Most bosses know just enough about AI to know that it can have a transformative impact on their business. Often enough, they don't know how and worse still they view AI as something like a "one weird rick", a magic spell or an efficiency hack.
Armed with enough knowledge to be dangerous, leaders make all manner of demands on their team to "use AI", to "use all of our data" in order to do...something which is never really clear.
Watch out for words and phrases like: "integrate", "collaborate" or "work with AI" or "just put it into ChatGPT". These indicate profound naivety or plain willful ignorance when it comes to the complexities of using AI to create business value.
Below are some ready replies to well-meaning but inane requests from bosses that allow you to look smart as an AI non-specialist. They may even help your team avoid a few footguns, timesinks and disaster projects in the process.
Question: Why don't we just put this into ChatGPT?
Answer: What exactly are we putting into ChatGPT? First off, I'm totally on board with using more generative AI in our workflows where that makes sense. To clarify: do you mean asking ChatGPT to create or prospect specific solution for us based on a particular prompt? Do you mean fine-tuning ChatGPT with prompting on some of our data? Can you provide a bit more color there?
Question: We have all this data. Why can't we use AI to [INSERT BUSINESS CAPABILITY OR FUNCTION]?
Answer: We certainly can explore whether a Large Language Model or another AI solution is appropriate for that use case. What data you feel is most pertinent to realizing that feature? Do we know where that data is? Is it in pristine Excel spreadsheets or is it spread across GDrive, random emails, Jira and Slack? Do we know what condition it's in? How much of the data is at the quality such that we could train a model for...what is it we're trying to accomplish again...?
Question: Can't ChatGPT already do this?
Answer: LLMs alone are general tools with a lot of general data about the world. The data that we have here at BigCo. is not only proprietary it's highly specific. No LLM, no matter how good it is, is going to have that high fidelity data. We could fine-tune ChatGPT or some other foundational model with our data to get quality outputs. Quick question: what makes you think an LLM or a chatbot is the right move here? What data from the business or our users or customers makes you believe this?
Question: I want everyone to think about how we can use AI to improve efficiency. How can we enhance the quality of operations with this technology?
Answer: Where do we think the greatest business value will emerge from efficiency improvements? We could make a lot of efficiency improvements across teams and still not move the needle on our most important metrics. Can you be specific about where in our company you see improved efficiency having measurable impact? What data do we have about current operations to measure any AI-driven efficiency efforts against?
Question: I want to see a proof of concept for an "AI approach" to [INSERT BUSINESS PROBLEM]. I won't take 'no' for an answer. How do we get started? We'll probably need to hire some data scientists soon, right?
Answer: Let's first figure out what we're trying to achieve here. Do we want predictions? Do we want classifications? For example, using AI to do sentiment analysis on customer chats or feedback messages to give us a sense of how people are feeling about our service in real time? Once we settle on that, let's find the most pertinent data and ensure that data is clean, consistent and coherent. Then we can use something like HuggingFace's AutoTrain feature to do some small experiments.
This is obviously just a smattering of the uniformed and downright asinine directives workers can expect from bosses in the throes of AI fever.
This technology isn't going anywhere, so we should absolutely embrace in the workplace. At the same time we need to ensure we remain results-focused, not matter what new tech comes down the pike and that above all we are honest with ourselves and our teams about its risks as well as its rewards.
Top comments (0)