The tech world has a long-standing love affair with hype. Every new product is hailed as “groundbreaking” or “the dawn of a new era”. While this can be exciting, it can also be misleading, especially when it comes to artificial intelligence (AI). Generative AI, in particular, has become the latest victim of this hype cycle.
Companies like OpenAI and DeepSeek are making bold claims about their AI systems, suggesting they can “think” and “reason” like humans. While these systems are impressive, they don’t possess human-like cognitive abilities.
AI Doesn’t Think, It Predicts
The truth is, AI doesn’t think or reason. It makes predictions based on patterns it finds in data. While this can be incredibly powerful, it’s crucial to understand the difference between prediction and cognition.
Confusing the two can lead to serious business risks.
The Risks of Anthropomorphising AI
When we assign human-like qualities to AI, we tend to overestimate its capabilities. This can lead to an over-reliance on AI for critical decision-making and a misplaced trust in its ability to handle sensitive information.
Here are some of the risks of anthropomorphising AI:
- Dependence on AI for critical decision-making: This can lead to poor decisions if the AI system is not properly trained or if it encounters unexpected data.
- Misplaced trust in its ability to handle sensitive information: AI systems can be vulnerable to security breaches and data leaks.
- Over-reliance on AI outputs for forecasting and planning: AI predictions are based on past data, which may not always be accurate in predicting future events.
The consequences of these risks can be significant, including:
- Operational failures
- Regulatory breaches
- Reputational damage
The Problem with Tech Demos and Marketing Hype
Tech demos and marketing campaigns often present AI in a highly idealised light. They showcase AI systems performing flawlessly in controlled environments, but these demos rarely reflect the complexities of real-world applications.
In reality, AI systems often encounter incomplete, noisy, or biased data, which can lead to unexpected and potentially harmful results.
A Pragmatic Approach to AI
To avoid the pitfalls of AI hype, businesses need to take a pragmatic approach. This involves:
1. Defining Clear Objectives
Before implementing any AI solution, clearly define the specific problems you are trying to solve and how AI can help. Ask yourself:
- What specific problems am I trying to solve?
- How can AI help?
- What are its limitations?
2. Focusing on Data Quality
High-quality data is the foundation of successful AI. Even the most advanced AI tools will fail without good data. Here are some steps to ensure data quality:
- Organise your data: Conduct a data audit to ensure consistency, accuracy, and high quality.
- Label your data: Clean, label, and update datasets to provide AI models with structured, meaningful inputs.
- Control access: Establish strong data governance to ensure sensitive data is accessed only by authorised personnel.
3. Starting Small and Experimenting
Don’t let the process of sorting out your data become overwhelming. Start small by focusing on specific use cases where your data is already well-structured. This allows you to build your AI capabilities gradually and learn from your experiences.
Key Points to Note
- Don’t believe the hype: AI is a powerful tool, but it’s not a magic bullet.
- Focus on practical applications: Identify specific problems that AI can solve for your business.
- Prioritise data quality: Ensure your data is accurate, consistent, and relevant.
- Start small and iterate: Experiment with different AI solutions and learn from your experiences.
- Combine AI with human expertise: Leverage the strengths of both AI and human intelligence to achieve optimal results.
By adopting a pragmatic approach and focusing on the real-world capabilities of AI, businesses can unlock its true potential and avoid the pitfalls of hype.
