#50 - The Illusion of AI Quick Wins (Part 2/2)
How to Avoid Building AI Products That Aren’t Worth Owning
#beyondAI
In my last article, I shared why so many AI quick wins turn into long-term burdens. Today, let’s talk about how to avoid that illusion — and how to ensure we build only what’s truly worth owning.
✅ Why Most Quick Wins Aren’t Wins at All
✅ How to Avoid the Illusion
✅ The Service Provider Perspective
✅ Closing Thoughts
Why Most “Quick Wins” Aren’t Wins at All
It’s easy to understand why the idea of AI “quick wins” has captured the imagination of so many organizations, especially in an era where every company feels the pressure to demonstrate technological innovation and where leadership eagerly seeks visible proof points to justify investments in artificial intelligence.
The notion that a small team, armed with modern APIs and a few clever prompts, can deliver something that appears intelligent, valuable, and even transformative within a matter of days or weeks is not merely appealing — it’s intoxicating, because it suggests that the barriers to impact have all but vanished.
Yet the sobering reality, which emerges time and again in the aftermath of these rapid development sprints, is that most of these so-called quick wins are not wins at all, at least not when measured against the full economic equation required to sustain an AI product in a live business environment.
For while it is true that the cost and speed of initial development have fallen dramatically, this reduction has done little to eliminate the far more stubborn costs that arise once a solution is expected to perform reliably, safely, and in alignment with business objectives over time.
Indeed, it is precisely because prototypes can be built so quickly and with such dazzling technical fluency that organizations often rush ahead, mistaking technical feasibility for product viability, and underestimating the layers of complexity that lie between a working demo and a sustainable, value-generating product.
One of the most insidious illusions of AI quick wins is that the moment a model produces correct outputs in controlled scenarios, it is ready for real-world deployment, when in truth, the journey from demo to product is where the true costs — and risks — begin to accumulate.
Consider the governance overhead alone: the legal teams who must review every possible output to ensure regulatory compliance, the security audits required to protect sensitive data, the need for explainability if the AI’s decisions affect customers or employees, and the documentation that must be created to satisfy auditors and internal stakeholders alike.
Layered atop these governance concerns is the relentless evolution of business processes themselves, because no matter how elegant an AI model may be, the realities of enterprise life dictate that business rules change, systems are upgraded, organizational priorities shift, and what was true yesterday might be obsolete tomorrow — all of which demand ongoing adjustments, retraining, and validation work that quietly consume time, budget, and human attention.
Then there is the human side of AI, which is so often overlooked in the rush to build: the time and effort required to train users, to earn their trust, to manage their expectations, and to support them when the AI inevitably produces an output that confuses, disappoints, or outright fails to meet the nuanced needs of their real-world tasks.
Even the most straightforward tools, once released into production, attract a steady stream of enhancement requests, support tickets, edge cases, and new use scenarios that were never foreseen during the initial build, each demanding attention, prioritization, and — ultimately — additional cost.
And so what begins as a deceptively affordable technical exercise becomes, over time, an ongoing drain on organizational resources, often far exceeding any value the initial prototype seemed poised to deliver.
This is the essence of why most quick wins are not wins at all: because they are evaluated only through the narrow lens of build effort, without a sober analysis of the cost of ownership and the total economic impact required to sustain them as real products.
An AI solution that costs €10,000 to build but €150,000 annually to maintain, while delivering only €50,000 of value, is not a win — it is a liability masquerading as innovation.
The true discipline of AI Product Management, therefore, lies not in celebrating how quickly something can be built, but in developing the discernment to know which solutions are worth owning, and in having the courage to say no to ideas that, while technically feasible, cannot deliver sustainable, economic value once all costs are accounted for.
Until we embed this discipline into our processes, we risk chasing the illusion of quick wins, filling our roadmaps with projects that impress in demos but quietly erode resources, distract teams, and ultimately fail to justify their existence in the harsh light of economic reality.
How to Avoid the Illusion
It’s one thing to recognize the illusion of AI quick wins; it’s quite another to develop the discipline and the practical methods required to avoid falling into that trap, especially in organizations eager to proclaim their leadership in AI innovation and where the pressure to deliver visible results can sometimes override sober assessment of long-term sustainability.
Yet avoiding this illusion is not merely a matter of caution — it is a fundamental responsibility for anyone tasked with building AI products, because the costs of getting it wrong are not confined to technical rework but extend into wasted resources, eroded trust among stakeholders, and the opportunity cost of having pursued initiatives that were never destined to generate meaningful returns.
So how, then, does one navigate the seductive pull of building quick, impressive prototypes while protecting the organization from the hidden liabilities of ownership?
Separate the Cost of Building from the Cost of Owning
The first and perhaps most crucial step is to consciously and systematically separate the cost of building from the cost of owning, treating them as two entirely distinct phases of the product’s economic life cycle.
It is no longer sufficient to ask only how many weeks it will take a developer to connect to an LLM API or to produce a functioning prototype; we must equally ask how many people will be required to maintain it, how frequently the model or prompts will need to be updated, and how many systems it must integrate with — each of which contributes directly to the total cost of ownership.
This separation forces product teams and stakeholders alike to look beyond the glamour of the initial demo and to confront the practical realities of sustaining a product once it enters the complex, ever-changing environment of real-world operations.
Ask the Right Questions Early
Avoiding the illusion begins with asking better questions — questions designed not merely to confirm technical feasibility but to expose the true shape of the ownership burden that will inevitably follow any AI solution.
Questions such as:
How dynamic is the problem domain? Do rules, policies, or user needs change frequently?
How many users will this serve, and how diverse are their roles and expectations?
Will this solution need to integrate into other systems, and if so, how tightly?
Does the solution handle personal or regulated data, triggering privacy or compliance requirements?
How critical is this solution to business operations, and what is the risk if it fails?
Are there existing teams prepared to own and support this product long-term?
Each of these questions serves as a signal for potential costs that may not be visible in the initial build estimate but will certainly materialize once the product is live.
Keep ROI Front and Center
Yet even as we strive to separate costs and estimate them with as much realism as possible, there remains one final, indispensable lens through which every AI initiative must ultimately be viewed: the lens of return on investment.
For no matter how elegantly we may build, or how rigorously we may estimate the costs of owning an AI product, these efforts are meaningful only insofar as they allow us to judge whether the value created by the product will, in the end, exceed the total costs required to build and sustain it.
Having a clear ROI mindset is not a constraint but a strategic compass, one that empowers product leaders and organizations to make deliberate choices about which AI ambitions are truly worth pursuing.
When we force ourselves to ask — even at the earliest stages — how much economic benefit a solution might realistically deliver, and when we set that potential benefit against both the known costs of building and the less visible but equally real costs of ownership, we transform decision-making from an exercise in technological enthusiasm into a discipline of informed, economically grounded choices.
It is through this lens of ROI that we gain the courage and clarity to prioritize not merely what we can build, but what is genuinely worth owning — and it is this discipline that will distinguish the fleeting illusions of AI quick wins from the sustainable successes that endure and create true business value.
Align Technical and Product Perspectives
One of the most significant risks in AI development is the disconnect between technical teams, who are often eager to demonstrate what is possible, and product leaders, who are responsible for ensuring that solutions translate into sustainable value.
To avoid the illusion of quick wins, these two groups must collaborate closely from the outset, jointly evaluating both the technical feasibility and the economic sustainability of any proposed initiative.
Technical teams must be transparent about the assumptions and hidden complexities in their solutions, while product leaders must challenge optimistic timelines and push for clarity around governance, integration, and support costs.
It is in this partnership — between those who build and those who own — that the best defenses against the illusion are forged.
The Service Provider Perspective: Shifting, Not Erasing, Ownership Costs
As we grapple with the challenge of distinguishing between the cost of building and the cost of owning AI products, it’s worth pausing to consider a business model that seems, at first glance, to escape the burden of ownership altogether: the model of the service provider.
There exists a substantial segment of the technology landscape composed of companies whose business is not to own products themselves, but to build solutions on behalf of others, delivering precisely what has been specified, collecting their fees, and moving on to the next project.
For these service providers, the economic calculus appears refreshingly simple: scope is defined, requirements are gathered, code is written, the solution is delivered, and payment is rendered. In this model, the cost of ownership — with all its complexities and potential liabilities — seemingly vanishes from the service provider’s concerns, because their financial and operational obligations end the moment the solution is handed over.
Yet this absence of ownership costs in the service provider’s books does not mean those costs disappear from the world. They are merely shifted — often in full — onto the shoulders of the client who commissioned the work.
For the buying company, the reality remains unchanged: every AI solution brought into production becomes part of a living ecosystem that must be integrated, supported, governed, and maintained over time. The same challenges apply, whether the code was written by internal developers or by an external partner.
Data pipelines still require monitoring and updates. Models still drift and demand retraining. Compliance audits still loom, demanding documentation and explainability. Users still generate tickets, seek enhancements, and raise concerns when outputs fail to align with their expectations. And when the inevitable changes in business processes arrive, someone must be there to adjust the solution so that it continues to deliver value rather than becoming a liability.
In many ways, the illusion of quick wins can be even more seductive in service-driven contexts, because clients may mistakenly believe that by outsourcing the build, they have also outsourced the burden of ownership.
But the truth is unavoidable: the cost of ownership can be transferred, but it cannot be eliminated.
It is a debt that must always be paid — either by the builder or by the buyer — and wise organizations recognize that even when they choose to partner with service providers, they must plan and budget not only for the cost of building, but for the far greater and longer-lasting cost of owning the solution once it comes home.
Thus, whether acting as a product company, a service provider, or a client engaging external partners, the fundamental discipline remains the same: to distinguish the immediate thrill of building from the ongoing responsibility of ownership, and to make choices that ensure the solutions we deploy are not merely technically impressive, but economically sustainable.
A Discipline, Not a Constraint
Avoiding the illusion of AI quick wins is not about stifling innovation or rejecting the incredible possibilities that modern AI unlocks; rather, it is about practicing a discipline that ensures the solutions we choose to build are not only technologically possible but economically sustainable.
It is about remembering that in the realm of AI products, the real measure of success is not how quickly something can be built, but whether it can endure, scale, and deliver value over time without consuming more resources than it is worth.
And it is about recognizing that in the end, the true quick win is not the speed of development, but the wisdom to build only what is worth owning.
Closing Thoughts
In the race to stake a claim in the future of artificial intelligence, it is understandable — and perhaps inevitable — that we find ourselves captivated by the apparent ease and speed with which AI solutions can now be built, fueled by the astonishing capabilities of modern language models and the growing arsenal of accessible developer tools that promise to transform even modest ideas into impressive technical demonstrations.
Yet amidst this newfound agility, there lies a fundamental truth that every AI product leader must carry forward: an AI solution that can be built quickly is not necessarily an AI product worth owning.
For while it is undeniably exhilarating to create something that works, that speaks fluently, that classifies data or forecasts outcomes with mathematical precision, the real test of success lies not in the initial build, but in the far less visible domain of sustained operation, governance, and economic viability.
It lies in the long, patient work of ensuring that what we deploy today remains relevant, accurate, and trustworthy tomorrow — that it continues to deliver value without quietly accumulating costs that outweigh any benefits it was meant to produce.
This is the quiet discipline at the heart of AI Product Management: the insistence on looking beyond the glittering surface of technical feasibility to examine the deeper, more demanding questions of adoption, integration, governance, and total cost of ownership.
It is the willingness to say no to ideas that cannot justify their presence in a real-world business environment, no matter how technically brilliant they may appear in a controlled demo.
It is the courage to protect an organization’s focus and resources, to ensure that the AI products we choose to build are those that can not only be delivered swiftly but sustained wisely.
Because ultimately, the role of AI product professionals is not merely to prove what is possible, but to guide organizations toward building only those solutions that stand the test of time, that create more value than they consume, and that contribute meaningfully to the strategic objectives we exist to serve.
Now, my question is:
How many real AI Quick Wins have you ever seen?
JBK 🕊️