This article was written by Troy Gedlinske (CDI’s Expert in Residence) along with US based colleagues at CDI Services, a global leader in Conversational AI training, Auditing, and Consulting services.
This is the third article in a program designed to help customer service leaders develop a realistic, effective AI strategy.
The first two pieces can be accessed below:
Article #1: Four tips to help CS leaders up their game
Article #2: Managing CS Quality in the new era of AI
Each article is a summary of a more detailed, hands-on workshop designed exclusively for CS leaders at the VP level and above. To learn more about CDI’s suite of workshop offerings, consulting and auditing services you can contact CDI’s Global Head of Sales in Ben Taylor on [email protected]
Executive Summary
- Your company ultimately thrives, survives, or dies according to its finances. Hence your CS Ops team is working constantly to improve CS Finance by reducing costs.
- The good news is that AI-related savings opportunities are real and abundant. These aren’t guaranteed: proper planning and execution are required. Nor are they without risk, especially to Quality. But they definitely exist beyond the imagination of AI advocates.
- While no two companies are the same, we can define Finance with the context of CS Ops in a way that helps us think about CS Ops + AI. Specifically, we can divide CS costs into OpEx vs CapEx, and then subdivide OpEx into direct vs indirect costs.
- One of the advantages of using CS Ops + AI to improve CS Finance is that tools like AI-powered chatbots target the same OpEx as traditional programs and tools, often using the same methods. For example, a carefully-planned and implemented AI-powered chatbot is a killer app for reducing contact rates, handling time, and labor inefficiency.
- Even though AI-related savings opportunities are real, CS Ops teams should be thoughtful when pursuing them. For example, poorly-conceived and/or implemented programs to reduce direct labor costs can also reduce the customer perception of Quality. Staff reductions can also hurt morale if they’re not measured or paired with reskilling to give displaced team members a role in the new era of AI.
Why the best companies are using CS Ops + AI to reduce costs
All companies ultimately thrive, survive, or die according to their finances. Hence every operational team in your company is working constantly to improve company finances by cutting costs and increasing revenue.
When it comes to improving CS Finance (one of the five pillars of CS management), CS Ops teams are no different. However, because most CS teams function as a cost center, CS Ops teams are highly-focused (possibly singularly-focused) on reducing costs. Hence most discussion about CS Ops + AI focus on cost savings as well.
💡 The good news is that savings are real and abundant.
Today, some of the best companies in the world are already using ready-for-primetime AI tools like AI-powered chatbots to lower the costs and complexities of CS.
And both they and their customers are better for it.
Thinking about Finance in the CS context
No two companies are identical, so your company’s approach to improving CS Finance is unique in some ways. Still, we can define Finance with the context of CS Ops in a way that helps us think about CS Ops + AI.
As you can see, CS Finance can be divided into three buckets:
- OpEx (Operating Expenses), which includes the day-to-day expenses your company incurs to run operations. Because these show up as expenses on the income statement during the period in which they occur, changes in OpEx (up or down) can have a powerful effect on profitability in the short term.
- CapEx (Capital Expenditures), which includes long-term investments that your company uses to acquire, upgrade, or maintain physical assets such as property, buildings, or equipment. Because these are capitalized on the balance sheet and spread over the useful life of the investment, changes in CapEx (up or down) can have a powerful effect on profitability in the long term.
- Revenue, which includes any activities that generate more sales for your company. Depending on the nature of your business, this can include activities that increase revenue directly, e.g., by improving sales processes or collections. It can also include activities to increase revenue indirectly, e.g., by increasing Downstream Impact (DSI) through reduced churn.
With these three buckets defined, let’s zoom into OpEx—the bucket that most CS Ops teams are focusing on—by dividing it into two buckets:
- Direct costs, which are mostly related to labor, e.g., CS agents plus 2-3 levels of management. Importantly, these costs are generally variable because they can adjust (up or down) relatively quickly in response to demand (actual or forecast).
- Indirect costs, which are mostly costs related to other types of labor, e.g., senior management, IT support, data support, planning and program management, vendor management, or rents. Importantly, these costs are generally fixed costs because they are harder to adjust (up or down) in the short term, regardless of demand.
How AI can help you reduce direct OpEx costs
One of the advantages of using CS Ops + AI to reduce direct labor costs is that tools like AI-powered chatbots enhance the strategies you’re already using. For example:
- Contact reductions. An AI-powered assistant is the ultimate self-service option for customers: they’re more effective and versatile than an FAQ or help doc; they’re available 24/7/365 without breaks or unplanned interruptions; and they can be updated and optimized in perpetuity. This makes them a killer app for contact reduction.
- Handle time reductions. AI-powered assistants can help reduce handling time by gathering as much information as possible about the customer and their issue before facilitating a clean, effective handoff to a human agent. They can also reduce handle times by relieving agents of tedious post-contact tasks like summaries and escalations, or by coaching agents mid-contact.
- Labor efficiency improvements. AI-powered assistants are natural skill blenders because they can be trained to handle multiple types of interactions and tasks. They can also cover multiple channels, including voice—which is why some of the largest companies in the world are busy replacing relatively successful IVR systems with AI-powered voice assistants.
These are just a few examples that stick closely to traditional tools and programs. Thus they don’t include all the new ways AI can improve CS Finance as AI tools spread within your organization. For example, savvy CS Ops teams can use the computing power of AI (in addition to their in-house or 3P planning tools) to analyze opportunities that may have been difficult to identify previously. This includes much better forecasting of the impacts from direct labor contact and handle time reductions, which even the best CS Ops teams know is as much art as science.
How AI can help you reduce indirect OpEx Costs
You can also use CS Ops + AI strategies to reduce indirect (fixed) labor costs.
In particular, large organizations with multiple layers of CS management may be able to streamline operations after deploying AI. The reason is simple: more efficient use of direct labor reduces the need for indirect labor to manage it. This could even include teams responsible for tasks that seem indispensable today, such as training and scheduling.
It’s important to remember that indirect labor savings will most often trail direct labor savings. Given the power of compounding, this means it’s often better to focus on direct labor improvements and efficiency first, then look for indirect labor savings opportunities as they emerge later.
Warning: Proceed thoughtfully
We get it: AI can be very tempting for CS Ops teams under constant pressure to improve Finance at a time when existing tools and best practices have plateaued. Just ask anyone tasked with wringing an extra percentage of containment from a 10-year old IVR system.
💡 But CS Ops teams should take care about the pace and nature of AI-powered cost reduction.
- Poorly-conceived and/or implemented programs to reduce direct labor costs can also reduce the customer perception of Quality, creating new costs and headaches for your company.
- Staff reductions can hurt morale and undercut other CS pillars. Companies are wise to adopt a realistic, measured approach that relies on natural attrition without rehiring to carefully and thoughtfully reduce both direct and indirect headcount.
- AI-driven improvements require careful planning and continuous investment. Adding an AI-powered chatbot isn’t a project; it’s a new channel (like adding a website during the 1990s or an app during the 2010s). Also, launch to product is Day 0—most of the value from AI comes over time through optimizations and the power of compounding.
- New roles will need to be created to manage AI programs, including both technical and non-technical roles. This makes it easy to overstate cost savings. On the plus side, some of these new roles can be filled by displaced managers and senior agents, creating a win for everyone involved.
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