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Gaproc Media Leadership

Change Your Thinking About AI ROI

AI is helping to reshape aspects of the service experience and its economics, and with it, the way you’re likely thinking about return on investment. We are at a critical impasse in CX where new types of interaction models can be deployed to drive unprecedented levels of service.
 
The upside is significant, but many AI business cases live or die on labor cost: heads removed, or attrition not backfilled. It’s often the 
only lever a CFO finds credible, and the one you can (theoretically) bank up front. But labor is the floor of AI value, not the ceiling. And actually realizing those benefits? Not easy! This explains some of the dismal press that AI has received around ROI.

AI is not some get-rich-quick scheme through head count reduction. AI fundamentally shifts the operating model and changes what we expect from talent. Reduction has validity, but redeployment unlocks greater value.
Redeployment is a management allocation decision. You can bank freed capacity as savings (the headcount story), or you can treat it as an investment fund and redeploy it into work the human was never doing. The latter requires visionary management and a true business – technology partnership.
1. Why the model changes: A fundamental shift in the CX model.
AI is reshaping how many organizations approach sales and service models. Organizations can use AI tools to deliver new experiences at greater efficiency, with deep data-driven understanding. AI can help agents not only be more effective, but also truly provide differentiated touches and help with new use-cases.
The ability to do more with less opens up long tail effects that are now worth going after. A well-structured AI program can realize lower cost to serve, allowing humans to flex into new roles and most importantly allow CX to evolve into the new era of where it should be.

2. Where capacity goes: The case for restructuring around AI

In a typical contact center, a five-minute call typically loses two minutes to authentication and figuring out the problem. AI tools can help reduce the time spent on authentication and diagnosis through virtual agent triage. The reflex is to bank the savings as headcount. The opportunity is to reinvest them.
One of our leading principles at Zoom is conversation to completion. There are five levers around restructuring for AI that we feel provides tactical ways to execute on this reality.
Lever 1 : Capitalizing on capacity.
Time saved could be used for educating customers, cross-selling, building relationships, and preventing the next call. This is the lowest effort shift; simple behavioral changes where agents are encouraged not only to be more efficient, but capitalize on the time that’s now opened up on each interaction.
Lever 2: Mining new data
For many supervisors, AI can replace 2% QA sampling with 100% coverage. But the real prize isn’t oversight, it’s the data. Every conversation becomes structured signal: a live feed of customer needs, product gaps, and competitive moves that once stayed buried in unreviewed calls.
At minimum, managers can be freed up to coach intelligently, not just listen to random call samples. But a role-redesign is where value compounds: redirecting strong agents to become data scientists is entirely feasible.
The contact center turns into a discovery engine for the roadmap, not just a place to resolve tickets. This is the most underappreciated lever AI offers.
Lever 3: Going after long tail use-cases.
Additional capacity allows organizations to go after long-tail cases that were previously deemed uneconomic.
The 2am contact, long-tail languages, volume previously abandoned. All of this becomes possible with AI.
Lever 4: Flexing the proactive muscle
Retaining revenue is cheaper than net new capture of revenue, and proactive service is almost always cheaper than reactive service. Reducing the need for manual handling of some L0 and L1 cases allows you to redeploy resources to actively manage relationships and proactively solve challenges.
 
Vignette: A major global airline randomly surveys frequent flyers to evaluate satisfaction from their recent flights. Top travelers who indicate dissatisfaction will get a call from a customer advocacy team who attempts to understand the problem and review the feedback. The simple act of the proactive outreach in response to a poor service event is a significant loyalty lever. Deploying humans to conduct this sort of exercise is the type of work that is enabled with additional capacity.
Lever 5 : Re-deploying agents to new roles
Agents typically understand an organization’s operational, process, and product gaps better than the functions that own them. That knowledge is wasted on ticket resolution alone.
Redeployed, agents can act as CX multipliers: anticipating customer needs and uncovering insights that can drive stronger relationships and potential revenue opportunities.
These agents may move into roles focused on AI design, data scientist pusuits, or focusing on long-tail use cases. It’s critical to think about rebuilding and optimizing operating models allowed by AI’s enhanced workforce capacity

3. The Flaw, and the reframe

AI requires active management: building new use cases, optimizing tools, and continuously improving performance. This work forms the foundation of an emerging skill set, and organizations must invest in robust training programs to help their best talent retool into these roles. The future of your workforce depends on how seriously you take that development today. Robust service design thinking is required as you retool for the five levers mentioned above and the many others that we don’t have time to dive into.
Reduction is made to look great on paper, but is very messy in real life. The dollar value of any AI capacity is a forecast that hinges entirely on a management decision: redeploy it, or let it dissipate back into slack.

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