Generative AI: Holy Grail or Distraction?
Generative AI applications (like content creation, image generation, or creative tools) have attracted much attention and the limelight in the broad public. From that personal experience, business executives feel the pressure to finally “get active” with AI in their companies as well. What they see, read, and hear in public and have tried out personally at home is the frame of reference for their briefings. But while in certain specific industries, generative AI is already changing the game (think movie-making or other industries producing creative output), in 99% of all businesses today, the much broader utility of AI applications still lies in automating processes and enhancing decision-making through intelligent, data-driven insights, and not in generative AI. That might not be perceived as sexy, but it is super important for competitiveness and definitely much better for the numbers. Here's, in very short, where the use of Artificial Intelligence will actually still make the biggest positive impact on most businesses today and why:
1. Automating Processes for Efficiency, Accuracy, and Cost Savings
- Repetitive Task Automation:
Many businesses deal with routine, repetitive tasks (e.g., data entry, invoice processing, customer support). Automating these tasks with AI-driven tools reduces labor costs, eliminates errors, and speeds up operations.
For example: Robotic Process Automation (RPA) combined with AI can handle invoice matching or payroll processing with near-zero errors. - Scalability:
Automation allows businesses to scale operations without a proportional increase in workforce. For example: AI-powered chatbots can handle thousands of customer queries simultaneously, 24/7. - Operational Efficiency:
AI optimizes workflows by analyzing bottlenecks and enabling real-time adjustments.
For example: AI can streamline supply chain logistics by predicting demand, optimizing routes, and reducing waste.
2. Intelligent, Data-Driven Decision-Making
- Data Overload:
Modern businesses generate massive amounts of data (from sales, customer interactions, marketing campaigns, etc.). AI analyzes this data to uncover
actionable insights that humans might miss or would never be able to produce at that scale.
For example: A retailer can use AI to analyze purchase patterns, helping decide what
products to stock or discount in specific locations. - Predictive Analytics:
AI-powered models can forecast trends, enabling businesses to make proactive decisions rather than reactive ones.
For example: Predictive maintenance in manufacturing prevents costly machine
downtime by forecasting equipment failures before they happen. - Personalization:
Businesses can use AI to deliver truly personalized experiences to customers, improving retention and loyalty.
For example: AI systems analyze customer data to recommend products, tailor email campaigns, or adjust pricing dynamically.
3. Support Core Business Strategies and Transformation
- Ressource Allocation:
Automating routine processes and decision-making tasks frees up employees to focus on higher-value activities, like strategic planning, innovation, and
customer relationship building.
For example: AI-powered tools take over day to day IT support tasks, allowing IT teams to work on digital transformation projects. - Improved Agility:
AI systems provide real-time insights, helping businesses adapt quickly to changing market conditions, customer preferences, or supply chain
disruptions. - Enhanced Employee Motivation and Satisfaction:
AI-powered platforms can enable a more personalized, efficient, and engaging work environment and automated processes can free employees from repetitive tasks that are seen as boring or tyring.
For example: AI-powered collaboration tools facilitate seamless communication, helping remote or hybrid teams stay connected and aligned and automate note taking, agenda management etc.
KEY TAKEAWAYS (Why Generative AI is Secondary for Most Businesses)
While generative AI has exciting applications, in the majority of industries these use cases
often serve niche purposes or complement broader automation strategies (e.g. providing
hyper personalized content for automated consumer interactions).
In contrast, automating processes and enabling intelligent decision-making:
1. Solves foundational business challenges.
2. Impacts the bottom line directly by reducing costs and boosting efficiency.
3. Ensures long-term sustainability through data-driven growth strategies.
In Conclusion
Technology will develop fast and generative AI will in more and more industries reshape business models and processes through content creation, simulation of ideas and fast innovative solutions at scale (e.g. real-time ideation, dynamic ad generation, prototyping and
manufacturing on demand etc.).
Today however focusing on and investing in process automation and decision intelligence simply yields an incredibly larger measurable ROI, scalability, and competitive advantage than most generative AI applications. This is neither a new realisation nor rocket science but
unfortunately not how we experience businesses thinking in daily life.
"While generative AI captures the spotlight, the real game-changer for most businesses today lies in automating processes and enabling data-driven decision-making. Streamlining operations, enhancing efficiency, and leveraging AI for predictive insights drive measurable ROI, scalability, and long-term competitiveness—far beyond the hype of content generation."
