Customer engagement using AI orchestration: Blending AI to maximize value

Businesses are increasingly relying on artificial intelligence (AI) and advanced analytics to improve customer engagement, but not all tools are created equal. AI-powered customer engagement and marketing solutions must seamlessly blend the most effective AI and analytic technology-such as deterministic rules, process automation, predictive AI, and generative AI-into a holistic solution. This approach empowers organizations to make faster, smarter decisions - resulting in personalized recommendations and enhancing the overall customer experience. 

However, simply adopting AI technologies is not enough. To maximize the potential of these tools, businesses need an AI orchestration platform. This platform must be built on a foundation of data and process management, vertical expertise, customer engagement best practices, and practical use cases. Its goal is to ensure that organizations achieve a faster time to value, allowing them to respond to customer needs efficiently and effectively.

The role of AI in customer engagement 

In terms of AI orchestration, different types of AI excel in specific areas: 

• Generative AI (Gen AI) and large language models (LLMs) are powerful tools for generating and summarizing content, categorizing unstructured data, and understanding the context of customer interactions. These models can interpret voice, text, and other forms of communication to determine customer intent. 
• Statistical AI, on the other hand, is highly effective at predicting and testing the effectiveness of that content. It forecasts the likelihood of a particular piece of content being useful to a customer, and it tests this prediction in real-world scenarios by leveraging adaptive analytics and data-driven techniques. 

But blending AI technologies is more than just switching between different models for isolated tasks. AI orchestration involves using these models in concert-working together in a closed-loop system to continuously learn, adapt, and improve. 

AI orchestration in action

Let's take a practical example of AI Orchestration in the context of customer engagement:

Imagine a customer reaches out to a company through various channels-such as voice, click, or chat. Generative AI, like an LLM, can immediately analyze the customer's input to determine their intent. This information is then fed, in real time, into an adaptive learning model that predicts the most effective way to respond based on the customer's past behavior and preferences. The same system might also be responsible for orchestrating AI to generate and test multiple content variants to find the most effective communication. This closed-loop system re-evaluates and adjusts its actions in real time, ensuring that the company responds with personalized, relevant content -further enhancing the customer experience.

At the same time, the orchestration platform is balancing all these processes against a deeper understanding of the customer's overall value to the business, ensuring that the actions taken benefit both the customer and the organization.

Removing Bottlenecks with AI

In many organizations, creative, development, and testing cycles can be time-consuming and riddled with bottlenecks. AI has the potential to accelerate these processes, and when multiple forms of AI are blended, this acceleration can reach new levels.

For example, if an AI-powered statistical model identifies underperforming content, an LLM could be prompted to generate new creative variations tailored to fill those engagement gaps. Creative teams can review and approve these variations more quickly, with AI agents assisting in ensuring that brand guidelines, compliance rules, and other best practices are followed.

With AI orchestrating and automating many of these processes, teams can significantly reduce time spent on routine tasks like drafting content fragments, assembling, reviewing, and approving them. This leads to faster iteration cycles and ultimately to rapid and trusted deployment of new content and strategies, ensuring businesses stay agile and responsive to customer needs.

The Future of AI Orchestration Platforms 

Looking ahead, the future of AI in customer engagement lies in platforms that are adaptable, interactive, and context-aware, providing business teams with the ability to make quick, informed decisions. They will need to facilitate smooth workflows by managing input data, AI prompts, models, templates, and interaction memory, while ensuring the outputs (next best actions and experiences) are both effective and timely.

Ultimately, the choice between generative AI and predictive AI-or, more likely, the blending of both-should be driven by the specific needs of the business and the use case at hand. By understanding the strengths and limitations of each AI technology, and effectively blending them, organizations can unlock their full potential and achieve greater time to value and lasting relationships with their customers. 

Learn more about AI-Powered decisioning by exploring its benefits with organizations like Rabobank, Google and Forrester.

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