<\/span><\/h2>\n\n\n\nThe word \u201cgovernance\u201d may conjure images of large corporations, regulations, and committees, but the concept is much simpler and applies to any medium-sized company as well.<\/p>\n\n\n\n
Governance of AI means being able to answer very specific questions<\/strong>: who can use it, what it can be used for, what information it can provide, what sources we consider valid, what decisions it can recommend, which ones it can carry out, when it needs authorization, how we can verify what it has done, and who remains responsible when something goes wrong.<\/p>\n\n\n\nIt's not about monitoring every prompt a worker writes. The goal is to establish some ground rules<\/strong> before AI is integrated into so many processes that it becomes difficult to tell where its involvement begins and ends.<\/p>\n\n\n\nAnd here, governance ceases to be a bureaucratic layer that we add after implementing the technology. It should be part of the design from the very beginning.<\/strong><\/p>\n\n\n\n<\/span>Governance does not mean holding back AI<\/span><\/h3>\n\n\n\nA company may view governance as a set of prohibitions: don't use this tool, don't enter that data, don't automate that. That would be a mistake.<\/p>\n\n\n\n
Good governance should, in fact, make it possible to use AI more in areas where it adds value, because we know what its limitations are<\/strong>. It's similar to any other business process. Having permits, designated personnel, controls, and procedures doesn't get in the way of work; it allows you to do it with less uncertainty.<\/p>\n\n\n\n<\/span>Implementing AI also requires figuring out how the company really works <\/span><\/h2>\n\n\n\nWhen we try to explain to a machine how to correctly perform a task, another reality quickly emerges: Many processes are not actually documented<\/strong>. They're in the minds of certain people.<\/p>\n\n\n\nAn experienced professional knows that before making changes to a campaign, it\u2019s a good idea to check certain metrics; that a specific source isn\u2019t reliable; that there\u2019s an exception for a certain type of customer; or that a seemingly simple decision requires checking with someone else first. That a series of small decisions<\/strong> It constitutes a very important part of business knowledge.<\/p>\n\n\n\nThat is why the implementation of AI may have an unexpectedly positive consequence: it may force us to convert tacit knowledge into organized knowledge<\/strong>. To achieve this, we must answer questions that we may never have explicitly asked ourselves before: how do we actually carry out a task, what do we always check, what distinguishes good work from mediocre work, which decisions require authorization, and which mistakes are acceptable and which are not.<\/p>\n\n\n\nAI may require us to document our criteria before attempting to automate them<\/strong>.<\/p>\n\n\n\n<\/span>A medium-sized company doesn't need to start with the tool <\/span><\/h2>\n\n\n\nOne of the most common mistakes is to start the conversation with \u00abWhat AI tool should we use?\u00bb Before selecting a technology, you should Identify where there is a real opportunity and what the risk is if you're wrong.<\/strong><\/p>\n\n\n\nOne simple way to do this is to evaluate each process using three variables:<\/strong> how much work a task requires, how much value AI can add, and what the consequences of an error would be.<\/p>\n\n\n\n<\/span>Where would a medium-sized company start tomorrow?<\/span><\/h3>\n\n\n\nThere's no need to immediately set up an AI committee or write a 100-page manual. But you do need to bring some order to the process. Here are eight specific steps:<\/p>\n\n\n\n
\nFind out what's happening right now.<\/strong> A simple inventory of tools, departments, uses, and types of information employed. Before designing a strategy, it is important to understand actual usage, including any that has emerged informally.<\/li>\n\n\n\nSet a few red lines.<\/strong> Information that cannot be entered into unauthorized tools, actions that AI cannot perform on its own, and decisions that always require a person.<\/li>\n\n\n\nChoose between three and five low-risk, time-consuming processes.<\/strong> It's better to learn about specific processes than to try to \u00abimplement AI across the entire company.\u00bb.<\/li>\n\n\n\nDocument how to do the job right.<\/strong> Before automating a process, define which sources it uses, what steps it should follow, what a professional checks, and what exceptions exist.<\/li>\n\n\n\nAssign a person in charge.<\/strong> Every relevant use case should have a process owner\u2014not necessarily a technical person, but someone capable of determining whether the result makes sense.<\/li>\n\n\n\nGrant permissions gradually.<\/strong> First, read and analyze. Next, make recommendations. Then, develop a plan of action. Only when there is sufficient trust should you allow autonomous execution within clear boundaries.<\/li>\n\n\n\nMeasure productivity as well as errors.<\/strong> Time saved, quality, and results\u2014but also necessary corrections, incidents, and situations in which human oversight prevented a problem.<\/li>\n\n\n\nCheck the model periodically.<\/strong> Capabilities are changing too quickly to consider any definitive policy. What we reject today may very well be safe a year from now.<\/li>\n<\/ol>\n\n\n\nThis makes implementation a gradual process: observe > test > measure > document > increase autonomy<\/strong>. Not in a race to integrate AI into the entire company.<\/p>\n\n\n\n<\/span>Three Questions to Ask Before Granting Autonomy to AI<\/span><\/h3>\n\n\n\nBefore allowing an agent to act independently in a process, it is important to be able to clearly answer three questions:<\/p>\n\n\n\n
\nWhat happens if you make a mistake?<\/strong> Determine the actual level of risk. An error in an internal report and an error in a transfer or a message to a customer do not have the same impact.<\/li>\n\n\n\nHow will we know that he made a mistake?<\/strong> Determine the level of oversight. If there is no clear way to detect the error, the AI should not act autonomously in that process.<\/li>\n\n\n\nWho will be responsible when it happens?<\/strong> Determine accountability. If no one is in charge of the process, there is no guarantee of effective oversight: AI does not assume responsibility, but people do.<\/li>\n<\/ul>\n\n\n\nIf a company cannot clearly answer all three questions, it probably should not yet grant autonomy to AI in that process.<\/p>\n\n\n\n
<\/span>And what would I reject right off the bat? <\/span><\/h3>\n\n\n\nIt may also be helpful to establish certain decisions that should immediately trigger a<\/em> warning sign<\/em><\/strong>:<\/em><\/p>\n\n\n\n\nAutomate a process that we don't yet fully understand.<\/em><\/li>\n\n\n\nConnecting sensitive information to tools whose data processing practices we are unaware of.<\/em><\/li>\n\n\n\nAllow irreversible actions without approval.<\/em><\/li>\n\n\n\nUsing results that no one within the company knows how to verify.<\/em><\/li>\n\n\n\nDelegate important decisions simply because the system is usually right.<\/em><\/li>\n\n\n\nImplementing AI simply because a new feature exists, without having identified the problem it solves.<\/em><\/li>\n<\/ul>\n\n\n\nThere is a<\/em> a particularly useful rule<\/em><\/strong>: \u00abIf we can\u2019t explain how a task should be done correctly without AI, we\u2019re probably not ready to automate it with AI yet.\u00bb<\/em><\/p>\n\n\n\n<\/span>The future may not lie in using more AI, but in better designing how it works with us <\/span><\/h2>\n\n\n\nAs of September 2026, we are still in a early and rapidly changing stage<\/strong>. We're now talking about prompts, agents, co-pilots, and different models because they are some of the tools and architectures available. It's quite likely that in a few years, some of this terminology will have become outdated.<\/p>\n\n\n\nWe may end up with systems connected to virtually the entire organization that are capable of continuously understanding the business context. Professionals may emerge who specialize in designing the methods, boundaries, and criteria by which these systems operate. Most likely, both of these developments will evolve simultaneously.<\/p>\n\n\n\n
What The need to decide what information we provide is unlikely to disappear<\/strong>, what level of autonomy we grant, what results we verify, and who is responsible.<\/p>\n\n\n\nThat's why perhaps the competitive advantage doesn't lie in becoming the company that automates the most. It will lie in to become the company that knows best what to automate<\/strong>, what knowledge to transfer to the machine and which decisions it wants to keep in human hands.<\/p>\n\n\n\nArtificial intelligence can help us work faster, analyze more information, and carry out processes that until recently seemed impossible to automate. But as its capabilities grow, so too must our ability to manage it.<\/p>\n\n\n\n
Because we can delegate work, streamline processes, and integrate AI with virtually every aspect of our business. What What we cannot delegate is the responsibility to understand what he is doing and why<\/strong>.<\/p>","protected":false},"excerpt":{"rendered":"The question is no longer what AI can do, but what we\u2019re willing to let it do. A few days ago, we discussed which sectors are leading the adoption of agent-based AI and where we\u2019re headed by 2027. Now it\u2019s time to take it a step further. AI is already part of the daily work of many companies, and with the arrival of agents capable of accessing systems, chaining together actions, and executing tasks, the need to set limits is also growing. This is where AI governance comes into play: the set of criteria, rules, and controls that determine how artificial intelligence can be used within an organization, what decisions it can make, and which ones must remain under human supervision. Because the greater the autonomy we grant AI, the more important it is to know what it\u2019s doing, what information it\u2019s using, and how far we\u2019re willing to let it go. What AI Is Already Doing in Businesses Marketing is one of the areas where this transformation is happening most rapidly. Salesforce reports that 75 % of the professionals surveyed already use AI. The potential is clear: AI can generate twenty newsletter topics, ten versions of a landing page, fifty ads, or a hundred social media adaptations at virtually no marginal cost. However, 84 % of those same professionals admit to continuing to develop generic campaigns. Issues with data quality, fragmentation, and availability remain the main barrier to achieving the personalization that the technology promises. AI has greatly reduced the cost of producing content. It hasn\u2019t reduced the cost of having something interesting to say. And the more accessible content generation becomes, the more valuable the assets that aren\u2019t available to everyone will be: personal experience, proprietary data, real-world case studies, customer insight, judgment, methodology, reputation, and the ability to connect the dots. From the Prompt to the Method: How AI Begins to Work in Your Company During these early years, we\u2019ve devoted a great deal of attention to learning how to ask AI for things: how to write better prompts, how to provide context, or how to get more accurate answers. The prompt was the first way we interacted with it: a direct, one-off instruction to obtain a specific response. Useful, but limited. Every time you started a conversation, you had to start from scratch. The next step came in October 2025 with Skills: reusable sets of instructions that you define once for a specific type of task, and the AI applies them continuously without having to repeat them. It\u2019s not a prompt; it\u2019s the criteria that remain constant. A skill can define how a campaign should be analyzed, what steps to follow, which sources to consider valid, what exceptions exist, or which decisions require human approval. It\u2019s configured once and always works the same way, with your company\u2019s logic built right in. The challenge now is to go further: to get the AI to operate in accordance with the way an entire organization works. Let\u2019s consider a Google Ads analysis. It\u2019s not enough to simply say, \u00abAnalyze this campaign.\u00bb A company may want any analysis to first check the quality of tracking, validate the comparison periods, review spend, conversions, CPA, or ROAS, search terms, historical changes, promotions, and seasonality\u2014always distinguishing facts from hypotheses. That\u2019s not a prompt\u2014or even a standalone skill. It\u2019s that company\u2019s way of working, transferred to the machine. We don\u2019t know exactly what form this will take in the coming years. What does seem clear is the direction: every company needs AI to be an integral part of it, with its own knowledge, rules, processes, and limits. A generic AI that doesn\u2019t understand how your company operates can generate a lot of volume. One that\u2019s integrated into your way of working can deliver real value. And that difference isn\u2019t created by the technology alone; it\u2019s built by each organization.\u00a0 It\u2019s likely that within a few years this will even give rise to a new professional specialty: people whose role isn\u2019t just to know how to use AI, but to design how AI should function within a company. Leveraging Your Own Context as a Competitive Advantage Salesforce has identified a significant difference between teams that simply use AI and those capable of feeding it connected business data. Marketing teams with unified data are 2.4 times more likely to rank among the top-performing professionals and are 60% more likely to use agents to scale their operations. It\u2019s not just a matter of technology. An AI that doesn\u2019t know who the customer is, what they\u2019ve purchased, what problems they\u2019ve encountered, what products are available, which ones generate profit margins, or what the business strategy is can automate a lot but contribute little. If we all have access to similar AI models, a growing part of the competitive advantage will lie in the context that each company is able to provide them. CRM, documentation, historical records, customer data, and accumulated experience can become the true raw material of enterprise artificial intelligence. It\u2019s also important to keep in mind what happens to the information we provide to these systems. Some language models may use conversations to improve future versions, unless corporate settings prevent it. A company that systematically enters strategic information into free tools without reviewing their terms of use is unwittingly making a decision about the ownership of its own knowledge. Do we use it to think\u2014or to avoid thinking? AI eliminates friction. Research that used to take several hours can now be started in minutes. It can summarize hundreds of pages, compare competitors, analyze thousands of customer reviews, or propose ten different scenarios almost instantly. The advantage is clear, but it also creates a temptation: to stop researching because asking is so much faster. In the past, competitive analysis involved searching for information, reading it, verifying it, comparing it, interpreting it, and discussing it before making a decision. Now we can type, \u00abAnalyze these five competitors and tell me what my company should do,\u00bb and have a proposal ready thirty seconds later. The issue isn\u2019t giving up that ability, but understanding<\/p>","protected":false},"author":10,"featured_media":10520,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[13],"tags":[],"class_list":["post-10513","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-inteligencia-artificial"],"yoast_head":"\n
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It turns ideas into action and opportunities into results, with a focus on adding value and contributing to the growth of each business.","url":"https:\/\/www.geotelecom.es\/en\/blog\/author\/maria-lapresa\/"}]}},"_links":{"self":[{"href":"https:\/\/www.geotelecom.es\/en\/wp-json\/wp\/v2\/posts\/10513","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.geotelecom.es\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.geotelecom.es\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.geotelecom.es\/en\/wp-json\/wp\/v2\/users\/10"}],"replies":[{"embeddable":true,"href":"https:\/\/www.geotelecom.es\/en\/wp-json\/wp\/v2\/comments?post=10513"}],"version-history":[{"count":2,"href":"https:\/\/www.geotelecom.es\/en\/wp-json\/wp\/v2\/posts\/10513\/revisions"}],"predecessor-version":[{"id":10522,"href":"https:\/\/www.geotelecom.es\/en\/wp-json\/wp\/v2\/posts\/10513\/revisions\/10522"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.geotelecom.es\/en\/wp-json\/wp\/v2\/media\/10520"}],"wp:attachment":[{"href":"https:\/\/www.geotelecom.es\/en\/wp-json\/wp\/v2\/media?parent=10513"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.geotelecom.es\/en\/wp-json\/wp\/v2\/categories?post=10513"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.geotelecom.es\/en\/wp-json\/wp\/v2\/tags?post=10513"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}