The Future Is Unfolding
The X-Factor in Support
The critical question is this: How will the new advancement in AI outperform the present new-age technologies in a customer support context? What kind of newness does the technology deliver when measured against the yardstick of customer experience?
Deploying this technology will level up CSAT through hyper-personalized responses, broader customer reach across languages, faster resolutions, and scalability to meet expanding business needs in the future.
Be Wary of Blind Spots
Brands must address biases, data privacy laws, copyright issues, human-verified output, transparency, security audits, and diverse and inclusive representative data sets through an ethical and responsible AI governance framework.
- Are sufficient data guardrails in place?
- What’s the differentiating factor of a particular service offering? Can other vendors also replicate the results with the same API?
- Is this the “real deal” integration with a high-profile generative model or another duplicate?
- Is this LLM trained in specific domain use cases (like telecom, contact center) and possesses topic-centric grounding?
Consider domain-specific large or a small parameter set to train the LLM models for successful outcomes. Such LLM models trained on a small set of parameters would outperform larger models at less cost. Accurate, complete, unified data and enhanced cybersecurity measures are paramount for trustworthy innovation.
What Should Contact Center Leaders Do?
Customer service and support leaders need to capitalize on this opportunity to extract the maximum value for the contact center. Start the journey by working with the right tech vendor to:
- Conduct an enterprise maturity assessment to see how to integrate generative AI into the current ecosystem.
- Assess current data and integration requirements.
- Ascertain top use cases to see early success.
- Chart a detailed enterprise roadmap involving data integration and technical architecture.
Here are some key foundational steps in the journey to build a Generative AI roadmap.
Define and document a formal enterprise AI policy that covers ethical AI guidelines with audit mechanisms to verify outcomes.
Find the right use cases. Use data analytics from CRM/ITSM systems to narrow down on low-complexity, high-impact use cases. ChatGPT’s information-summarization capability can synthesize distributed customer feedback, NPS & CSAT data to provide meaningful insights.
Evaluate “Build” vs “Buy” decision as it takes a wider team effort to make the journey successful. Given the diversity of technology and growing industry-specific solutions, defining the adoption journey can be daunting. Investing in business differentiation features on top of standard GPT-enabled platforms will be key for proper outcomes. Start using secure GPT-enabled CRM platforms trained on customers’ internal data with knowledge curation and contextual training.
Strive for accuracy and build trust. Most organizations have data integrity problems; the existing knowledge base needs enrichment and curation before deploying it to train the model. Establish a KM governance framework focusing on selection, enrichment, and training process with apt approval workflows and human validation—Finetune first-draft ChatGPT responses for accuracy in subsequent iterations. Managing the algorithmic dial is critical for accurate outcomes.
Define the security governance process with a human-integrated holistic approach. Governance entails architecture, data readiness, model training process, data residency, and authentication techniques. End-to-end encryption, SSO, MFA, ISO & IEC adherence, GDPR & CCPA compliance are table stakes.
The last step is organizational change management. Communicate enterprise adoption plans, create bridging courses, and help employees and partners to align with organizational goals in advance.
A Watershed Moment
The disruption is unlike any other in the last decade or two and now is the time to realize its benefits. Gartner says innovation in AI is accelerating and creating numerous use cases in generative AI across industries. With a combination of cloud and new-age technologies, generative AI is set to open new frontiers to bridge the physical and digital worlds. A pivotal moment in AI poised to reinvent business and revolutionize CX is unfolding.
Though a ton of excitement is in the air, leaders need to communicate the benefits of AI’s new dawn to their support teams; they need to address the market disruption in the new future of work and quell the collective anxiety around the buzzy technology.
Here’s an excerpt from ChatGPT’s response on what the future of CX would be with generative AI:
“…Overall, the future of CX with generative AI holds promise for enhancing personalization, efficiency, and customer satisfaction. As the technology advances, it will be crucial to balance automation and human touch, creating seamless and meaningful interactions between businesses and their customers.”