The demonstration described by Adsmurai moved from a generated video to an advertisement created in Meta. Its July 6, 2026 release presents an MCP connection with 287 tools in Adsmurai Marketing Platform, AMP. The commercial step is allowing the conversation analysing a campaign to reach the objects executing it.
The provider reports a Madrid session the preceding week, without publishing expenditure, sales or a measured labour saving. The demonstration establishes the scope the company communicates; it does not turn creating an advertisement into an assessment of its profitability. The economic test begins when the correct object is ready for its intended use and the decision produces a result that can be examined.
Businesses are trying different places to incorporate AI
A Deloitte survey reported by Statista puts public language models at 62% in a sample of 100 technology and line-of-business leaders in Spain. Productivity tools account for 60% and corporate platforms 49%. Fieldwork took place from July to September 2024. These are overlapping uses within a leadership sample, rather than a representative adoption rate across all Spanish companies.
Spain: generative AI uses reported by business leaders
100 technology and business leaders in Spain, July–September 2024. Organizations using AI daily and running generative-AI projects.
Tap or hover for details. All values are also available in the data table.
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Deloitte / Statista. Selected leaders at organizations using AI daily and running generative-AI pilots or daily implementations, rather than all Spanish businesses. Uses overlap and must not be added. Original methodology, p. 29, confirms eligibility. This does not measure savings, output quality or the analyzed provider’s customers.
Explore the data table
| Category | Reported use |
|---|---|
| Public language models | 62.0% |
| Productivity tools | 60.0% |
| Generative AI applications | 56.0% |
| Corporate platforms | 49.0% |
| Code generation | 31.0% |
The comparison shows several entry points without establishing that connecting them automatically produces value. An assistant can help draft; a corporate platform retains business information; an operating tool changes an asset. Buying a connection becomes more specific when it defines the transition the account requires, rather than selling the mere proximity of those capabilities.
AMP needs to preserve evidence of what actually changed
Adsmurai’s MCP product page keeps AMP as the operational centre and describes tool selection under each user’s existing permissions. Account identity remains consequential. A reasonable recommendation applied to another brand, market or campaign produces an incorrect operation even when the original calculation was sound.
Reading a report and changing a budget have different consequences. The first supplies evidence; the second changes a commercial decision. A useful record preserves which information motivated the action, which object received it and which result the system returned. A persuasive assistant response does not replace the campaign state retained in AMP.
The same applies to multi-step work. Creative may have been created while its association with the campaign remains pending. Executing the entire request again without checking can duplicate completed work. Operational confidence depends on recognising the confirmed state of each part, as well as understanding the initial intention.
Public confidence introduces another condition for the offer
AIMC’s Navegantes en la Red study, reported by Statista, supplies a different reference. Among participants who had heard of AI, 77.1% fairly or strongly agreed that work done with it should be identifiable. A 32.6% share expressed that level of trust in companies using it ethically. Fieldwork ran from Oct. 15 to Dec. 9, 2024. The 14,735 useful responses describe the complete study rather than the verified size of that question. The online sample was weighted to EGM data, but respondents were more active online than the general internet population. This is not an assessment of Adsmurai.
AIMC: expectations of identification and trust in AI
Participants who had heard of AI. Spain, Oct. 15 to Dec. 9, 2024.
Tap or hover for details. All values are also available in the data table.
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AIMC / Statista. The question concerns the subgroup who had heard of AI; its exact size is not published. The complete study has 14,735 useful responses from internet users aged 14 or over. Online questionnaire weighted to EGM; respondents are more active online than the general internet population. One agreement category is preserved without normalising others. This does not measure product customers, autonomous shopping or commercial outcomes.
Explore the data table
| Category | Fairly or strongly agreed |
|---|---|
| AI should be identifiable | 77.1% |
| AI develops faster than society can absorb | 72.7% |
| Trust in companies using AI ethically | 32.6% |
For a provider enabling campaign actions, the practical question is making the decision maker and recorded operation understandable. The percentages do not prescribe an interface or certify a secure implementation. They do help separate technology use from confidence: executing more tasks does not itself explain how to recognise their effects and responsibilities.
Savings need to be counted through to completed operation
Removing navigation between screens can save work. Additional review, context correction or recovery of incomplete actions can also appear. Evaluation needs to follow the task from preparation to an accepted operation, including that effort. Counting invocations or available tools does not establish how much work became usable.
Adsmurai’s proposal connects interpretation and operation on existing infrastructure. Its most defensible advantage will be an account where the transition from evidence to action can be reconstructed without losing permissions, current offer conditions or confirmed state. The budget and recorded asset remain the decisive evidence of what happened. The next assignment deserves expanding when that continuity completes usable commercial work, beyond making it quicker to request.
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Marian Vasile
Marian Vasile is a MarTech and growth professional. His work spans product marketing, SEO, paid advertising and deep online tracking, including behavioral analytics and attribution.