Empathy can’t wait

AI for helplines is not only about crises

At Tranquilla, we create empathetic AI (the bot is named Kathleen) that engages sensitively with users for three scenarios we call Concierge, Coach and Care, which you can think of as information, guidance and a kind of considerate, conversational support.

In all these modes, Kathleen can help people effectively, quickly and at scale. This matters because with any helpline or service agent, being on hold is not good enough. Waiting is, at best, obstructive and, too often, a source of harm in itself.

In any discussion of AI and helplines, the conversation gravitates toward cases of acute crisis. We think about suicide prevention, severe mental health episodes, and people at the edge. These situations matter greatly, of course, and the moral urgency is real. But this framing may lead us astray because it is so vivid and emotionally compelling. This is the availability heuristic at work: our minds weigh memorable, intense scenarios more heavily than common, mundane ones. We end up designing systems for catastrophes rather than daily needs.

There’s an important social component to this: crisis justifies intervention, making the work of helpers visible and valued. And crisis maintains our power structures. When people are in desperate need, they’re not in a position to question how they got there or what systemic failures created the conditions for their suffering. They’re trying to survive.

So, there’s moral drama in rescue that doesn’t exist in the daily work of prevention, which, if done well, would not only fend off crises but enable people to question the systems of power that continue producing crises.

This is reminiscent of how we approach poverty politically. We’re very concerned with feeding people in famine, which is dramatic and visible. We’re much less concerned with the economic systems that create conditions for famine, which would require examining power structures and the distribution of resources.

Research on helplines reveals a wide spectrum of needs. While crisis lines specifically serve people in acute distress, many helplines, such as those providing medical information, benefits guidance, or general support, primarily field questions from people seeking information or early-stage help. Even on the most critical helplines, many users contact services long before reaching a critical point, looking for support while ordinary human difficulties are still developing. The adolescent who attempts suicide, the person having a psychotic break, are compelling concerns, but they are often the endpoint of a much longer process that we could have interrupted earlier.

Earlier help doesn’t require the same intensity of intervention and may be a scenario much more suitable for AI’s capacity for scale and the relatively light touch empathetic conversations AI makes possible. For someone who needs information about whether their experience is normal, or validation that their concern deserves attention, or guidance about a straightforward next step, AI should work well.

If we build systems that treat all contact as if it requires intensive clinical expertise, we create bottlenecks that leave people stranded. When we create accessible touchpoints, people may come with small concerns that we can address; they leave with what they need. But when those touchpoints don’t exist, the same people may show up months later on the crisis line with compounded difficulties that now require much more intensive work.

Waiting is Obstructing

Researcher Anat Rafaeli has said of the all too familiar on-hold experience: “It is not an issue of time; it is an issue of obstacle.”

Each failed attempt to access help, whether for customer service or emotional advice, doesn’t register as a neutral delay. It reinforces a sense of futility: that seeking help was itself a mistake.

Too often, in the call center business, when we think about waiting as duration, we naturally focus on reducing minutes, improving queue management, or at least making the wait more pleasant. We play music and provide updates: your call is 4th in line. We apologize for the delays. These feel like solutions because they address the time we are spending on the system, but politeness is not accessibility.

But Rafaeli’s insight reveals why these interventions often fail. Where the call center operator sees passive time passing, the caller experiences active obstruction. Someone calling a helpline has already crossed a threshold, acknowledging they cannot resolve something alone. They have decided to seek help; to then encounter an obstacle at precisely that moment of acknowledged need intensifies the experience of being stuck. This is why waiting is best thought of as an obstacle, just as much as a complex form or a demand for rigorous verification.

Research confirms this. Studies on telephone waiting times show that even waits of twenty seconds feel twice as long to callers. Why? Because the psychological experience isn’t so much about time on the clock as the sense of how long you remain blocked from what you need.

Why Current Solutions Miss the Mark

Every obstruction of this kind requires an obstructer and someone obstructed. Once you see this, the moral landscape changes completely.

The person reaching out, even with a slight problem, is already vulnerable, knowing they need help. The obstruction of waiting is not a refusal to help, which would at least be honest, but something more insidious: the appearance of availability coupled with the reality of inaccessibility.

This raises questions for teams like Tranquilla about what we’re actually offering. Will we use our empathetic conversational technology to remove obstacles or to momentarily reduce distress? It’s important here that we refuse the contrast many people make: human good, technology bad. Instead, we can ask which obstacles require human capability to remove, and which don’t? That’s a more useful question.

Where obstacles are informational, as so often in customer service, AI can work very well. But where the difficulties are relational, perhaps feeling isolated or unworthy of care, AI can’t remove that particular blockage.

There are, for sure, times when only human contact can work. The philosopher Emmanuel Lévinas wrote about the face of the other, making an ethical claim on us. When someone reaches out in need, their face (metaphorically) demands a response. To turn away, to make them wait, is to refuse that ethical claim. We’re saying, in effect, “your face doesn’t command my attention enough to warrant immediate response.”

But, and this matters, that same AI might still play a valuable role if it can quickly identify that someone needs this human connection rather than information, and route them accordingly.

The systems I most distrust are those that force everyone through the same gatekeeping process regardless of need. Someone who needs immediate human connection shouldn’t have to spend twenty minutes providing demographic information or completing screening questionnaires. That’s obstruction presented as due process.

So if we took this seriously and designed a system primarily for removing obstacles rather than crisis response, what would actually change?

How We Work

For one thing, system design would be moral work, not merely technical work: accessibility would become the primary design criterion. Instead of asking the call-center question, “How do we manage demand within our capacity?” we can ask, “What blocks people from getting what they need, and how do we remove those blockages most effectively?” We are, in effect, making decisions about whose suffering we’ll leave unattended, whose needs we’ll obstruct, whose vulnerability we’ll ignore. These are ethical decisions too easily disguised as administrative ones.

For this reason, at Tranquilla, we are not building a generic emotional chatbot where everyone goes through intake, everyone gets assessed using the same criteria, and everyone follows the same pathway. We can reframe AI deployment in emotional and practical support away from the usual “can machines be empathetic?” toward something more tractable: what specific obstacles can different systems remove? To do this, we work with service providers (for example, in education, social support or healthcare) because we can then address their very specific issues that need to be unblocked. This is not a standardized approach, but differentiated for each scenario at scale.

Naturally, there are dangers here that we should be wary of. Algorithmic conversations go wrong if the system is trained on data reflecting existing biases. For example, if a military veteran uses jargon the chatbot doesn’t understand, they may get routed to a human, but what is meant only as clarification may then feel like escalation. This is another reason why our work is most effective with specialized service providers, who can inform this process, rather than building a generic service.

There’s also widespread concern about depersonalizing care, replacing human warmth with algorithms or treating people as problems to be processed. Sadly, human operators can also become impersonal, cold and systematic: their role is highly stressful, demanding and exhausting. We humans can’t turn on empathy as needed, and we often get things wrong.

What AI can provide is not connection in isolation, or love in distress, but a kind of scaffolding: ways of breaking down decisions, questions that help clarify values and priorities, frameworks for weighing options.

Designing for failure

Of course, as I have suggested, AI gets things wrong too. If an automated system misidentifies someone’s need, routes them incorrectly, or provides guidance that doesn’t fit their situation, what’s the recovery mechanism? With human interaction, miscommunication can be repaired relationally. The person can say “no, that’s not what I mean,” and the human can adjust. With AI, if the person experiences the response as unhelpful, do they try again? Give up? Conclude that help doesn’t exist for their particular problem?

From a design perspective, how would we know what happened? Or, from a systems management perspective, how do we measure success in a system designed around removing obstacles to help? Not by throughput, average wait times, or how many people we processed. Instead, how many felt unblocked, or how many left before small concerns became crises?

How do we track what didn’t happen: the crisis averted, the person who got what they needed early and didn’t require ongoing intervention? Remember, prevention is invisible, therefore less compelling and undervalued.

If we measure success through prevented crises, we’re implicitly accepting crisis prevention as the goal. But is that the right goal? Maybe the goal should be flourishing, or growth, or developing the capacity to handle difficulties. These are much harder to measure but potentially more meaningful.

New measures of success

So, we have to work with our partners in service to value different kinds of success. There’s a subtle message in current approaches: if you can manage with information or brief guidance, your problem wasn’t serious enough to warrant concern. But that’s backwards. The person who can resolve their difficulty with minimal support demonstrates exactly the kind of early access and efficient obstacle removal we should optimize for.

One important measure is trust: an empirical question we can test, even if subjective. Do people trust AI systems that are helpful, even when they know those systems don’t “truly” understand? Similar measures can be collected by qualitative research, but also have to be sensitively haxndled. If I’ve just struggled through accessing a helpline, barely gotten the help I needed, and now I’m asked to rate my satisfaction on a scale of one to five, there’s something almost insulting about it: I am sure you have all had this experience. So, evaluating the quality of our work is likely best done closely with our clients, who in turn are working with their users.

Final word

I suspect my focus here on obstacle removal is both helpful and limiting. Helpful, I hope, because it prevents us from romanticizing human contact in situations where information or structure is genuinely what’s needed. Limiting because it may treat human needs as somewhat transparent: as if people always know what they need and we only have to provide it, or as if an AI can reliably identify those needs for us.

At Tranquilla, our aim is not efficiency with a touch of empathy; we’re addressing a form of harm that existing systems inflict on vulnerable people. When someone acknowledges they need help and we make them wait, we’re not neutral. Every moment adds to the burden of someone who is already carrying too much.

Please note that I write as the Futurist at Tranquilla, so my role is to look ahead and beyond where our work is today. I mean that you shouldn’t take what I write as a product description or roadmap, but as my personal reflections on areas where our work is leading.