What Is Reflective Intelligence?

By Chance Whittley, Principal AI Consultant, PTP

Gen AI and the Subjective Customer Experience

In the rapidly evolving digital landscape, the power of Generative Artificial Intelligence (AI) is undeniable, yet its potential is often undermined by a critical oversight: the neglect of the customers’ subjective experience. Enterprises deploying one-size-fits-all solutions fail to engage customers effectively, overlooking the nuances of human communication and their own brand identity. This not only diminishes the customer experience but also hampers the success of an Enterprise’s Generative AI initiatives. Without incorporating Reflected Intelligence (RI), these systems overlook enterprise-specific knowledge, processes, and branding, which are crucial for meaningful AI interactions.

Enhancing Gen AI with Reflected Intelligence

Reflected Intelligence (RI) is a solution that bridges this gap, ensuring that Enterprise Generative AI fulfills its promise of transforming business operations. RI goes beyond technical proficiency by mirroring an organization’s human intelligence and capturing employee knowledge, communication styles, and company culture. By understanding the customer and enterprise context, RI customizes responses, fosters trust, and drives adoption. This article explores the core principles, benefits, and implementation strategies of RI, paving the way for a new era of customer service-centric AI experiences.

The Problem with AI

Experience experts have emphasized the technological potential of personalization and efficiency through Generative AI, overlooking the critical aspect of how customers perceive their interactions with AI systems subjectively. This standardized approach, where AI interactions remain inherently neutral and objective despite being augmented with human-like conversational elements and response indicators, results in experiences that can feel unengaging and detached from the brand identity customers have come to expect. Such generic interactions, rooted in limited data sets and an absence of contextual understanding, fail to capture the subtleties of human communication and knowledge, thereby impeding user adoption and satisfaction.

There are two basic types of AI models:

1. Discriminative AI models are designed to classify and categorize data based on their training data sets. These models are often used for predicting the next word in a sentence, classifying information, and following predetermined steps. The output of discriminative AI models is selected from the existing options provided in workflows or datasets. These models are commonly used for tasks such as routing calls, sentiment analysis, chatbot interactions, IVR NLU, and spam filtering.
2. Generative AI models can combine reasoning with content creation (text, voice, or images) to convey accurate understanding and results to the end user. These modules are commonly used for various applications, such as IVR Natural Language Understanding (NLU), Generative Agents or Assistants for Voice and Chat, and Generative Analysts for Voice, Text, and Data.

The Solution

RI addresses this gap and is a transformative solution enabling AI to mirror an organization’s employee knowledge, communication style, and culture. RI aims to elevate the customers experience, fostering a more personalized and engaging interaction that resonates with the enterprise’s identity and values. This integration challenges the prevailing norms of AI interactions and signifies a pivotal shift toward creating enterprise-acceptable AI solutions.

RI Core Principles

At its core, RI allows AI to capture, understand, reflect, and mirror an enterprise’s peer-to-peer (employee) knowledge, existing business processes, common communication styles, and the enterprise’s culture. The result is to improve the customer’s experience when interacting with the AI, leading to increased adoption.

• Employee Knowledge: RI captures the collective knowledge and expertise shared between employees during interactions. These are often situations and/or information not collected or captured properly within Knowledge Management databases. This type of intel may also discover employee practices still in use that are different from the preferred method of resolution.
• Business Processes: RI also expands the Generative AI’s Reasoning and Acting (ReAct) approach to better understand all the business processes and workflows in place, allowing the Generative AI to predict the following best action(s) within the desired process(es). This reflects how tasks are typically completed within the organization.
• Communication Styles: RI can also understand how employees communicate with each other and with customers, which is essential to maintaining the brand image and keeping people engaged in the interaction.
• Company Culture: Absorbing the overall corporate values, attitudes, and behaviors that define the company’s work environment is another benefit of AI. It enables an understanding of the organization’s decision-making process and further fine tunes a Generative AI’s ReAct approach.

RI Benefits

The concept of RI underscores the importance of automation being technically proficient but also being deeply integrated with and reflective of the organization’s specific context and nuances. It enables a more natural and helpful AI experience that will boost trust and adoption, leading to improved efficiency and return on investment. Some of the key benefits include: 

• Increased Knowledge: Bridge information gaps between departments by capturing and sharing tacit knowledge exchanged between employees.
• Context-Aware Responses: Consider the user’s role, department, and current task when providing information or completing requests, leading to more relevant and helpful responses.
• Increased Adoption: The adoption of RI models leads to AI interactions that are more relevant and engaging for both employees and customers.
• More Natural Interactions: By understanding communication styles and company culture, RI can tailor its responses to feel familiar and natural to customers, leading to smoother and more engaging interactions.
• Enhanced Brand Identity: RI ensures that AI interactions resonate with the brand identity and values, creating a more cohesive and satisfying customer experience.
• Increased Trust: customers are likelier to trust an AI that reflects their company’s values, understands their work and communication environment, and provides accurate information.

RI Challenges

It is important to acknowledge that any innovative technology comes with unique challenges that need to be considered beyond the typical concerns of privacy, confidentiality, risk, and compliance. However, a proactive and thoughtful approach can effectively address and manage these challenges.

• Passive knowledge: Captured from real-world interactions can sometimes lead to inaccurate or biased information. This, in turn, may require data cleaning and filtering techniques to be applied, which are typically carried out by technical specialists who are knowledgeable about the relevant tools and techniques. By using these techniques, it is possible to mitigate the impact of any unwanted biases or inaccuracies that may have been introduced during the knowledge capture process.
• Scalability and Adaptability: Scaling RI to handle large organizations with diverse departments and workflows can be complex. Additionally, adapting RI to evolving company cultures and communication styles requires ongoing maintenance and adjustments.
• Technical Implementation: Extracting meaningful insights from unstructured data (like conversations) requires technical specialists who can navigate the complexities of Generative AI models, reasoning systems, and unstructured data processing.

Implementation and Controls

To successfully implement an RI model, it is crucial to establish a strong partnership between business leaders and technical experts. They need to work together with confidence to define an overall strategy and roadmap that encompasses the right combination of innovative Generative AI tools and techniques and a robust architecture for data acquisition, processing, knowledge representation, reasoning, and multiple levels of safeguards and controls.

• Incorporate an “adversarial” Generative AI in real-time to fact-check the generative outputs before they are presented during an interaction. This adversarial Generative AI would act as a discerning reviewer or “Critic Agent,” identifying any potential biases, inaccuracies, or hallucinations produced, thus enhancing the overall accuracy and reliability of the system.
• To prevent biases and errors with RI, a human-in-the-loop (HITL) approach can be used. This involves presenting the captured data to human experts for validation before the Generative AI uses it. Human reviewers can check the data’s accuracy, completeness, and potential biases and make necessary corrections to increase the level of confidence.

Leverage Explainable Artificial Intelligence Techniques

• Feature Attribution to explain how individual pieces of data contribute to the AI’s final output.
• Model Visualization, which can help people understand the inner workings of the AI model, would outline the high-level steps for the critical decision elements.
• Counterfactual Reasoning allows people in the HITL feedback process to explore how the output would change if certain input features were modified.

Summing It Up

An RI model integrates seamlessly with business processes, reflecting an enterprise’s workflow, decision-making, and operational strategies. This allows it to begin absorbing and adapting to the enterprise’s culture and considering the prevalent value norms and behaviors. It also ensures that the interactions and outputs are relevant and appropriate.

• Generative AI’s focus on technological capabilities often misses the critical aspect of customer subjective perception, leading to interactions that fail to use enterprise knowledge, lack branding, and do not help customers.
• Standard AI approaches result in generic interactions that lack contextual understanding and do not capture the nuances of human communication.
• RI challenges the prevailing norms of AI interactions by integrating deeply with an organization’s unique identity and values. It aims to create AI solutions that are technically proficient but also meaningful and customer accepted.
• The adoption of RI models, designed to reflect and adapt to an enterprise’s culture and workflows, significantly improves customer experience. It also increases adoption by making AI interactions more relevant and engaging.

Current AI prioritizes technology but neglects customer experience and enterprise knowledge. Reimagine AI interactions with Reflected Intelligence. RI infuses AI with your organization’s DNA, fostering natural, engaging experiences that resonate with your brand. By understanding your company culture and communication style, RI builds trust and drives user adoption. Unleash the true potential of AI with Reflected Intelligence.