GenAI has broad applicability across the marketing cycle and will have a significant impact on how future customer experiences will be designed, delivered, and scaled.
Future value exchanges between customers and brands will be premised on data and customer insights. In turn, the imperative for earning and sustaining customer trust increases while accountability for the security of customer data and respect for customer privacy unequivocally falls on the enterprise.
As customer demands and expectations continue to rise, additional pressure is placed on relational aspects of the experience life cycle, such as delivering empathetic customer outcomes, proving customer value, and mitigating churn risk. By allowing better personalization and contextualization in customer-facing content, GenAI has the potential to improve — and create — the experiences customers have with businesses.
35% of customer experience (CX) executives agree that the acceleration of innovative capabilities such as GenAI and Web3 will most impact future CX strategy.
The C-Suite – particularly CMOs – must start with an assessment of their business needs before considering which technology or product to adopt through GenAI consulting services. The aim of these consulting services is to help organizations understand the potential for GenAI technology to reduce operational costs, cut time to market for new products and services, grow existing revenue streams, and identify and drive new revenue streams. They can help with ideating, prioritizing, triaging, piloting, and later scaling GenAI use cases across the organization.
IDC predicts that by 2026, 45% of the Global 2000 will use AI/ML to elevate context and nudge customers into unfamiliar and novel experiences that simultaneously improve sentiment metrics and brand upselling potential, and GenAI will play a role in this transformation.
Some of GenAI’s potential uses — and risks — are still being worked out; CMOs should paradoxically take an adoption approach that is both bold and cautious. Bold in the sense that the organization should experiment with something that is immature and has the potential for misuse, financial damage, and even brand degradation. Cautious in the sense that this experimentation should be done with close oversight and strong guardrails.
Because GenAI is a new and disruptive technology, ready-made uses cases may not be available, or they may have little short-term and medium-term performance data to validate the likely return on investment for the enterprise (let alone any long-term data). In these cases, adoption may start from a different — and more ideational and experimental — perspective, as organizations ask what the business benefits of the new technology could be.
Two of the biggest benefits of GenAI to enterprises are cost reduction and speed. Because GenAI is still maturing as technology and is in the nascent stages of adoption by enterprises, metrics are not standardized and formalized.
For these reasons, it’s a good idea to seek advice, project management, and implementation expertise from business and IT consultancies that have experience with AI and organizational change.
In terms of cost, GenAI obviously has the potential to reduce spending on human activity in parts of the marketing cycle, notably data-heavy research, reporting and analysis, and content generation and management. GenAI’s greatest potential benefit for marketing may be its ability to empower organizations to update existing content and re-factor it for new contexts of new distribution channels.
In terms of speed, GenAI’s nature as a relentless, always-on technology with massive processing power gives it the potential to significantly shorten delivery cycles for data-intensive work, such as market research, reporting, and analysis and for the production of creative content in draft form.
As with any new technology, there are benefits, but there are also risks. There are two forms of risk for GenAI: generic risk associated with any business and technology change project and risks specific to GenAI.
Generic risks include the possibility that the business case for GenAI may be miscalculated, that the technology may not perform as it should, is not configured properly, and that the “human side” of the project is not addressed adequately. For example, if training is inadequate, the purpose of the project is not explained to ground-level users of the new applications, or stakeholders do not buy into the project, they will not have success.
Risks that are specific to GenAI relate in part to its combination of immense processing power, unpredictability, and its ability to mimic human communication.
- Offensive and brand-degrading content. GenAI does not truly understand human ideas and emotions and because it cannot make the culturally and politically nuanced decisions that humans make, it can create inappropriate and offensive and brand-degrading content.
- Over-personalization. If GenAI is used at scale to produce personalized content it is possible that it will flood recipients with content that is both too frequent and possibly too personalized, perhaps even “creepy”.
- Environmental cost. Widespread GenAI use may entail significant levels of net-new datacenter and network usage, which would not sit well with any public commitments your brand has made to safeguarding the environment.
- Sameness. This is not a cheap technology to produce and train at scale, and it’s possible that enterprises will use GenAI services that are based on just a handful of large language model (LLM) providers globally, which can produce text and images in styles that are recognizably similar. Consumers will quickly spot GenAI produced content and may come to discount this content, reducing its powers.
- Bias. Because GenAI is trained on large volumes of existing content, it can adopt the cultural, racial, or sexual biases of this content, lessening the ability of a brand to communicate with certain social groups.
- Legal liability. Because GenAI content is based on large data sets of existing data, there may be legal issues surrounding copyright laws.
- Organizational conflict. GenAI has the potential to enrich existing jobs, but it also has the potential to deskill and degrade people’s experience of work and to reduce the perceived quality of the output of their work.
As organizations prepare to deploy GenAI they must think through the technology change and process, workflow, and organizational structural changes that will be required to make best use of its business potential. It will also require close attention to the “human side” of the changes that GenAI will bring.