As AI adoption accelerates in customer support, the real challenge is moving from AI-first messaging to AI-effective operations. In this conversation, Hiver CEO Niraj Ranjan Rout explains how reducing cognitive load, protecting human empathy, and embedding AI directly into workflows are redefining customer service performance at scale.
In this interview, Niraj Ranjan Rout, co-founder and CEO of Hiver, discusses why the next phase of AI in customer operations will be defined less by surface-level automation and more by measurable impact on agent experience and customer outcomes. Drawing on Hiver’s evolution from email collaboration to an AI-powered customer service platform, Niraj outlines how high-performing teams embed AI across the support lifecycle, reduce cognitive load, and preserve human judgment in emotionally sensitive workflows. He also shares where automation delivers real value, where it risks eroding trust, and what responsible, human-centered AI should look like as support organizations scale.
AIFN: Niraj, you’ve said it’s easy for companies to call themselves “AI-first,” but much harder to be AI-effective. What’s the difference and why does that distinction matter right now?
“AI-first” is often a positioning statement.
“AI-effective” is an operational outcome.
Anyone can bolt AI features onto a product and talk about transformation or pick up a small part of a problem and solve it using AI. But effectiveness shows up in very unglamorous places. Lower cognitive load for agents, fewer escalations, faster resolution without robotic replies, better customer sentiment over time.
The distinction matters right now because AI has crossed the hype curve. We are no longer asking whether it works. We are asking whether it works reliably, at scale, in messy real-world conditions. In customer support, that means handling ambiguity, emotion, context switching, and incomplete information.
AI-effective companies do not start with technology. They start with the workflow. They ask: where does friction exist today? Where are agents mentally exhausted? Where are customers waiting unnecessarily? Then they apply AI precisely there.
It is the difference between saying “we use AI” and being able to say “our team goes home less drained.”
Customer support teams operate under constant pressure and emotional load. Where does AI genuinely help humans do better work and where does it risk getting in the way?
Support work is cognitively heavy. An agent might juggle dozens of threads, each with different tone, urgency, and background. AI genuinely helps in three areas: summarizing context, suggesting relevant knowledge, and handling repetitive interactions that do not require judgment.
When an agent opens a long email thread and immediately sees a clean summary, that is meaningful relief. When AI surfaces the exact help article needed instead of forcing a search, that is time and energy saved. When password resets or order status queries are automated end-to-end, that is capacity returned to the team.
Where AI risks getting in the way is when it overreaches. If it drafts responses that sound polished but miss nuance, agents spend more time correcting than benefiting. If it pushes automation for emotionally charged situations, billing disputes or service failures, it can damage trust.
AI should remove friction, not flatten judgment. The moment it starts dictating tone or replacing discretion in complex cases, it stops being helpful.
What patterns do you see in how high-performing teams use AI differently from those that struggle?
We see three consistent patterns in high performers.
First, they start small with precise workflows, like AI-assisted summarization or draft replies, and iterate based on agent feedback. They do not rush into full automation.
Second, they treat AI as a co-pilot, not a replacement. Real-world data suggests that thoughtful AI adoption frequently increases agent satisfaction without reducing headcount dramatically. A Gartner survey showed only 20 percent of service functions reported headcount reduction due to AI, validating that augmentation, not elimination, is where value often lands.
Third, they invest in clean, structured knowledge systems. Garbage in, garbage out holds doubly for AI. Poor input means poor suggestions.
Teams that struggle skip these foundations and chase vanity metrics instead of agent experience.
You often frame AI as reducing cognitive load rather than replacing human judgment. What does that look like in practice?
In practice, it looks like an agent opening a ticket and not starting from zero.
They see a thread summary. They see suggested next steps based on similar historical cases. They see relevant policy snippets surfaced automatically. They might see a draft response, but it is clearly labeled as a draft.
The human still decides: is this the right tone? Is this customer frustrated? Does this require flexibility?
Cognitive load is about mental switching costs. Searching for context, remembering policy details, manually tagging conversations. These are small drains that accumulate across a day. AI can handle those background tasks.
Judgment, empathy, exception handling. All those remain human. And they should.
Emotional labor is an under-discussed part of customer support. How should AI support empathy rather than erode it?
AI should recognize emotion, not suppress it. Sentiment detection can flag when a customer is frustrated or anxious, prompting the agent to slow down and respond thoughtfully. That is supportive.
Additionally, AI-generated replies should be suggestions, not final outputs. If teams blindly auto-send templated empathy, customers feel it immediately. Authenticity cannot be automated. It can only be assisted.
Finally, AI should reduce the volume of low-stakes work so agents have the bandwidth for high-emotion cases. Empathy requires energy. If an agent spends all day on repetitive tickets, they have less capacity left for someone genuinely upset.
The goal is not synthetic empathy. It is protecting human empathy by managing workload intelligently.
Many AI tools promise efficiency gains, yet burnout persists. What tells you AI is actually improving day-to-day work?
Efficiency metrics do not tell the whole story. Globally, employee stress levels remain high despite productivity gains across industries. That tells us speed alone does not fix strain.
At Hiver, we train our product to look for different signals. Are agents escalating fewer tickets because they feel more confident? Are they spending less time searching and more time resolving? Are new hires ramping faster because context is clearer? And the simplest signal: do agents voluntarily use the AI features?
If AI is truly helpful, adoption does not need enforcement. It becomes part of the rhythm of work. But if burnout worsened, it is often because AI increased expectations without reducing chaos.
As AI agents become more capable, where do you draw the line between automation and human accountability?
The line should follow risk and ambiguity.
Low-risk, predictable workflows like order tracking, subscription updates, and appointment confirmations can be automated safely. But if a decision impacts revenue, reputation, or trust, a human should own it. If something goes wrong, responsibility cannot sit with an algorithm.
AI executes, while accountability remains human. That principle should guide system design.
What mistakes do companies make when deploying AI that look good in theory but fail in practice?
One mistake is over-automating before stabilizing foundations. If knowledge is inconsistent, AI scales inconsistency.
Another is failing to design graceful escalation. When AI hits ambiguity, the handoff to a human must be seamless. Customers should never feel trapped in loops.
Another subtle mistake that customer support platforms make is optimizing purely for deflection. High deflection looks efficient on paper. But if customers feel unheard, it leads to trust erosion over time.
What cultural changes are required for teams to trust AI as a partner rather than fear it?
Trust does not start with technology. It starts with the conversation.
The moment leadership introduces AI, people try to read between the lines. Is this about helping me do better work? Or is this about cutting costs and measuring me more closely? If the intent is unclear, people assume the worst. Once that happens, adoption becomes performative at best.
Leaders need to speak plainly about why this is happening, what it will and will not be used for, and what success looks like. Then behavior must match the message. If leaders say AI will reduce workload but quietly raise output expectations, teams will notice.
It also helps to involve agents early. Let them test features. Ask what is useful and what is frustrating. Share results openly. When people see their feedback shaping the system, it stops feeling imposed.
Framing matters. If AI becomes a silent scoring engine that tracks response times and flags underperformance, people resist it. If it becomes a coach that helps draft better replies, surface helpful context, or spot knowledge gaps, perception shifts.
Trust is not built into the product. It is built into the leadership around it.
Looking ahead five years, what does responsible, human-centered AI in customer operations look like?
The computer scientist Mark Weiser, who coined the term “ubiquitous computing,” once said the most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they become indistinguishable from it. That is how AI in support should evolve.
It should not perform. It should not announce itself. It should quietly remove friction.
Customers will not remember the automation. They will remember that their issue was understood and resolved without unnecessary effort.
For agents, that quiet intelligence should translate into clarity. Less time reconstructing context. Less searching and less duplication. More space for judgment, empathy, and discretion, the parts of support that remain inherently human.
Quiet cannot mean opaque. The industry will have failed if support becomes emotionally thinner, if reaching a human becomes harder when it matters, or if agents are reduced to supervising scripts instead of solving problems. That would be the wrong trade-off.
