INBOX INSIGHTS, September 18, 2024: Overcoming AI Resistance, OpenAI o1 Model - Trust Insights Marketing Analytics Consulting

INBOX INSIGHTS: Overcoming AI Resistance, OpenAI o1 Model (9/18) :: View in browser

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How to Overcome Resistance to AI Integration in Your Organization

At MAICON last week, I talked about managing the people who manage AI. In this talk, I mentioned that AI integration is not a digital transformation. It’s a culture shift. A culture shift means that your team, your people, need to change. That’s not an easy ask.

Adopting new technologies, especially AI, can be a tough sell within an organization. People tend to resist change—whether it’s fear of job loss, discomfort with new tools, or concerns about losing the human touch. But the good news is that, with the right strategies, you can turn that resistance into enthusiasm for innovation.

Understand the Root Cause of Resistance

Resistance to change, especially AI, often stems from a few fears: job loss, a lack of understanding, and anxiety over its impact on creativity and decision-making. If you’re hearing hesitation in your organization, take the time to listen and understand the specific fears. This is key. You have to really hear what people are saying. I’ve often found that even if they know it won’t change things, people still want to be heard. They want to know that their voice matters. If you don’t take the time to hear your people out, you will not be able to effect change. AI is a culture shift. You’re asking people to do something new and different. There is bound to be resistance.

Communicate the Benefits in Their Language

It’s easy to talk about AI in terms of its efficiency, but not everyone will see that as a positive. Tailor the messaging to the specific needs of each department. For marketing, highlight how AI can provide deeper customer insights, making campaigns more targeted. For operations, emphasize increased efficiency and reduced human error. It’s not about replacing people, but rather how to make their work product better and give them time to do more high value tasks. Keep the conversation about the people, not the technology.

Provide Education and Training

One of the most significant reasons for pushback is a lack of understanding. If your team doesn’t know what AI does or how it works, they’re less likely to feel comfortable using it. Run training sessions, webinars, or quick tutorials on AI. This will help your team get comfortable with new tools. You can also ask neutral third parties (like me!) to help with education.

You can also invest in courses, like Generative AI for Marketers

Start Small, Show Quick Wins

You don’t need to overhaul your entire business overnight. Start with small, low-risk projects where AI can clearly demonstrate its value. Maybe it’s automating a repetitive task that everyone dreads or enhancing a process that’s already working well. Once you have some quick wins under your belt, you’ll find it easier to build trust and reduce skepticism. Small victories that the teams can feel good about will go a long way.

Involve Key Players in the Process

If you want widespread buy-in, you’ll need the support of your internal champions. Identify key influencers—department heads and respected team members—and get them on board early. When your culture carriers support change, you’ll have an easier time getting others on board. People look to their peers and leaders for cues. Getting your “influencers” excited about AI can help you shift the broader mindset.

Emphasize AI as a Collaborative Tool

One of the biggest misconceptions about AI is that it’s here to take over. In reality, AI is a partner that helps with tasks like data analysis, automation, and analysis. By reframing AI as a collaborative tool that complements creativity and decision-making, you can reduce fear and build enthusiasm. It’s like having a really capable assistant at all times. AI isn’t here to replace anyone. It’s here to help them do their jobs better.

Overcoming resistance to AI integration is about more than pushing a new tool. It’s about understanding and addressing your team’s concerns, educating them, and demonstrating value. By starting small, involving key players, and communicating benefits in a way that resonates, you can mitigate concerns and anxieties.

Change is a marathon, not a sprint. With patience and persistence, you can lead your organization through a successful culture shift and integrate AI into your daily workflow.

Are you meeting resistance from your teams about using AI? 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 their key takeaways from the Marketing AI Conference (MAICON) 2024. Learn why scaling AI beyond consumer tools like ChatGPT is crucial for businesses looking to leverage its full potential. Discover why process decomposition is essential for successfully integrating AI into your workflow and how it allows you to identify specific tasks where AI can truly shine. Understand the importance of establishing clear business requirements and standard operating procedures before diving headfirst into AI implementation. Gain insights into how to evaluate new AI models as they emerge and learn to differentiate the hype from the practical applications.

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 use generative AI for content personalization. Catch the episode replay here!

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👉 New: Generative AI for Hospitality

In this week’s Data Diaries, let’s talk about the new OpenAI model, 01. It was released last Thursday to much fanfare, formerly known as Strawberry, and it’s a new model that is supposedly capable of greater reasoning. So, what does this mean? What in the world is this, and how do we make use of this?

First of all, we should be clear that a reasoning model is really more about knowing that the model is thinking things through in a greater capacity than today’s standard models.

There’s a prompting technique in generative AI called chain of thought. What chain of thought does is it asks a machine to think through and evaluate its work step by step—to say, “Show me how you solved this problem.” This is akin to what you used to do in high school, where you would do things like show your proofs in your math class to demonstrate you actually know how to solve the problem.

Today’s average generative AI model does not do this. You give it a prompt, it follows the instructions for the prompt, and it tries to generate the answer you want. A reasoning model requires the model — and you, to a lesser degree — to think through not just the goal you’re trying to accomplish, but how you’re going to accomplish that goal. Within the o1 model, there is allegedly a reward framework as well, in which the model self-evaluates its work as it is trying to show its work.

I say allegedly because OpenAI has been especially cagey about what’s actually going on inside the model.

OpenAI has suppressed the output of the chain of thought in exact words; what it only shows is a brief summary of what it thought through. We will leave discussion of this feature to another time, but for now, we know that the o1 model is essentially a chain of thought model where you cannot turn off the chain of thought process.

Now, who needs this? Who asked for this? Who wants this? This model, and models like it, are going to most strongly benefit people who don’t put a lot of thought into their prompts, people who write very naive prompts, like, “Hey, write me a blog post about B2B marketing,” where they provide insufficient detail and insufficient information. For those very naive prompts that generate extremely bland, boring outputs or have no sense of self-evaluation, a reasoning model like o1 will be a significant improvement in the quality of what they can get generative AI to do.

If you are a person who already writes highly detailed prompts with lots of chain of thought of your own and lots of data you provide, you will still see benefit in a reasoning model, but you will see less benefit because you’re already leveraging the power of generative AI models to think things through, to self-evaluate, and perhaps even to have a reward function built in, such as building a scoring rubric and having the model score its own work and then try to improve itself.

That’s essentially what a reinforcement learning model with chain of thought built in is doing. It is programmatically providing the same infrastructure as you writing prompts that say, “Evaluate your work, score it against a rubric, and then refine your work until it reaches the minimum score level.”

There are three key takeaways here. First, reasoning models, because they have to do so much self-evaluation, are approximately 100 times more computationally expensive than a regular model, which means that things like cost and consumption of electricity go up substantially. That’s one of the reasons why the o1 preview has such strong limits — 50 messages per week as of September 17.

Second, if your prompts are already robust and already incorporate things like chain of thought or tree of thought and evaluation metrics, o1 will be a small incremental improvement on the work you’re already doing. The added benefit is that if you are already learning and mastering techniques like self-evaluation and scoring, you can apply these to any AI tool like Claude or Gemini and not be constrained to the OpenAI system.

The third key takeaway is that reasoning models, by very definition, and especially because you can’t see what’s going on underneath the hood in OpenAI’s system, will tend to create less creative outputs. If you’re using it for something like content generation, you’re going to get more thorough outputs, but they will be less creative, and you will probably need to put it through a model that is a more creative writer to reduce the monotonous and robotic tone that a reasoning model is going to spit out.

This is inherent to reasoning models because reasoning models, by definition, are applying logic and thought and process to your generative outputs. Those are all high-probability tasks, and interesting writing is often characterized by low probability use of words, words that are interesting and rare and uncommon. Reasoning models will not provide that by definition, and to create that will require fairly advanced prompting, which will in turn cost significant resources.

As a result, you will need to give greater thought to your use of generative AI in terms of whether the task you are pursuing is a reasoning task or a creativity task and then choose the appropriate model for the task.

The days of one model for everything are rapidly coming to a close. Instead, we will pick up models like tools and use them for the appropriate task in the same way that we pick a word processor or a spreadsheet or presentation software for specific tasks. Can you, for example, use Microsoft Excel during a presentation? Yes. Should you use it as your presentation software? Probably not.

Give o1 a try with your current book of prompts and see how it performs. If you see a significant improvement on some prompts, that’s a sign that those prompts themselves need to be improved.

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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