The rationale here is that layoffs today save companies money that can be channelled into their AI research. In turn, this is expected to advance their technology to push forward without the need for manpower.
It’s not just the big tech that is looking at AI as a suitable replacement for human resources. In fact, a recent study from ZenBusiness showed that even small businesses today see AI as a necessity to survive and thrive. The days of it being a ‘nice to have’ are long gone.
Is the customer a casualty or beneficiary?
The rather aggressive shift in business priorities towards AI over the past couple of years has been disruptive for all stakeholders. However, the biggest impact has been felt by customers - the stakeholder who funds the entire operation.
In some cases, the deployment of AI has taken away the human element that made business engagement real. As a customer, would you rather speak to a human or an AI voicebot while calling customer care?
However, this has also allowed businesses to optimize processes and help customers resolve their issues more seamlessly. Customer support has often been riddled with frustratingly long wait times. With tools like Kapture, AI agents can now attend to customer queries instantly and help resolve most of the routine issues. This leaves human agents responsible for the 10% that needs human intervention.
All in all, the answer to the question of whether AI deployment has made customers a casualty or a beneficiary can best be summated through its impact on customer experience. Are customer satisfaction scores like NPS and CSAT higher or lower when customers interact with AI agents instead of humans. That is indeed the true test.
Managing customer expectations in the age of AI
Here is a general rule of thumb when it comes to overhauling any business process; not just with respect to AI - if the change does not improve productivity or reduce costs, do not do it.
But here’s the thing - how sure can you be that the cost reduction project does not impact customer experience and expectations? It is for this reason that it is important to ensure that productivity improvements and cost reduction projects are undertaken while ensuring baseline customer satisfaction metrics are met.
Let’s take the use of AI in customer support for illustration. There are multiple scenarios where AI can be deployed in this segment. Chatbots are perhaps the most visible elements of AI in customer engagement. However, a pure bot-based interaction has often been derided by customers. Some surveys show that bot interactions can in fact bring down customer satisfaction.
The ideal way to manage customer expectations then would be to identify the strengths and weaknesses of human and bot interaction and establish a hybrid approach where the strengths of each component overpower the weaknesses.
As mentioned earlier in this article, wait times and off-business hour support can be quite frustrating for customers. This is especially true when you have a global clientele from across different timezones coming up with support requests through the day.
To bridge after-hours gaps without sacrificing empathy, many teams adopt an AI-first, human-backed phone workflow. A modern AI answering service like Quo (formerly OpenPhone) can greet callers 24/7, capture intent, route by topic or priority, and escalate to a live agent when sentiment or complexity requires it. Features such as automatic transcripts, shared inboxes, and SLA-based routing shorten time-to-first-response while preserving context for handoffs—resulting in lower wait times and better NPS/CSAT across time zones.
An AI agent that can handle basic customer enquiries can be a gamechanger with respect to bringing down average resolution time, and first resolution time. At the same time, human agents now have the necessary bandwidth to work on the more complex requests. Overall, this can improve productivity, bring down costs while ensuring exemplary customer satisfaction scores.
Navigating AI use in prospective customer engagements
Most of our discussion on AI in customer engagement revolves around customer support. However, this does not include prospective customers who form their impressions and build trust about your business through your engagements; a lot of which is driven by AI today.
Use-cases here include the use of AI writing tools to build your marketing assets, but also other uses like predictive analytics, lead engagement and nurturing, to mention a few.
One difference you see between paying customers and prospective leads is that unlike paying customers, it is not easy to see when a prospective lead becomes warmer or colder towards your offering. Ideally, you should engage warmer leads more, and drop your cold leads because they may not need your product or service.
AI based marketing automation tools use metrics like open rate, click-through rate, and time spent on the assets to establish a score for each prospect. You can integrate this with TCPA tools that manage user consent to identify your engagement strategy. A lead who has consented for your call but not engage with your assets could perhaps be handed to an agent responsible for re-engaging prospects while one with an active consent and is actively engaging could be channeled towards deal closure.
Such an engagement strategy ensures that customers who are no longer interested do not get bombarded with your messages while you continue to engage those who are interested.
Striking the right balance
Despite everything you have seen AI do in the past few years, it is a sobering thought that we are still in the very early stages of AI. We are going to be witness to dramatically significant developments and the use of AI in business over the next several years.
As it stands today, AI-driven technologies like chatbots, predictive analytics tools, personalized recommendation engines can help companies proactively address customer needs and deliver timely solutions. All this is a step towards improving customer experience.
However, there is no gainsaying the fact that striking the right balance is crucial. At the end of the day, humans continue to be the decision makers. A human intermediary is always necessary for critical emergencies. Knowing when to channel a business process through AI, and when to retain humans will serve as a critical differentiator and define the success of businesses in future.
