The majority of enterprise GenAI initiatives stall somewhere between a promising demo and a production deployment. According to DCF Research's 2026 analysis of over 80 active genai consulting engagements, roughly 70% of proofs of concept never advance to a funded production phase. The failure is rarely technical. It is almost always a problem of scope definition, data readiness, or a consulting firm that sold excitement instead of engineering rigor.
This guide is written for CTOs, VP Engineering, and technical program managers who are evaluating genai consulting proposals or who have already signed a contract and want to know what good execution looks like. It covers what a PoC should prove, what a credible timeline and cost structure look like, what deliverables to demand in writing, and the specific failure modes to watch for before you commit budget.
For context on how PoC engagements fit into the broader AI project lifecycle, see our AI consulting projects hub. For firm selection guidance, the AI consulting firms buyer's guide is a useful companion to this piece.
What Is a GenAI Proof of Concept?
A GenAI proof of concept is a time-boxed, narrowly scoped technical engagement designed to answer a specific feasibility question: can this use case be solved with generative AI, using your data, within an acceptable cost and latency envelope? A PoC is not a product. It is not even a pilot. Its sole purpose is to reduce technical uncertainty before the organization commits production-level investment.
The definition matters because many firms blur the boundary between PoC, pilot, and MVP in their proposals — often because larger scopes mean larger contracts. A genuine ai consulting proof of concept should target a single use case, operate on a representative but limited data sample, run for four to eight weeks, and produce a working prototype alongside a rigorous evaluation report. If a firm is proposing a "PoC" that spans twenty weeks and costs $400K, you are looking at a pilot or early MVP, not a proof of concept.
What a PoC must prove:
- Technical feasibility — the use case is solvable with current LLM or GenAI tooling at the accuracy threshold the business requires.
- Data viability — your existing data (or a realistic subset of it) can support the model without prohibitive cleanup or enrichment.
- Cost profile — inference, storage, and orchestration costs at projected production volume fall within a range the business can absorb.
- Integration surface — the system can connect to the relevant internal data sources and downstream applications without requiring a full data platform rebuild.
A PoC that cannot answer all four of these questions has not done its job, regardless of how impressive the demo looks.
Typical GenAI PoC Timeline
A well-run genai poc runs four to eight weeks for most enterprise use cases. Six weeks is the most common duration in practice. Engagements shorter than four weeks typically cannot produce a meaningful evaluation; engagements longer than eight weeks usually indicate scope creep or a consulting team working through a learning curve on your budget.
The standard phases map as follows:
| Week | Phase | Key Activities |
|---|---|---|
| 1–2 | Discovery and Use Case Definition | Stakeholder interviews, use case scoring, data landscape audit, success metric definition, LLM/tooling selection |
| 3–4 | Data Preparation and Architecture Design | Data pipeline design, embedding strategy selection, vector store setup, chunking experiments, system architecture documentation |
| 5–6 | Prototype Build | RAG pipeline or agent construction, prompt engineering, integration connectors, internal evaluation harness |
| 7–8 | Evaluation and Reporting | Accuracy benchmarking against defined metrics, latency and cost profiling, failure mode analysis, production roadmap drafting |
The discovery phase is where most PoCs are quietly doomed. If weeks one and two produce only a slide deck and a signed-off use case statement without a real data audit, the prototype built in weeks five and six will almost certainly fail the evaluation. Insist that the data audit — including volume, quality scores, and access constraints — is a formal deliverable of the first phase, not an assumption carried into the build phase.
For use cases involving structured internal data (e.g., querying a data warehouse via natural language), six weeks is achievable. For use cases involving unstructured documents, multi-modal data, or complex agent orchestration, eight weeks is more realistic and should be your baseline expectation when reviewing proposals.
What Does a GenAI PoC Cost?
A credible six-to-eight week genai consulting proof of concept from a reputable firm will typically cost between $50,000 and $150,000 all-in, including infrastructure costs. The range is wide because team composition, use case complexity, and firm tier drive significant variation.
For detailed context on how these rates are constructed, see our AI consulting pricing guide.
Typical Role Composition for a 6-Week PoC
| Role | Allocation | Blended Weekly Rate | 6-Week Total |
|---|---|---|---|
| AI/ML Architect | 50% (3 days/wk) | $2,400–$3,000/day | $21,600–$27,000 |
| Senior AI Engineer | 100% (5 days/wk) | $1,800–$2,400/day | $54,000–$72,000 |
| Data Engineer | 50% (3 days/wk) | $1,400–$1,900/day | $12,600–$17,100 |
| PoC Lead / Engagement Manager | 25% (1.5 days/wk) | $1,600–$2,000/day | $7,200–$9,000 |
At standard US onshore rates, a lean but competent team runs $95,000–$125,000 for six weeks before infrastructure costs. Nearshore-blended teams (LatAm or Eastern Europe for engineering, US for architecture and management) typically deliver a $60,000–$85,000 total for the same scope. Offshore-only teams can quote as low as $30,000–$45,000, but the evaluation and production roadmap quality is frequently insufficient to make sound investment decisions.
Infrastructure costs — LLM API calls (OpenAI, Anthropic, Google), vector database hosting, and compute — typically add $2,000–$8,000 for a six-week PoC at representative data volumes. Some firms mark up cloud infrastructure; always request direct billing or itemized cost transparency.
Proposals below $50,000 for a six-to-eight week genai poc from a US-based firm are a signal worth investigating. Either the team is understaffed, junior resources are being substituted for the senior talent quoted in the proposal, or the firm has productized the work to the point where your use case will be forced into a template rather than evaluated on its own merits.
Key Deliverables from a GenAI PoC
The deliverables are what you are actually buying. According to DCF Research's 2026 analysis, fewer than 40% of PoC proposals enumerate specific deliverables in the contract. The firms that produce the best post-PoC outcomes are uniformly those that treat deliverables as binding commitments, not advisory outputs.
Five deliverables should be non-negotiable in any genai consulting engagement:
1. System Architecture Document A detailed technical specification of the prototype architecture: data ingestion approach, embedding model selection and rationale, vector store configuration, LLM selection and version pinning, orchestration layer (LangChain, LlamaIndex, custom), and API surface. This document should be complete enough for your internal team to understand, maintain, and extend the system without the consulting firm present.
2. Working Prototype A functional, deployed prototype — not a Jupyter notebook, not a recorded demo — that your team can interact with against real data. The prototype should operate in a controlled environment (staging, not production) with documented setup instructions, dependency pinning, and a test harness. Code must be delivered to your version control system under your ownership.
3. Evaluation Report A structured assessment of prototype performance against the success metrics defined in week one. This should include: accuracy or task-completion rates, latency benchmarks (p50, p95), hallucination rate for RAG use cases, failure mode taxonomy, and a comparison against baseline (current manual process or rule-based system). If the prototype does not meet the success threshold, the evaluation report should explain why and what it would take to close the gap.
4. Production Roadmap A phased plan — ideally twelve to eighteen months — describing what production deployment would require: team composition, infrastructure scaling, data pipeline maturation, security and governance work, integration development, and model fine-tuning considerations. This is a scoping document for the follow-on engagement, not a sales pitch. It should include honest statements about what is uncertain.
5. Total Cost of Ownership (TCO) Estimate A twelve-month operational cost model covering: LLM inference costs at projected query volume, vector database storage and retrieval costs, engineering time for maintenance and iteration, and monitoring/observability tooling. This estimate is what separates a firm doing genuine genai consulting from one running a demo factory. Without a TCO model, you cannot make a defensible build-vs-buy decision for the production phase.
PoC vs. Pilot vs. MVP: What Consulting Firms Are Actually Selling
The terminology is inconsistently used across the industry, and consulting firms have commercial incentives to expand the definition of a "PoC" to justify larger contracts. Understanding the distinctions protects your budget and your internal stakeholders from misaligned expectations.
A PoC answers: Can this work? A pilot answers: Does this work at limited production scale with real users? An MVP answers: Can we ship something customers or employees will rely on?
| Dimension | Proof of Concept | Pilot | MVP |
|---|---|---|---|
| Primary question | Technical feasibility | Operational viability | Minimum shippable value |
| Scope | Single use case, representative data sample | Single use case, real user cohort | One or more use cases, production data |
| Typical timeline | 4–8 weeks | 2–4 months | 4–9 months |
| Typical cost (US onshore) | $50K–$150K | $150K–$400K | $300K–$1M+ |
| Primary output | Working prototype + evaluation report | Measured user outcomes + operational data | Deployed system + adoption metrics |
| Decision it enables | Fund or kill the use case | Fund or descope production deployment | Iterate or scale |
When a firm proposes a "PoC" with a four-month timeline and a $300K price tag, you are being sold a pilot. That may be the right engagement — pilots produce much richer data than PoCs — but you should enter it with pilot-level expectations for production readiness, governance, and user testing. Mislabeling inflates expectations and corrupts the go/no-go decision at the end of the engagement.
See our RAG implementation consulting guide for a detailed breakdown of how these phases apply specifically to retrieval-augmented generation projects.
How to Evaluate a GenAI PoC Proposal
Most PoC proposals look similar on the surface: a methodology slide, a timeline, a team roster, and a price. Differentiation lives in the details that most buyers do not know to look for.
Four criteria separate credible genai consulting proposals from well-packaged guesswork:
1. Specificity of success metrics A credible proposal names the exact metrics by which the prototype will be evaluated before the engagement starts. For a document Q&A use case, this might be: "Answer accuracy of 85%+ against a 200-question golden dataset, p95 latency under 3 seconds, hallucination rate below 5% as measured by the custom evaluator." A proposal that says "we will evaluate output quality" has not committed to anything. Reject it or require amendment before signing.
2. Data audit as a phase-one deliverable Any firm proposing to build on your data without first auditing it is planning to find the problems in week five, not week one. The proposal should explicitly describe what data discovery will produce: a data quality scorecard, identified gaps, access requirements, and a go/no-go checkpoint before build phase begins.
3. Code and IP ownership language All prototype code, documentation, prompts, and evaluation artifacts should be transferred to you at engagement close. Proposals that are silent on IP ownership, or that reference firm-owned "proprietary frameworks" that will not be licensed to you, create a follow-on dependency that was not priced into the PoC.
4. Explicit failure conditions Good proposals state what would cause the project to recommend against proceeding to production. This is a trust signal: a firm willing to define its own failure conditions is doing real engineering analysis, not selling you toward the next engagement regardless of outcome.
What to be wary of: proposals that lead with model names (GPT-4o, Claude 3.5, Gemini 1.5 Pro) as differentiators rather than methodology; proposals that cannot name a specific team member who will lead the engagement; and proposals that compress discovery to less than one week to hit a lower quoted price.
Why 70% of GenAI PoCs Never Reach Production
According to DCF Research's 2026 analysis, the transition from PoC to production is where the majority of GenAI investment is lost. The failure is almost never that the technology does not work. It is that the conditions for production success were never established during the PoC. Four failure modes account for the overwhelming majority of stalled genai poc engagements.
1. Data quality assumptions that do not hold at scale PoCs routinely use a curated, cleaned data sample that does not represent the full volume, variety, and quality of the production data environment. The prototype achieves impressive accuracy on 10,000 clean documents; the production system is expected to handle 2 million documents spanning fifteen years of formatting conventions, multiple source systems, and inconsistent metadata. The gap surfaces only after the production engineering work begins, at which point the cost to resolve it is three to five times what it would have been if addressed during the PoC data audit.
2. Governance and security gaps that block deployment Enterprise security reviews, data residency requirements, PII handling regulations, and model output audit requirements are routinely treated as post-PoC problems. By the time legal, compliance, and infosec have reviewed the architecture, the production timeline has slipped by six to twelve months and the original consulting team has moved on. A PoC that does not produce a security and governance assessment as part of its architecture deliverable is setting the production team up for delays the PoC sponsor cannot defend internally.
3. Cost surprise at production volume LLM inference costs that are manageable at PoC query volumes (hundreds of queries per day) can become prohibitive at production scale (hundreds of thousands of queries per day). Without a TCO estimate anchored to realistic usage projections, the production funding request arrives with a cost model that shocks the finance committee. Projects get descoped or cancelled not because the technology failed, but because the economics were never pressure-tested.
4. Capability transfer failure The consulting team builds a system that works. They write documentation. They deliver a handoff. And then they leave. Three months later, the internal team cannot debug a retrieval quality regression, cannot evaluate the impact of a new model version, and cannot extend the system to a second use case without re-engaging the vendor. The PoC never developed the internal competency to operate what was built. Genuine genai consulting includes structured knowledge transfer — pair programming, architecture review sessions with internal engineers, and runbook development — not just documentation delivery.
Avoiding these failure modes requires addressing them explicitly in the PoC contract, not hoping the consulting firm volunteers to solve them.
Choosing the Right Consulting Firm for Your GenAI PoC
The difference between a PoC that produces a defensible go/no-go decision and one that produces a polished demo is almost entirely a function of which firm you hire and how the engagement is structured.
Evaluate firms on three dimensions before committing:
Demonstrated production deployments, not PoCs. Any competent team can build a demo that works on clean data with hand-selected queries. Ask for case studies that describe a production system: the data volume it handles, the user base it serves, the operational metrics it is measured against, and what happened during the first six months after launch. If a firm's case library stops at "successful PoC," their core competency is selling the next engagement, not delivering production outcomes.
Team composition transparency. Insist on knowing who specifically will be assigned to your engagement — not job titles, but named individuals with verifiable backgrounds. Request LinkedIn profiles for the architect and lead engineer. Confirm that the people presenting the proposal are the people doing the work. Bait-and-switch resourcing (senior talent on the pitch, junior talent on the engagement) is common enough in the genai consulting market that it should be a standard due diligence question.
Reference checks on comparable use cases. A firm with ten successful RAG implementations for legal document review is a better choice for your legal document review use case than a firm with fifty diverse AI projects and none matching your domain. Domain data familiarity — knowing the quirks of medical records versus financial filings versus customer support logs — shortens the data audit phase and improves prototype quality.
For a curated list of vetted firms with transparent capability data, the AI consulting projects hub is the right starting point. For a structured decision framework comparing firm tiers, specializations, and engagement models, the AI consulting firms buyer's guide provides detailed evaluation criteria.
Conclusion
A well-scoped genai consulting proof of concept is one of the most valuable investments an enterprise can make before committing to a production AI program. It is also one of the easiest engagements to get wrong — because the incentives of many consulting firms push toward impressive demos rather than honest feasibility assessments.
The markers of a credible engagement are straightforward: a six-to-eight week timeline with defined phases, a data audit in week one, specific success metrics agreed before the build begins, five clearly enumerated deliverables including a TCO model and production roadmap, and explicit failure conditions written into the contract.
Budget $50,000 to $150,000 for a US onshore engagement, $60,000 to $85,000 for a nearshore-blended team. Treat proposals below $50,000 with skepticism. Treat proposals above $150,000 that still use the word "PoC" with equal skepticism — you are being sold a pilot, and your expectations should adjust accordingly.
The 70% failure rate for genai poc engagements is not inevitable. It is the predictable outcome of underspecified contracts, skipped data audits, and firms that have no incentive to tell you the use case is not ready. Fixing that starts with knowing what to ask for before you sign.
Ready to compare specific firms for your GenAI PoC? Browse AI consulting firms or contact our research team for a free proposal review.