The Spanish market is looking for 2000+ specialists in professional services.
Let’s be frank. “Professional services” is an elegant name for an army of consultants, analysts, lawyers, and project managers. These are people whose main job is to gather information, analyze it, and neatly package it into reports, presentations, or legal opinions. They are the outsourced brain for companies that lack the time or expertise to do it themselves.
And now Spain, like the entire business world, declares: “We need more than two thousand such brains. Right now.” The pain is understandable: business is becoming more complex, data is growing, and decisions need to be made faster. Companies are willing to pay huge sums for expertise that will help them outmaneuver competitors, optimize costs, or launch a new product. They are looking for people capable of transforming information chaos into a clear plan of action.
But what if I told you that hiring a legion of expensive specialists for this is like building pyramids with slave labor when you have a modern construction crane right next to you? Let’s talk about how this army can be replaced by one well-tuned artificial intelligence.
Imagine a dialogue between two directors over a cup of coffee. Let’s call them Skeptic and Pragmatist.
Skeptic: We’re looking for a lead analyst again. Tired of interviews. Everyone wants a hundred thousand in a nanosecond, but in reality, they can’t even link two reports without errors.
Pragmatist: And we stopped looking. We created our own “analyst.”
Skeptic: Did you hire a genius from an incubator?
Pragmatist: Better. We set up a system based on a language model. We named it “Alfonso.” Now, when we need market analysis, we don’t wait a week for a person to collect data and draw slides. We write a query: “Alfonso, analyze the widget market in Andalusia for the last quarter. Competitors, prices, trends. Report in presentation format, 10 slides, focus on weaknesses X and Y.” In 15 minutes, the presentation is in my inbox.
Skeptic: Sounds like science fiction. And who checks this data? A machine can lie, after all.
Pragmatist: Of course, it can. That’s why we implemented a few simple rules.
Here are these rules that Pragmatist might describe. These are the implementation steps.
Approaches and tools. This isn’t about simply opening ChatGPT and asking it to create your business strategy. It’s about building a specialized system.
1. Data Collection. The foundation of everything. We connect the AI to reliable sources: paid databases (Statista, Nielsen), internal CRM and ERP systems, news aggregators, industry reports. The model doesn’t “fantasize,” but operates with facts from verified channels.
2. Contextualization. We “train” the model on our business context. We upload our past reports, strategies, and presentation templates into it. It learns not just to analyze, but also to speak our language, understand our priorities, and even format slides in our corporate style. Tools? These could be solutions like Azure OpenAI Service or Google Vertex AI, which allow for creating closed, secure environments for working with your data.
3. Agent Creation. Different “specialists” for different tasks. One AI agent monitors legal risks in contracts, another builds financial models, a third writes code for parsing competitor websites. This isn’t a monolith, but a flexible team of digital employees.
How to reduce distrust? This is the most important question. No one will entrust the fate of a company to a black box.
1. The “Co-pilot” Principle. AI doesn’t replace a human; it becomes their assistant. The first, most routine 80% of a report is prepared by the machine. The remaining 20% – critical reflection, strategic conclusions, fact-checking – is done by an experienced specialist. We don’t fire the analyst; we give them a tool that saves them 80% of their time. Now they can do not one, but five analyses a week.
2. Source Transparency. Any statement in an AI report must be accompanied by a link to its source. Is a figure taken from a Deloitte report? Here’s a link to page 48. Is a conclusion based on an analysis of 500 customer reviews? Here’s a link to the dashboard with those reviews.
3. Gradual Implementation. Start small. Assign the AI a low-risk task. For example, compiling a weekly news digest for your industry. When everyone sees that it works, quickly and efficiently, trust will begin to grow.
How to validate the result?
Very simply. Set up a competition. Give the same task to two teams: a team of three junior consultants and one senior specialist armed with an AI assistant. Evaluate four parameters: speed of execution, cost (man-hours), depth of analysis, and number of factual errors.
Repeat this experiment three times on different tasks. I assure you, after the third time, the question “Why do we need a staff of 2000 consultants?” will disappear on its own. You will start asking another question: “How quickly can we scale our AI assistant to meet all these needs and leave competitors far behind?”
Источник: https://www.linkedin.com/jobs/view/4407754065/
