company this year, it didn’t come at the expense of quality. Customers were able to resolve their issues in 2 minutes – compared to 11 minutes previously – and repeat enquiries also dropped by 25%.
AI intermediaries: challenging traditional search engines
Such stories raise concerns about potential job losses. However, in a surprising turn of events, it might actually be the customers rather than the customer support representatives who will start to be replaced by AI systems. Companies that are challenging the traditional notion of a search engine by utilising LLMs to directly answer users’ questions are growing rapidly. One of the leaders in this space is You.com. Their deeper search functionalities hint at a future in which AI agents will be able to analyse countless pages and perform multiple steps of reasoning to arrive at a close-to-perfect product or service recommendation for a potential customer.
The end of dark patterns: AI as customer advocate
The introduction of AI intermediaries between human customers and sellers will mean bad news for companies that have been lured into employing dark patterns or questionable practices in dealing with their customers. An AI system would not be dissuaded by a needlessly complex process to unsubscribe from a service, would never forget to cancel a free trial and would be immune to impulse buying. An example of a company that has made an early entry into AI customer advocacy is DoNotPay. Their chatbots take the customer’s side in resolving issues with companies, from cancelling subscriptions to negotiating better rates for services.
AI natives and wearables: the future of customer experience
People who increasingly rely on these AI intermediaries might become known as ‘AI natives’, and sellers will have to make adjustments to accommodate their customer journeys – or rather, the customer journeys of their AI representatives.
Further innovation in customer experience will be driven by the adoption of AI wearables. These are devices that use multi-modal AI models to continuously monitor what the user hears or sees and open new ways of interacting with generative AI models. Many such devices have been announced recently and seem to be taking a wide variety of different shapes – pins, pendants, phone-like devices. But it seems that the ones taking the form of glasses will be especially relevant – the Frame glasses by Brilliant Labs are a very interesting example.
However, the significance of AI wearables lies less in new human-computer interactions and much more in their ability to create an incredibly comprehensive and evolving record of an individual’s life. They could enable AI intermediaries to gain an in-depth understanding of the individual’s circumstances, wants and desires that could never be matched by any individual seller’s attempts to understand the customer.
The shift towards product-centric competition in the age of AI
The comprehensiveness of this understanding will lead to data privacy and trustworthiness of AI systems being central issues that influence the adoption of these technologies. It presents a big opportunity for systems built on top of open-source AI models and designed to protect the privacy of the customers using them.
If customer-privacy-preserving AI intermediaries become common, it might – somewhat paradoxically – lead to sellers having less information about their customers than they currently do. If we combine this with the expectation that it could be quite hard to capture the attention of AI systems with marketing techniques and that the systems will likely make rational decisions and patiently scrutinise the market for the optimal products, this really invites the question: in what ways will companies still be able to gain a competitive advantage? And the answer is… with the product itself.
Leveraging AI for customer feedback and product improvement
Expanding the line of products to include ones that better cater to the needs of specific customer groups might create an advantage. But the key component for developing and maintaining product quality is being highly sensitive to customer feedback. Fortunately, generative AI is already improving both how customer feedback is given and how it can be interpreted and incorporated into product decisions. An interesting example of a company finding new ways of collecting feedback is Tesla. Their customers get the option to record a quick voice note each time they disengage their self-driving system.
However, having a lot of user feedback is only useful when you can analyse it systematically. And for this task, large language models are an excellent fit. They can not only summarise a large amount of customer feedback but also point out the items that are particularly relevant or actionable.