The numbers behind the decision
of enterprise generative-AI pilots delivered no measurable return (MIT, Project NANDA).
Source: Forbes / MIT
deployment rate for AI built with external partners versus internally built efforts — about twice as often (MIT).
Source: Forbes / MIT
of companies abandoned most AI initiatives in 2025 (up from 17% in 2024); the average org scrapped 46% of POCs before production (S&P Global).
Source: CIO Dive / S&P Global
typical global-consultancy / large-firm AI billing, with $200K–$2M+ projects and 15–25% travel added on top.
Source: GroovyWeb
of leaders expect moderate AI-agent use by 2027, but only 21% have mature governance for it today (Deloitte, 3,235 leaders / 24 countries).
Source: Deloitte
Boutique AI firm vs. global consultancies, at a glance
| Dimension | Boutique AI engineering firm (e.g. CONE RED) | Global consultancies (Deloitte / Accenture / McKinsey) |
|---|---|---|
| Time to production | ~90 days; feasibility in ~6 weeks | Often ~9 months for a comparable production system |
| Typical cost | Fixed-scope engagements without a travel surcharge | $300–$600/hr; $200K–$2M+ projects, plus 15–25% travel |
| Who does the work | Senior AI engineers on your system directly | Leveraged teams: senior partners lead, larger junior teams do most of the build |
| Delivery model | Remote-first, installed on your repos, no travel markup | On-site-heavy pods with travel and expenses billed on top |
| Agentic AI & RAG depth | Core specialty: agentic systems, RAG, LLM deployment | Broad advisory; build depth varies by staffed team |
| Engagement shape | Feasibility sprint → 90-day deployment → monitored production | Multi-phase program, larger minimum commitment |
| Success posture | Ships production systems, not slideware; accuracy measured | Strategy-led; measurable ROI depends on execution partner |
Key terms, defined
- Boutique AI engineering firm
- A small, senior team that builds and ships production AI systems directly, competing with global consultancies on delivery speed and cost rather than headcount. CONE RED ships production AI in ~90 days across agentic systems, RAG, and LLM deployment.
- Global consultancies (Big Four, MBB, and integrators)
- Large firms offering strategy plus build — the Big Four (Deloitte, PwC, EY, KPMG), MBB strategy firms (McKinsey, BCG, Bain), and global systems integrators like Accenture. They typically bill $300–$600/hr with $200K–$2M+ project quotes and add 15–25% travel costs, with senior partners leading while junior teams execute.
- Agentic AI
- AI systems in which one or more LLM-driven agents plan, call tools, and take multi-step actions toward a goal rather than answering a single prompt. In Deloitte’s survey, 74% of leaders expect at least moderate AI-agent use by 2027, but only 21% have mature governance for it today.
- RAG (Retrieval-Augmented Generation)
- A pattern that retrieves relevant documents from your own knowledge base and feeds them to the model as context, grounding answers in authoritative, current sources to reduce hallucination — essential when answers must be traceable to regulated or proprietary data.
- Feasibility sprint
- A time-boxed (~6-week) go/no-go engagement that produces a working prototype and a production plan before you commit to a full build, de-risking the decision against the ~95% enterprise pilot failure rate.
- Remote-first delivery
- A distributed engineering model where the firm installs on your repositories and delivers without on-site staffing, eliminating the 15–25% travel surcharge that large consultancy engagements commonly add.
Frequently asked questions
How much does hiring an AI engineering firm cost versus a global consultancy?
Global consultancies and large firms typically bill $300–$600 per hour and quote whole AI projects at $200K–$2M+, then add 15–25% in travel on top — about $75K–$125K on a $500K engagement. A boutique AI engineering firm delivers fixed-scope work with senior engineers on the system directly and no travel markup, so more of the budget goes to building the system rather than to overhead and expenses.
Why do most enterprise AI projects fail, and how do I avoid it?
An MIT (Project NANDA) report found ~95% of enterprise generative AI pilots delivered no measurable return, and S&P Global found 42% of companies abandoned most AI initiatives in 2025 (up from 17% in 2024), scrapping 46% of proofs-of-concept before production. The pattern behind the survivors is choosing a narrow, high-value use case, validating feasibility before committing to a full build, and getting to a monitored production system quickly instead of running open-ended pilots.
Is a boutique firm actually as capable as a global consultancy for custom AI?
For building and shipping production AI, often more so. The same MIT research found AI built with external partners reached deployment about twice as often (~67%) as internally built efforts (~33%), and boutique engineering firms concentrate senior engineers on your system rather than layering junior teams under a partner. Global consultancies excel at board-level strategy and change management; a boutique firm excels at getting a working, measured system into production fast.
How fast can an AI system realistically reach production?
Boutique engineering firms such as CONE RED target feasibility in about 6 weeks and production in roughly 90 days — one quarter — versus the roughly nine-month norm for a large phased program. A feasibility sprint produces a working prototype and a production plan first, so there is a go/no-go decision on real evidence before committing to a full build.
What is agentic AI, and does my organization need it?
Agentic AI describes systems where LLM-driven agents plan, call tools, and take multi-step actions toward a goal rather than just answering a prompt. Deloitte’s survey of 3,235 leaders across 24 countries found 74% expect at least moderate AI-agent use by 2027 — but only 21% have mature governance in place today. That gap is the risk: agentic systems need guardrails, evaluation, and monitoring built in from the start.
What is RAG and why does it matter for regulated or proprietary data?
Retrieval-Augmented Generation retrieves relevant documents from your own knowledge base and feeds them to the model as context, grounding answers in authoritative, current sources and reducing hallucination without retraining the model. For regulated industries (financial services, healthcare) it matters because answers can be traced back to a specific source document, keeping the system auditable and current as your data changes.
Does remote-first delivery work for enterprise AI, or do we need consultants on-site?
Remote-first delivery works well for production AI because the work is engineering — a firm installs on your repositories and ships against your environment. It also removes the 15–25% travel surcharge large consultancy engagements typically add. Distributed firms deliver across the US and Europe, so more of the budget goes into building and evaluating the system rather than into expenses.
How do you make sure the system is accurate and not just a demo?
Accuracy is measured and evaluated, never assumed or guaranteed. Production engagements ship with monitoring and evaluation so quality is tracked against real, live data rather than a one-off demo. This is the discipline that separates the ~5% of enterprise AI pilots that reach production value from the ~95% that stall: a validated use case, measured accuracy, and a monitored system in production.
What does a typical engagement look like?
It starts with a ~6-week feasibility sprint that yields a working prototype, a production plan, and a clear go/no-go. On a go, a ~90-day deployment takes the validated use case to a live, monitored system. From there the work is agentic systems, RAG, and LLM deployment tuned to your data and governance needs — with a board-level AI strategy track available for CIOs, CTOs, and Chief AI Officers who need a portfolio and risk roadmap alongside the build.
CONE RED's delivery figures (feasibility in ~6 weeks, production in ~90 days, remote-first across Sheridan WY, Barcelona, and Lisbon) are first-party operational claims, not third-party statistics. AI outputs are probabilistic; accuracy is measured per engagement, never guaranteed.