Deb Friar

Everything begins with the domain expert! Too many data science projects fail because there is a knowledge and communication gap between the data scientists and the domain experts. Until the data scientist fully understands the nuanced business case, their models and techniques will be off the mark. This knowledge gap results from multiple factors including functional territorialism, human nature to not admit one's shortcomings, and even communication style differences.

Any major task can be broken into smaller subtasks. In my experience, applying AI to a subtask is the key. AI works best with smaller very specific tasks. For example, a 3,000 word blog can't be written as a single AI task. Instead, chunk it into obvious sprints such as opening statement, problem definition, causal thesis, hypothesis, recommendations, final summary. Tackle each as its own task, then add two addition tasks: stitching each element together and uploading the full draft into an LLM to have it "graded" and identify gaps. Modify and publish! This chunking process can work as a method to introduce the power of AI into virtually any workflow.

It’s easy to build an “interactive” UI using excel and dummy data. Create navigation prompts that link to content pages to show the progression of the user experience. No need to spend thousands on a working prototype. It’s important to get this in front of as many potential users as possible and becomes a no cost way to iterate the UI as one gains user feedback. Spend the money on dev once a customer bites.

WIIFM. I would align on the business challenge first. This builds trust and creates a foundation. Next, have them imagine how their work or results would be different if that challenge was reduced or eliminated. Only then would I educate about the techniques and in layman’s terms, with visuals wherever possible, how this project will solve the challenge. Finally I’d share the metrics that will help demonstrate that the chosen technique does in fact resolve the use case.

Internal. Providing a truly searchable full library of enterprise product and service info to assist CSRs in answering customer inquiries quickly and accurately is a huge time saver. Customer satisfaction will improve, the CSR team will have time to upsell and cross sell. It’s a win-win!

With AI, it’s never been easier or faster to track Brand Sentiment in the wild! Let’s dig into 3 ways AI and machine learning revolutionize reputation management. Real-Time Sentiment Analysis: AI monitors social media and online forums, providing immediate insight into public sentiment. Predictive Analytics: Machine learning identifies trends, helping brands stay proactive and ahead in their strategies. Personalized Customer Interactions: AI-driven tools enhance customer satisfaction and build a positive brand image. AI isn't just an advantage in brand management; it's a necessity. Let's discuss! How do you see AI transforming your brand management strategies?