Let’s say your company wants to build a machine learning solution or launch a Generative AI assistant, but you’ve never done anything like this before. Where do you even start?
For many businesses, the smartest first move is an AI proof of concept (PoC). Why? Because it gives you just enough runway to validate an idea, test real data, and reveal hidden blockers, without committing to full development.
At the same time, running one isn’t as simple as it might sound. There are strategic decisions to make, success metrics to define, and plenty of room for costly detours if the process isn’t well structured.
That’s why we spoke with Olga Hrom, Director of Pre-Sales Strategy and Delivery at Master of Code Global. Drawing from dozens of projects, she shared firsthand insights on when an AI PoC delivers the most value, what mistakes to avoid, and how to approach this stage with a clear outcome in mind.
Whether you’re testing a new model, pitching an idea internally, or still trying to figure out how to implement AI in business, this guide will help you navigate the phase with confidence—and a lot less guesswork.
Table of Contents
What Is an AI PoC?
Launching an AI initiative without testing the waters can lead to wasted months and burned budgets. That’s where an AI PoC—or AI proof of concept—comes in.
In simple terms, it is a short-term, low-risk project that helps your team answer one critical question: Can this technology actually work for us? It’s not about building a full product. It’s about checking whether your idea is technically feasible, using real data in a controlled setting.
In some cases, businesses choose to run a Proof of Value (PoV) instead. While similar in setup, a PoV typically has a broader scope—it also explores whether the solution can deliver visible impact or resonate with end users. And further down the line, when you’re ready to release a real application to market, you move toward an MVP, or Minimum Viable Product.
So, how do these approaches compare?
AI PoC vs Proof of Value vs MVP: What’s the Difference?
For artificial intelligence projects, jumping straight into development without validation can be risky and expensive. That’s why many companies start with a phased approach, beginning with a PoC or a PoV before moving on to a full MVP.
This progression helps de-risk decisions, align employees, and avoid building the wrong application. But the lines between these stages can get blurry—especially when internal teams or stakeholders treat an AI proof of concept like a half-finished product.
Here’s how Olga breaks it down: “If the goal is just to check whether the chosen model or tech stack works, an AI PoC is enough. But if you also want to see how the solution performs in on-the-ground conditions—whether users understand it, whether it delivers measurable outcomes—then that’s a Proof of Value.”
In other words:
- AI PoC = Can this work technically?
- PoV = Will it deliver real-world benefits?
- MVP = Let’s launch it for actual users.
These aren’t interchangeable terms—they serve different purposes. A proof of concept is narrow and quick. A PoV adds layers like UX testing or KPIs. And an MVP is a working product ready to meet business or user needs. You’ll find a broader side-by-side comparison in the table below.
What about the cost?
- An AI PoC typically costs $10,000–$20,000 and runs for 3–4 weeks.
- A PoV starts around $25,000, depending on scope and complexity.
- An MVP can range anywhere from $10,000 to $150,000 or more, in line with feature depth, integrations, and infrastructure.
The key takeaway? Start with the smallest commitment that answers your biggest uncertainty. If you’re unsure whether a technology will perform as needed, or if your team is still aligning on strategy, an AI PoC or PoV allows you to move forward with confidence.
Next, we’ll look at when a trial actually makes sense—and when it might be more of a distraction.
AI PoC: When You Need One and When You Really Don’t
Not every AI idea needs to begin with a proof of concept. So, how do you know when it’s the right move?
Let’s start with what we’ve learned from dozens of client engagements. According to Olga Hrom, there are several clear scenarios where an AI PoC—or Proof of Value—delivers the most benefit:
Signs You Should Start with an AI PoC
- You need to validate the technical stack. Unsure which approach, large language model (LLM), or infrastructure will best support your use case? AI proof of concept gives you the chance to compare approaches, test drive the stack, and make informed architecture decisions before you’re locked into costly technology.
- You want to measure potential ROI before scaling. Intelligent solutions require both time and money. A pilot allows you to test whether the value you’re aiming for—such as increased efficiency, lower churn, or better user experience—is actually achievable based on early results.
- Your AI use case is complex or unfamiliar. Off-the-shelf models won’t solve every problem. If the use case involves sensitive data, rare edge cases, or a workflow that doesn’t follow typical patterns, an AI PoC offers a safe environment to build, experiment, and iterate.
- You’re trying to reduce bias or internal resistance. Skepticism is common, especially when teams worry about hallucinations, compliance, or loss of control. A trial run helps decrease fear by demonstrating that the solution is measurable, explainable, and aligned with business goals.
- You suspect hidden blockers. Sometimes the biggest problems aren’t visible at the start. An AI PoC can surface unexpected issues—like gaps in GDPR compliance, poor data structure, or reliance on outdated tools—before you hit them in production.
When a PoC Might Be a Waste of Time
- You already know the tech works—and so does your competition. If you’re solving a problem that’s been addressed many times, like chatbot FAQs for retail or basic product recommendations, an AI proof of concept might not add value. In fast-moving sectors, speed matters more than validation.
- Your business isn’t ready to support AI. Even the best trial run won’t fix missing infrastructure, low data quality, or fragmented internal systems. If these are the real issues, you’re better off starting with a strategic audit or discovery phase.
- You treat an AI PoC like an MVP. Overloading a proof with features makes it hard to test anything clearly. It also raises expectations and leads to scope creep. A focused proof of concept should be lean and purpose-driven, not a pre-product.
- Your objectives aren’t clearly defined. The early-stage test needs to answer a specific question. If your team can’t agree on what success looks like or what you’re attempting to validate, it’s not time for a proof yet.
Other Factors to Consider Before You Start
- Your team’s AI maturity matters. If this is your first initiative, an AI PoC can double as a learning phase. It gives internal teams the opportunity to understand how technology works, how to monitor it, and where to course-correct. But if you already have deep experience, too much validation may slow down momentum.
- Data availability and access can delay everything. Having information isn’t the same as having the right one, in the right format, with the right permissions. A trial run will reveal gaps—but if your organization can’t move quickly to provide access, the entire effort may stall.
- Infrastructure may need to scale sooner than you expect. A successful AI proof of concept can trigger demand for broader rollout faster than planned. If your foundation (cloud, on-prem, or hybrid) isn’t ready to adapt or integrate new workflows into live systems, that success becomes a bottleneck.
- You may need to pivot midstream—and that’s okay. The experimental phases aren’t always linear. In some cases, they uncover deeper issues with legacy tools, UX friction, or integration requirements. Be ready to adjust the course if the project turns into a more profound business or operational audit.
- Stakeholder alignment still drives adoption. Even the best results go unnoticed if key stakeholders aren’t bought in. An AI proof of concept is also a communications tool—it helps align leadership, IT, compliance, and operations around a shared vision of success.
So if the stakes are high and the answers aren’t obvious, a focused AI PoC may be the smartest next move. But is it just about risk management, or does it actually create measurable value?
That’s exactly what we’ll break down next.
The Business Value of AI PoCs
An AI proof of concept isn’t just a technical checkpoint—it’s a decision-making accelerator. When done right, it ensures clarity, reduces uncertainty, and brings tangible advantages well before any full-scale rollout.
Here’s what you can expect a successful AI PoC to deliver:
1. Faster Time to Insight
Instead of debating the potential of an artificial intelligence use case for months, a well-structured proof of concept delivers real-world results in weeks. It enables decision-makers to pick things up quicker, whether the answer is to move forward, pivot, or pause. Olga calls it “a way to learn faster, get early results—and even fail faster if needed. It’s a classic lean approach.”
2. Cost Savings of 50–70% on Development
By catching flawed assumptions early, AI PoCs help bypass sunk costs. In our experience, projects that start with an early-stage test often avoid 2–3 months of unnecessary development effort. The outcome? Reduced scope, cleaner architecture, and less rework.
3. Strategic Clarity for the Road Ahead
An AI proof of concept isn’t the end—it’s a springboard. It surfaces the real blockers (like missing APIs, weak data pipelines, or infrastructure gaps) that might otherwise only appear mid-project. This prepares your team for smoother scaling and clearer budgeting.
4. Easier Stakeholder Buy-In
Whether you’re selling internally to execs or externally to clients, a pilot gives you something to show. A working prototype, even if basic, helps communicate value more effectively than pitch decks or spreadsheets.
5. Clearer AI Architecture Decisions
Artificial intelligence stacks aren’t one-size-fits-all. During an AI PoC, you can test different models, LLMs, or cloud configurations and make evidence-based decisions. That’s a safeguard against over-engineering or vendor lock-in.
6. Real Performance Metrics
Instead of guessing about accuracy or business impact, you get real KPIs to work with. Our experts frequently use this stage to define success criteria and build a measurement framework that carries into the next phase.
As AI adoption accelerates, organizations are under pressure to act—but not to waste. The proof of concept acts as a bridge between inspiration and execution, allowing teams to test their thinking and de-risk their investments.
So what does this look like in practice?
In the next section, we’ll walk through field-tested AI proof of concept scenarios from our portfolio that helped businesses save time, avoid wrong turns, and enable fresh opportunities.
Real AI PoC Examples to Learn From
Theory is helpful, but what does a successful AI proof of concept look like in action?
Here are three examples from our portfolio that illustrate how an early-stage test guides clients to clear goals, lower risk, and real results.
Example 1: Multinational Retailer Uncovers Hidden Process Gaps
A large retail enterprise operating across 14 countries relied on telesales teams to sell magazine subscriptions. Due to regulatory requirements, agents had to read a five-minute terms and conditions script aloud before closing any sale. This not only led to frequent call drop-offs but also created unnecessary strain for call center staff.
We launched a PoC machine learning solution to test an alternative flow: delivering the legal text via WhatsApp during the call, allowing users to review and confirm separately. While the initial goal was to reduce friction and maintain compliance, the validation uncovered much more. It revealed:
- Unrecognized churn reasons linked to script timing and tone
- Data input inconsistencies across call centers and markets
- Missed automation opportunities in consent collection and follow-up
In just a few weeks, the AI proof of concept gave the client a better understanding of where manual effort could be reduced, where workflows were misaligned, and how user experience could be improved with minimal disruption.
Example 2: Restaurant Chain Warms Up to Generative AI
A fast-growing group wanted to modernize its ordering process but was hesitant about using large language models. The leadership team raised concerns about hallucinations, brand tone mismatch, and data security, especially within SMS channels.
To address this, we designed a Generative AI PoC: a conversational SMS ordering experience powered by a fine-tuned LLM. It was tightly scoped to handle core menu interactions, while respecting channel limitations and compliance constraints.
The results demonstrated how GenAI could provide accurate, practical responses in a highly controlled flow. More importantly, the pilot gave stakeholders something tangible to evaluate, changing perceptions from skepticism to curiosity. It laid the groundwork for future expansion into richer channels, with a stronger case for budget and buy-in.
Example 3: GenAI Bot Boosts Sales Ahead of Black Friday
A global electronics manufacturer wanted to test the value of AI-powered chat for direct-to-consumer sales and support. With the holiday shopping season approaching, they aimed to validate whether an on-site Apple Messages for Business chatbot could enhance user experience and boost revenue, especially among iOS users.
Working in partnership with Infobip, our team developed a real-time shopping assistant fully integrated with Shopify. The chatbot leveraged a generative component to understand user inquiries, suggest relevant products, and guide customers through the purchasing journey, without ever leaving the chat.
Key features included:
- Personalized product recommendations powered by GenAI
- Seamless cart management and checkout support
- Instant answers to technical product questions
- Easy handoff to live agents when needed
The proof of concept was completed in time for Black Friday campaigns. The results spoke for themselves:
- 84% session engagement rate
- 80% average CSAT
- ~$300 average order value
How to Approach an AI PoC: Stages, Readiness, and Roadblocks
In the previous section, we explored how a well-designed AI proof of concept opens the door to real impact. To achieve those kinds of outcomes, you need the right foundation. In addition, you require a structured strategy, stakeholder alignment, and a clear definition of success. Here’s the correct way to do it.
Questions Every Business Should Answer Before Starting an AI PoC
Before diving in, Olga recommends that companies ensure their team can confidently address the following questions:
1. What specific problem are we aiming to solve with AI?
Clearly express the business challenge or opportunity. A well-articulated problem statement ensures that the PoC remains focused and relevant.
2. Why is now the right time to explore this AI solution?
Assess the urgency and relevance of the issue. Factor in market dynamics, competitive pressures, and internal preparedness to adopt AI-driven solutions.
3. Do we have the necessary data to support this initiative?
Evaluate the availability, quality, and accessibility of information required for the AI proof of concept. Data gaps can significantly hinder progress and outcomes.
4. What does success look like for this AI PoC?
Establish clear, measurable benchmarks. Define key performance indicators (KPIs) that align with company objectives and validate the trial’s effectiveness.
5. Are all key stakeholders aligned and supportive?
Confirm that decision-makers, end-users, and technical teams are on the same page regarding the goals, scope, and potential impact.
6. What is our plan post-PoC?
Consider the next steps if the AI trial is successful. Outline a roadmap for scaling, integration, and full deployment to guarantee continuity and maximize ROI.
The Key Stages of a Winning AI PoC
A well-executed AI proof of concept follows a clear progression. Each phase is designed to minimize risk, maximize learning, and prepare the firm for what comes next.
1. Planning and Goal Definition
- Start by identifying the specific business challenges you want to solve.
- Set detailed plans and trackable outcome metrics—whether it’s accuracy, cost reduction, or customer satisfaction.
- Align goals with the overall strategy and engage stakeholders early to ensure buy-in.
- Prioritize use cases with strong impact potential and quick feedback loops.
- Assemble the right team, including data scientists, AI engineers, and domain experts.
2. Data Collection and Preparation
- Collect high-quality, relevant information from internal and external sources.
- Clean and structure the datasets—remove inconsistencies, fill in gaps, normalize formats.
- Confirm the data is representative and suitable for training and testing models.
- Implement proper governance protocols to maintain integrity and security.
3. Model Development
- Choose the right AI tool and technology stack for your use case.
- Train and fine-tune the model using prepared data.
- Build a scaled-down prototype or early demo to showcase functionality.
- Keep it lean—focus only on what needs to be validated, not polished.
4. Testing and Evaluation
- Run the model against your defined success criteria.
- Use real or synthetic data to test performance under various conditions.
- Conduct cross-validation, edge case analysis, and error reviews.
- Gather early feedback from stakeholders to assess usability and trust.
5. ROI and Feasibility Analysis
- Evaluate the AI PoC’s results against the expected impact.
- Estimate cost savings, operational improvements, or revenue potential.
- Utilize this data to make a go/no-go decision or revise the approach.
6. Pilot and Scaling Preparation
- Move into a pilot with a broader user base or department.
- Refine the solution based on real-world feedback.
- Prepare for full-scale deployment with plans for infrastructure, ML Ops, and internal adoption.
Common Blockers That Can Hinder AI PoC Success
Several roadblocks impede the progress and success of a project. Being aware of these can help in proactive mitigation:
- Inaccurate, incomplete, or biased data leads to unreliable models. Ensuring high-quality data is foundational to any AI initiative.
- Ambiguity in goals results in misaligned efforts and unclear outcomes. Clear, measurable objectives are crucial.
- Without buy-in from key stakeholders, the early-stage test may lack the necessary support, resources, or alignment with business needs.
- Inadequate infrastructure or a lack of tech expertise restricts model development and deployment. Assess and address technical readiness early on.
- Overlooking legal and ethical considerations causes compliance issues. Ensure that data usage and applications adhere to relevant regulations.
- Resistance to change within the organization slows down adoption. Implementing effective change management strategies is essential for smooth integration.
At the end of the day, a successful AI PoC isn’t just about proving something works—it’s about learning fast, surfacing challenges early, and preparing the business for what comes next. Handled correctly, this phase can make the difference between a promising idea and a production-ready solution.
What’s Next?
If you’re researching AI for your company, a proof of concept is one of the smartest ways to move forward without overcommitting. But the outcome depends heavily on how it’s done—and who you partner with.
At Master of Code Global, we take a different approach. Instead of rushing to prototype, we spend time up front helping you ask the right questions, define measurable goals, and surface blockers that could stall progress later. It’s a process that’s helped us turn 90% of our discovery engagements into successful development projects.
We’ve seen that AI PoCs aren’t just about testing technology. They’re often the first step toward bigger decisions: rethinking workflows, aligning stakeholders, or exploring automation opportunities that weren’t obvious at the start. That’s why we treat every initiative as both a learning phase and a launchpad.
Our team also brings hands-on AI experience from across industries, and we practice what we preach—trying it internally before recommending it to our clients. This helps us deliver practical, business-ready guidance, not just tech demos.
So if you’re looking for a partner who won’t just build fast, but build smart—one who understands both the landscape and the real-world pressures behind your decision-making—let’s talk.
AI proof of concept is just the beginning. Let’s make it count.
Ready to build your own Conversational AI solution? Let’s chat!