AI Proof of Concept (PoC): Guide for Businesses

Gartner predicts that through 2026, organizations will abandon 60% of AI projects - not because AI doesn't work, but because they weren't clear on what they were actually trying to prove.

The difference between AI initiatives that scale and those that quietly get shelved often comes down to how the proof of concept was designed from the start: the right scope, the right data, the right question.

This guide breaks down what an AI proof of concept is, how it differs from a prototype, pilot, or MVP, and what it takes to run one that gives you a real answer.

An AI proof of concept is a bounded, time-limited experiment designed to answer one question: can this AI approach work for this specific problem, in this specific context, with this data?

It is not a product. It is not a demo to present at a board meeting. It is a structured test of a hypothesis - designed to produce a decision.

The output of a well-run POC isn't a working application; it's a clear answer. Should we invest further in this approach, or redirect resources before committing serious budget? A well-scoped POC delivers that answer in four to eight weeks, at minimum cost and with maximum clarity.

A POC is not:

• A prototype you're going to demo to stakeholders
• A pilot you intend to scale across the business
• An MVP with early users in a live environment
• A generic exploration of "what AI can do for us"

Each of those things is valuable in the right context. None of them is a POC.

These four terms are used interchangeably in most organizations. They shouldn't be - each stage answers a different question and carries a different level of investment and risk.

Confusing a POC with an MVP is a common causes of early AI project failure. Stakeholders expect a production-ready product; the team delivers a technical feasibility test. The result is frustration, misaligned expectations, and a project that gets cancelled for the wrong reasons.

When to Use Each

The transition between these stages is where most AI projects fall apart. A POC might prove that an LLM can summarize a contract with 90% accuracy - but the subsequent MVP phase might reveal that the cost of running that query at scale makes the solution economically unviable.

• POC: Before committing meaningful budget. You don't yet know whether the approach is technically feasible.
• Prototype: Feasibility is established. You need to demonstrate the workflow or validate the user experience with stakeholders.
• MVP: The case is made. You're building the minimum feature set for real users to test in a controlled environment.
• Pilot: The product is ready. You're testing it in a live environment before full rollout.

Traditional software POCs test whether something can be built. AI POCs test whether a probabilistic system can be trusted - and that's a harder question to answer.

In traditional software, a POC is largely a binary check: does System A communicate reliably with System B? The code either works or it doesn't. If it works in the test, it works in production.

AI systems don't work that way. They are probabilistic. The same prompt can produce different outputs on different days, with different phrasing, or against slightly different data. A AI model might perform well on your sample dataset and fail on real production data. It might be accurate 90% of the time - and wrong in ways that matter the other 10%.

This means AI POCs require a fundamentally different evaluation approach:

• Accuracy is measured against thresholds, not as a binary pass/fail. A hypothesis like "correct answers ≥85% on human-validated test cases" is specific enough to be useful. "It seems to work" is not.
• Latency is a success criterion. A response that takes twelve seconds may be technically accurate but operationally useless for real-time workflows.
• Data is the biggest variable. Poor-quality, fragmented, or inconsistent data doesn't just slow the AI model down - it poisons the output. Garbage in, garbage out remains the immutable law of AI. This is why a data first approach is critical for an AI strategy. 
• AI Governance enters earlier than in traditional builds. Questions about data ownership, PII handling, and compliance affect the architecture from day one - they cannot be left until after the build.
• User trust is a success criterion. A system that employees don't adopt has failed, regardless of its technical metrics.

AI projects fail for four predictable reasons. A well-scoped proof of concept surfaces all four before you've committed serious resources.

Up to 70-80% of AI initiatives never reach production. They stall in what the industry calls "POC Purgatory" - technically functional in a sandbox, but unable to clear the bar for business viability, data quality, or organizational readiness. The POC is the mechanism that prevents you from discovering that bar at the wrong point in the investment cycle.

The Four Risk Dimensions a POC Tests

A well-designed AI POC tests four risks in parallel:

• Technical feasibility. Can the model reason accurately over your domain data? Can it meet your latency requirements? Can it handle edge cases at the volume your use case demands?
• Business viability. Does solving this problem move a metric that matters? Is the cost of inference, infrastructure, and maintenance justified by the value generated?
• Data readiness. Is your data clean, accessible, and sufficient? Organizations routinely discover that data they assumed was available is siloed, inconsistent, or legally restricted.
• Operational scalability. If it works with 500 records in a controlled sandbox, will it hold up against 50,000 in a live environment?

Miss any one of these, and the project fails - at a stage where the cost of failure is far higher than it would have been during a four-week POC.

The most technically impressive use case is rarely the right starting point. The right use case sits at the intersection of high business value and high data readiness.

A AI POC that tests a complex, multi-system agentic workflow against data that doesn't yet exist will teach you nothing useful. A POC that tests a focused hypothesis against clean, accessible data will give you a defensible Go/No-Go in four weeks.

The Value Concentration Principle

Research from McKinsey indicates indicates that approximately 75% of the economic value of generative AI concentrates in four business functions, with an estimated annual value between $2.6 trillion and $4.4 trillion.

Prioritizing these areas maximizes the likelihood that a successful AI POC leads to meaningful ROI.

1. Customer Operations. AI can increasingly automate complex customer interactions by 30% to 45%, to reduce average handle time, and improve first-contact resolution - all directly measurable, all tied to cost and satisfaction.
2. Marketing and Sales. Personalization at scale, outreach generation, and synthesis of sales signals from unstructured data. Conversion rates can validate a POC result in this area quickly.
3. Software Engineering. AI coding assistants and test automation deliver productivity gains measurable in story points and cycle time.
4. Research and Development. In manufacturing and pharma, AI accelerates discovery and generative design. Highly specialized, but high value concentration.

A structured POC process turns an experiment into a defensible business decision.

The most common reason POCs produce no useful output is that they were never structured as an experiment. They started with enthusiasm and ended with "it kind of works." The following eight steps produce a decision, not a demo.

1. Define the business problem, not the technology

Start with a measurable KPI, not a feature wishlist. "We want to use AI for customer support" is not a testable hypothesis. "We want to test whether AI triage can handle 40% of incoming tickets with >90% accuracy" is. One of them produces a Go/No-Go signal. The other produces a prototype.

2. Scope ruthlessly

One use case. One dataset. One question. Every additional use case added at this stage doubles the complexity and halves the clarity of the output. If the first hypothesis proves positive, you'll have the foundation to run the next POC in half the time.

3. Assess data readiness - before writing a line of code

Data readiness is the most common POC killer. Before any build starts, audit your data: Can you access it? Is it clean enough to test against? Does it contain PII that needs to be handled before it enters the POC environment? Is there a sufficient volume to validate the hypothesis?

If the answer to any of these is unclear, the data assessment is your first deliverable - not the AI build.

4. Choose your tooling tier

HSO recommends matching tooling to the fidelity the POC requires - not defaulting to the most complex option available:

• Low-code (Microsoft Copilot Studio): Fastest to a working proof. Ideal for knowledge worker and customer support use cases. Best when speed matters more than full technical control.
• Managed platform (Azure AI Foundry / Microsoft Fabric): Balanced approach. Retains IP, integrates with existing Azure infrastructure, and supports RAG pipelines and structured data use cases.
• Custom engineering (Semantic Kernel / HSO accelerators): Maximum flexibility and lowest long-term operational cost. Requires engineering resource, but produces a build that is representative of what production will look like.

5. Build with security from day one

Security is not a final step. Define roles and access controls before the first resource is provisioned.

HSO's guidance is clear: request only the roles you need, enforce least-privilege access, and keep production data out of the sandbox unless it has been properly AI governed and anonymized. This is not just good practice - it is how you avoid a compliance incident mid-POC.

6. Define success criteria upfront

Set your thresholds before you see any results. What accuracy level constitutes a pass? What is the maximum acceptable latency for the use case? What cost-per-query makes the solution economically viable? What user satisfaction score would confirm adoption?

Defining these after seeing results is not evaluation - it's post-hoc justification.

Examples:

• Accuracy: "Responses must be factually correct at least 95% of the time to go live."
• Latency: "Each query must return a response within 2 seconds for customer-facing use."
• Cost: "Cost per query must stay below $0.03 to keep unit economics viable at scale."
• User satisfaction: "At least 80% of pilot users must rate the experience 4 out of 5 or higher."

7. Use Infrastructure as Code (Bicep / AVM)

Treat the POC environment as ephemeral and codified. Using Bicep and Azure Verified Modules means the environment is reproducible: if the POC succeeds, you can rebuild and harden it for production without starting from scratch. An environment built by hand cannot be audited, replicated, or trusted at scale.

8. Measure against business KPIs, not just model metrics

An AI model that hits 92% accuracy on a test set but doesn't reduce processing time or operating costs has not proved its value. Always map technical metrics to business outcomes: accuracy to first-contact resolution rate, latency to user adoption, cost-per-query to cost-per-transaction saved.

The stakeholders who fund the next phase will ask about the business number - not the score.

Most AI POC failures are not random. They follow predictable patterns - and two high-profile examples make those patterns impossible to ignore.

The failures that attract attention are rarely pure technical disasters. They are the result of applying the wrong process to a problem that required rigor: insufficient scoping, no real evaluation criteria, and operational conditions that were never properly tested.

McDonald's AI Drive-Thru (Cancelled 2024)

McDonald's deployed IBM Watson-powered AI order-taking to more than 100 US locations. The system was removed in 2024 after a string of failures - including orders being misheard and incorrectly processed - became widely documented.

The technical limitations were entirely foreseeable. The system struggled with accents, competing background noise, and complex or modified orders. None of these conditions were adequately tested before rollout. A voice AI that performs acceptably in a quiet environment is a fundamentally different problem from one operating in a fast-food drive-thru with ambient noise, dialect variation, and real menu complexity.

The lesson: Operational conditions are not optional POC scope. If the use case involves real-world noise, edge cases, or complex input variation, those must be in the test - not discovered after rollout.

Klarna AI Customer Service (Success)

Klarna deployed an AI assistant that handled 2.3 million conversations - two-thirds of their total customer service volume - within its first month. The system performed the equivalent work of 700 full-time agents while customer satisfaction scores held steady.

The reason it worked is straightforward: Klarna chose a single, well-scoped use case with clean data from years of customer interactions, defined a clear success metric (resolution rate), and phased the rollout. They did not attempt to automate all of customer service at once. They proved one hypothesis and scaled from there.

The lesson: Narrow scope, clean data, and a clear success metric produce a POC that answers the question. The Klarna approach is not sophisticated - it's disciplined.

HSO's approach to AI POCs starts with the business problem, not the technology - and uses the Microsoft stack to build reproducible, governed environments that are ready to scale if the POC succeeds.

The most expensive mistake in AI is building something impressive that can't be repeated, audited, or hardened for production. HSO structures POC engagements as if the environment might become a production system - because the ones that succeed will.

Tooling Selection

Choosing the right tooling is a strategic decision, not a default. HSO recommends matching the tooling tier to the level of fidelity and control the specific POC requires.

What HSO Delivers in a POC Engagement

An HSO AI POC engagement produces four specific outputs:

• A AI solution tested against your defined use case and sample data, with logging and telemetry built in from day one.
• An evaluation report documenting performance against pre-agreed success criteria, including accuracy metrics, error analysis, and edge-case behavior.
• A security and governance baseline - least-privilege access controls, a data handling and PII assessment, and an IaC-provisioned environment that can be rebuilt and hardened for production.
• A clear next-step recommendation - scale to MVP, iterate on the current approach, or redirect budget to a better use case. The POC produces a decision, not an open question.