This article was written by Troy Gedlinske (CDI’s Expert in Residence for CS Ops + AI) along with colleagues at CDI Services, a global leader in Conversational AI Auditing, Training, and Consulting services.
This is the sixth article in a program designed to help Customer Service leaders develop a realistic, effective AI strategy.
The first five articles can be accessed at these links:
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
Article #4: Achieving CS Scale in the new era of AI
Article #5: Achieving CS Flexibility 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 workshops offerings, you can contact Ben Taylor ([email protected]), CDI’s Global Head of Sales.
You can also register for CDI’s February webinar, on Thursday Feb 20th, 4pm CET time that will involve Troy and members of the US Services team that will bring the series to its conclusion here.
Executive Summary
- CS Risk is the fifth pillar of CS and arguably the most important. This is because managing CS Risk means managing issues that can negatively affect the other pillars of CS: Quality, Finance, Scale, and Flexibility.
- Human beings are notoriously bad at managing risk. Fortunately, CS Ops leaders have a range of risk management best practices available to them—no matter how big or small their operation.
- New AI tools are transforming CS Risk management by introducing new tools to manage risk—as well as new risks to manage.
- Smart, thorough planning remains the best approach for managing CS Risk related to AI. This allows CS Ops teams to collect quick wins today while building up capabilities to succeed in the new era of AI.
The more things change, the more important managing CS Risk becomes
CS Risk is straightforward to define. It’s any potential issue that can negatively affect any of the other four pillars of CS: Quality, Finance, Scale, or Flexibility.
Beyond that, things get a little more complicated.
For starters, humans are bad at managing risk due to cognitive biases that encourage us to overestimate rare risks and underestimate common ones.
Risks also compound, with risk in one CS pillar quickly spreading to others, such as when a CRM system failure blocks customer interactions and leads to missed SLAs and reputational damage.
Finally, risks change with time. As we discussed in the second article in this series, CS has changed dramatically since the industry's infancy 60 years ago. Back then, teams were small, interactions were local, communication was slow, and expectations were low. Today, the opposite is true. This revolution in CS has delivered huge benefits to customers and companies alike. But it has also introduced new risks for you to manage—from technology and security risks in the data center to political and weather risks in far away countries.
Thanks to AI, CS is being transformed again, introducing both new benefits and risks—from deploying AI improperly to mismanaging expectations among both customers and stakeholders. This makes managing CS Risk both more important, and more complex, than ever.
Placing new AI risk in the context of existing CS Risk
All CS Risks, and how you prioritize them, vary from company to company, we can organize traditional risks into three categories:
- Operational Readiness Risks are risks related to the technology, systems, and processes you’ve implemented as the foundation of your CS Ops. These risks may affect everything from your CRM system to the strategies and processes you use to route contacts to agents with the appropriate skills.
- Geographic Risks are risks related to the geographic distribution of your CS teams and systems. Such risks include political, labor, and weather/climate risks unique to where your teams are located. There is also a risk that you’re too geographically concentrated, reducing resilience.
- Topology Risks are risks related to how you distribute contacts between different agents based on a variety of factors, such as their location (on shore or off shore, in office or remote). Mismanagement of these risks can undercut the efficiency of your operations and unnecessarily hurt the customer experience.
With AI, we must extend these traditional categories of risk with a new one: the risks CS Ops teams face as they embrace, at varying speeds and depths, new AI tools.
These CS-specific risks fall into three broad categories:
- First Mover Risks occur when CS Ops teams move too quickly with new AI tools that haven’t been proven and/or that they don’t understand. This wastes time and money, creates confusion and disappointment, and stifles future investment.
- Late Mover Risks occur when CS Ops teams wait too long to adopt transformative AI technology due an excess of caution or underinvestment. This forfeits quick wins and all the compounding benefits, which in turn hurts competitiveness, recruitment, and morale.
- Adaptation Risk occurs when CS Ops teams fail to adapt their legacy strategies to maximize the benefit of AI, e.g., by protecting human agents from being overwhelmed by complex tasks (and difficult customers) that AI-powered assistants can’t handle. Poor adaptation to the existence of new AI tools reduces their ROI.
How AI can help you mitigate CS Risk
If you’ve read any of the previous articles in this series, then you know that we’re huge advocates of AI-powered assistants. When properly planned and implemented, such assistants are the ultimate self-service option for customers because they’re always available, readily scalable, and can be deployed flexibly.
Within the context of CS Risk, AI-powered assistants mitigate or even eliminate most common risks associated with the supply of human agents by reducing the demand for them—not just day-to-day, but also in the face of unforeseen challenges. As a result, such assistants can reduce your company’s overall CS Risk profile, giving your team more time and money to focus on other risks.
CS Ops teams who are ready for more sophisticated options can invest in AI-powered predictive analytics tools to identify and manage risks. Such technologies can be used to analyze vast amounts of data to detect patterns, predict disruptions, and recommend actions. Practical applications include attrition reduction, security risk detection, and NPS prediction.
That said, you should take care when investing in predictive AI tools today. Using them responsibly requires proper oversight to prevent algorithmic bias or reliance on inaccurate data, and in no circumstance should then be used to replace human decision-making.
All things being equal, your first CS Ops + AI investment should be in an AI-powered assistant.
How you can mitigate the risk of AI
While there is a lot of focus on the big picture risks of AI to society and human safety, we’d like to close this series by focusing on the more specific AI risks we discuss above. In doing so, we’d also like to highlight the best risk mitigation strategy available to you: proper planning.
It’s imperative that your CS Ops team “start smart” with AI. In general, this requires four steps:
- Begin with a unified vision for CS Ops + AI. This vision should emerge from a sober, top-down assessment of your current state (business + technology + team). It should also clearly explain the rationale behind investing in AI (costs + benefits).
- Define a strategy as a pathway to the unified vision. This strategy should be as comprehensive as possible, with special care taken to define target domains, the customer experience, the required organizational structure, and success metrics. You should resist pressure to do too much, too soon, and with too few resources.
- Think very carefully about technology. CS Ops teams should work with their internal technology partners to review the current tech stack and commit to fixing weaknesses, such as poor interoperability, excess cost and complexity, and inattentive vendors. You should also avoid defaulting into a technology or vendor, instead letting the right platform choice emerge from a thoughtful analysis.
- Embed ethical AI governance in programs from the beginning. If AI is to become an integral part of CS Ops, you must ensure it aligns with your company’s governance, ethical, and regulatory requirements. Be sure to include AI in your overall risk management framework before you begin implementing it!
These steps are really just the first of what’s required to properly plan and execute your AI strategy, then evolve it over time. But we call them out here because they’re also the steps that many CS Ops teams skip in the rush to embrace AI. This introduces risk that you should, and can, eliminate up front.
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