If it’s true that AI allows us to build anything, and to build it more efficiently than ever before, are we entering an age of software abundance? Or are we about to experience a deluge of useless software that no one actually needs? The answer depends largely on what we choose to build, and this is a choice that humans must make, not AI.
At a recent AI Expo in Mexico City, I had the chance to speak with Osvaldo Ramírez Hurtado from the Panamerican Business School for the Scaling Tech Podcast.
[Watch the interview: Creating Real Business Value with AI: Osvaldo Ramirez Hurtado | Scaling Tech Podcast in Mexico]
Osvaldo talked with me about the importance of building a “Proof of Value.” The idea has stayed with me since our conversation as the concept offers a path to building incredibly useful software, instead of just building software quickly.
Osvaldo explained that a Proof of Concept (PoC) shows that something can work, and that it is technically possible. That’s good, especially if that software is doing something innovative and we need to confirm the technical feasibility in order to de-risk the development of the rest of the application.
A Proof of Value (PoV), by contrast, shows that the application will actually deliver something of value to the users or the business. It helps us to know if we should build this feature, instead of just if we can build it.
In the age of AI-assisted engineering, that distinction matters more than ever.
The Temptation of the Vanity AI Project
Today, tools like Claude Code and OpenAI Codex make it easier than ever to build applications quickly. With LLMs readily accessible and APIs available in minutes, engineering teams can spin up impressive demos at record speed.
That speed is powerful. But it is also dangerous because it makes it tempting to build things just because we can, without thinking if we should.
As Osvaldo warned in our conversation, it is easy to fall into what he called “vanity projects.” These are AI initiatives that look impressive, generate internal buzz, and maybe even attract investor attention, but do not tie back to measurable business value.
It is tempting to:
- Add an LLM-powered chatbot because competitors have one
- Publish a press release announcing an AI feature
- Track model metrics like hallucination rates without tying them to revenue or cost savings
If the AI feature does not solve a real user problem or improve a real business KPI, it is just an expensive experiment.
Why Proof of Value Matters More in the AI Era
Before AI-assisted engineering, building software was expensive and slow enough that teams were often forced to think carefully before shipping something. Now, we can prototype in days and integrate new features in hours. We can deploy features continuously with automated testing providing assurance that it is functionally correct.
That doesn’t mean that the application is actually ready to scale in production. There will still be labor intensive efforts needed to test, deploy, monitor and maintain the application at scale. Nonetheless, building software is more efficient than it used to be.
That acceleration is a gift. But it removes a natural constraint that once protected us from building things no one wanted.
The result is a higher risk of:
- Rapidly shipping features that do not meaningfully improve user outcomes
- Investing in AI infrastructure without clear ROI
- Confusing technical novelty with business innovation
AI does not automatically create value. It amplifies whatever product thinking you already have. If your strategy is vague, AI will help you move faster in the wrong direction.
That is why Proof of Value must come first.
What Real Proof of Value Looks Like
A real Proof of Value is not just a demo in front of leadership. It includes:
- A clearly defined business problem or user pain point. Not “we want to use AI.” Rather, “we want to reduce customer onboarding time by 30 percent” or “we want to decrease support ticket resolution time by 20 percent.”
- Explicit success metrics tied to financial outcomes or user satisfaction. Revenue growth, cost reduction, customer retention, conversion rates. Not just model accuracy.
- A controlled pilot with real users. Real feedback, real behavior, real data. Not just internal testing.
- A plan for iteration. LLM driven applications are not finished products. They require tuning, retraining, guardrails, and governance.
In our interview, Osvaldo put it bluntly:
“Otherwise, you are just losing time and money.”
That is not necessarily a technology problem (although technical teams have a critical role to play in creating and deploying a Proof of Value).
Good UX and User Research Are More Important Than Ever
Ironically, in a world obsessed with AI, some of the most important disciplines are the oldest ones in software development.
- User research
- UX design
- Journey mapping
- Usability testing
- Continuous Discovery
If anything, they are more important now. When LLMs are involved, small UX decisions can dramatically impact perceived quality. Prompt design, context management, fallback behaviors, transparency about model limitations, and response timing all affect user trust.
A poorly designed AI feature can feel unreliable or even dangerous, especially in sensitive domains like healthcare, finance, or legal services.
The good news is that AI also makes user research more efficient:
- We can prototype functionality in days instead of weeks
- We can simulate flows and test them with users earlier
- We can analyze qualitative feedback faster
- We can iterate on prompts and flows without rewriting entire systems
AI accelerates experimentation. But experimentation only creates value when guided by real user insight. “The classroom”, as Osvaldo told me, “is everywhere”. The same is true for user research. Feedback is everywhere. The teams that win are the ones who systematically gather it and act on it.
Why Technical Talent Matters More in LLM-Driven Applications
There is another risk in the current AI cycle: underestimating the complexity of production grade LLM integrations.
It is easy to call an API. It is much harder to:
- Design robust guardrails
- Handle edge cases and hallucinations
- Secure sensitive data
- Manage token usage and cost
- Integrate LLM outputs into existing systems reliably
- Properly test and validate non-deterministic outcomes
- Implement proper monitoring and governance
This is a rapidly evolving field. Best practices from just 12 months ago may already be outdated.
Building a true Proof of Value requires a multidisciplinary team that understands:
- Software architecture
- Prompt engineering / AI Workflows
- MLOps and DevOps
- Data privacy and governance
- UX and product strategy
- Business metrics and financial modeling
At AgilityFeat, we have been helping companies build and scale engineering teams in Latin America for over a decade. In recent years, we have invested heavily in LLM driven applications, voice agents, AI powered workflows, and real time integrations.
That experience matters. When exploring AI integrations, you don’t just need developers who can code. You need engineers who understand system design, can translate business needs into AI-enabled architectures, and connect technical decisions to measurable outcomes.
How to Start Building a Proof of Value
If you are considering an AI initiative, here is a practical starting framework:
- Define the business objective in financial terms with SMART (Specific, Measurable, Achievable, Relevant, and Time-bound) goals. What specific revenue, cost, or retention metric are you trying to improve?
- Map the user journey. Where does friction exist today? Where could AI meaningfully reduce effort or increase clarity?
- Identify a narrow, high impact pilot. Start with a specific workflow or user segment. Avoid broad “AI transformation” mandates.
- Build a fast prototype. Use modern AI tools to move quickly, but keep the scope tight.
- Test with real users. Observe behavior, gather feedback, and measure impact.
- Decide based on data. If the pilot shows measurable value, scale it. If not, refine or stop.
This is a Proof of Value mindset.
Ready to Build a Proof of Value?
If you are exploring LLM integrations, AI-powered workflows, or new AI-driven products, we would be glad to help you design and execute a true Proof of Value. Our internal nearshore development team can support you with:
- UX and user research to validate real needs
- Rapid prototyping with modern AI tools
- Secure and scalable LLM integrations
- Nearshore engineering teams with deep AI experience
- A clear path from pilot to production
Once your Proof of Value shows the true impact possible by building AI driven applications, we also have the expertise to expand it into a production ready and scalable application.
Contact AgilityFeat to start building a Proof of Value for your AI initiatives and ensure your next AI project delivers measurable impact where it matters most!
Further Reading:
- Best Practices for AI-Assisted Software Development
- How to Hire LLM Engineers: Why Latin America Solves the AI Talent Crisis
- When Does a Serverless MVP Approach Work Best?
- Using an Evidence-Driven Approach to Build Applications Customers Actually Want (David Bland on the Scaling Tech Podcast)
- Blending People, Processes, and New Tech for Faster Software Delivery
- AI Video Editor Development: Building Verbolo’s Intelligent Content Creation Platform
- Five Tips for Integrating LLMs into Software Products
- Deploying AI Agents in Production with AWS
