The study "The ROI of Gen AI and Agents 2026" from Snowflake/Omdia sounds like a blueprint for AI success: 49% return, 92% success rate, Germany among the top-3 global adopters. But anyone who reads the methodology notices: only enterprises with 500+ employees were surveyed — not the 99.5% of German companies that qualify as SMBs under the EU definition. This article puts the Snowflake numbers in context and shows which insights actually translate to smaller organizations.
The Snowflake Study at a Glance ▲
The study was fielded between August 13 and September 17, 2025, and published in March 2026. 2,050 business and IT leaders across nine global regions were surveyed. The key findings:
| Metric | 2026 Value | Prior Year 2025 | Context |
|---|---|---|---|
| Average ROI | 49% ($1.49 per $1) | 41% ($1.41) | +20% YoY, but different research firms (Omdia 2026 vs. ESG 2025) |
| Early adopters with positive ROI | 92% | >90% | Self-selected sample, not representative of the overall market |
| Agentic AI in production | 32% | N/A | Another 25% plan to roll it out within 12 months |
| Expected failure rate (36 months) | 41% | N/A | Respondents' self-forecast, not a measured historical rate |
| Budget overrun | 60% | N/A | Primarily storage and compute costs |
| Shadow AI | 57% | N/A | Ungoverned AI usage within the organization |
Core answer: The numbers look impressive — but they only tell half the story. The decisive question isn't what 2,050 corporate managers say. The question is what applies to a trades business with 12 employees or a §34d advisor with 20 employees.
Who was surveyed — and who wasn't? ▲
Minimum company size: 500 employees. According to the study's methodology section (page 40), only organizations with 500+ employees were included:
- 34% with over 5,000 employees (enterprise)
- 49% between 1,000 and 4,999 employees
- 17% between 500 and 999 employees (labeled "midmarket" by Snowflake)
For the DACH region, that means: under the EU definition, companies with fewer than 250 employees count as SMBs. In Germany, under § 267 HGB, businesses with 250+ employees are already classified as "large corporations." The category Snowflake labels "midmarket" (500–999 employees) would long since be classified as a large enterprise here.
Specifically excluded: trades businesses, regional financial advisory firms with 10–50 employees, owner-run manufacturing companies, municipal administrations, and the entire breadth of the German SMB market under 500 employees — that is, 99.5% of all German companies.
Sample bias: the companies surveyed typically have dedicated IT departments, data engineering teams, and already-implemented cloud infrastructure. Many are likely Snowflake customers or operate within the data warehouse ecosystem. The 92% success rate reflects this pre-selected environment — not the overall landscape of German companies.
DACH limitation: the study captured Germany with 7% of the sample (about 144 respondents); Austria and Switzerland are missing entirely. A genuine "DACH ROI" can't be derived from this data.
A 15-person trades business in Thuringia faces completely different challenges than a 5,000-employee software corporation in Munich — the Snowflake numbers simply don't capture the former group. Comparable studies that actually include SMBs regularly arrive at markedly more sobering results: higher failure rates, longer payback periods, lower adoption.
Similar sample effects also show up in the Anthropic Economic Index: it surveyed ~9,700 Claude power users — likewise no representative cross-section of the SMB market.
41% Failure Rate: Portfolio Bet vs. SMB Reality ▲
The study contains a remarkable self-assessment: 41% of the agentic AI projects launched over the next 36 months are forecast by respondents to fail. That's four out of ten initiatives.
Snowflake frames this in its companion blog as "acceptable iterations" — acceptable within the scope of experimental technology. That framing works for enterprises running parallel AI portfolios: if ten agents are launched and six of them succeed, the bottom line is still a positive outcome.
Different standards apply for SMBs. A trades business with 20 employees can't afford to launch four projects hoping that 2.4 of them work out. Every failed project ties up budget, management capacity, and creates change fatigue on the team. The "portfolio logic" of large organizations — diversify, experiment, discard quickly — isn't a workable path for smaller units.
Important: the 41% is a forecast by respondents for the coming three years, not a measured historical failure rate. The report's own wording (page 18): "They estimate that 41% of agentic initiatives launched over the next 36 months will ultimately fail."
What matters: even the most optimistic early adopters expect considerable attrition. Snowflake's message that "92% succeed" and its simultaneous expectation that "41% will fail" only appear contradictory at first glance — the former figure refers to past projects within a pre-selected sample, the latter to future initiatives across a broader rollout.
The consequence for SMBs: instead of portfolio bets, focused, well-prepared single projects with a clear vertical fit. A financial advisory firm doesn't need a generic "AI agent" — it needs a phone agent for §34d-compliant initial qualification. A trades business doesn't need a chatbot for "everything" — it needs an emergency answering system that qualifies and routes jobs outside business hours.
Germany in International Comparison ▲
Germany plays a special role in the study — with some surprisingly progressive numbers:
- 50% of German respondents use gen AI across many use cases (vs. 38% globally) → top-3 adoption alongside the US and India
- 69% have deployed agentic AI or definitely plan to in the next 12 months (vs. 57% globally)
- 58% report additional jobs from AI automation (vs. 42% globally — the highest rate in the panel)
- 45% see vendor solutions maturing quickly (vs. 23% globally)
At the same time, German respondents report concerns well below average: 36% cite data quality as a challenge (vs. 50% globally), 27% see a skills shortage (vs. 36% globally).
Interpretation: large German companies (reminder: 500+ employees) appear better prepared — likely due to higher regulatory demands (GDPR, NIS2, the upcoming EU AI Act) and a traditionally strong engineering culture. The high job-creation rate might also suggest that German firms deploy AI primarily as augmentation (humans + AI) rather than substitution.
Quote, Benoit Dageville, Snowflake co-founder: "AI has arrived so quickly that we don't yet know how the world is going to rebalance or reorganize itself."
But: these numbers apply at the enterprise level. A regional financial advisory firm with 25 employees faces different realities: less data-engineering capacity, no dedicated AI team, often still on-premise legacy systems. The SMB-specific challenges in Germany — §34d compliance in financial services, the skilled-labor shortage in the trades, data-privacy sensitivity — aren't reflected in the study. Especially for smaller organizations: don't blindly copy enterprise playbooks — adapt selectively.
60% Budget Overrun: What It Means for SMBs ▲
60% of respondents report budget overruns — primarily from unexpected storage and compute costs. Cloud-based AI scenarios generate ongoing costs for data storage, model inference, API calls, and embedding generation that are difficult to forecast.
For enterprises with multi-million-euro IT budgets, a 20% overrun might be annoying but manageable. For a trades business with a €5,000 annual budget for "digital," an unexpected doubling can feel existential.
The typical cost traps:
- Token-based billing: Claude, GPT-4, and Gemini charge per token processed. A "simple" agent that handles 100 calls a day and analyzes transcripts can quickly cost €500–1,000/month — an unpleasant surprise in Q2 if it's not budgeted for.
- Embedding storage: RAG systems (retrieval-augmented generation) require vector databases. 10,000 documents at 2,000 tokens each mean storage and indexing costs that add up month over month.
- Unexpected scaling: a successful agent gets used more heavily than planned — more calls, more data, more cost.
Fixed-price models as SMB protection: instead of usage-based cloud billing, specialized providers offer flat-rate packages for SMBs. Example: €990 setup + €149/month for a phone agent with a defined call volume. The budget stays predictable — no surprises on the monitoring dashboard.
Shadow AI makes the problem worse: 57% report ungoverned AI usage within the organization, according to the study. Employees use ChatGPT Plus accounts on their own initiative, upload sensitive data to Claude, or build "quick and dirty" solutions with free tools. For trades and manufacturing that may be a smaller risk — for financial advisors with §34d licensing and IDD obligations, shadow AI is a compliance nightmare. A single advisor analyzing client data through a public LLM can jeopardize the entire broker license.
More on the compliance architecture: AI Architecture for German SMBs — The 5 Layers of Robust Systems
What SMBs Can Learn From the Study ▲
The Snowflake numbers aren't worthless — they just need to be put in the right context. Three transferable insights for SMBs:
1. Focus beats breadth
The most successful projects in the study were vertically specific: financial services focused on analytics and strategic recommendations (60% primary goal for agentic AI), manufacturing on predictive maintenance, retail on personalization. No successful adopter built "the universal AI assistant."
For SMBs: better one agent that does one thing excellently than three agents doing everything mediocre. A trades business needs an emergency-call qualifier, not a "digital all-rounder." A financial advisory firm needs a §34d-compliant lead qualifier, not an "advisory chatbot for everything."
2. Data readiness is the bottleneck
Only 20% of unstructured and 32% of structured data is "AI-ready," according to the study. 40% of respondents cite data quality as the biggest hurdle. That holds at small scale too: an agent for quote generation needs structured product data, price lists, template text. Without them, the agent becomes a source of errors instead of relief.
Before agent deployment: a data audit. What information does the agent need? What format is it in? Who maintains it? A two-day data-review workshop saves months of frustrating "why doesn't the agent understand this?" debugging.
3. Fixed price beats portfolio experiment
The 41% failure forecast reflects enterprise logic: many parallel bets, some fail, the overall portfolio stays profitable. SMBs need the reverse approach: one project, well prepared, with a clear ROI case and limited risk.
Concretely: instead of "let's just try AI," pick a use case with a 3-month pilot: a clear goal (e.g., "20% fewer missed calls outside business hours"), measurable success, a defined budget. If the KPIs aren't right after three months, it gets stopped — but with insights, not flying blind.
Bottom line: the Snowflake study proves that AI works for well-prepared, large organizations. It doesn't prove that the same approaches transfer 1:1 to a 30-person manufacturing business or a 12-person financial advisory firm. But it does show the levers that matter: focus, data quality, budget control, governance. Anyone who sets these levers correctly at SMB scale can benefit from AI — without portfolio bets and without nasty surprises in the invoice inbox.
More on the multi-model strategy: Anthropic Export Restriction: Why SMB AI Now Needs a Multi-Model Approach
FAQ ▲
Are the 49% ROI figures from the Snowflake study applicable to German SMBs?
No. The study surveyed exclusively companies with 500+ employees and mature IT infrastructure. SMBs under 250 employees — 99.5% of German companies — weren't covered. The numbers reflect enterprise reality, not SMB reality.
What does the 41% failure rate actually mean?
It's a self-forecast by respondents for the next 36 months, not a measured historical rate. Early adopters expect four out of ten planned agentic AI projects to fail. Snowflake frames this as "acceptable iterations" within portfolio experiments — an approach that doesn't hold up for SMBs with limited budgets.
Why is the study relevant anyway?
It shows the levers that matter: vertical focus beats generalist approaches, data quality is the bottleneck, budget overruns are the norm with cloud billing, and shadow AI is a governance risk. These insights hold at a smaller scale too — once translated to SMB size.
What should SMBs do instead?
Focused single projects with a clear use case (e.g., an emergency-call agent for trades businesses, a lead qualifier for financial advisors), an upfront data audit, fixed-price models instead of usage-based cloud billing, and a governance layer from day one — especially in regulated industries like financial services.
Does the study apply to DACH?
Only for Germany, and even then with caveats: 7% of the sample (about 144 respondents), all from companies with 500+ employees. Austria and Switzerland are missing entirely. A genuine "DACH ROI" can't be derived from this basis.
How current is the data?
Fielding period: August 13 to September 17, 2025. Events after that period (e.g., the OpenAI DeployCo announcement, Anthropic's export restriction in June 2026, new Gemini market-share figures) aren't reflected. The report's "2026" title is marketing, not the fielding date.