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 fourth 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
Article #3: Improving CS Finance 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
- Scale is how companies become wildly profitable, and stay that way. This is because it allows them to grow their operations while their costs grow slower than revenue.
- Scale is critical for CS Ops teams, few of which generate revenue on their own. This is because any cost savings and efficiencies you earn by scaling CS go right to the bottom line, boosting company profit.
- Historically, CS Ops teams have achieved and protected CS Scale by investing in technology (especially self-service tools), centralizing and streamlining processes, and making better use of data and analytics. These strategies work for all types of growth.
- You should be investing in AI, period. And not just any random technology or implementation, but a blend of AI-powered assistants and human agents.
- No matter what you do, start today. CS Scale is achieved with investment and planning, not magic (with or without AI). And because AI-powered assistants enjoy natural economies of scale, your payback/ROI will be higher than you may think.
Scaling CS is about getting smart before you get big
What separates a good company with solid earnings from a wildly successful company that has more money than it knows what to do with?
The answer: scale.
Specifically, economies of scale. This is the magical state when your company grows its operations, often fantastically, while your costs grow slower than revenue.
In the beauty pageant or gladiator pit of the business world (pick your metaphor), some companies can scale easier than others. Just think of all the wealth that platform and high-margin software companies have created over the last twenty years.
But even for the most advantaged companies, scaling doesn’t just happen. You have to plan for it and make the right investments.
As a CS leader, you know that scale is even more important for CS. This is because most CS Ops teams are cost centers. So when your company experiences high growth, your costs can quickly get out of hand.
On the other hand, when you achieve CS Scale, cost savings go right to the bottom line. And thanks to AI, achieving and maintaining CS Scale is easier than ever before.
Reminder: growth and scale are not the same thing
In common parlance, people often use “growth” and “scale” interchangeably. But in business, they’re very different.
Specifically, growth refers to increasing size, revenue, or output by an often proportionate growth in resources or investment. This means costs typically grow at the same rate as revenue—sometimes even faster.
Economies of scale—or simply scale for short—refers to increasing revenue or output in a way that favorably alters the relationship between revenue and costs. Costs may still grow, but they grow slower than revenue or output, thereby increasing profit.
As these simple definitions remind us, growth and scale are also related. As your company grows, scale comes under pressure—hurting profits (among other things). For your company to grow sustainably and profitably, it must increase scale so that revenue grows faster than costs.
Scaling CS in different growth scenarios
All growth scenarios put pressure on scale. But different scenarios pressure CS Scale in different ways, and in different intensities.
For starters, let’s divide the different growth scenarios into organic growth, which occurs in the course of a company’s overall operating strategy, and non-organic growth scenarios, which are one-off events that occur in direct response to specific company decisions.
Of these scenarios, hypergrowth is probably the most well known (and perhaps infamous). With hyper growth, companies experience a phase of sharp and rapid expansion in revenue, usually driven by proportionally sharp increases in costs. While hyper-growth can lead to outsized profit in the future, it puts extreme pressure on company infrastructure, culture, and resources.
In particular, CS Ops systems, people, and processes suffer in the mad scramble to keep up with the rest of the organization, generating huge costs. Meanwhile, any effort by CS Ops teams to protect key pillars like CS Quality and CS Finance are often resisted.
Non-organic growth scenarios cause similar challenges to CS Scale. But each scenario features a few unique wrinkles worth noting.
For example, if a new product introduces a new customer type (e.g., B2B in addition to B2C) or channel (e.g., direct-to-customer instead of third-party retailer), it will do more than just increase your costs. It will alter the “shape” of your entire CS strategy.
Similarly, if entering a new market means localizing customer support (e.g., by supporting a new language), you may discover that surprisingly little of your processes or SLAs can be ported without an overhaul. Cultural sensitivities can be particularly challenging to manage.
How AI can help you achieve and protect CS Scale
Historically, CS Ops teams have deployed many proven strategies to achieve and protect CS Scale, such as: investing in better technology (especially self-service tools), centralizing and streamlining processes, and using data and analytics for forecasting and labor management.
Success with CS Scale is also reinforcing. For example, CS Ops teams who get really good at forecasting soon discover that growing companies generate a lot of data, making forecasting more effective—further reinforcing scale.
Now we can add AI to the mix. And not just any AI technology, but AI-powered assistants blended thoughtfully with a team of human agents.
Let’s start with self service. As we mentioned in our previous article on CS Finance, 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. They can even cover multiple channels, including voice.
Now consider what happens when you blend AI-powered assistants with human agents. While the assistants focus on low touch or “easy” interactions, human agents can focus on the high touch interactions that may be the most valuable in terms of CS Quality. This is a powerful combination with some great benefits.
- In an organic growth scenario, a blended team reduces the number of new human agents required to support a given quantity of growth. Slower headcount growth means lower costs.
- If you’re launching a new product that’s an evolution of an existing product with 80%+ similar functionality, you can really lean into AI-powered assistants. This is because CS Ops teams should have all the data needed to properly plan and implement an AI-powered assistant. This frees live agents to handle the most complex and risky interactions—which are usually the most valuable in a product launch.
- If you’re entering a new market that requires localization, you can deploy AI to assist live agents directly instead of interacting with customers. For example, you can use AI translation services for real-time interaction translation or for transcribing interactions and notes back into the company’s “native” language for review and analysis.
Some parting tips for your next CS + Ops strategy session
- Don’t wait to get started. It’s hard to plan for the future when you’re under pressure today. But CS Scale doesn’t happen magically, with or without AI. The sooner you get started, the better.
- If you’re projecting high growth, boost your payback/ROI calculations for AI. AI-powered assistants have natural economies of scale because upfront costs can be spread over many more interactions while variable costs decrease with volume (thanks to volume discounts common among enterprise platforms). So if you’re forecasting high growth, you can feel comfortable forecasting a correspondingly high ROI.
- Don’t worry about any decreases in the efficiency in human agents. It’s true: in a blended operation, human agents who focus on exception handling will experience labor “drag” in the form of reduced efficiency. They’ll also need to be managed with greater care because their jobs will have substantively changed. Still, this is a red herring. With more volume running through AI, you will be more efficient organizationally—and that’s what really matters when thinking about CS Scale.
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