Integrating your AI agent with existing systems: challenges and solutions

Integrating an AI agent into a company involves connecting it to existing information systems (IS) used for customer relationship management and internal operations. These systems primarily include:

• CCaaS (Contact Center as a Service): Platforms for managing customer interactions, such as Genesys and Kiamo
• CRM (Customer Relationship Management): Tools like Salesforce that centralize, analyze, and optimize customer interactions
• Knowledge bases: Software like Mayday that enables companies to centralize, structure, and enrich their information to provide accurate and consistent responses to users
• Other internal IS: Including ERPs and company-specific internal solutions

These systems form the backbone of customer interaction management and are often deeply embedded in business processes.

Integrating an AI agent presents two main use cases:

1. The AI agent for customer service

The AI agent integrates with existing information systems to enhance the customer experience by contextualizing interactions. It can query databases, a CRM, or an ERP to tailor responses based on the customer’s context, considering their interaction history, preferences, or request status. This personalization helps reduce response times and improve customer satisfaction by providing more accurate and relevant answers.

Additionally, the AI agent can send information to internal systems to enrich customer records, log complaints, or qualify a request before a human agent takes over. For example, when a customer contacts support for a request update, the AI agent can automatically retrieve logistical details and notify relevant teams in case of delays, without human intervention. Similarly, in technical support, the AI can analyze the reported issue and suggest a solution from a knowledge base before escalating the case to a human advisor if needed.

2. The AI agent for augmented advisors

In this scenario, the AI agent acts as an add-on integrated into business tools used daily by customer service teams. It can suggest relevant responses or actions in real time, based on CRM data or ongoing interactions, enabling proactive assistance. When a customer service representative handles a complex request, AI can recommend solutions based on similar past cases or extract key information to speed up diagnostics and resolution.

Furthermore, by facilitating access to essential information, the AI agent reduces request processing time and enhances decision-making for advisors. With seamless integration into CRM, CCaaS, and knowledge base tools, it provides instant access to customer histories, optimized response scripts, and strategic recommendations based on best practices. Real-time conversation analysis also helps detect customer emotions and adjust the advisor’s approach accordingly, ensuring more effective and satisfying interactions.

1. Compatibility with existing systems

Integrating an AI agent into an existing technological environment is an opportunity to modernize and optimize current processes. While some companies still rely on legacy systems (“legacy systems”) that were not originally designed to interact with advanced technologies, many solutions now facilitate this transition. Smart connectors (APIs) effectively link AI with existing tools, ensuring smooth compatibility.

Some businesses already use a variety of integrated software accumulated over time. Rather than being a constraint, this diversity opens up new synergies and optimizes information flow by integrating solutions tailored to each organization’s specific needs. By evaluating modernization and connectivity options, companies can leverage their existing infrastructure while enhancing it with artificial intelligence.

Partnering with AI specialists, such as DialOnce, provides valuable expertise in selecting the best connectors and ensuring a seamless transition. These experts possess the skills to analyze existing infrastructures and recommend the most effective integration strategies. They can advise on the best connectors based on specific requirements, ensuring optimal compatibility between the AI chatbot and existing tools, whether it’s a CRM, ERP, or internal knowledge base. If no pre-existing connector fully meets the needs, they can also develop custom solutions for a smooth and efficient integration.

2. Return on investment

Integrating AI is a strategic investment that can quickly generate significant added value. Rather than being a cost, it serves as a performance lever, as a well-integrated AI agent helps optimize interactions, reduce team workload, and enhance customer satisfaction. Adopting a progressive approach is an effective way to spread expenses over time. By starting with high-impact use cases, such as automating responses to frequently asked questions, businesses can quickly measure benefits and adjust their strategy before expanding AI to other processes.

With technological advancements, AI solutions are becoming more accessible and scalable, allowing businesses to tailor deployment according to their needs and resources. Many AI agent providers, like DialOnce, offer flexible pricing models that enable a gradual and controlled expansion.

3. Data management and utilization

AI relies on high-quality data to deliver optimal performance. Efficient data management not only ensures reliable interactions but also uncovers new opportunities for personalization and optimization. Regularly structuring and cleaning databases guarantees a more relevant and effective AI agent.

Data security is also a key factor. Today’s advanced protection technologies (encryption, access control, security audits) ensure a high level of protection. By adhering to strict protocols and implementing strong data governance, businesses can leverage AI’s full potential with confidence.

Integrating an AI agent is not just a technical challenge—it’s a strategic opportunity to optimize operations and improve customer experience. With the right approach and tools, businesses can fully capitalize on this technological advancement and position themselves for long-term innovation.

The integration of an AI agent does not end with its deployment. Once in place, its performance must be continuously evaluated to prevent efficiency degradation or misalignment with the evolving needs of the business. Without a structured monitoring process, it can be challenging to measure its real impact, identify areas for improvement, and resolve any issues related to its interaction with existing infrastructure.

It is essential to establish a monitoring framework based on key performance indicators (KPIs) aligned with business objectives, such as the automated resolution rate, average response time, or user satisfaction levels. A continuous data collection process helps detect performance gaps and implement necessary adjustments promptly.

A plan for continuous improvement should be in place, including regular updates to the conversational AI agent, refinement of its conversational capabilities, and the integration of new features. Additionally, analyzing user feedback and internal team insights is a critical factor in ensuring the AI agent remains aligned with business processes.

Regular real-world testing helps assess the AI agent’s robustness in response to market changes and user expectations. This proactive approach ensures that the chatbot continues to provide value to the company and its customers while adapting to new integrations and organizational developments.

Integrating artificial intelligence into existing systems is a strategic step for customer service teams looking to stay competitive in a constantly evolving world. While it comes with challenges, careful planning and the use of tailored solutions can turn these obstacles into opportunities. By adopting a progressive approach and leveraging expert support, businesses can fully harness the potential of AI agents to revolutionize their operations and enhance the customer experience.