Mind the guardrails! Control, Security and Consistency
This blog derives from a Webio Credit Shift podcast with guest Chris Booth, Product Owner for NatWest Group's AI assistant Cora
The world over, AI agents are taking centre stage in contact centre software solutions. These virtual assistants, powered by language models, can hold conversations, answer questions, and even personalise interactions.
But before you unleash a fleet of AI agents on your customers, it's important to understand the AI and automation scene, its limitations and possibilities.
Large language models (LLMs), while incredibly powerful, are not infallible and they have inherent limitations that must be addressed. Their responses are based on patterns in the data they were trained on, which means they can inadvertently produce biased or inappropriate content.
So, as builders of AI agents, you must recognise these limitations and implement safeguards to prevent unintended consequences.
One such safeguard is using custom language models that can be contained.
The Power of Tiny: Big Results, Small Packages
The future of AI agents could lie in "tiny models", which are more targeted, custom versions of LLMs. Using multiple small models instead of a single large model offers several advantages:
Local Models for Control
Small models provide granular control and they can reside locally, reducing latency and privacy concerns. By specialising AI agents for specific tasks, you gain this greater control - think of it like having a team of experts, each with a dedicated area of knowledge.
Smaller models can also be kept local, reducing the need for extensive computational resources and data transfer. This approach allows developers to fine-tune each model for specific tasks, leading to more efficient and targeted AI solutions.
Languge models evolve rapidly, and small models empower developers to fine-tune and adapt swiftly. Updating language models without disrupting existing systems can be daunting, but by focusing on specific tasks (e.g., emotion detection or intent recognition), you can maintain agility.
Addressing Version Control in AI Platforms
As AI agents evolve and company needs change, managing different versions can become tricky and resource-intensive. However, by embracing a modular approach, you can ensure your AI agents remain responsive and up-to-date as these models are easier to update and maintain.
The Importance of Governance and Consistency in Customer-Facing AI
LLMs are incredibly powerful tools, but with “great power comes great responsibility”. These models, while highly capable, can produce unexpected or inappropriate outputs if not properly controlled.
When used in customer-facing environments, careful governance and risk management are non-negotiable. Implementing frameworks and protocols ensures that AI agents behave predictably and responsibly, safeguarding both the customer and the organisation's reputation.
These guardrails include: ensuring data privacy, preventing bias in responses, and having clear escalation procedures for complex issues. Just like training a human employee, AI agents need well-defined parameters to operate effectively and ethically. Companies must establish guidelines for acceptable behaviour, monitor model outputs, and intervene when necessary.
Building Bridges, Not Walls
AI agents shouldn't be an exclusive club. Making them accessible and easy to use is showing consideration to your customers. Imagine a customer struggling to navigate a complex menu, only to be met by an AI agent that speaks in technical jargon: not a winning first impression. On the other hand, by focusing on clear, concise language and intuitive interfaces, you can create a smooth and frustration-free experience for everyone.
This involves ensuring that the interface is user-friendly and that the AI can understand and respond to diverse user inputs accurately. By focusing on accessibility, developers can build AI agents that cater to a wider audience. This not only fosters better customer relationships but also paves the way for widespread adoption and trust in AI-driven solutions.
The Magic Touch: Personalised Engagement AI
Now for the exciting part – personalisation! Imagine an AI agent that remembers your preferences and has accurate customer data on hand, immediately. This personalised touch can build stronger customer relationships and build brand loyalty.
Custom language models for focused conversations
However, striking the right balance is important. Overreliance on LLMs may lead to generic, insecure and unhelpful responses, whereas using a custom language model (e.g. conversational AI CLM for the debt collection industry), enables businesses to build customer conversations that are personalised, useful and secure.
The AI game is competitive and expensive to play. However, all is not lost for smaller players. Startups, and small businesses can tap into fine-tuning existing language models and RAG (Retrieval Augmented Generation) to stay ahead of the curve.
By harnessing the flexibility of smaller, more specialised models, these organisations can deliver innovative and customised AI solutions. Innovation thrives when AI is accessible and by going this route, small businesses can build AI solutions without massive infrastructure and extensive financial resources.
The world of AI agents is brimming with potential. By understanding the limitations, implementing best practices, and embracing focused models, businesses can create personalised customer experiences that are both effective and a pleasure to use. So, aim to build AI agents that bridge the gap between businesses and their customers.
If you need to improve your customer engagement, talk to us and we'll show you how AI and automation via digital messaging channels work.
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