After two decades in and around contact centres, I’ve learned this: every conversation about service investment boils down to the same challenge — do more with less. Service teams are under pressure to scale support, reduce costs, and meet rising customer expectations — all without increasing headcount.
Service AI offers a way forward. But turning that potential into business-wide momentum takes more than vision. It takes a bulletproof business case. That’s the topic of our latest guide, Building a Business Case for Service with AI, your roadmap to making service AI a reality in your organisation.
Whether you’re just starting with out-of-the-box generative AI or looking to expand toward Agentforce — a team of autonomous agents that work side-by-side with your employees to extend your workforce and serve your customers 24/7 — we’ll show you how to frame the opportunity, prove the impact, and get the buy-in you need.
Why Build a Business Case for AI in Service?
Today’s service teams know AI has potential. But many still struggle to articulate its tangible business value. That’s where a well-crafted business case comes in.Start by identifying the real challenge: nearly two-thirds (61%) of agents’ time is spent on administrative tasks away from customers. Customer service representatives are consumed by swivel-chair work, manual note-taking, and navigating disconnected systems.
AI tackles these inefficiencies at scale — and the compounding impact is massive.
In ANZ, 9 in 10 service organisations using AI report measurable time and cost savings. Over the past decade, service teams have also steadily become more central to business growth. The number of contact centres tracking revenue targets has nearly doubled since 2018, rising from 51% to 91%. This shift reflects a broader evolution: service is increasingly seen as a strategic growth lever, not just a cost centre.
Securing buy-in for AI investment goes beyond recognising potential — it requires proving tangible impact. You need to show how AI delivers on business priorities. That’s where the most compelling arguments come in:
- Target horizontal inefficiencies: Focus on tasks that occur in every interaction, like case classification, conversation summaries, and after-call work (ACW). These inefficiencies affect more than isolated processes. It can extend across your entire operation.
- Prove it with volume maths: Contact centres are volume-driven. A 60-second saving on ACW, multiplied across thousands of interactions each day, unlocks significant cost savings and boosts capacity without increasing headcount.
- Lead with augmentation, not automation: Agentforce may be the long-term vision, but assistive AI delivers faster ROI with lower risk. It supports service representatives in real-time, builds trust in the tech, and lets your people focus on complex, high-value interactions.
Building a Business Case for Service with AI
Your guide to kickstarting a service transformation with Agentforce.
A strong business case helps service leaders bridge the gap between possibility and profitability, which allows decision-makers to see AI as a strategic asset, not just another tool. Framing your business case through this lens also makes it easier to answer important questions such as:
- What operational efficiencies will AI unlock?
AI helps reduce resolution times, streamline repetitive processes, and free up human reps for more strategic work.
- How will AI enhance customer experience?
With AI-powered personalisation and proactive service, you meet rising expectations and reduce churn.
- What’s the projected return on investment?
Agentforce can increase self-service adoption, improve first-contact resolution, and identify new revenue opportunities through cross-sell and upsell.
Real-World Service AI Transformations
Top ANZ service organisations are already transforming service with AI and Agentforce, improving efficiency, reducing response times, and enhancing customer experiences — all while keeping costs in check. In our guide, service leaders from Magentus and Fisher & Paykel Appliances share exactly how they made AI work for their organisations, covering key strategies, lessons learned, and real results.
Magentus: Leading with metrics
As a healthcare technology company, Magentus saw AI as an opportunity to improve efficiency and offer more convenient service options for its customers. After adopting Service Cloud and launching a pilot program with AI, the team saw immediate results: customer wait times dropped by 60%, chat deflection reached 38%, and average response times fell from 15 minutes to just 3.
Gavin Slade, Salesforce and Systems Enablement Manager at Magentus, shares why measurement is key to gaining support for a service AI transformation, “You need to demonstrate not just the technological improvements but the real-world value and efficiency gains to get stakeholder buy-in.”
Fisher & Paykel: Scaling service without scaling costs
With customers in more than 50 countries, Fisher & Paykel turned to Agentforce to help scale its service operations without increasing costs. Today, AI agents resolve 30% of live chat queries, while automation and self-service tools save the team an impressive 3,300 hours each month.
For Rudi Khoury, Chief Digital Officer at Fisher & Paykel, this has been key to maintaining efficiency as the business grows.“Despite significant growth over the last ten years, our service team hasn’t needed to expand. AI has enabled us to meet high customer expectations while maintaining efficiency,” he said.
Want to see how they did it? Download the full guide for deeper insights, expert advice, and a step-by-step framework to build your own business case for AI in service.
How to Build Your Business Case for Service AI
A well-structured business case is essential to securing leadership buy-in.
In Building a Business Case for Service with AI, we provide a ready-to-use business case template, covering:
- Strategic goals and challenges: Clarify why AI matters now. What are the pain points in your service organisation? Make sure your goals align with broader business objectives and KPIs.
- Projected ROI: Demonstrate the value with numbers. Use data to estimate cost savings, efficiency gains, and potential revenue growth. For example, estimated cost savings, efficiency gains, and revenue growth.
- Implementation risks and mitigation strategies: Address leadership concerns, show you’ve thought through the hurdles and how you’ll overcome them. Address each concern with practical steps, such as starting with AI assisting customer representatives, investing in knowledge management, and providing agent training.
- Next steps: From pilot to scale: Outline a phased approach. Start with assistive AI that supports teams internally. Prove quick wins in a controlled environment. Then gradually expand to more advanced use cases like Agentforce once your data, processes, and people are ready.
It’s Time to Lead
Service AI is no longer a future ambition. It’s happening now. If you’re still waiting to act, you’re already behind. But the good news? You don’t have to do it all at once.
Start small. Show value. Bring your customer representatives with you. And build the business case that sets your service team and your company up for long-term success.
Companies like Magentus and Fisher & Paykel are proving how AI can reduce costs, increase efficiency, and improve the customer experience, all while strengthening the business case for future investment.
Want to see what AI and Agentforce could do for your service organisation? Download the full guide to access insights, case studies, and a step-by-step business case template.
Building a Business Case for Service with AI
This guide delves into the “how” behind transforming service with AI agents.
