Agentforce: Good to Great Strategies for Autonomous Service Agents

The goalposts in customer service are always shifting. As technology advances, what was “great” yesterday is simply “good” today. To stay ahead, your service strategy must evolve — and that’s where AI agents, specifically Agentforce Service Agents, step in.

Unlike your traditional chatbots, these agents can work autonomously alongside your team, handling routine and complex tasks alike, and taking action to resolve issues in real time. By combining your team’s human intuition with AI’s speed and efficiency, agents open up new ways to deliver faster, smarter, and more scalable customer service.

In this blog, we’ll cover three essential strategies to unlock the full potential of AI agents and transform both customer and employee experiences.

Aligning your AI strategy with your service strategy

With so many new capabilities at your fingertips, where do you start? If Service Cloud and Agentforce can do a myriad of amazing things, the first step is knowing which align best with your business’s needs. Before you can define an AI strategy, you need to understand your organisation’s service strategy.

Service strategies usually fall into one of three categories and are dependent on the complexity of cases:

1. Action-based: Service organisations like Australia Post or Uber Eats handle high-volume, repetitive tasks that require quick resolutions. Automation is essential to keep operations efficient.
2. Order-based: For companies like MECCA and Telstra, the majority of customer interactions represent potential value and revenue. Balancing efficiency with customer satisfaction and brand love is crucial, as each contact opens up upsell and cross-sell potential.
3. Knowledge-centred: These businesses manage complex, long-resolution cases that require deep expertise to help resolve cases — think of service interactions at Salesforce or Xero.

Identifying where you fit helps pinpoint where AI can deliver the most value.

For example, in action-based service organisations, a high deflection rate might be the goal. Autonomous service agents can take care of rescheduling appointments or tracking orders by pulling relevant customer data and reasoning based on your knowledge articles and FAQs.

In order-based organisations, AI can play a critical role in assisting and augmenting the human experience by providing recommendations and offers. It can also help ensure organisational policies are executed accurately and consistently. 

In knowledge-centred organisations, AI agents can support the most complex queries by accessing both structured and unstructured data, answering technical questions and escalating cases to human representatives when necessary. They even help customer representatives by summarising those multi-interaction cases, generating replies, and recommending next steps. 

1. Define a channel strategy for humans with AI agents

Each type of customer inquiry has its ideal channel, and getting this right is essential for effective AI-powered service. Simple tasks, like updating an address, are well suited for no-touch processes, while interactions that add value, like cancellations or product inquiries, might need a high-touch approach.

To craft your channel strategy, bring your team together to review each contact driver and determine which channels serve each best. This approach lets customer representatives focus on high-value interactions, while Agentforce handles the rest.

Need more help on channel strategy?

2. Map out a maturity journey for your AI agents

Where are you on your AI journey? Knowing what your AI agents can do today — and how they can evolve to do more — is essential for long-term success. A maturity plan helps you guide your Agentforce deployment from “good” to “great” and beyond.

There are four levels to the journey, each with an increasing percentage of containment: 

• Level 1 – Conversational bots: These experiences respond by regurgitating knowledge articles or replying with answers like, “Click here to check your order status”, rather than actually resolving the problem for the customer. They’re limited by a lack of authentication, integration, and guardrails that can help execute autonomous actions. 
• Level 2 – Personalisation with authentication: At this level, autonomous agents can recognise customers and personalise interactions using CRM data, creating a slightly more tailored experience. They still lack access to transactional data and the integration to execute automation. 
• Level 3 – Access transactional data: Here, AI agents get serious. They can access real-time transactional data, allowing them to provide personalised answers like “Your order has shipped. Here’s the tracking number.”
• Level 4 – Take action: At the highest level, fully autonomous AI agents go beyond responding to actively resolving issues. They process refunds, reschedule deliveries, and handle complex requests on their own. Trust, guardrails, and integration play key roles in attaining level 4. 

Knowing where you are on this journey — and what it takes to reach the next level — is key to unlocking the full potential of humans seamlessly working with AI agents in customer service.

3. Plan a data strategy

Moving from a conversational experience to fully autonomous AI agents requires a solid data foundation. Clean, accessible data is essential for unlocking levels 3 and 4, where AI agents start delivering real value to customers, employees, and the business.

• Level 3 requires real-time access to transactional data. Data Cloud brings all structured and unstructured data together, giving AI agents a 360-degree view of the customer, so they can move from simple responses to actually solving issues.

• Level 4 demands integration across all systems and processes, where MuleSoft comes in. It connects every system and workflow so agents can take action across the enterprise. With AI agents solving inquiries and performing work on behalf of customers and employees, you’ll see real drops in contact centre volume.

Take your customer service from good to great

With human employees working alongside AI agents, customer service reaches new heights. But before you dive in, make sure your AI strategy aligns with your business goals, map out a channel strategy and maturity journey, and prepare your data to support AI agents.

With the right strategy and data foundation in place, you can elevate your service from good to great — boosting efficiency, increasing customer satisfaction, and driving real business impact.

Ready to go from good to great? For more practical advice, watch the Good to Great: 7 Digital Support Best Practices webinar and learn how to take your service strategy to the next level.

Explore more service tips

• Customer Service Makes a Strategic Shift for ANZ Organisations
• 3 Ways to Transform Service and Outpace Rising Expectations with AI
• 7 Best Practices to Take Customer Support From Good to Great