Media attention surrounding ChatGPT has predominantly focused on the transformative potential this technology has to reshape the character of labor.  Nonetheless, the larger story is about how generative AI will transform the client experience. A McKinsey study finds that 80 percent of customer tasks will be automated across channels, leading to a 20 percent savings for cost-to-serve.

ChatGPT and similar tools will be leveraged to support quite a few use cases, across business functions corresponding to marketing and sales, supply chain, customer support, product development, and more. By increasing worker productivity, enabling proactive outreach and problem solving, and addressing common friction points, generative AI solutions may help teams rapidly evolve customer-facing capabilities. To attain this vision, nonetheless, enterprise teams might want to overcome five different obstacles and deploy two different architectures: one for human-augmented interactions and one for fully automated interactions.

5 Challenges to Solve to Get ChatGPT Ready for Primetime

So, what are among the roadblocks or risks to implementing generative AI – and the way can they be mitigated?

1. ChatGPT doesn’t personalize messages: Current generative AI tools can’t personalize messages, yet personalization is essential to driving product and repair sales, increasing per-purchase spending, gaining repeat sales, and enhancing customer loyalty.Marketers need enterprise-class generative AI technology to have the option to personalize names, imagery, offers, product recommendations based on recent purchases, and cart abandonment messages.
2. ChatGPT hallucinates content: Generative AI solutions use prompts and leverage past learning to create content. Which means they fill within the gaps with content learned from statistical patterns, often “hallucinating” information that isn’t true.To leverage generative AI and scale it across customer segments and use cases, enterprises have to have the option to discover and take away this erroneous content before it reaches users and approvers or is distributed to customers.

3. Generative AI can’t apply business rules: Business rules streamline customer interactions. Narrow AI chatbots have excelled at detecting these similarities and serving up approved answers.Generative AI can’t detect these commonalities and can create original responses to reply each query, creating customer confusion and introducing errors into interactions.An enterprise-grade technology architecture that mixes a generative AI tool with the corporate’s predefined business policies would help standardize these responses, providing consistent responses across customers.
4. Generative AI isn’t capable of ensure compliance: Customer-facing content typically goes through legal reviews, to be certain that imagery, text, offers, and guarantees comply with an organization’s legal, regulatory, and customer policies. This process protects corporations from customer mishaps, regulatory censure and fines, and other sorts of business harm.Generative AI can’t create compliant content, because it doesn’t understand these nuances. Consequently, technology that leverages generative AI must embed legal guardrails to discover and take away non-compliant content before it’s distributed or used publicly.
5. Ungoverned use of ChatGPT is creating security risks: ChatGPT use is an enchanting case study in what happens when individuals aren’t checked by security policies. Media stories abound about employees inputting sensitive data into this publicly accessible chatbot, risking data exposure and the lack of mental property.Enterprise data and IT teams can mitigate these issues by segmenting information: sending sensitive content to domain chatbots, that are guarded by security controls and systems, and routing general inquiries to ChatGPT.

Evaluating Recent Architectures for Generative AI

To enable human-augmented B2C and B2B operations and fully automated B2C operations, enterprises will need two different architectures.

Each architectures leverage open-source generative AI tools like ChatGPT and other solutions that guide processes from prompt input; to data synthesis; to content creation, cleansing, and personalization; and governance.

Using ChatGPT to Streamline Human-Augmented B2C/B2B Interactions

Let’s consider a standard scenario. A marketing skilled enters a prompt into an enterprise interface, using a predesigned questionnaire to guide content development, corresponding to for an email campaign.

The worker enters key information, including the e-mail directions, desired audience, product name, marketing claims and product characteristics, and any usage directions.

The architecture then harnesses customer personas to counterpoint instructions with information that may appeal to this segment, providing these data models can be found. The improved query is then sent via an external API to ChatGPT or any similar generative AI tool.

Next, a curator applies business rules and legal guardrails to be certain that the content will meet enterprise and regulatory standards. The marketing skilled would then review and approve the resulting email before sending it to the client base.

Using ChatGPT to Automate B2C Interactions

So, what about interactions that will be fully automated?

After a user enters a matter, it’s enriched with customer persona data, as before. Nonetheless, the updated query is then routed one in every of two ways: to a site chatbot that may personalize responses for business-specific content or via an external API to ChatGPT for routine questions. The domain chatbot personalizes content, while ChatGPT doesn’t.

The resulting content is then scrubbed for errors and compared against business rules and guardrails before being mechanically distributed to customers.

Reap Recent Business Value from ChatGPT by Deploying Recent Technology Architectures

The race is on to drive ROI from generative AI. Enterprise leaders are analyzing business processes for cost and waste, talking to vendors to grasp their approach and solutions, and developing proofs of concepts. They’re looking for insights and solutions that they’ll harness to attain speed to value and speed to scale.

As they do that necessary work, these leaders can vet all providers by their ability to unravel these five common generative AI challenges and enable each human-augmented and fully automated interactions.

Using these two different foundational architectures will enable enterprises to perform myriad business gains. They’ll have the option to spice up team productivity, enhance the client experience, decrease service interaction costs, and drive recent product sales.