Imagining a new era of customer experience with generative AI

Opening minds to new possibilities

Broadly, we see the potential impact of generative AI across four key domains: commerce, service, sales, and marketing, but the potential goes well beyond this when we consider combining the touchpoints that make up important interactions with customers, resulting in orchestrated, personalized journeys.

Clearly, generative AI can be a potent content, sales and marketing tool – and customer experience is one of the biggest areas where this technology can make a significant impact. But, as with any new frontier, there are risks. Organizations must navigate new complexities, including intellectual property risks and responsible and ethical usage, and prepare for the possibility of data leakage and irrelevant or biased outputs. An obvious risk is presented when generative AI is provided the whole internet as its data resource, meaning it draws on both safe, reliable data as well as potentially misleading or copyrighted information. That’s why delineated boundaries must be defined around relevant data sets to exclude false or misleading information and increase the quality and safety of AI-generated content. Such guardrails and other guidance are also needed to protect more intangible aspects, such as brand identity and reputation.
 
Despite these hazards, 40% of organizations have already created dedicated teams and budgets for generative AI,¹ but most still haven’t considered how important the next step is: choosing the right advisor and solution partner. Although generative AI can create significant standalone value, it is only truly revolutionary when combined with existing capabilities. An experienced and reliable technology partner can identify the areas within the organization where its integration can bring the greatest benefits to transform the customer experience across the whole customer life cycle. They can provide the innovation, the transparency of data source and usage, and the type of features and experiences that will step-change how organizations engage with their customers, at scale.

True personalization through the lens of generative AI

Perhaps generative AI’s greatest capability is the hyper-personalization possibilities. Customers deal with multiple, fragmented touchpoints and inconsistent personalization at every turn. Just consider all the interactions involved in planning a trip abroad. There’s the transportation (buying tickets, securing taxis, arranging transfers), the accommodation, and everything else in between such as planning activities, making dining reservations, and managing local travel logistics. With so many interdependent elements, one disruption can have a ripple effect on the whole itinerary. Wouldn’t life be easier if someone (or something) helped manage all this? Although still a bit futuristic, we’re drawing closer to an age where generative AI, in conjunction with workflow and execution, will consolidate multiple touchpoints and act as a personal assistant for customers.

Suppose you’re on your way to the airport but find yourself stuck in heavy traffic. Not knowing if you’ll catch your flight, you open the airport’s app and inquire about available options. Generative AI then quickly assesses various factors such as your airport arrival time and if there’s a chance of a flight delay. Using voice interaction, it suggests personalized actions it can do on your behalf like prepare your shopping in advance, reserve a convenient short-term parking spot, or arrange fast-track service that allows you to speed through airport check-in.

The assistant then goes beyond merely providing recommendations. It connects the necessary workflows of separate touchpoints and coordinates the execution of the suggested actions. This may mean that if you don’t make your flight, the virtual assistant can seamlessly rebook airline tickets, change accommodation dates, make new restaurant reservations – and even send the letter of complaint and compensation claim to the airline.

Today, large consumer products brands simply aren’t equipped to provide each individual customer with accurate, consistent, yet always personalized, contextual content. Generative AI can make what was once unfeasible attainable. The visionary concept behind the 30-year-old groundbreaking book, The one-to-one future: building relationships one customer at a time,can finally be embraced and scaled in all its glory.

Making it work in CX

While generative technologies can help us create useful and contextual content, they still require a holistic framework to be used by enterprises to improve customer experience. At a high level, any enterprise will need four key elements to adopt generative AI in CX (in addition to standard elements like data, algorithms, integrations):

1. Business use cases: While there are many use cases imagined for generative AI in CX, it’s important to understand the feasibility and value each will bring. An enterprise will require a refined strategy to select the right use cases that will deliver tangible outcomes (applicable to their business).
2. CX orchestration: Generative content can be used to create a more engaging and personalized customer experience. However, it is important to carefully orchestrate this content in order to ensure that it is consistent with the brand’s values (tone, voice), target audience and overall CX goals. By carefully considering these factors, businesses can use generative content to create a more cohesive and memorable customer experience.
3. Guardrails: A powerful layer of CX guardrails (brand guidelines, core values, vision of brand etc.) need to be applied to prompts and inputs, and most importantly, the security of models (scope of data and usage). By putting guardrails in place, businesses can ensure that generative AI is used in a responsible and ethical way. This can help to protect the brand, the customers, and the data.
4. Adoption methodology: Generative solutions cannot be seen in isolation as they become part of existing work been done by team in CX space (marketing, sales, service or commerce). Enterprises need to have an adoption methodology that ensures all elements of technology, people and process are fine tuned to embrace changes brought in by adoption of generative technologies.

A strategic approach for controlled impact

Even though full maturity of generative AI isn’t expected for another 2-5 years, 70% of global organizations have already started exploring the technology’s probable future.^[1] This has regulators scrambling to create guidance and restrictions around its use. As a first of its kind – before the fantasy of AI became reality – the European Parliament has put together a draft law, the AI Act, set to be released later this year. More regulations will undoubtedly soon follow.

Of the organizations that have kick-started their AI experimental journey, most haven’t considered the implications these regulations will have on their final creations. They could be forced back to the drawing board, increasing costs and delaying progress. This is where a skillful advisor can be most beneficial. They’ll know what to expect and can provide foresight to avoid the common pitfalls, especially if they’ve successfully overcome the challenges of previous technological evolutions. Ideas will be fast-tracked, efforts will be minimized, and the transformative value of generative AI will permeate across any organization ready to spark unprecedented change to customer experience.

^{[1] https://www.gartner.com/en/newsroom/press-releases/2023-05-03-gartner-poll-finds-45-percent-of-executives-say-chatgpt-has-prompted-an-increase-in-ai-investment}

^{[2] https://www.capgemini.com/insights/research-library/generative-ai-in-organizations/}