Purpose-built AI for mental health has a bright future, if it abides by mindful precepts and strong leadership.
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In today’s column, I examine the stepwise, gradual, and inevitable adoption of purpose-built AI for mental health, which will be increasingly used by large segments of society.
Here’s the backstory. You might be vaguely aware that there are specialized generative AI systems and large language models (LLMs) devoted to providing mental health guidance. These are known as purpose-built AI (PBAI). Right now, they garner a tiny portion of users when compared to the massive volume of people using general-purpose AI (GPAI) for mental health advice. There are perhaps hundreds of millions of people using generic chatbots such as ChatGPT, GPT-5, Grok, Gemini, CoPilot, Llama, and other popular LLMs to routinely get psychological assistance. Only a small fraction of those people have found their way toward using a purpose-built AI that provides mental health insights.
One question that I get asked frequently during my talks on this evolving topic is whether the purpose-built LLMs for mental health have a chance at growth or will they fall by the wayside, ultimately being pushed out by the general-purpose AIs.
My emphatic answer is that well-devised, purpose-built AI for mental health is destined to become remarkably popular and will not be knocked out by general-purpose AI. The rightfully designed purpose-built AI for mental health has a capability that will not be matched by the broader LLMs. People will grow weary of the shallowness of generic chatbots and seek out alternative AI that provides robust mental health guidance.
My prediction is that the adoption process, when it comes to leaning into purpose-built AI for mental health, will abide by a classic evolutionary stratification typified by the now-classical theory involving the diffusion of innovation.
Let’s talk about it.
This analysis of AI breakthroughs is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here).
As a quick background, I’ve been extensively covering and analyzing a myriad of facets regarding the advent of modern-era AI that produces mental health advice and performs AI-driven therapy. This rising use of AI has principally been spurred by the evolving advances and widespread adoption of generative AI. For an extensive listing of my well over one hundred analyses and postings, see the link here and the link here.
There is little doubt that this is a rapidly developing field and that there are tremendous upsides to be had, but at the same time, regrettably, hidden risks and outright gotchas come into these endeavors, too. I frequently speak up about these pressing matters, including in an appearance on an episode of CBS’s 60 Minutes, see the link here.
Millions upon millions of people are using generative AI as their ongoing advisor on mental health considerations (note that ChatGPT alone has over 900 million weekly active users, a notable proportion of which dip into mental health aspects, see my analysis at the link here). The top-ranked use of contemporary generative AI and LLMs is to consult with the AI on mental health facets; see my coverage at the link here.
This popular usage makes abundant sense. You can access most of the major generative AI systems for nearly free or at a super low cost, doing so anywhere and at any time. Thus, if you have any mental health qualms that you want to chat about, all you need to do is log in to AI and proceed forthwith on a 24/7 basis.
There are significant worries that AI can readily go off the rails or otherwise dispense unsuitable or even egregiously inappropriate mental health advice. Banner headlines last year accompanied the lawsuit filed against OpenAI for their lack of AI safeguards when it came to providing cognitive advisement.
Today’s generic LLMs, known as general-purpose AI, such as ChatGPT, GPT-5, Claude, Gemini, Grok, CoPilot, and others, are not at all akin to the robust capabilities of human therapists. Meanwhile, specialized LLMs are being built to attain those desired qualities, though such AI is still primarily in the early development and testing stages. For more about purpose-built AI apps in mental health, see my in-depth coverage at the link here and the link here.
Consider these five major patterns of how people dip into using AI for getting mental health advice:
Users are fluidly moving from category to category as they become more aware of GPAI limitations and become cognizant of BPAI availability.
The mainstay of the world right now is currently in category #1 of using general-purpose AI exclusively as their mental health guidance. These users either don’t know about the purpose-built AIs or are unsure of which purpose-built AI is safe and trustworthy to utilize. The users are not making a conscious decision to avoid purpose-built AI for mental health. Instead, they are comfortable using general-purpose AI and do not know of or perceive a suitable need to use any purpose-built AI to do so.
The next in popularity would be category #2 of mainly using general-purpose AI for their mental health advice, plus sometimes using purpose-built AI. These users are considered “innovators” and “early adopters” who are the first to opt to try using purpose-built AI for mental health and see if it is any good. They are sitting on both sides of the fence for the moment. They still like using general-purpose AI and the convenience of seeking mental health guidance while logged into the GPAI. Nonetheless, they are also attracted to the purpose-built AI and use it too.
Category #3 is inching gradually upward, whereby BPAI for mental health is a mainstay, and those users consider GPAI-based mental health advisement to reside lower on the totem pole. This is admittedly making slow headway. Meanwhile, a very small portion of people are in category #4, whereby they always and only use BPAI for their mental health guidance. They stridently avoid doing so in general-purpose AI. There aren’t many like this when compared to the vast volume of AI users all told.
Category #5 consists of those who might be using GP AI for a variety of uses but refuse to use it for any mental health advice. Likewise, they aren’t using purpose-built AI for mental health guidance. Why the overarching reluctance? Several plausible reasons. Perhaps they don’t believe AI can be helpful or prefer not to divulge their innermost secrets to AI.
The overarching process of people adopting purpose-built AI for mental health will abide by the now-classic theory regarding the diffusion of innovation (DOI). You probably have read about or seen talks covering the diffusion of innovation. It was devised and popularized in the 1960s by Everett Rogers and other sociologists and anthropologists. The idea is that when an innovation arises, the adopters can be prudently bracketed into five major groups:
Innovators are those who eagerly adopt the innovation at the onset. The early adopters come in slightly on their heels. The early majority slowly comes on board. The late majority wait until they clearly discern that the early majority is in there. Finally, the laggards are quite late to the game, sometimes never adopting the innovation at all.
The maker of a purpose-built AI for mental health can and should segment their users into those five groupings. Doing so is sensible. Cater to your users based on the grouping that they tend to fall into.
The handy rule-of-thumb is that not all users are the same. For example, if you do a survey of your users, suppose that only the innovators and early adopters respond. This will give you a skewed perspective on the total set of users. The ones that are in the other groups aren’t necessarily going to favor and disfavor aspects of the PBAI in the same ways that the responding initial adopters do.
In the case of purpose-built AI for mental health, there is a significant additional factor associated with the five diffusion stages. Here it is. The key factor is whether someone is already using general-purpose AI for mental health or whether they aren’t doing so.
The higher likelihood is that people who are already using GPAI for mental health are going to be interested in and willing to dip into PBAI. For any PBAI makers that have been around for a while, they might still be rooted in the past, whereby few were using GPAI, and fewer still were using GPAI for mental health purposes.
That’s no longer principally the case. The mainstay of gravitating toward PBAI is now what I have been referring to as being governed by a gateway effect. Users first try GPAI for advice. If they hear or discover a BPAI that seems worthy, they are open to trying it. They will then persistently migrate to the BPAI as their primary source for mental health guidance if it is suitable and satisfying. Meanwhile, they will still use GPAI, but not so much for mental health reasons anymore, and instead for its generalized capabilities.
I’m not suggesting that the segment of society that isn’t using GPAI for mental health should somehow be ignored when trying to attract them toward using BPAI. The gist is that the biggest bang for the buck will be to turn the GPAI users into BPAI users. You might falsely think it is a harder sell because you must convince them that BPAI is a better choice for them. The thing is, they already accept the notion of using AI for mental health. This is a huge barrier to overcome, which is the case for those who aren’t using GPAI for mental health guidance.
The bottom line is that makers of PBAI for mental health should spend their precious strategic and marketing dollars wisely and put their bets on the gateway effect. Aim at the GPAI users foremost, then the others on a secondary basis.
I see a bright future for purpose-built AI in the mental health realm. This doesn’t mean that all PBAIs that are devised for mental health will succeed. Nope, a lot of them will fade, some of them will collapse. It all depends on the leadership that is guiding the effort. The greatest ideas and cleverest AI can be destroyed by lousy leadership.
Abraham Lincoln famously made this notable remark: “The most reliable way to predict the future is to create it.” Makers of purpose-built AI for mental health are in the driver’s seat. New ground is being pursued. Proceed with mindful determination, keen leadership, and true grit.
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