Bringing Trust to the Forefront of Customer Experience as We Enter the Generative AI Era
Customer Experience (CX) is about building more robust relationships with your customers and prospects. When lacking trust, brands usually end up having a very transactional relationship.
With Artificial Intelligence (AI) and Generative AIon the rise, integrating them into your customer relationships is critical. Thanks to the large language models (LLMs) that generative artificial intelligence uses, these can be used with high efficiency in the trust-building process. By leveraging such technologies, brands can infuse an element of trust across their operations, data, and core systems. According to our most recent report, 80% of the respondents claim they aim to implement AI across their organization in the next five years.
Generative AI: Using Technology to Build a Solid Foundation of Trust
Generative AI spans many benefits, ranging from increased efficiency, personalization, relevancy, scalability, and more. However, the significant contribution of generative AI is that it can be used to secure a higher trust factor in terms of data management and data privacy. This concern is significantly increasing in regions such as Europe and Australia, where data sovereignty is highly valued. Such areas make a point of keeping customer data within the region. As you would expect, this aspect poses a series of challenges for global organizations. Being mindful of where data is stored and being fully transparent with it are critical in such scenarios, and they act as a solid foundation of trust.
Challenges of Using Generative AI to Enhance Customer Experience (CX)
While Generative AI tempts many organizations, many haven’t considered the challenges of operating similar engines. When deploying generative AI models, you must be mindful of how data will be processed, stored, and used. Besides, data needs to be anonymized before being sent to generative AI engines. Data anonymization strips away the personally identifiable information (PII) data, so these engines can’t be used to identify and use the information negatively.
While pre-processing data needs to be considered, organizations considering using such models in their operations must also think about post-processing steps to eliminate false representations and incorrect responses before generating information.
Properly Deploying Generative AI In Business Operations
When considering deploying generative AI models in their operations, companies must first build customer trust. This requires building a team that values integrity and good corporate citizenship. A company’s culture and trust profile must be visible and well-marketed. For this, make sure you publicize your actions, expectations, and behaviors and that your efforts need to match your statements.
CX leaders are also responsible for helping team members build new skill sets concentrated in two main areas: soft skills and hard measures.
- Soft Skills: teach and train your team members on accountability, integrity, responsiveness, transparency, communication, and expectation management.
- Hard Measures: conduct and provide process optimizations, certifications (i.e., SOC2 Type2, ISO 27001), system access, awareness, measurement methods with checks and balances, routine audits, root-cause analysis, and more.
Building a resilient organization in the face of the challenges posed by integrating generative AI technologies into your operations starts with building resilience with each team member through educational courses such as drills, tabletop conference room pilots, mock tests, etc.
With a solid foundation of what “building trust” means to your team and making sure they are good “corporate citizens,” you can layer on the corporate aspects of protecting your customer data to cement trust and, as a result, build customers for life.
If you’re interested in learning more about how we at Sugar handle generative AI and infuse it into our solutions, watch our on-demand webinar, 2024 State of CRM: Key Insights for Companies Investing in CRMs.
This article is based on this piece, published initially in CXScoop.
