1. The Reality of AI in Companies: Hype or Real Impact?
Artificial intelligence (AI) is at the center of many business conversations. There’s talk of automation, algorithms optimizing workflows, and models making better decisions than humans. But when we take a closer look, we find that many companies claim to be using AI when, in reality, they’re still in the exploratory phase.
This isn’t necessarily a bad thing. There’s nothing wrong with admitting that we’re still analyzing and identifying the areas where AI will have the greatest impact in terms of cost-benefit. The real issue arises when the narrative gets ahead of reality (we saw this happen with Big Data), and AI becomes more about branding than actual operational value.
The challenge isn’t just to “use AI” but to determine where to apply it to generate real and sustainable impact. Business resources are always limited, and the key remains the same: prioritization. Strategic decision-making is a matter of choices, and within that, there is always a prioritization in resource allocation and the projects that receive those resources.
Let’s look at real cases—industries where AI is truly transforming processes and others where AI is more talk than execution.
2. AI in Consumer Goods: Demand Prediction with Measurable Impact
In the consumer goods sector, AI is driving value, especially in demand forecasting and personalization.
Coca-Cola and AI in Demand Planning
Coca-Cola leverages AI to anticipate trends and adjust production based on real demand. Essentially, it’s taking predictive models one step further by using the power of AI-driven solutions. This is data analysis with a clear business purpose.
Lessons for Other Consumer Goods Companies
✅ They don’t use AI just for the sake of it—they use it because it helps them sell more without creating excessive inventory. Poor stock optimization in consumer goods can lead to huge financial penalties.
✅ The return is clear: Less waste, greater efficiency in the supply chain.
This is a great example of smart implementation: AI applied with a clear, measurable objective.
3. Organizations Like SGAE: AI in Beta Mode
The copyright industry is an area where AI could work wonders—if applied correctly.
YouTube Content ID: A Success Story in Copyright Management
YouTube’s Content ID system scans videos to detect copyrighted content. This is AI with a clear impact: it automates processes that would be impossible to handle manually. Naturally, organizations like SGAE (Spain’s copyright management society) were eager to jump on board—after all, a big part of their business involves tracking down violators and enforcing penalties through hefty fines.
And SGAE? Lots of Talk, Little Real Application
SGAE and other entities in the sector mention AI in their speeches, but real implementation remains limited. Truly disruptive solutions could be developed, such as blockchain-based systems for automatic and transparent payments, but we are far from seeing this applied at scale.
It’s not enough to say, “We use AI.” If the impact is minimal or implementation is tokenistic, it’s better to be honest and say, “We are in the exploration phase.” And that’s completely fine.
“Many organizations and brands still need to realize that AI is not a starting point—it’s a tool to serve strategic decisions. The correct sequence is not ‘let’s implement AI and figure out where it fits,’ but rather defining what business improvements we seek and then assessing whether AI can enable them. When technology leads without a clear purpose, time and resources are wasted. AI should be a means, not an end.”
4. AI in Retail: Improving Experience While Keeping Business Fundamentals in Check
In retail, AI is enhancing customer experience and reducing costs. But here’s the key: the priority isn’t having the “techiest” store—it’s having the most profitable one.
Amazon Go: AI to Eliminate Checkout Lines
Amazon Go has eliminated checkout registers using computer vision and machine learning. But this isn’t just about innovation—it’s about removing a pain point in shopping while cutting labor costs. It’s pure business logic.
AI in Traditional Retail: Not Everything Has to Be Disruptive
✅ Well-applied AI simplifies processes and improves margins.
❌ But not every retailer is Amazon. If a traditional retailer tries to replicate this without a clear strategy, they could burn millions without achieving real ROI.
Not every retail business needs cutting-edge AI. Sometimes, optimizing with simpler tools can deliver better results without requiring massive investments.
5. AI in Education: From Real Personalization to Pure Marketing
The education sector talks a lot about AI, but real implementation is still weak.
Coursera and edX: AI for Adaptive Learning
These two platforms are great examples of how AI personalizes learning. They use technology to enhance the student experience by adjusting the course pace based on individual performance.
How Do They Do It?
🔹 Adaptive learning models: If a student struggles with a topic, the platform suggests additional exercises or provides more detailed explanations before allowing them to move forward.
🔹 AI-powered automated feedback: Real-time assessments that identify error patterns and offer personalized recommendations.
🔹 Chatbots and virtual tutors: Assisting students with common questions and guiding them through their progress.
Traditional Universities: Lots of Hype, Little Substance
Many universities claim to be using AI, but in reality, they just have an LMS (Learning Management System) with basic automation.
What does this mean in practice?
🔹Some universities incorporate AI-based course recommendation tools, but without true personalization.
🔹An LMS with simple automation is not advanced AI (and that’s fine, but let’s be honest about it).
🔹AI for grading multiple-choice exams or managing enrollments is useful, but it doesn’t transform the learning experience.
Conclusion: If AI Doesn’t Radically Improve the Student Experience or Learning Outcomes, Maybe It’s Not the Right Solution.
6. The Key: AI Should Be Business-Oriented, Not Hype-Oriented
AI is not an end in itself. It’s a tool to improve processes, optimize costs, and drive business growth.
If it makes sense to implement AI, then do it. If not, there’s no shame in saying, “We’re not using AI yet.” The important thing is that when AI is implemented, it delivers real impact—not just headlines in press releases.
At the end of the day, the real question isn’t “Are we using AI?” but rather “Are we making better business decisions?”
