The Illusion of AI Quick Wins (Part 1/2)
Cheap to build. Expensive to own.
#beyondAI
Nowadays, every developer feels empowered to build their own AI solution.
At least a certain type of AI solution, the kind that doesn’t require training a model from scratch.
With the rise of powerful LLM providers, building AI has become remarkably accessible. You browse the provider’s docs, write a few lines of code, and within hours you’ve built something that looks like magic.
The cost of building AI has never been lower.
But just because an AI solution works technically doesn’t mean it’s an AI product.
An AI product is an economic proposition. It’s not just code that runs, it’s a solution that solves a real problem for a specific user group, earns adoption, and generates enough value to justify its costs. Ultimately, it’s about building something that makes more money than it costs to sustain.
And this is where many builders get blindsided.
People forget that real AI products carry two types of costs:
The cost to build them.
And the cost to own them.
And once you understand this, you realize that the majority of AI use cases we see today would never make it past the idea stage if we honestly accounted for the true costs of ownership.
That’s what my article today is about:
The illusion of AI quick wins. Part 1 - The Problem.
The Rise of “Cheap to Build” AI
We’re living through a remarkable moment in technology history, one in which the barrier to building AI solutions has dropped so dramatically that it’s now possible for nearly any developer, regardless of their background in machine learning, to assemble something that looks intelligent in a matter of hours.
The reason for this sudden accessibility lies, of course, in the emergence of large language models and the thriving ecosystem of APIs and developer tools that surround them, making it feasible to build conversational agents, text analyzers, summarization tools, or countless other applications without ever training a single model from scratch.
Where once deploying AI required deep knowledge of algorithms, the painstaking preparation of training data, and expensive computational resources, it now often requires little more than reading API documentation, crafting a few prompts, and wiring the output into an existing user interface or backend service.
This shift has created an intoxicating sense of speed and empowerment among developers, because for the first time, the dream of embedding intelligence into digital products feels tangible, immediate, and relatively inexpensive to prototype.
Yet this very ease has become a double-edged sword, because while the act of building has become democratized and dramatically cheaper, it can foster a dangerous illusion: that the low cost and speed of initial development somehow translates into a sustainable, low-cost product over the long term.
One can build a working demo in a hackathon, showcase it internally, and impress stakeholders with the apparent sophistication of natural language understanding or smart decision-making — and in doing so, create the impression that the AI problem is “solved” simply because the technical proof of concept runs without errors.
However, the reality that lurks beneath the surface is that the cost of writing code and hooking into an AI API is often the smallest fraction of what it takes to turn an AI solution into a real product — a product that not only functions reliably but integrates into workflows, complies with governance requirements, adapts to shifting business needs, and continues to deliver economic value year after year.
The temptation to believe in AI “quick wins” stems from this new reality: that building a prototype has become so deceptively easy, it masks the far greater costs and complexities involved in truly owning and operating an AI product over time.
And unless we consciously separate the cost of building from the cost of owning, we risk filling our backlogs and our organizations with solutions that look brilliant on the surface but quietly drain resources, erode trust, and fail to deliver a sustainable return on investment.
Why an AI Solution ≠ an AI Product
It’s a subtle but critical distinction — one that often gets overlooked in the current rush to showcase technological capability — that an AI solution, impressive though it may be from a purely technical standpoint, is not by default an AI product, because a product is defined not merely by its existence but by its ability to consistently solve a problem for real users, in a way that is sustainable, adopted, and ultimately economically viable.
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While an AI solution might be a clever script, a working demo, or a functional prototype that answers questions, generates text, or classifies data with uncanny accuracy, it remains fundamentally an internal artefact until it crosses the far more challenging threshold of delivering value to a defined group of users who choose, repeatedly and willingly, to integrate it into their daily tasks.
An AI product, in contrast, exists within an ecosystem of human expectations, business objectives, and operational realities; it must earn trust, fit seamlessly into workflows, comply with often stringent governance requirements, and deliver an experience robust enough that users are not only willing to try it once, but to depend on it over time, perhaps even to the point of paying for it — either directly or through its contribution to broader business performance.
This transition from “solution” to “product” represents the true crucible of AI product management, because it is here that the technical marvel of AI collides with the stubborn complexities of human behavior, regulatory constraints, and the shifting sands of organizational priorities.
Too often, teams celebrate the technical feasibility of an AI initiative as though that alone were sufficient proof of its value, pointing to a working chatbot, an elegant classification model, or an automated report as evidence that the problem has been solved — when in reality, these artifacts are little more than prototypes until they can prove that users actually want to adopt them, that they can survive in production environments, and that the economics of ongoing operation make sense when weighed against the benefits they bring.
I have seen, time and again, solutions that worked beautifully in a controlled testing environment but fell apart in the real world, not because the underlying AI was flawed, but because the product as a whole lacked the infrastructure, the support mechanisms, and the organizational alignment necessary to transform a clever idea into a sustainable asset.
Consider, for instance, a chatbot designed to answer policy questions within a large enterprise. While the technical implementation might be straightforward — wiring an LLM API to a document database, perhaps — the true challenge arises when policies change, when users begin asking nuanced or politically sensitive questions, or when legal and compliance teams intervene to scrutinize every possible hallucination or misinterpretation that the model might produce.
Or imagine a forecasting model built to predict customer churn, which dazzles stakeholders with its precision during a pilot phase, only to collapse under the weight of integrating into live systems, dealing with data refreshes, and explaining predictions in terms that business users can trust and act upon.
The difference between an AI solution and an AI product, therefore, is not just a matter of technical sophistication, but a question of economic sustainability and operational maturity — the capacity to deliver value continuously, safely, and in a manner that justifies both the initial investment and the ongoing cost of ownership.
This is why, in the discipline of AI Product Management, we must always look beyond the seductive glow of working demos, and insist on asking the harder questions:
Who will use this?
Will they truly adopt it?
How often will it need to change?
What governance or compliance hurdles must it clear?
And above all — will it generate more value than it costs to build and maintain?
Because in the end, it is not the elegance of our code, nor the cleverness of our models, that defines success in AI, but our ability to build products that persist, scale, and deliver a return on the resources invested in them — products that serve real needs, in the real world.
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The Two Costs of Real AI Products
When we speak of AI products, it’s tempting to focus almost entirely on the exhilarating act of building — on the prototypes, the architecture diagrams, the proof-of-concepts that light up demo days and reassure stakeholders that progress is being made.
And yet, the true story of an AI product is told not merely in the cost and time it takes to build it, but in the often invisible, relentless costs that follow long after the first lines of code are written, costs that determine whether the product becomes a sustainable asset or an expensive curiosity that fades from memory once the initial excitement has worn off.
Every real AI product carries with it two fundamental categories of cost:
the cost of building, and equally — if not more importantly — the cost of owning.
The Cost of Building
The cost of building encompasses all the one-time efforts that go into transforming an idea into a functioning prototype or a first release.
It includes the hours spent by developers exploring data sources, selecting algorithms, testing prompts, designing user interfaces, and navigating the labyrinth of system integrations necessary to embed AI into existing business processes.
It covers the technical work of setting up pipelines, the collaboration sessions between product managers and data scientists to frame the problem correctly, and the sometimes intense periods of iteration required to move from a promising proof-of-concept to a solution robust enough to be demoed to stakeholders.
In many organizations today, thanks to the availability of powerful pre-trained models and API-driven services, this initial cost of building has plummeted, allowing teams to produce impressive prototypes at a fraction of what it would have cost only a few years ago.
And this, paradoxically, is precisely where the illusion of quick wins begins — because it creates the impression that the bulk of the work is done once a model produces credible outputs or an LLM responds with human-like fluency.
The truth, however, is that while building has become easier, it remains only the first, often smallest, part of the journey.
The Cost of Owning
It is in the cost of owning where the real weight of AI product development reveals itself — the weight that so often remains hidden during the euphoric days of building, only to emerge as an increasingly heavy burden in the months and years that follow.
Owning an AI product means maintaining not just the technical components — the models, the code, the integrations — but the entire ecosystem required to keep the product relevant, accurate, and safe in the face of continual change.
It means monitoring model performance to detect drift, updating prompts as business rules evolve, retraining models when new data becomes available, and ensuring that the AI’s outputs remain consistent with shifting regulatory requirements and legal standards.
It involves integrating the AI into production systems in a way that remains resilient even as upstream or downstream systems change, and preparing for the reality that what works perfectly in a lab environment may encounter unexpected edge cases or operational challenges in real-world conditions.
The cost of owning also includes the human side of AI: supporting users as they learn to trust and adopt new systems, providing documentation and training materials, handling requests for enhancements or bug fixes, and dealing with the inevitable questions and complaints that arise when AI makes errors or delivers results that users don’t fully understand.
Moreover, ownership carries with it the burden of governance — the processes of security reviews, legal assessments, and risk management, all of which are non-negotiable in enterprise environments, particularly when AI is involved in decisions that might affect customers, employees, or regulated business activities.
These costs of ownership are neither optional nor trivial. They are the ongoing price we pay for transforming clever prototypes into real products — products that not only work once but keep working, safely and reliably, over time.
This is why the notion of AI quick wins can be so dangerously seductive: because it blinds us to the reality that while the cost of building has indeed fallen, the cost of ownership has remained stubbornly high, and in many cases, has even increased as AI systems become more complex, regulated, and deeply integrated into the heart of business operations.
Until we account for both sides of the ledger — the cost of building and the cost of owning — we cannot truly judge whether an AI initiative is a quick win or a long-term liability in disguise.
Examples of the Ownership Trap
To truly appreciate why so many AI solutions, though cheap to build, become expensive to own, we need only look at the real-world examples that emerge time and again in enterprises attempting to harness the promise of artificial intelligence.
These are not failures of technology per se, for the algorithms often perform precisely as designed; rather, they are cautionary tales about what happens when the seductive ease of building blinds us to the relentless realities of ownership.
Example 1: The Chatbot That Kept Growing
Consider the seemingly innocuous decision to deploy a chatbot designed to help employees navigate internal policies, an initiative that, on paper, appeared to be a perfect quick win.
The technical work was modest: a few calls to an LLM API, some prompt engineering to ensure the bot referenced the correct documents, and a lightweight web interface for employees to submit questions.
Within weeks, the prototype was working well enough to be demoed to leadership, and its creators rightly felt a surge of pride — for here was an AI solution that could answer policy questions quickly and reduce the load on human support teams.
Yet, as soon as the chatbot went live, a different reality unfolded.
Employees, delighted by the initial utility, began asking increasingly complex questions that blended policy interpretation with subtle organizational politics — queries the model was never designed to handle and which introduced significant risks if answered incorrectly.
Meanwhile, the legal department intervened, demanding rigorous controls to ensure no confidential or outdated information was served, triggering a new wave of compliance reviews, prompt adjustments, and the need for an auditable log of every interaction.
Worse still, business units outside the original scope began requesting versions of the chatbot tailored to their own specialized policies, fragmenting the development effort and multiplying the maintenance burden.
What began as a small, low-cost experiment had now evolved into an ongoing product with legal risks, governance overhead, and a growing queue of change requests — a perfect example of how low initial build costs can mask the true cost of ownership.
Example 2: The Forecasting Model That Couldn’t Survive the Real World
Another example arises from the widespread enthusiasm for predictive modeling, particularly models designed to forecast critical business outcomes such as customer churn.
In one enterprise, a team built a sophisticated churn prediction model using historical customer data, leveraging advanced machine learning techniques that achieved impressive accuracy during testing.
The prototype dazzled stakeholders, who were eager to deploy it as a tool for proactive retention strategies.
However, as soon as the model was moved toward production, its creators discovered that the very data pipelines feeding it were prone to frequent schema changes, driven by evolving business definitions and new marketing initiatives.
Each time upstream systems changed, the model broke, requiring urgent intervention from data engineers and data scientists to re-map features and re-run validations.
Moreover, business users, once enthusiastic, began demanding clear explanations for why certain customers were flagged as high churn risks — explanations the model was ill-prepared to provide, especially under tight timelines.
What had seemed like a technical triumph quickly transformed into a fragile solution requiring constant care, communication, and firefighting — its ownership costs far exceeding the initial estimates.
Example 3: The One-Team Tool That Became Everyone’s Problem
A final example comes from a small tool built by a team to automate the categorization of customer feedback into topics for analysis.
Originally conceived as a simple internal solution, the AI model used text classification to tag feedback into a handful of business categories, helping one analytics team speed up their reporting.
Initially, the build was straightforward: the team fine-tuned an existing model, connected it to their feedback database, and produced a simple dashboard.
But success quickly brought attention.
Other departments, seeing the tool’s usefulness, requested their own categories, more languages, integration into enterprise reporting systems, and compliance reviews for customer data privacy.
Each request seemed small on its own — a new tag here, another language there — but together they transformed a low-maintenance script into an enterprise-grade product requiring dedicated ownership, funding, and continuous upgrades.
What started as a clever side project became a sprawling responsibility nobody had planned to sustain, draining resources that could have been focused on higher-value initiatives.
The Pattern Across All Examples
In each of these stories, the initial build was fast, inexpensive, and technologically feasible.
But the unseen costs — governance, integration complexities, user support, evolving requirements, and compliance obligations — turned these “quick wins” into enduring commitments, often without delivering proportional economic value.
This is the essence of the ownership trap: the seductive belief that because we can build AI solutions quickly and cheaply, they will naturally become sustainable products — when in reality, ownership costs often dwarf the initial effort and can transform even the most promising initiatives into long-term liabilities.
Understanding this trap is not simply a technical concern but a core responsibility of AI Product Management, because only by acknowledging and planning for the full cost of ownership can we ensure that the solutions we build become products that survive, scale, and create lasting value.
These examples reveal the hidden dangers behind so-called AI quick wins. But if building has become cheap, and owning remains expensive, how can we avoid falling into the same trap? That’s what I’ll explore in the next article.
The illusion of AI quick wins. Part 2 - The Solution.
JBK 🕊️