Jack Rowbotham

Integrating AI into your daily work requires forming a habit, much like any other practice. You should be intentional about the tasks you choose to use AI for—consider setting three AI-related tasks each day. This approach helps build the muscle memory of recognizing how AI can assist you. Start with small tasks, think big in terms of possibilities, and always be willing to experiment and learn from your prompts.

To stand out in AI, simplicity is key. When sharing knowledge, break down complex terms into easily digestible content that anyone can understand. Typically, this involves translating technical jargon into business-focused outcomes, starting with the WHY.

Personal assistants are a fantastic way for employee efficiencies. Typically, they excel in two key categories: 1. Knowledge Discovery and Research: Personal assistants can assist with answering questions, conducting research, and uncovering valuable insights. 2. Task Automation: They can interact with your line-of-business applications, handling administrative or critical business processes. From scheduling meetings to managing repetitive workflows, they streamline your workload. When the assistant excels in both these areas, it truly starts to demonstrate its value. However, awareness, usage, and adoption play a crucial role. Getting users to fully embrace the solution remains a challenge—one that deserves a separate discussion!

Exploring online courses is an excellent way to kickstart your journey in developing and nurturing your AI knowledge. Platforms like LinkedIn Learning, Khan Academy, and Udacity provide courses suitable for learners of all levels. Keep in mind that while some courses are free, others may require funding. If you’re part of a company, consider checking whether they offer personal development funding—you might be pleasantly surprised! Note: The tech landscape is evolving incredibly fast so courses can become out of date quickly. Remember to cross-check the content with real-world developments. News sites can be your trusty sidekick for that.

Whenever I talk about the value of AI, I break it down into two pillars. AI that generates blue money (saves costs) and AI that generates green money (generates revenue). Blue Money: Represents operational efficiency like cost savings and time saved. It can be very hard at times to quantify savings with this bucket if those efficiencies are not being reallocated to other innovative activities. Green Money: Refers to benefits that can help increase generate revenue. This is usually the most undiscovered benefits when AI can help serve and upsell to customers. This is the real differentiator when assessing AI’s impact on the bottom line.

Following last year’s disruption in the chatbot industry, many organizations have implemented LLMs with prompt engineering, believing it's an end-to-end conversational app capable of meeting all customer needs. The reality is that traditional chatbot frameworks which include escalation to human agents/contact centers, remain one of the most critical components of a chatbot’s workflow. There are pros (customer experience) and cons (cost to serve) to this approach. There will always be instances when a customer needs to speak to a human, especially for critical issues that the chatbot is not yet trained to handle. I recommend implementing LLM enabled chatbots with an orchestration layer to hand off the conversation when required.