You said "we want AI." Here's the part nobody sells you.

A demo proves the model can do the task once. Production means evals, guardrails, monitoring, data pipelines, security and human oversight, the unglamorous 90 percent. Then inference compounds: a multi-step agent resends its whole context on every call, so one task can burn roughly 10 to 100 times the tokens of a single chat turn (Gartner puts it at 5 to 30 times per task). Unit prices keep dropping, but Bain found that as token prices halved, usage grew about 450 percent, so the total bill still climbs. Budget for the system, not the API call. Gartner predicted in 2024 that at least 30 percent of generative AI projects would be abandoned after proof of concept by the end of 2025, naming escalating cost as one reason.

Buy the commodity (transcription, generic chat, coding assistants); building it yourself is lighting money on fire. Build only where you have proprietary data or a workflow nobody else can copy, because that is the only place a custom system earns its keep. MIT's 2025 data is blunt on this: tools bought from specialist vendors or built through partnerships reached deployment about twice as often as tools built internally. And sometimes the right answer is wait: if your data is not ready, a pilot now just produces an expensive lesson you could have read here for free. Use the tool above to get the same verdict we would give you.

The demos work. What kills the project is the boring middle: messy data nobody wants to clean, integration into systems never designed for it, a workflow people will not actually adopt, and a last mile of evals and guardrails that turns a clever prototype into something you can trust in front of a customer. The numbers around this are stark and worth reading carefully. MIT's Project NANDA reported that only about 5 percent of integrated enterprise AI pilots showed measurable profit-and-loss impact (the widely quoted "95 percent failed" framing is contested, and applies most cleanly to custom internal builds). RAND estimates more than 80 percent of AI projects fail, roughly twice the rate of non-AI IT projects. McKinsey found 88 percent of organizations now use AI somewhere, yet only about 39 percent report any enterprise-level EBIT impact. When a vendor's plan skips the boring middle, that is the tell.

A good first project is almost boring: one well-chosen workflow, AI wired directly into the tools people already use, and a metric that existed before AI did (handling time, error rate, throughput). MIT's 2025 work found the 5 percent that captured real value did exactly this: they narrowed to one high-value workflow, customized deeply, and started at the edges before scaling into the core. McKinsey, testing 25 organizational attributes, found redesigning the workflow had the single biggest effect on whether gen AI moved EBIT, and that the clearest returns showed up in unglamorous back-office automation, not the flashy front office. The feedback such a project generates becomes proprietary data that makes the next version better. That compounding loop, not a launch, is what turns into an advantage.

A 20-person company that builds its own model is making an expensive mistake; the right move is to adopt good tools well and move on. A mid-sized firm earns a custom build only on the one workflow that is genuinely its edge. An enterprise needs the boring layer most vendors skip (governance, access control, a platform) before any single use-case is worth scaling. On timelines: Deloitte's 2025 survey found only 6 percent of organizations saw AI payback in under a year, with most reaching satisfactory ROI in two to four years, against the seven to twelve months typical of conventional tech. BCG found only 5 percent capturing value at scale and put about 70 percent of the gap on people, process and culture rather than the models. The real divide is leaders versus laggards, not company size.

A US-headquartered provider can be compelled to produce data regardless of where the server physically sits, under the CLOUD Act (2018, 18 U.S.C. 2713), so a datacenter in Frankfurt owned by a US company is not actually sovereign. US foreign-intelligence law (FISA Section 702 and Executive Order 12333) can reach non-US persons' data held by US providers with limited redress; Section 702's authority lapsed in mid-2026 but collection continues under court certifications into 2027. The legal ground keeps shifting: Schrems II (2020) invalidated Privacy Shield, and the 2023 EU-US Data Privacy Framework was upheld by the EU General Court in 2025 but is now under appeal at the Court of Justice. Real sovereignty means the operator, the hardware and the jurisdiction are all European. We run our own infrastructure on EU soil with no US middlemen in the data path. If your data is regulated, privileged, or a genuine trade secret, this is the difference your legal team flagged.

Be suspicious of "AI strategy" with no implementation attached, of fine-tuning pitched when a prompt would do, and of rip-and-replace where integration would do. Gartner calls it "agent washing": vendors rebranding chatbots and old automation as "agentic." Andreessen Horowitz, an AI investor with every reason to talk it up, argues the opposite is true at the margin: AI gross margins often run 50 to 60 percent against 60 to 80 percent for comparable software, and the model "may largely be a pass-through" to the underlying product and data. Benedict Evans puts it plainly: frontier models are converging, so the moat is not the model but how you use it. What is actually defensible is unglamorous: proprietary data and the feedback loops that improve it, deep integration into how work happens, switching costs, and a quality system you own. The model is rented.

We start with a scoping pass: what data, what jurisdiction, what actually needs to be true for this to pay off. We build the narrow, integrated version that ships, run it on infrastructure we operate on EU soil, and own the loop with you. And when the honest answer is that you should buy a tool or wait six months, we say that. That is the whole point of this page.

Nobody is exactly lying. "Deployed in weeks" means the demo. "It learns your business" means retrieval over the documents you upload. "Enterprise-ready" means SSO and a log page. "99 percent accurate" means on their benchmark, not on your data. The account executive is not the person who will wire the system into your ERP, clean ten years of your files, or sit in the eval meetings, and by the time the gap surfaces the commission has cleared. AI made this old software pattern worse, because generative systems are at their most impressive in the first five minutes and at their most fragile in month three. Two habits protect you. First, run every phrase in the deck through the translator above. Second, do the thing a sales team cannot fake: ask for the engineer who will own your integration to join the next call, and ask them what will be hard. If no engineer shows up before the contract does, that is your answer.

Predicting model capability is guesswork, so this page does not try; the LIVE strip below tracks the fast-moving part. What can be forecast is structure. One: unit prices keep falling while agent workloads multiply token use 10 to 100 times per task, so budgets follow usage, not price lists; run your own numbers in the calculator above. Two: frontier models keep converging, which means whatever advantage you rent, your competitor can rent next quarter; only the data, evals and integration you own compound. Three: regulation arrives on a schedule, not a vibe. The EU AI Act phases in obligations through 2027, and the EU-US data framework is again being tested in court, so where your systems run stops being an IT detail and becomes board-level risk. Four: the winner gap widens. Adoption is already near universal, and the roughly 5 percent who capture value at scale are compounding while everyone else pilots. Waiting for the model that makes it easy is not a strategy; the bottleneck is your data and your workflow, and those do not improve on their own.