INBOX INSIGHTS, July 17, 2024: Corporate Culture, Programming Generative AI - Trust Insights Marketing Analytics Consulting

INBOX INSIGHTS: Corporate Culture, Programming Generative AI (7/17) :: View in browser

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What is Corporate Culture?

When I talk about integrating AI into your company, I talk about how it’s a culture shift. The response I get to that statement is usually, “What if we don’t know what our culture is?”

I asked our Slack community, Analytics for Marketers, how they define corporate culture. Here are some of the responses:

Seems to be that for culture to work, your values must be:

Defined in a clear and concise way
Rooted in actions, not beliefs. if these two are covered, then also:
Understood by everyone – ideally before being hired – where they learn it once, but that message is reinforced through action in more organic ways where it isn’t forced down an employees throat – giving people the opportunity to continually buy-in

It’s the outline of what is acceptable and what isn’t that is unique to a company

Culture is one of those things that you know when you see it.

I’ve found that a lot of organizations have become antagonistic internally, often out of necessity with all the layoffs and restructures, and the looming threat of AI. That makes it difficult to build a culture that’s more than just a section in the employee handbook.

Oh, this is a great question. There’s no commonly accepted definition so discussions go all over the place. Company culture definitions usually include leadership, how people are treated, how customers are dealt with, how work gets done.

I think it’s interesting to apply elements of culture in general to a company: What are the:

Norms
Values
Language
Symbols
Artifacts

The thinking is that corporate culture is how you’re treated and what’s acceptable. For example, I used to work at a company where the VP would openly scream at me and others during team meetings. That was accepted behavior. That VP never faced consequences for his actions. It created an environment of stress and fear. It became challenging to care about the work when you had to wonder when you were getting yelled at next. You became terrified to step out of line or give bad news. It led to a lot of resentment and turnover from team members.

There’s a quote, “you reap what you sow,” that applies well to how you should approach corporate culture. It’s the old, “treat people the way you want to be treated”. Ok, enough throwing quotes at you. So, what is corporate culture and why does it matter?

Think about it as your business guidelines. Are you promoting transparency and productive conversation, or are you encouraging performance plans as easy solutions? Are you tolerating bad behavior from one staff member and chastising others? Are you setting the tone and leading by example? It doesn’t matter what your role is, your attitude and actions are a reflection of your environment. If you show up to work every day and you hate what you do, and you can’t tolerate your coworkers, that’s a toxic culture. Whether you’re bringing that negativity to the table, or you’ve inherited it, you need to make a change.

Toxic culture can bring down a business. If you’re wanting to make a transformation, like integrating AI into your company, you need to do a temperature check. Are your people happy? Are they productive? Are they an accurate representation of your company? AI is a culture shift. Why? Because you’re changing how your business runs. You’re asking your staff for different skills and outputs. You’re evolving the products and services that you sell to your customers. You’re adapting the processes of how work gets done. You’re giving your team new measures of success.

Take a minute and assess your company culture. Do you like what you see? If you don’t, you’ll need to fix those problems before introducing AI or other changes into the fold.

How healthy is your corporate culture? Reach out and tell me, or come join the conversation in our Free Slack Group, Analytics for Marketers.

– Katie Robbert, CEO

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how the capabilities of generative AI are changing how executives think about building a team. Learn why simply cutting staff to save money is not a viable long-term strategy. Discover the questions you should ask to accurately assess your needs and build a plan. Understand the importance of including your team in the decision-making process to foster transparency, ownership, and innovation.

Watch/listen to this episode of In-Ear Insights here »

Last time on So What? The Marketing Analytics and Insights Livestream, we looked at how to do audience analysis with data like GA4. Catch the episode replay here!

This week, we’ll be doing some generative AI hot seat work, diagnosing prompts in real time. Are you following our YouTube channel? If not, click/tap here to follow us!

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In this week’s Data Diaries, let’s talk about an advanced generative AI prompting technique for tools like ChatGPT and Google Gemini. As we’ve discussed on previous podcasts and livestreams, the words we use to prompt language models are software, are code. Suppose you wanted to write some haiku, as an example. The beginner prompt would be to say something like “Write a haiku about (topic)”. That will get you an okay result.

But these models are capable of running software in plain words. What does this look like?

Open up the large language model of your choice and follow along – copy/paste the italics text in as you follow along.

First, you’d start with the Trust Insights RACE and PARE frameworks to build a priming prompt like this:

You’re a Pulitzer Prize-winning poet. Today, you’re going to do some short form poetry, haiku. First, what do you know about this topic?

Then you’d follow the rest of the PARE framework to populate the session with mistakes to avoid:

What are some common mistakes less skilled poets make with haiku?

…expert tips:

What are some expert tips only you would know that we haven’t discussed yet?

…and an evaluation system:

Great. Using all these best practices, lessons learned, expert tips, and things to avoid, build a scoring rubric to evaluate a haiku.

This follows the PARE framework closely. You’ve now got a chat session filled with not only knowledge about writing haiku, but also a way to evaluate whether or not a haiku is any good. You’ll spend some time correcting any mistakes the model has in its knowledge.

So how do we transform this conversation into software? A software developer might think, well, we want to have the software create the outputs, and then we have to QA the outputs, make adjustments, and run it again. That’s your most basic iterative software development.

Language models can do this, so that’s what we will build – a prompt that acts as a piece of software, executing steps in order, evaluating its own work, and refining based on its evaluations. Try this prompt – and you paste the whole thing in at once, in its entirety.

Strictly follow these instructions in order: 1. I’m going to give you a topic. 2. You will generate a list of 20 synonyms and adjacent words to this topic. These are to spark your imagination and confirm your understanding of the topic. 3. You will identify 5 emotion words for the topic based on your knowledge of the topic. 4. With this topic and these related terms, you will devise 4 different haiku candidates. 5. You will score each candidate haiku based on your scoring rubric. 6. You will choose two highest scoring candidates and refine them both. (Round 1) 7. You will score your refinements against the original two winning candidates using the scoring rubric. 8. You will choose the highest scoring candidate. (Round 2) 9. You will make one additional refinement based on the highest scoring candidate from Round 2. 10. You will score both. (Round 3) 11. You will return the ultimate final winning candidate.

When you run this, it will ask you for a topic and then run the rest of the program.

You may have seen computer programming languages before, like Python or Java. You may have even tried your hand at writing them. But this is writing code, executable code, in plain language – and unlike computer languages, you can read, interpret, and make use of it.

Conceptually, what if you swapped out haiku with email subject lines or blog post titles? You could build your own software to create self-judging marketing tools, all in the coding language you’re most comfortable in: your native language.

Want to share your haiku? Pop by our Analytics for Marketers Slack group and share it.

Here’s a roundup of who’s hiring, based on positions shared in the Analytics for Marketers Slack group and other communities.

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Imagine a world where your marketing strategies are supercharged by the most cutting-edge technology available – Generative AI. Generative AI has the potential to save you incredible amounts of time and money, and you have the opportunity to be at the forefront. Get up to speed on using generative AI in your business in a thoughtful way with our new offering, Generative AI for Marketers, which comes in two flavors, workshops and a course.

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