Mental health service leader Alexander Amatus considers how clinicians can work with clients who have already used AI therapeutically, and what that shift means for formulation, therapeutic alliance and risk.
26 March 2026
In my work across mental health service delivery, I am seeing more people arrive at care having already had a meaningful conversation about their distress with AI. Sometimes they have used a chatbot before booking. Sometimes they have drafted what they want to say before a first session. Sometimes, especially late at night, they have turned to it because it was immediate, private and available when no one else was. That shift matters, not because AI replaces therapy, but because it is increasingly shaping what happens before therapy even begins.
Psychologists are now more likely to meet clients whose first structured conversation about their distress did not occur in a consulting room, a GP surgery, or even with a trusted friend. It happened with a chatbot.
The disclosure is often not dramatic. It appears almost in passing:
"I asked ChatGPT what this might be."
"I used it to organise my thoughts."
"It helped me write what I wanted to say."
"I just needed something at 2am."
This development is neither surprising nor inherently problematic. AI systems offer what mental health services often struggle to provide at scale: immediacy, privacy, low friction and a non-judgemental interface. In contexts where shame, uncertainty and inertia commonly delay help-seeking, such features are not trivial. Digital mental health interventions have long demonstrated that accessibility and anonymity can reduce barriers to engagement (Andersson & Titov, 2014). Large language models extend this accessibility further by offering interactive, personalised responses in real time.
The clinical question, therefore, is not whether AI sits within the client journey – it already does. The more pressing question is what this means for formulation, therapeutic alliance, risk assessment and care pathways when the first articulation of distress occurs in dialogue with a machine.
The pre-edited narrative
One of the subtler changes practitioners may now encounter is the increased coherence of client's initial accounts. Individuals who might once have struggled with where to begin can arrive with timelines, summaries and even tentative formulations. Some bring text they have written with AI assistance. For clients who find spontaneous verbal expression difficult, this can be helpful. It may reduce the cognitive load of starting therapy and allow earlier movement into meaningful discussion.
Yet coherence is not neutral. Psychologists do not work only with content; they also work with process. In psychodynamic traditions, attention to slips, contradictions and defensive organisation is central. In cognitive behavioural approaches, discrepancies between stated beliefs and behavioural evidence can be diagnostically informative. In systemic practice, what is omitted may matter as much as what is included.
Large language models are designed to generate coherence. They summarise, categorise and tidy. An emotionally fragmented account can be transformed into a plausible and linear narrative within seconds. What may be lost in that transformation are precisely the ruptures, hesitations and inconsistencies that provide access to underlying affect and meaning.
An experience not to be dismissed
Research into human-computer interaction suggests that users can attribute empathy, understanding and companionship to conversational systems (Bickmore & Picard, 2005). More recent analyses indicate that people may experience forms of perceived social support from companion chatbots (Ta et al., 2020). In some contexts, users report feeling heard or validated by these systems (Miner, Milstein & Hancock, 2017).
That subjective experience should not be dismissed. But feeling understood is not the same as being held in a therapeutic relationship. AI can reflect language back smoothly and responsively; it cannot assume responsibility, tolerate relational complexity, track risk over time, or participate in the negotiated work of therapy. In psychotherapy, empathy is only one component. The alliance also involves shared goals, agreed tasks, rupture and repair, accountability and movement toward change (Bordin, 1979).
When clients present with AI-assisted narratives, the task is not to dismiss them but to locate them clinically. It may be useful to ask: what part of this account feels genuinely yours, and what part feels shaped by the tool? What became clearer when you wrote it this way, and what became flatter, cleaner or more certain than it really felt? A practical stance is to treat AI-generated material as one artefact among others rather than as the definitive account. A clinician might say, "Let's use what you brought in, but let's also slow it down. What felt hardest to put into words? What still feels unresolved underneath this version?" The aim is not to undo coherence, but to reintroduce texture.
False completion and the illusion of resolution
A second phenomenon warrants attention. Some people experience transient relief after interacting with AI, and subsequently delay or avoid entering care.This can be understood as a form of false completion: an experience that resembles progress without constituting it. In behavioural terms, the interaction may function as negative reinforcement. Distress is reduced in the short term, which decreases motivation to pursue more effortful forms of support. Similar dynamics are familiar in reassurance-seeking cycles within anxiety disorders.
Digital mental health interventions have long grappled with engagement and adherence challenges (Christensen, Griffiths & Farrer, 2009). AI chat systems, because of their immediacy and conversational fluidity, may intensify these dynamics. A person can rehearse difficult conversations without having them, explore diagnoses without seeking assessment, or repeatedly query their symptoms until they receive an answer that feels sufficiently containing.
The clinical implication is not that AI use is inherently avoidant, but that its function matters. Is it scaffolding movement towards care, or substituting for it?
AI as part of the help-seeking pathway
Help-seeking in mental health rarely occurs as a single decision. It is a pathway shaped by appraisal, informal consultation, information-seeking and, eventually, formal engagement (Rickwood et al., 2005). AI now occupies a place within this pre-therapy landscape.
For some clients, AI supports emotional labelling or reduces task friction in booking appointments. For others, it assists with drafting messages, organising thoughts or reflecting between sessions. In these contexts, it may function as temporary cognitive scaffolding.
From a CBT perspective, the critical question is whether AI use increases behavioural activation and exposure or reinforces avoidance. From an attachment-informed perspective, reliance on AI may reflect difficulty tolerating relational vulnerability. From a systemic perspective, we might consider how AI-mediated preparation influences conversations within families, workplaces or broader support networks.
These are not abstract concerns. They bear directly on assessment. It may now be clinically prudent to ask, alongside questions about sleep and substance use, whether clients have engaged with AI tools in relation to their distress. A neutral enquiry such as, "Did you use any online or AI tools to think this through before coming?" can yield insight into coping patterns without inducing defensiveness.
The therapeutic relationship in context
Public discourse sometimes conflates linguistic responsiveness with therapeutic capacity. While AI systems can simulate empathic language, they do not hold responsibility. They do not track risk across sessions, manage safeguarding obligations or co-construct goals over time within a bounded ethical framework.
Meta-analytic evidence consistently demonstrates the centrality of therapeutic alliance to outcomes across modalities (Flückiger et al., 2018). Alliance involves agreement on tasks and goals as well as the bond. It unfolds in a relational field shaped by history, transference, countertransference and embodied presence. AI interactions, however sophisticated, are not situated within such a field.
This distinction is structural rather than sentimental. Psychotherapy is embedded within professional accountability, supervision and regulatory oversight. Risk assessment is not merely a conversational exchange but a clinical responsibility. AI systems do not bear duty of care.
At the same time, dismissing clients' experiences of support from AI would be clinically counterproductive. If a person reports feeling understood by a chatbot at 2am, that subjective experience matters. It may represent unmet needs within existing service models, particularly around accessibility and immediacy. The task is to integrate that reality without conflating simulation with relationship.
Formulation in an AI-influenced landscape
Formulation may require subtle adaptation. When a client presents with a self-diagnosis generated via AI, the clinician's role is not simply to confirm or refute it, but to explore how that diagnosis functions psychologically. Does it provide relief, identity coherence and permission to seek help? Or does it close down curiosity too early?
Similarly, if a client has rehearsed conversations with AI, it may be useful to explore what aspects felt safer in that context. Was it the absence of perceived judgement? AI systems often present information in a confident tone. Clients may arrive with apparently well-informed conceptualisations that blur distinctions between psychoeducation and personalised assessment. Clarifying the limits of generalised information without undermining the client's agency becomes part of the work.
In service contexts, consistent language may also be required across intake teams and clinicians to manage cases where AI use intersects with risk. Relief derived from an AI interaction should not substitute for formal triage in situations involving suicidality, safeguarding concerns or severe deterioration.
Beyond polarisation
Debate around AI in mental health often oscillates between utopian replacement and existential threat. In everyday clinical practice, the reality is quieter. People are using AI as an emotional and cognitive buffer before deciding whether to disclose vulnerability to another human. Sometimes this facilitates engagement; sometimes it delays it.
For psychologists, the appropriate response is unlikely to be either prohibition or enthusiasm. It is adjustment. Updating assessment questions, refining formulations and differentiating between relief and progress are practical steps. So too is curiosity about what clients are seeking in these interactions: speed, anonymity, structure, containment. Those needs are real, and services may need to respond to them without compromising relational depth. If clients now arrive having already spoken to AI, the task is not to compete with the machine. It is to understand the function it served, and to situate that function within a broader therapeutic process that remains, at its core, human.
Alexander Amatus works at the intersection of AI and mental health service delivery. He is part of the leadership team at TherapyNearMe.com.au, a national Australian mental health service, where his work focuses on safer, more human-centred pathways into care and the clinical implications of emerging technologies.
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References
Andersson, G. & Titov, N. (2014) 'Advantages and limitations of Internet-based interventions for common mental disorders', World Psychiatry, 13(1), pp. 4–11. doi:10.1002/wps.20083.
Bickmore, T.W. & Picard, R.W. (2005) 'Establishing and maintaining long-term human-computer relationships', ACM Transactions on Computer-Human Interaction, 12(2). doi:10.1145/1067860.1067867.
Bordin, E.S. (1979) 'The generalizability of the psychoanalytic concept of the working alliance', Psychotherapy: Theory, Research & Practice, 16(3), pp. 252–260. doi:10.1037/h0085885.
Christensen, H., Griffiths, K.M. & Farrer, L. (2009) 'Adherence in internet interventions for anxiety and depression', Journal of Medical Internet Research, 11(2), e13. doi:10.2196/jmir.1194.
Flückiger, C., Del Re, A.C., Wampold, B.E. & Horvath, A.O. (2018) 'The alliance in adult psychotherapy: A meta-analytic synthesis', Psychotherapy, 55(4), pp. 316–340. doi:10.1037/pst0000172.
Miner, A.S., Milstein, A. & Hancock, J.T. (2017) 'Talking to machines about personal mental health problems', JAMA, 318(13), pp. 1217–1218. doi:10.1001/jama.2017.14151.
Rickwood, D., Deane, F.P., Wilson, C.J. & Ciarrochi, J. (2005) 'Young people's help-seeking for mental health problems', Australian e-Journal for the Advancement of Mental Health, 4(3), pp. 218–251. doi:10.5172/jamh.4.3.218.
Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E. & Loggarakis, A. (2020) 'User experiences of social support from companion chatbots in everyday contexts: Thematic analysis', Journal of Medical Internet Research, 22(3), e16235. doi:10.2196/16235.
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