A Critical Moment for Point Solutions
Point Solution: A specialized software tool designed to solve one specific problem or workflow, as opposed to a comprehensive platform that handles multiple related functions in one place.
My hot take is that point solutions are facing an existential moment in the age of tools like Cursor, Claude Code, and MCPs. A case in point is a situation that my Solutions Architecture team faced recently. While exploring tools to enhance our API demos, we came across many point solutions for this type of requirement. There is no shortage of tools for showcasing the capabilities of your API products, freeing you from the typical Postman show and tell.
Many of the options we found in this sea of tools charged $40k+ for annual licenses for a handful of users with little extensibility and control. The alternative was building something in-house, which has its own issues: access to internal resources, hosting, development time, and more. We decided to test the latter route and discovered a key learning on that journey: building your own solutions has never been easier.
There is, of course, some polish and scalability lost when “rolling your solution,” but the flexibility, extensibility, and speed to live make the trade-offs worth it. The outcome we achieved required many iterative cycles, but we gleaned insightful lessons from every loop. Working from this builder first mindset has a major impact on the practicality of paying for applications. With AI, it makes sense to at least try to build your own point solutions where the lift is low and a viable experiment can be conducted in a day.
So how does this tie back to point solutions and the moment they are facing? It means that there is a pressing urgency to transition from single solutions to platforms. The point solution is ephemeral in nature. It solves a very specific step in a workflow and has dozens, if not hundreds, of competitors’ options. What do these companies need to do to transform into platforms? To be an essential service, you need to have some key components:
DATA: varying data sources combined with insights that are proprietary and are resource-prohibitive to aggregate yourself.
AUTOMATION: workflows and logical orchestration that leverages the aforementioned data and couples it with domain expertise.
ACTIONS: triggers and deep integrations to other platforms that perform complex outputs which are a critical part of the outcomes the user is seeking.
Platforms also support more than one use case. Twilio is a great example of this. They offer video, messaging, CDP, and many other solutions that when connected together execute critical business flows that span a myriad of industries and use cases. We are likely a year or two from this transition reaching a do-or-die state.
One last existential point. All of this had me thinking more broadly about what AI really should be doing for us. Corporate management is obsessed with using AI as a human replacement tool; reducing overhead by automating processes. I think this is a weak, short-term, and deeply late-stage Western capitalist position. Moreover, the reliance on large LLMs and not small expert models is proving in many cases more costly than the humans they replace. Simply put, AI should be a human enhancement tool.
There is an approach in all this where we join expert model horsepower with human experience, domain knowledge, and the curiosity to solve problems. These combined forces can be pointed at five foundational human problems that desperately need solving. Housing Security, Food Security, Education, Healthcare, and Occupation; in that order. Maybe then we can avoid some dystopian future. Perhaps this is the platform we need. A technology company building solutions in one integrated platform with a singular mission to improve the human condition.
