Building better customer relationships and crafting more engaging and personalized experiences by tapping the prowess of this disruptive technology.
By: Sid Victor, Head of Support Services at Movate
AI’s new inflection point, generative technology, has captured the world by storm and could be the next giant leap to unleash the innovation and productivity we need. The market will reach over $109B by 2030, with a projected CAGR of 35.6%. ChatGPT’s meteoric rise is attributable to generative AI’s capability to augment human effort. Enterprises envision building better customer relationships and crafting more engaging and personalized experiences by tapping the prowess of this disruptive technology.
The Future Is Unfolding
The talk of the town is about leveraging this technology with “human-in-the-loop” workflows, automating repetitive tasks, creativity, content creation, supercharging human support, and finetuning Large Language Models (LLMs) to specific domains, enterprise use cases, and customer scenarios.
Going by a survey of global business leaders, AI foundation models will play a pivotal role in enterprise strategies over the next three to five years. An estimated 60% of IT leaders are looking to implement Generative AI. Existing tech service providers have their roadmaps chalked out to accelerate the advancement of existing AI platforms infused with Generative AI models. OpenAI has released API for accessing their flagship AI models. Vendors are exploring API integrations with the latest generative LLM models (like Stable Diffusion, DALLE-E) and are inking partnerships with the pioneers to level up their CX capabilities.
The landscape of customer support strategy is witnessing a shift as benefits of Generative AI unravel use cases with a human-centric approach. Around 95% of enterprise leaders believe generative AI is ushering in a new dawn of enterprise intelligence. The game-changing technology will likely transform CX to provide unprecedented customer delight across service operations. ChatGPT, for example, is known for being content-oriented, having deep language understanding, contextual response generation capabilities, and flexibility for open-domain conversations.
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?
A piece from The Wall Street Journal cited how enterprises are leveraging the significant breakthrough in NLP to make customer service bots even smarter. Over the years, we witnessed how the journey has been from linear chatbots to cognitive, conversational AI, and now the step up with generative bots—highly adaptable and interactive agents. LLM-powered chatbots facilitate conversations and tap knowledge gleaned and retained from previous interactions.
We now enter the language proficiency era that marks the start of language-driven generative models that are proficient in tackling customer support interactions at a humanized level of maturity. Generative bots deliver supreme contextual and intricate language understanding compared to costly, rigid, manually configured FAQ chatbots that return many articles to read. The “out-of-the-box” models mine deeper into the troves of data from knowledge bases and untapped touchpoints. Automation takes a turn for the better as models deliver deep predictive intelligence and content aggregation capabilities.
As human agents receive assistance from co-pilots, the technology reduces manual effort. It amplifies agent experience—NPS & CSAT monitoring, extracting relevant info from multiple articles, and generating human-like summaries. Gen AI-powered solutions deepen customer engagement via multi-lingual support, next-best actions, precise recommendations, onboarding process, appointment setting, and scheduling. Decision-makers can accurately decode customer emotions and sentiments, use empathy reasoning, and topic clustering, and summarize content from various channels.
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
A survey of IT leaders indicates that 67% are prioritizing generative AI for the next 18 months, and one-third have it as a top priority, but challenges remain. The advancement in AI is still in its nascent stages, with rapid rounds of experimentation and innovation in progress. A lot of research, validation, and testing iterations are underway.
Leaders need to be wary of the reputational risks and brand damage arising from hallucinated responses (coherent nonsense) amidst sensitive customer interactions—for example, providing a false credit card interest rate to a customer seeking information.
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.
With all the buzz around the new AI arms race, clients need to ask service providers the tough questions around security, accuracy, and governance. These include:
- 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.