Most people still think Large Language Models (LLM) like ChatGPT are best used for creative writing. While these applications can be helpful, they often yield mediocre results.
What if I told you that AI's real magic lies in its ability to perform menial, data-intensive tasks faster, cheaper, and more accurately than humans? Let me take you on a journey through a recent client experience that showcases AI's power in solving real, messy business problems.
A travel sector client approached me with a treasure trove of customer data from a questionnaire. They wanted to analyze responses to client questionnaires to determine travel destination trends, but they faced a massive, chaotic wall of messy text that resisted all attempts at analysis.
There were a variety of issues:
Volume: Tens of thousands of free-text responses. Imagine trying to manually extract country names from prose text responses a thousand times over.
Variety of Spellings: Take "Cambodia" for example. We found it spelled 15 different ways! Here's a taste:
Now multiply this chaos across every country mentioned.
Outdated or Inaccurate Names: “Burma” instead of “Myanmar,” “Zanzibar” instead of “Tanzania.”
Irrelevant Information: Responses like "I don't know yet" or "First we go to Germany, then we are thinking of flying directly to LA".
Multiple Countries in One Response: "Thailand Malaysia Laos Vietnam Phillipinen Indonesien".
Abbreviations and Language Variations: "USA" vs. “United States of America,” “Zanzibar” vs. “Sansibar.”
Can you feel the data analyst's headache forming? Unfortunately, this is the reality of raw, human-generated data – messy, inconsistent, and resistant to traditional analysis methods.
Before we dive into our AI-powered solution, let's consider the typical approaches many businesses might take:
Manual Processing: You could have an employee painstakingly standardize each entry. However, that would be slow, expensive, and prone to human error.
Outsourcing to Freelancers: Freelancer Platforms like Fiverr or Upwork have many freelancers who technically could do the project, but you'd still need to control their quality to ensure consistency.
Now, let's explore how we tackled this challenge using AI, providing a framework you can adapt to your own data processing needs.
Data Preparation: Respect Privacy, Reduce Risk Before proceeding, we scrubbed the data of personally identifiable information (PII). This isn’t just about complying with data protection laws—it's about being a responsible steward of clients’ information.
Initial Data Assessment: Showing AI the Ropes We selected about 10 of the most complex examples from our data set. Think of this as giving our AI a crash course in the messiest parts of our data problem. We're not just throwing data at the machine – we're curating a learning experience.
Prompt Engineering: AI Teaching AI In an innovative twist, we used one AI model (Claude) to generate instructions for another (ChatGPT). The result was a robust set of guidelines tailored to an LLM’s expectations.
Quality Check: Trust, but Verify We tested our AI solution on a sample set, meticulously checking its work. This isn't about blindly trusting the machine but refining and perfecting its approach. We're teaching the AI to learn from its mistakes, just like we would train a human team.
Error Handling: Embracing Uncertainty We defined clear protocols for how the AI should handle uncertainties or errors. Should it make a best guess? Flag for human review? This step balances efficiency with accuracy, ensuring our AI knows when to ask for help.
Scaling the Solution: From Sample to Tsunami After training and testing our AI, we created a script to process the entire dataset in batches using the OpenAI API.
By leveraging AI for this data processing task, we achieved results that would make any business leader sit up and take notice:
Look beyond the AI hype: AI's true power often lies in automating tedious tasks, not just in creative applications. It's not about replacing human creativity but augmenting human capabilities in data-intensive tasks.
Identify your data bottlenecks: Look for areas in your business where messy or inconsistent data hinders decision-making. These are prime candidates for AI-powered solutions.
Start small, then scale: Begin with a sample set to refine your AI approach before processing larger datasets. It's about building confidence and competence before tackling your biggest data challenges.
Combine AI models for superior results: Different AI tools can work together to create more robust solutions. Don't be afraid to mix and match AI capabilities to solve complex problems.
Prioritize data privacy and security: Always consider data protection regulations when working with AI and customer data. Responsible AI use builds trust with your customers and protects your business.
Automate for the future: Once set up, AI-powered data processing can continue to deliver value as you collect new data. It's an investment in ongoing efficiency and insights.
Embrace the learning curve: Understanding and implementing AI solutions may seem daunting, but the potential rewards in efficiency and insight are immense. It's about evolving your business for the AI age.
Focusing on these practical applications of AI can unlock significant efficiencies in your business operations. The key is to identify the right problems—those repetitive, data-heavy tasks that are bogging down your team—and apply AI strategically to solve them.